Final report of theJoint WCRP/SCOR Working Group on Air-Sea Fluxes(SCOR Working Group 110)INTERCOMPARISON AND VALIDATION OFOCEAN-ATMOSPHERE ENERGY FLUXFIELDSbyMembers of the WGASFEdited by P.K.TaylorSection A – Executive Summary,and Chapters 1 to 9June 2000Report of the Working Group on Air Sea Fluxes June, 2000Report of the Working Group on Air Sea Fluxes June, 2000TABLE OF CONTENTSPREFACE..........................................................................................................................................................viiEXECUTIVE SUMMARY ............................................................................................................................... ix1 INTRODUCTION............................................................................................................................................11.1 The Surface Flux Field Problem....................................................................................................11.2 The Joint WCRP/SCOR Working Group on Air Sea Fluxes......................................................11.2.1 Creation of the Working Group...................................................................................11.2.2 The remit of the Working Group.................................................................................21.2.3 The Working Group's Mode of Working....................................................................31.3 The variables to be considered ......................................................................................................31.4 The flux products assessed ............................................................................................................41.5 Layout of the report........................................................................................................................42 THE REQUIREMENTS FOR SURFACE FLUX DATA SETS..................................................................72.1 Introduction....................................................................................................................................72.2 NWP systems—assimilation and forecasting...............................................................................72.2.1 Introduction..................................................................................................................72.2.2 The needs of data assimilation systems for flux fields...............................................82.2.3 The needs of atmospheric forecast models for flux fields........................................112.2.4 Regional observing / nowcasting / forecasting systems...........................................122.3 Oceanic General Circulation Models (OGCM's).......................................................................132.3.1 Introduction................................................................................................................132.3.2 Parameterisation of the thermal feedback.................................................................142.3.3 Salinity Forcing..........................................................................................................152.3.4 Geophysical consistency of the ocean forcing function...........................................162.3.5 Forcing fields..............................................................................................................162.3.6 Discussion ..................................................................................................................172.3.7 Conclusions................................................................................................................182.4 Wave Models................................................................................................................................182.5 Sea Ice in Ocean Models and Sea-Ice Modelling.......................................................................192.6 Air-Sea Flux Fields and Ocean Data Assimilation.....................................................................202.6.1 Introduction................................................................................................................202.6.2 Ocean Data Assimilation...........................................................................................202.6.3 Example of Ocean Assimilation requirements - GODAE........................................212.7 Evaluation of Climate Models.....................................................................................................212.8 Climate change studies ................................................................................................................252.9 Conclusions..................................................................................................................................273 SPACE-TIME VARIABILITY OF THE FLUXES....................................................................................293.1 Introduction..................................................................................................................................293.2 Synoptic and mesoscale processes..............................................................................................293.2.1 Mid to High latitude variability.................................................................................293.2.2 Variability in the tropics............................................................................................313.3 Climate variability........................................................................................................................313.3.1 Introduction................................................................................................................313.3.2 Detection of climate trends........................................................................................323.3.3 Interannual to decadal scale variability.....................................................................333.4 Summary.......................................................................................................................................364 DATA SOURCES FOR FLUXES AND RELATED VARIABLES..........................................................374.1 Introduction..................................................................................................................................374.2 in situ data sources.......................................................................................................................374.2.1 Voluntary Observing Ships........................................................................................374.2.2 Ocean Weather Ships.................................................................................................394.2.3 Buoys..........................................................................................................................40Table of Contents ii.Report of the Working Group on Air Sea Fluxes June, 20004.2.4 Platforms, Rigs, etc....................................................................................................414.3 Satellite Data Sources..................................................................................................................414.3.1 Introduction................................................................................................................414.3.2 Non geostationary satellites.......................................................................................424.3.3 Geostationary satellites..............................................................................................454.4 NWP and Reanalyses...................................................................................................................454.4.1 Introduction................................................................................................................454.4.2 Fluxes from operational data assimilation................................................................464.4.3 Fluxes from reanalyses ..............................................................................................484.5 Residual methods (TOA + model divergences)..........................................................................544.6 Ocean Data assimilation ..............................................................................................................544.7 Data Sources for Fluxes and Flux-Related Variables over Sea Ice............................................545 DIRECT FLUX OBSERVATIONS - STATE OF THE ART...................................555.1 Introduction..................................................................................................................................555.2 Radiative fluxes............................................................................................................................555.2.1 Introduction.................................................................................................................555.2.2 Shortwave radiation...................................................................................................565.2.3 Longwave radiation. ..................................................................................................595.3 Turbulent fluxes...........................................................................................................................615.3.1 The Eddy correlation method....................................................................................615.3.2 The inertial dissipation method.................................................................................625.4 Precipitation Flux.........................................................................................................................645.4.1 Introduction................................................................................................................645.4.2 in situ rainfall using Funnel raingauges....................................................................655.4.3 Other methods of in situ measurement......................................................................666 PARAMETRISATION OF RADIATIVE FLUXES ................................................696.1 Introduction..................................................................................................................................696.2 Radiative fluxes from ship data...................................................................................................696.2.1 Parameterisation of SW flux......................................................................................696.2.2 Parameterisation of LW flux .....................................................................................716.3 Radiative Transfer Models - RTM’s...........................................................................................716.3.1 Longwave radiative flux (LW)..................................................................................716.3.2 Shortwave radiative flux (SW)..................................................................................726.3.3 COARE atmospheric soundings................................................................................746.4 Radiative Fluxes by Remote Sensing..........................................................................................756.4.1 SW and LW fluxes from TOA radiances..................................................................756.4.2 Longwave Net Flux from a combination of AVHRR and SSM/I............................766.5 Surface Albedo.............................................................................................................................776.5.1 Ocean Surface Albedo...............................................................................................776.5.2 Albedo over sea ice....................................................................................................786.6 Summary - Parameterisation of Radiative fluxes.......................................................................787 PARAMETERISATION OF TURBULENT FLUXES...............................................................................797.1 Introduction..................................................................................................................................797.2 Example of traditional "Bulk Aerodynamic" formulae..............................................................797.3 Determining the Transfer Coefficients........................................................................................817.3.1 Traditional bulk formulae..........................................................................................817.3.2 New bulk formulae.....................................................................................................837.4 Bulk formulae for light to moderate wind conditions.................................................................897.4.1 The TOGA COARE algorithm..................................................................................897.4.2 Other Algorithms .......................................................................................................927.5 Transfers in moderate and high winds .........................................................................................997.6 Summary of bulk algorithms........................................................................................................998 RANDOM AND SAMPLING ERRORS IN FLUX FIELDS. ................................................................. 1038.1 Introduction............................................................................................................................... 1038.2 Random errors and sampling density....................................................................................... 103Table of Contents iii.Report of the Working Group on Air Sea Fluxes June, 20008.2.1 Introduction............................................................................................................. 1038.2.2 Errors and Variability of Monthly Mean Fields .................................................... 1048.3 Systematic Errors...................................................................................................................... 1098.3.1 Introduction............................................................................................................. 1098.3.2 Example of a parameterisation error...................................................................... 1098.3.3 Determining errors in VOS reports........................................................................ 1098.3.4 Method of averaging fluxes.................................................................................... 1108.3.5 Further discussion.................................................................................................... 1118.4 Combining Errors...................................................................................................................... 1118.5 Mapping errors.......................................................................................................................... 1128.5.1 Introduction............................................................................................................. 1128.5.2 Successive correction.............................................................................................. 1128.5.3 Kriging of monthly means and error estimation.................................................... 1138.6 Sampling problems for satellite-derived quantities................................................................ 1188.6.1 Introduction............................................................................................................. 1188.6.2 Sampling errors for precipitation............................................................................ 1188.6.3 Scatterometer Sampling Errors............................................................................... 1199 METHODS OF EVALUATING FLUXES AND FLUX RELATED PRODUCTS................................. 1219.1 Introduction................................................................................................................................ 1219.2 Sources of reference data........................................................................................................... 1219.2.1 The accuracy of research quality measurements: experience from TOGA-COARE 1219.2.2 High quality buoy data............................................................................................. 1239.2.3 High-Quality Automated Data from ships.............................................................. 1259.2.4 Data for verification of satellite products................................................................ 1269.3 Intercomparison of field products.............................................................................................. 1279.3.1 Comparison to Specialised Products....................................................................... 1279.3.2 Comparison of Variability patterns in different flux products............................... 1279.4 Integral constraints..................................................................................................................... 1299.4.1 The global heat and water balance.......................................................................... 1299.4.2 Meridional heat and water transport........................................................................ 1299.4.3 Enclosed Seas Used for Flux Validations............................................................... 1339.4.4 Ocean Mixed Layer Budgets................................................................................... 13610 BASIC VARIABLES - STATE OF THE ART....................................................................................... 13710.1 Introduction............................................................................................................................. 13710.2 Sea Surface Temperature (SST)............................................................................................. 13710.2.1 Introduction - Definition of Sea Surface temperature......................................... 13710.2.2 in situ SST measurements..................................................................................... 13810.2.3 Remote sensing of SST......................................................................................... 14010.2.4 SST Analyses........................................................................................................ 14110.3 Sea Surface Salinity (SSS)...................................................................................................... 14110.3.1 Introduction...........................................................................................................14110.3.2 In Situ measurement of SSS................................................................................. 14210.3.3 Remote sensing of SSS......................................................................................... 14210.4 Air temperature ....................................................................................................................... 14410.4.1 in situ air temperature measurement .................................................................... 14410.4.2 Surface air temperature from the Satellite SSM/I sensor.................................... 14710.5 Humidity................................................................................................................................. 14810.5.1 in situ humidity measurement .............................................................................. 14810.5.2 Remote sensing of near surface humidity............................................................ 14910.6 Wind........................................................................................................................................ 15110.6.1 Introduction............................................................................................................ 15110.6.2 in situ wind data.................................................................................................... 15210.6.3 Remote sensing of surface wind........................................................................... 15510.7 Waves...................................................................................................................................... 15810.8 Cloudiness............................................................................................................................... 16010.8.1 Introduction............................................................................................................ 160Table of Contents iv.Report of the Working Group on Air Sea Fluxes June, 200010.8.2 in situ cloud data.................................................................................................... 16010.8.3 Remote sensing of cloud data................................................................................ 16010.8.4 Combined products................................................................................................ 16010.9 Precipitation ............................................................................................................................ 16110.9.1 Surface-based precipitation data........................................................................... 16110.9.2 Precipitation by ocean freshwater budgets........................................................... 16310.9.3 Satellite Remote sensing of precipitation............................................................. 16310.9.4 Intercomparison projects ..................................................................................... 16410.10 River inflow........................................................................................................................... 16610.11 Sea ice.................................................................................................................................... 16810.11.1 Sea Ice amount.................................................................................................... 16810.11.2 Flux-related variables over ice........................................................................... 16810.12 COADS Monthly Summary Trimmed Groups (MSTG) variables..................................... 16911. EVALUATION OF FLUX PRODUCTS ...............................................................................................17111.1 Introduction............................................................................................................................. 17111.2 Flux products based on in situ data........................................................................................ 17111.2.1 Introduction...........................................................................................................17111.2.2 pre-1990 flux studies............................................................................................. 17111.2.3 UWM/COADS...................................................................................................... 17411.2.4 SOC ....................................................................................................................... 17511.2.5 IFM........................................................................................................................ 17911.2.6 Comparative assessment....................................................................................... 18011.3 Flux products based on Satellite data..................................................................................... 18511.3.1 Introduction...........................................................................................................18511.3.2 Evaluation of HOAPS........................................................................................... 18511.3.3 US Satellite Radiation products........................................................................... 19611.3.4 Japan Surface Shortwave and Longwave Products............................................ 20011.3.5 Residual method................................................................................................... 20311.4 Evaluation of the reanalysis products..................................................................................... 20411.4.1 Introduction...........................................................................................................20411.4.2 Comparisons to in-situ observations.................................................................... 20411.4.3 Polar regions.......................................................................................................... 20711.4.4 Ocean Surface Energy Balance............................................................................ 20711.4.5 Evaporation........................................................................................................... 20811.4.6 Sensible heat flux.................................................................................................. 21511.4.7 Net short-wave...................................................................................................... 21811.4.8 Net long-wave....................................................................................................... 22511.4.9 Net heat flux..........................................................................................................23011.4.10 Evaluating the NCEP1 reanalysis by forcing an ocean model.......................... 23411.4.11 Winds................................................................................................................... 23411.4.12 Zonal surface stress............................................................................................. 23711.4.13 Meridional surface stress.................................................................................... 24111.4.14 Precipitation........................................................................................................ 24411.4.15 Conclusions......................................................................................................... 25111.5 Flux Products from Operational NWP model's ..................................................................... 25311.6 Evaluation of fluxes in sea-ice covered areas........................................................................ 25311.6.1 Cloud and surface radiative flux data sets in the Arctic...................................... 25311.6.2 Precipitation in the Arctic..................................................................................... 25511.6.3 Need for more assessment.................................................................................... 25611.7 Evaluation of Waves............................................................................................................... 25611.7.1 Visual wave observations...................................................................................... 25611.7.2 Altimeter wave data............................................................................................... 25711.8 Summary ................................................................................................................................. 25812. CONCLUSIONS....................................................................................................................................... 26512.1. Introduction............................................................................................................................. 26512.2. Summary ................................................................................................................................. 26512.3. Meeting the needs ................................................................................................................... 267Table of Contents v.Report of the Working Group on Air Sea Fluxes June, 200012.3.1. Budget studies....................................................................................................... 26712.3.2. Climate variability studies.................................................................................... 26812.3.3. Climate models...................................................................................................... 26912.3.4. Ocean general circulation models ........................................................................ 26912.3.5. Wave modelling.................................................................................................... 27012.3.6. Sea-ice modelling.................................................................................................. 27012.3.7. NWP and data assimilation................................................................................... 27012.4. Perspectives............................................................................................................................. 27212.5. Specific Recommendations .................................................................................................... 273ACKNOWLEDGEMENTS............................................................................................................................ 274REFERENCES................................................................................................................................................ 275APPENDIX A. MEMBERS OF THE WORKING GROUP ON AIR-SEA FLUXES............................... 303APPENDIX B. CATALOGUE OF FLUX DATA SETS AND FLUX RELATED PRODUCTS............. 305B.1 Introduction................................................................................................................................ 305B.2 Accessing the Catalogue........................................................................................................... 305B.3 Preliminary Initial Contents List............................................................................................... 305B.4 Information to be submitted...................................................................................................... 305APPENDIX C. ACRONYMS AND ABBREVIATIONS........................................................................... 307vi.Report of the Working Group on Air Sea Fluxes June, 2000Preface vii.Report of the Working Group on Air Sea Fluxes June, 2000PREFACEThis document is the final report of the Working Group on Air-Sea Fluxes (WGASF)which was jointly sponsored by the World Climate Research Programme (WCRP) and theScientific Committee on Ocean Research (SCOR). For the WCRP, because the question ofsurface fluxes is considered central to a number of its scientific projects, the WGASF reporteddirectly to the Joint Scientific Committee. For SCOR, the WGASF was SCOR Working Group110: "Intercomparison and Validation of Ocean-Atmosphere Energy Flux Fields". This jointsponsorship has impacted the work of the WGASF in a number of ways. On a logistic level, itresulted in more funding being available for meetings than would otherwise have been the case,which was clearly advantageous. However there have been more fundamental effects. Inparticular, although uniform terms of reference were agreed by WCRP and SCOR, theexpectation with regard to the Working Group outcome was somewhat different. Typically aSCOR working group consists of experts in a particular field, chosen to reasonably represent theinternational membership of SCOR. They are tasked with reviewing a subject in some depthand producing a monograph which may be formally published in book form; often this maytake several years. The requirements of the WCRP were somewhat different since the resultsof the WGASF were expected to have a direct impact on future WCRP projects. Thus theWCRP requirement was for an expert group capable of rapidly concluding its deliberations andof producing one or more reports within a restricted time scale. Fulfilling this last requirementwas particularly difficult since the inception of the WGASF coincided with the firstdissemination of the results from the "reanalysis" projects at the European Centre for MediumRange Weather Forecasting (ECMWF) and the US National Centers for EnvironmentalPrediction (NCEP); projects which were clearly of major importance with regard to theestimation of air-sea fluxes but which would take time to evaluate. Indeed a first detailed globalintercomparison of the various analyses is presented in this report.It was clear even from the first meeting of the WGASF that, both to justify anyevaluation of flux products and also to provide a suitable review for SCOR, it would benecessary first to summarise the requirements for surface fluxes, the state of the art with regardto surface flux estimation, and the methods available for flux validation. The first half of thereport deals with these issues. Two later chapters deal with the evaluation of the basicmeteorological variables (temperature, wind etc.) and of a number of available flux products.All the chapters have been assembled from many separate sections written by different WorkingGroup members. Inevitably the degree of detail varies considerably; in some cases acomprehensive review has been undertaken, in other cases a point has been made by quotingtypical examples. Often Working Group members have relied heavily on their own work -justifiable because it was directly relevant, but also very necessary because there was not thetime or resources for a more comprehensive review. Thus this report should not be consideredan exhaustive review on air-sea flux research. However the WGASF does believe that thereport represents a reasonably balanced assessment of the present state of air sea fluxdetermination.The conclusions with regard to the relative merits of different flux products are lessprecise than might have been hoped or originally anticipated. However it must be emphasisedthat this uncertainty does not stem from a lack of evaluation effort (although more work wouldclearly be desirable). The detailed discussions presented in the earlier chapters of the reportemphasise that there are a number of issues where our knowledge remains inadequate. Theseinclude, for example, the question of the correct transfer coefficients for the turbulent fluxes,and the reconciliation of measurements and radiative transfer modelling for the radiationbalance. Many of these issues have been the subject of research for many years; however therehas recently been significant progress both in measurement techniques and in ourunderstanding. It is likely that, within a few more years, the absolute values of the surfacefluxes over the earth's oceans will be much better known. It is hoped that this report representsa positive step toward that aim.Executive Summary viii.Report of the Working Group on Air Sea Fluxes June, 2000Executive Summary ix.Report of the Working Group on Air Sea Fluxes June, 2000EXECUTIVE SUMMARYThe Working GroupThe main aims of the Joint JSC/SCOR Working Group on Air Sea Fluxes1 were toreview the requirements of different scientific disciplines for surface flux data sets, to catalogueavailable surface flux data and flux-related data sets, and to review the strengths andweaknesses of each. The three meetings of the Working Group were (1) NOAA Headquarters(Silver Spring, Maryland, USA), 22nd to 25th October 1997; (2) KNMI (De Bilt, TheNetherlands) 14th to 17th April 1999; (3) SOC (Southampton, UK) 6th to 10th December,1999. The Working Group's findings are contained in this report, which has been prepared onan accelerated schedule at the request of the JSC.The Fluxes ConsideredThe Fluxes considered by the working group were the transfers of heat, water andmomentum between the sea and the air. The flux products considered were those which: werereadily available to all scientists; were a gridded, derived surface flux or flux related field;covered major ocean basin scales and upwards; and, were adequately documented. TheWorking Group has assembled a catalogue of flux products which meet these criteria. It will beavailable on the Internet2 and described in an appendix to this report. Given the limited timeand resources, it only proved practicable to evaluate a subset of the more recently available fluxproducts. The Working Group also summarised the State-of-the-Art with regard to the basicmeteorological variables (used in flux calculation), and other important components of theclimate system such as Waves and Sea Ice.Requirements and data sourcesThe Working Group has reviewed the requirements for flux fields and thecharacteristics of the various data sources available for flux estimation. The latter include insitu data from ships, buoys, rigs, etc.; instruments on satellites either in polar (or otherprecessing) orbits or geostationary; and models, run either in operational Numerical WeatherPrediction (NWP) or reanalysis modes. The requirements for surface flux fields are many anddiverse. However the Working Groups review suggests that there are four main types ofrequirement.One class of requirement is the need for surface flux fields on high time and spaceresolution; typically 3 hours and 50 km. Such data are needed, for example, for forcing oceangeneral circulation models, for wave modelling and forecasting, and for regional weathernowcasting and prediction. Such high space/time resolution is only possible at present usingmodels, however in future, high resolution satellite products will become available. Thequality of the wind fields is particularly important for these applications and operationalsatellite-borne scatterometers are expected to make an important contribution.A second class of requirement is for flux fields on longer space and time scales but withhigh absolute accuracy, typically within a few W/m2. Customers for these data include climateand sea-ice modellers. At present such needs are difficult to satisfy because of the problems ofquantifying systematic errors in the flux data. Only by comparing flux values obtained usingdifferent data sources and estimation methods are these stringent accuracy requirements likelyto be obtained. 1 (SCOR Working Group 110: Intercomparison and Validation of Ocean-Atmosphere EnergyFlux Fields)2 available from the WG web site: http://www-pcmdi.llnl.gov/airseawg/ (mirrored athttp://www.lmd.jussieu.fr/pcmdi-mirror/airseawg/ and at http://www.bom.gov.au/bmrc/clch/pcmdi-mirror/airseawg/ )Executive Summary x.Report of the Working Group on Air Sea Fluxes June, 2000Climate variability studies define a third class of requirement. Again high absoluteaccuracy is desirable, however consistency and continuity of the data over a suitable timeperiod (and spatial domain) is the vital need. The longest time data set available is from themerchant ship observations (as assembled in the Comprehensive Ocean-Atmosphere Data Set,COADS). However the ship data suffers from poor sampling in many regions and timedependent biases due to changing ship characteristics and observational technique.Comparison with the reanalyses suggests that the COADS data can give useful information oninterannual variability of monthly mean fluxes in the mid-latitude North Pacific and NorthAtlantic, and in parts of the South Atlantic; over much of the tropics and the SouthernHemisphere, north of 40S, the COADS data can only define a long-term monthly meanclimatology. However, the reanalyses also suffer from time dependent biases because of theassimilation of data from the evolving observational system. Thus, whether using models orobservations, the detection of long-term trends in the surface fluxes is difficult if not impossible.Satellite data has now been available for a significant and lengthening time period. Such datahas the potential to provide a consistent time record but only if great care is taken to inter-calibrate sensors on successive satellites.A final flux requirement is for high quality verification data. For example, NWPmodels need independent estimates of the basic meteorological variables and the fluxes forverification of the model physics. Such estimates must be associated with a realistic errorassessment and specification of the true resolution of the data. Similarly Ocean GeneralCirculation Model development would be aided by air-sea interaction experiments withmeasurements of the fluxes in both atmosphere and ocean, with ocean mixed layermeasurements sufficient to allow budget closure. Such high quality measurements are not easy.The "flux buoy" systems are an example of instrumentation capable of providing verificationdata for both models and satellite - borne instruments.Flux VariabilityFor many applications it is important that a flux product contains accurate informationon the variability of the fluxes. In some ways the determination of flux variability is easier thanestimating mean flux values; systematic errors are of less importance, and the main spatialscales of longer term variability tend to be large. Numerical weather models tend to performbetter with regard to determining the variability than they do in determining the mean; probablybecause they are targeted at forecasting the weather - the day to day variations in climate.However despite these considerations, and the undoubted importance of surface fluxvariability, the Working Group considered that a detailed assessment of variability in the fluxproducts could not be accomplished at this time. The report contains a brief discussion of fluxvariability to set the context for the flux field evaluations. It was noted that most previousstudies had concentrated on the variability of basic variables, such as sea surface temperature(SST), rather than the fluxes themselves, and that further studies focused on the fluxes weredesirable. The Working Group concluded that a thorough assessment of the variability ofavailable flux products must remain an important priority for further studies, perhaps by a futureworking group.Direct Flux measurementDirect measurements of the air-sea fluxes are too few to contribute directly to thecalculation of large scale flux fields. Rather they are important for developing, calibrating, andverifying the parameterisation formula used to estimate the fluxes from the basic variables.Thus the accuracy of the direct flux determinations represents an accuracy limit for the indirectestimates. The methods of direct measurement of the radiative fluxes, turbulent fluxes, andprecipitation were reviewed.Accurate measurement of surface radiative fluxes is not easy, and with regard to theinstrumentation needed there are contrasting views. Experts in radiation measurement assertExecutive Summary xi.Report of the Working Group on Air Sea Fluxes June, 2000that only sophisticated instruments and procedures, as defined by the Baseline SurfaceRadiation Network standards, will provide adequate accuracy. However such measurements aredifficult to implement at sea and will be restricted to very few sites. In contrast, scientistsinvolved in air-sea interaction studies believe that, by avoiding known pitfalls, consistentresults can be obtained from simpler instrumentation. Much depends on the accuracy and timeresolution sought. The simpler instrumentation should be adequate to highlight the biases in theradiative fluxes in many models (presently a few 10's Wm-2 in some regions).Estimation of the turbulent fluxes using fast response instrumentation mounted onresearch ships or buoys is becoming more common. The techniques used in the eddycorrelation method and the less direct, but more easily implemented, inertial dissipation methodare summarised. When implemented on a ship both methods are prone to errors but of differingnature. Thus in future it is desirable that both methods be used to fulfil an importantrequirement, the determination of the transfer coefficients for flux parameterisation (seebelow).Rainfall, particularly that from tropical convection, is highly sporadic both in time andspace. For most applications area averaged values are required. These can be obtained by shipor aircraft mounted radars on the local scale, or from satellites in the longer term and on largerscales. However the sensors and retrieval algorithms need to be verified by reliable ground-truth measurements. Accurate measurement of rainfall at sea presents difficulties in tworespects; there are measurement (instrumental) problems, and sampling problems. New rain-gauges have been developed for high wind conditions but air-flow distortion by the ship remainsa problem. Even during intensive measurement campaigns the spatial and temporal samplingmay be inadequate. Results of different satellite algorithm intercomparison projects suggest thatpresently available in situ measurements and radar estimates are not representative enough toserve as a verification data source.Parameterisation of the fluxesRadiative fluxesThe parameterisation formulae for obtaining the net surface shortwave and longwavefluxes from ship observations are relatively crude, relying on the estimate of cloud amount tocharacterise the effects of cloud on the fluxes. Satellite based estimates use measurements ofthe top-of-the-atmosphere radiation, and radiative transfer models (RTM's) to estimate thesurface value. For clear-sky conditions, the surface LW values from a sophisticated RTM within situ radiosonde data can be used to check the calibration of pyrgeometers. However undercloudy conditions longwave RTM's are limited by the information available on both cloud baseheight and cloud emissivity. This limits the reliability of satellite estimates of surfacelongwave; new techniques are being developed but still need evaluation. Knowledge ofatmospheric scattering is essential for shortwave RTM's under both clear and cloudy conditions.Uncertainty in the diffuse component may cause RTM's to over estimate the surface shortwavecompared to measurements, possibly by a few percent. Despite this, satellite estimates ofshortwave radiation are more reliable than for longwave. Atmospheric models (NWP andreanalyses) use simplified RTM schemes for computational efficiency. However at present thelimiting factor in most models is the representation of clouds and their radiative effects. Inparticular, low level stratiform clouds are often poorly modelled.Turbulent fluxesThe parameterisations for the turbulent fluxes are based on values of basicmeteorological variables such as wind speed, temperatures, and humidity. Since these "bulkformulae" are used for flux estimation from not only in situ data, but also satellites, and mostmodels, they were reviewed at some length by the Working Group. Despite years of researchthere is still uncertainty with regard to the behaviour of the various transfer coefficients, inparticular for sensible and latent heat at wind speeds over 10 m/s. For this reason, some newerparameterisations use wind stress estimates and surface renewal theory to predict the heatExecutive Summary xii.Report of the Working Group on Air Sea Fluxes June, 2000fluxes. However, the variation of the drag coefficient with sea state remains controversial,particularly in sudden storms, or in light wind, swell dominated conditions. The neweralgorithms also incorporate treatments of other physical processes, particularly light windphenomena such as the diurnal warm surface layer and wind gustiness.Error EstimationAn important requirement of flux products, which is rarely satisfied, is the realisticestimate of accuracy. The error estimates should consider as many contributing factors aspossible, both random and systematic. Representative error estimates may be difficult todetermine. The report reviews some theoretical approaches, gives practical examples ofquantifying random, systematic, and sampling errors, and shows that it is possible to estimatethe cumulative errors.Many applications need gridded flux fields that are complete both in space, and oversome significant time period. The non-uniform sampling of in-situ and remotely-sensed fluxesnecessitates the use of an analysis method to interpolate and, sometimes, extrapolate the data.Such techniques alter the error budgets of the flux fields. For example, depending on the choiceof method and averaging period, sampling errors in remotely-sensed data may dominate thesystematic biases. The advantages and disadvantages of two examples of mapping techniqueswere discussed. The successive correction technique is computationally simple and has beenwidely used. However it creates fields with spatially varying resolution, and spreadsinformation so as to degrade the flux values in well sampled areas. In addition, error estimatesare difficult to determine. Objective analysis techniques which take proper consideration ofobservational and sampling errors, and are capable of producing a posteriori error estimates,should be employed instead of the sucessive correction method. In particular, state-of-the-artvariational techniques developed in the context of atmospheric and oceanic data assimilationshould be applied to the analysis of surface marine fluxes.Evaluation of available productsMethods of evaluationThe methods of evaluation for the basic variables and the flux fields includecomparisons to reference data sets (such as direct flux measurements or those from high-qualitysources) and intercomparisons between products. For the fluxes it is also possible touse integral constraints such as the global heat and water balance, oceanic transports, andenclosed basin budgets, as well as comparisons with other indirect flux estimates.Basic Meteorological VariablesIt is important to know the accuracy of our estimates of the basic meteorologicalvariables. Errors in these variables affect the accuracy of the surface flux values. Using thebasic variables to verify model and satellite derived estimates avoids uncertainty due to the useof different flux parameterisation formula. As a rough guide, achieving 10 Wm-2 accuracy in aheat flux estimate requires knowledge of wind speed to a few tenths m/s, air and seatemperatures to about 0.2K, humidity to about 0.3 g/kg; these requirements vary regionallybeing generally more stringent in the tropics.For most of the basic variables, the Working Group reviewed the accuracy of highquality in situ measurements, standard in situ measurements, and satellite based estimates. Thehigh quality measurements, as might be obtained from research experiments and "flux buoys"are of value for verification and for parameterisation development. The standard in situ data,for example from Voluntary Observing Ships (VOS), represent our only near surfacemeasurement of many quantities for use both in flux estimation and model assimilationschemes. As instrumentation and retrieval algorithms improve, the satellite remote sensed dataare increasingly being used to define the spatial and temporal variations of a given variablewhile the in situ data are used for verifying the retrievals.Executive Summary xiii.Report of the Working Group on Air Sea Fluxes June, 2000The ocean affects the surface fluxes through the sea surface temperature (SST). In situobservations of "SST" are often made at a few metres depth; the SST difference due to the coolocean surface skin can be allowed for, but significant errors may occur if a near surface diurnalthermocline is present. Satellite estimates are limited by the effects of cloud and biases due tocloud contamination (particularly at night) and the effects of aerosols. The better SST analysesuse combinations of satellite and in situ data. Typical RMS differences between analyses inmost regions are a few tenths K, greater than that in the Southern Ocean, in coastal regions, andin sea ice areas.Variations of sea surface salinity (SSS) are not significant for air-sea flux calculation(although its mean effect must be allowed for in latent heat flux calculation). However SSS isimportant for flux verification and for the freshwater forcing of ocean general circulationmodels. In situ salinity data remain relatively few but will increase significantly due to projectssuch as ARGO. The potential for, and difficulty of, salinity measurement from space wasreviewed. Useful data may be obtained in regions of large salinity gradients, however the 0.1psu accuracy needed by ocean models is unlikely to be achieved by remote sensing in the nearfuture.Near surface air temperature is not easy to measure accurately in the marineenvironment. Biases due to radiative heating of the sensor (or its surroundings), sea spray, etc.,are common. VOS data tends to be biased high, probably by a few tenths K on average, but byseveral degrees on occasion. New techniques for estimating air temperature from satellite datahave been suggested; further evaluation is needed. Fortunately, except in high latitudes, thesensible heat flux is a minor part of the heat balance. However air temperature is also needed inall regions for stability corrections during latent heat flux calculation.The latent heat flux depends on the humidity of the near surface air. Humiditymeasurement on ships is in some ways easier than temperature determination. However it islikely that VOS data are biased high, particularly where fixed thermometer screens are used.Methods of determining near surface humidity data from satellites have improved significantlyand useful accuracy is obtained in many regions. However developing an algorithm whichperforms well both within and outside of tropical regions remains a problem.There is no accurate absolute standard for near surface wind measurement over the openocean. Anemometer-based and visual estimates from ships, and buoy measurements, all aresubject to systematic and random errors. "Beaufort scale" values for the visually estimatedwinds have been calculated which, in the mean, result in good agreement between visual andanemometer winds. Importantly, the main reasons for the differences from previous BeaufortScales are now understood. Buoy winds may be biased low in high sea state conditions.Satellite scatterometer winds are considered significantly superior to ship winds but thealgorithms were developed using buoy data so biases may still remain. Passive microwavederived wind speeds are useful for heat flux estimation. They have the potential to providegood sampling and an increasingly long time series.Waves are an important climatic variable in their own right and may, to some stilluncertain extent, modulate the surface fluxes. The amount of wave data from buoys isincreasing but the spatial distribution is poor. Wave data from ship reports is generallyconsidered to be of poor quality; however it has been used to construct wave climatologieswhich are still much in use. One problem is the separate reporting of, but unclear distinctionbetween, sea and swell. Satellite altimeters provide verified global estimates of wave height;algorithms for scatterometer wave estimation are still being developed. Wave modellers believethat, given an accurate wind field of high temporal and spatial resolution ( say 3 hours and 50km), then a good quality wave field can be calculated.Cloud amount is used in some radiative flux parameterisations and is also important forthe verification of NWP and climate models. Sources of cloud data reviewed included shipreports and the International Cloud Climatology Project (ISCCP). Precipitation also may beestimated from ship weather reports; however variable satellite based estimates offer betterExecutive Summary xiv.Report of the Working Group on Air Sea Fluxes June, 2000future potential. The Global Precipitation Climatology Project (GPCP) and the CPC MergedAnalysis of Precipitation (CMAP) have produced precipitation climatologies which merge insitu and satellite estimates.River inflow must not be neglected as a component of the ocean freshwater budget.Data are available through the International Hydrography Program (IHP) which published aGlobal River Discharge Catalogue, and from the Global Runoff Data Centre (GRDC).Sea ice is an important climate variable which significantly modifies the surface fluxesand is also a component of the ocean fresh water budget. At present, sea-ice models canreproduce a realistic annual cycle of the global sea-ice cover. Probably this is despite large butopposite errors in the fluxes over sea-ice. Now that multidecadal satellite and submarinerecords of ice extent and ice thickness are available, the challenge is to understand interannualvariability. This will require a higher level of accuracy in estimates of the radiative forcing ofthe ice surface and data on the interannual variability of snowfall. The errors in reanalysis andsatellite-derived surface flux fields must be better characterised and then reduced by morethorough comparisons amongst themselves and with in situ observations. Boundary layerformulations need to be examined for their applicability in predominantly stable polarconditions.Flux field productsNewer products based on in situ data3 have benefited from the valuable new data setsconstructed by the COADS project. For wind stress the newer climatologies are definitely to bepreferred since previous climatologies, such as Hellerman and Rosenstein (1983) or Oberhuber(1988), over estimated the wind stress in most areas. However poor in situ sampling in thesouthern hemisphere, and particularly in the Southern Ocean (where the winds areunderestimated in almost all in situ climatologies), suggests that the lengthening satellitederived wind data-set is a promising alternative. For the heat fluxes, our inability to balancethe long term mean global heat budget together with the uncertainty in the transfer coefficientvalues, remains a major hindrance to evaluating many of the flux products.To illustrate the problem: the global heat balance of the SOC climatology was 30Wm-2;almost exactly the same as the for the UWM product. This was despite the use of correctionsfor observation biases in the in situ data. In a tuned version of UWM, shortwave radiation wasdecreased (by about 10%) and latent heat flux increased (by about 14%) resulting in a balancedbudget. Previous flux studies have required similar adjustments, often to ensure that the heattransport in the ocean agreed with oceanographic estimates. The satellite estimates of net SWradiation do not support reduction of the shortwave flux (although they in turn may be over-estimated). However by adjusting the other fluxes in various ways a variety of balancedbudgets can be produced. As a result, the various estimates span the range of the values fromNWP models and reanalyses4. The overall conclusion is that for the global balance, and alsofor zonal averages, the differences between the various flux products are within the uncertaintyof the observational estimates (with only one or two outlying exceptions).Interestingly, almost all adjustments suggest that the latent heat flux must be increased,toward the higher values for latent heat flux predicted by most models. Such an increase wouldgenerally worsen the agreement between climatologies (such as UWM or SOC) and the highquality buoy data; however the latter also depend on transfer coefficients to determine thefluxes. Thus while comparisons of fluxes from ship observations and buoys suggest that 3 Here we will refer to in situ climatologies produced at the University of Wisconsin-Milwaukee (UWM), the Southampton Oceanography Centre (SOC) and the Institut f rMeereskunde, Kiel (IfM).4 The reanalyses considered were the ECMWF 15 year reanalysis (ERA15), the NCEP/NCARreanalyses (NCEP1 and NCEP2) and the Goddard GEOS reanalysis (GEOS1).Executive Summary xv.Report of the Working Group on Air Sea Fluxes June, 2000NCEP1, NCEP2 and ERA15 evaporation may be too large, we can not be certain. This alsoreflects the uncertainty in our knowledge of the strength of the hydrological cycle. New satellitebased products may help through better estimates of precipitation (although there is a lack ofverification data) and global estimates of evaporation. With regard to the latter, the HOAPSclimatology5 suggests that in situ climatologies under-estimate evaporation in the SouthernOcean. Further development of these satellite products is needed, for example, the HOAPSlatent heat flux is biased low in convective tropical regions.Other significant differences exist between the different products on a regional level andin some cases the cause can be identified. For example, the reanalyses all appear to havesubstantial errors in surface radiation fields, reflecting problems both with regard to clouds andclear sky radiation. NCEP1 had errors in the downward shortwave and the albedo which tendedto cancel. A major shortcoming for ERA15, GEOS1 and NCEP2 appears to be a lack of low-level stratiform clouds over the eastern subtropical oceans. Satellite Surface Radiation Budget(SRB) short wave radiation appears to have more realistic patterns than the reanalyses, but mayhave a positive bias. Even the estimates from crude in situ parameterisations are in betteragreement with SRB than are the models.Choice of ProductConsidering the recent, global climatologies based on in situ data, UWM and SOC aresimilar. The SOC climatology gives marginally better agreement with buoy measurements andmay be preferred for regional studies. However for a balanced global net heat flux (e.g. forforcing models) then the tuned UWM fluxes must be preferred; these also cover a longer timeperiod. More use should be made of the UWM sensitivity fields for producing new versions ofthe constrained fluxes. In all cases the sampling density in any particular region must beconsidered when using these products.Covering the Atlantic Ocean only, the IFM climatology has used a better mappingtechnique, has good error estimates, and implies a realistic meridional heat transport. The latteris due to the choice of radiation parameterisations and transfer coefficients which are notgenerally used for open ocean regions (but are within the bounds of uncertainty). Furtherverification of this new product is desirable.The NCEP1, NCEP2 and ERA15 reanalyses appear to have more realistic fluxes thanthe earlier GEOS1 reanalysis (the GEOS data assimilation system has since been substantiallydeveloped). At present the NCEP1 reanalysis covers the longest period and is the most widelydistributed and studied. It has the most realistic oceanic low-level stratiform clouds. NCEP2corrected mistakes in NCEP1 and used improved short wave radiation and boundary layerparameterisations. Its precipitation patterns, downward short wave radiation at the surface, andequatorial wind stress appear superior to NCEP1, but its sensible heat flux over the oceans islower than all other estimates. ERA15 is the highest resolution reanalysis now available. Itappears to produce the most realistic precipitation patterns of the four reanalyses and has morerealistic surface downward short wave radiation than NCEP1. Concerning month-to-monthvariability in surface fluxes, GEOS1 had, in general, the smallest variability, NCEP2 tends tohave the largest.For most of the world ocean, satellite based products are capable of providing muchbetter time and space resolution, and more consistent accuracy, than fluxes based on in situobservations. With passing years the restricted time period for which data is available isbecoming less of a disadvantage. The surface shortwave flux fields are superior to the modelestimates; and presumably also to in situ estimates. However on a regional basis the differencebetween satellite and ship estimates is similar in magnitude to the difference between differentsatellite fields. The satellite longwave estimates remain less reliable. Satellite scatterometerwind data has significantly less scatter than ship observations. Satellite estimates of the latent 5 Hamburg Ocean Atmosphere Parameters and fluxes from Satellite data.Executive Summary xvi.Report of the Working Group on Air Sea Fluxes June, 2000heat flux have improved substantially but problems still remain in some regions. Satelliteprecipitation estimates have not been evaluated here other than to review the variouscomparison projects that have been undertaken by others; lack of verification data is a majorproblem. For the future, satellite products should exploit combinations of different sensors,preferably flown on a single platform (as for example, the TRMM mission). Combinedsatellite and in situ data products have the potential to provide a data set which is independentfrom the models.In summary, all the various flux products that were reviewed in this report havedifferent advantages and disadvantages. However, it is also evident that the various fluxproducts are complementary rather than being in competition, and that the ensemble of air-seaflux estimates that they represent cover the time and space scales that ocean science studies areinvestigating.Specific RecommendationsThe main conclusions from the Working Group's endeavours appear throughout thebody of the report, are summarised in the final Chapter, and have been described above. At itsfinal meeting, the Working Group also adopted the following specific recommendations:❍ Reanalyses should be performed every 5-10 years by more than one centre; adequateresources should be provided to the reanalysis efforts to improve their surface fluxes, tocarry out and evaluate the reanalyses and to ensure that they are easily available to theentire scientific community. Surface fields should be output every 3 hours .❍ Evaluation of the surface fields and fluxes from global operational NWP systems willbenefit future reanalyses as well as provide critical guidance and product uncertaintyestimates to users of these flux products. The WGNE's plans to archive and evaluate thesurface fields and fluxes from a number of global NWP systems should therefore besupported.❍ A network of high quality “flux reference platforms” (combination of long-termmoorings and ships) should be established to deliver highly accurate values of stress andall components of the air-sea heat fluxes for, inter alia, verification of surface fields andflux estimates from satellites and models, and the long term calibration of satellitesensors.❍ There is continuing need to compare and assess the quality of fluxes derived fromvarious sources, and to evaluate the parameterisations used. Encouragement should begiven to efforts to enhance the reliability of momentum, net heat and freshwater fluxesby combining the best estimates from these various sources.❍ Support should be provided for the continuing assembly of flux and flux-related datasets (in particular Voluntary Observing Ship-based collections such as COADS andother historical data). Continued efforts are needed to remove non-stationaryobservational biases in historical data. Basic meteorological variables should beincluded as well as uncertainties, error estimates and adequate documentation for allflux data sets. A catalogue of flux data sets should be maintained on the Internet.❍ Research and field experiments are needed to improve boundary layerparameterisations and bulk formulae, especially in regions where our physicalunderstanding is poor. Adequate resources for complete analysis of the resulting dataare necessary to realise the full benefits of the field experiments.1 - Introduction 1 1.1 The Surface Flux Field problem1.2 The Working GroupReport of the Working Group on Air Sea Fluxes June, 20001 INTRODUCTION1.1 The Surface Flux Field ProblemThe extent to which the earth's climate may be changing, either due to the actions ofmankind or natural variability, is at present a major cause of societal concern. Numericalmodels of the earth's climate system are being used to quantify the possible effects of climatechange. A vital test of such models is to verify that the present day climate is correctlysimulated. An important aspect of that simulation is the transfer of energy between the oceanand atmosphere, the air-sea fluxes of heat, water, and momentum. These fluxes represent key-processes in the earth's climate system; they establish links and feedbacks between its maincomponents, the ocean and the atmosphere.In recent years there has been significant progress in our knowledge of the surfacefluxes. Air-sea interaction experiments have refined the formulae used for flux calculation, wehave better understanding of the errors in data and the corrections needed, methods ofobtaining fluxes from satellite data have been developed, and weather forecast models havebeen used to produce a consistent re-analysis of the past state of the atmosphere.Unfortunately, despite these advances, the observational flux data remains limited and variousattempts to produce climatologies of the flux fields, and to describe their climatic variability,differ in significant aspects. The major problem is that, while the magnitude of the fluxcomponents is of order a few 100 W/m2, both the inter-annual variability and the mean heatflux in any region is typically a few 10's W/m2, and the mean net heat flux must balance towithin a only few W/m2. This places stringent requirements on the accuracy of flux estimateswhich we are still struggling to meet. For example Figure 1.1 shows recent estimates of theannual mean distribution of the net heat flux. Two examples of fluxes calculated from weatherforecast models, which have been used to reanalyse the past weather, are compared to twoclimatological estimates based on ship weather observations ("COADS"). It is obvious thatthe fluxes calculated from the ship data have more areas where heat is going into the oceancompared to the other estimates. Since unrealistically large ocean heating would result, thesefluxes cannot be correct and various adjustment schemes have been proposed. However,where comparisons with high quality buoy data are available, the ship data often show goodagreement - better than that which the models typically achieve. Thus the bias can not beuniform but must vary both regionally and temporally. Similarly, while the heating andcooling calculated by the models is better balanced globally, in some regions large errors mayoccur both in the flux components and the net heat flux.Given this situation, the main aims of the Joint WCRP/SCOR Working Group on AirSea Fluxes (SCOR Working Group 110: Intercomparison and Validation of Ocean-AtmosphereEnergy Flux Fields) are to review the requirements of different scientific disciplines for surfaceflux data sets, to catalogue available surface flux data and flux-related data sets, and to reviewthe strengths and weaknesses of each. The working group's findings, contained in this report,will be of particular use for those developing numerical climate models for research andforecasting purposes.1.2 The Joint WCRP/SCOR Working Group on Air Sea Fluxes1.2.1 Creation of the Working GroupProposals for co-ordinated activities in the World Climate Research Programme(WCRP) in the area of air-sea fluxes stemmed from the WCRP Workshop on Air-Sea FluxFields for Forcing Ocean Models and Validating General Circulation Models in October 1995(White, 1996a). This workshop was a landmark in encouraging interaction and dialoguebetween the diverse scientific communities involved in producing and using air-sea fluxes;hitherto it had been limited. Among the principal recommendations from the workshop wasthe need to continue to foster interdisciplinary consultations in this area, and to catalogue and1 - Introduction 2 1.2 The Working GroupReport of the Working Group on Air Sea Fluxes June, 2000keep under review available surface flux and flux-related data sets. It was suggested that alimited-life group should be established to carry forward the needed activities.Figure 1.1 The annual mean net surface heat flux from the atmosphere into the ocean (negativeimplies ocean cooling). Shown as examples are values from the NCEP/NCAR reanalysis, theECMWF reanalysis, and the climatologies based on ship data in the COADS data set ascalculated by UWM and SOC (see Chapter 11 for a detailed discussion)The Scientific Committee on Oceanic Research (SCOR) is also very interested inair-sea fluxes, especially their use in the determination of meridional heat and freshwatertransports - basic features of ocean climate. Therefore a proposal was made to establish aSCOR Working Group on Intercomparison and Validation of Ocean-Atmosphere Flux Fields.Since the roles of the WCRP and SCOR groups overlapped and included several commonmembers, it was considered reasonable to merge the interests of the WCRP and SCOR in thisarea and to create a joint WCRP/SCOR Working Group on Air-Sea Fluxes. Theestablishment of the joint group, its terms of reference and membership were duly endorsed bythe Joint Scientific Committee (JSC) for the WCRP at its eighteenth session (Toronto, March1997).1.2.2 The remit of the Working GroupThe Terms of Reference (TOR) agreed between the JSC and SCOR were:TOR.1 to review the requirements of different scientific disciplines for surface flux data sets;TOR.2 to compile a catalogue of available surface flux data and flux-related data sets,including those becoming available from the various reanalysis projects, and to review, inconsultation with users and producers, the strengths and weaknesses of these data sets;TOR.3 to inform the scientific community of the work of the group by the use of the WorldWide Web, by the publication of the final catalogue, and by convening, at a suitable time, ascientific workshop;1 - Introduction 3 1.2 The Working Group1.3 The variables to be consideredReport of the Working Group on Air Sea Fluxes June, 2000TOR.4 to keep the JSC and SCOR informed of progress in the area and presentrecommendations for action as necessary.1.2.3 The Working Group's Mode of WorkingThe first meeting of the Working Group on Air-Sea Fluxes took place at NOAAHeadquarters (Silver Spring, Maryland, USA), during the 22nd to 25th October 1997. Themode of working was agreed and a draft table of contents for the final report was adopted.Over the following 17 months a draft report was assembled from the contributions of workinggroup members. This draft was discussed at the second Working Group meeting held 14th to17th April 1999, at KNMI (De Bilt, The Netherlands). Although the Working Group hadplanned to finish its work by summer 2000, in response to a request from the JSC, theschedule was brought forward to produce a completed draft by the end of 1999. Accordingly athird meeting was scheduled to be held at SOC (Southampton, UK) during 6th to 10thDecember, 1999. At this meeting the report was reviewed and the Working Groupsconclusions agreed. The final version of the Working Group report is the result of furtherediting following that meeting. Because of the shortened time scale it has not proved possibleto circulate the report within the air-sea flux community for comment and the final versiontherefore mainly represents the understanding and beliefs of the Working group members. 1.3 The variables to be consideredThe basic set of physical fluxes between the atmosphere and ocean are the transfers ofshortwave radiation (Qsw - wavelength 0.3 to 3 µm), longwave radiation (Qlw - wavelength 3 to 50µm), sensible heat (Qsen), water vapour (E ), precipitation (P ), and momentum (τ - windstress). For many purposes the radiative fluxes are characterised by the downwards componentof the flux, and the upwards shortwave and longwave fluxes are determined from the surfacealbedo, A, and the sea surface temperature respectively. Given this basic set of fluxes, otherflux variables which couple the atmosphere and ocean can be determined. Thus for the latentheat flux Qlat, net heat flux Qnet, and the freshwater flux, Fw, we have:QE lat ∝ QQQ QQ net sw lw sen lat =+++ FE P w =− (1)The dynamic coupling between atmosphere and ocean depends both on the temporal and spatialvariability of the fluxes. Water masses can form at the surface of the ocean in response to thebuoyancy flux Fρ(Qsw, Qlw, Qsen, E, P) and the mixing by the wind, and surface water is alsocarried into the interior of the ocean by Ekman pumping due to curl τ and by spatial differencesin the ocean's response to the buoyancy forcing. The ocean currents that redistribute heat aredriven both at the surface and in the interior by curl τ and the density gradients that result fromthe surface buoyancy flux.The air sea fluxes can be obtained in several ways. The most straightforward, but alsomost difficult, is the direct measurement of the fluxes. Due to its complexity, directmeasurement of fluxes is limited in space and time. However, it provides valuable data fordeveloping formulae to estimate the fluxes and for the validation of fluxes derived from othersources.Fluxes with a larger coverage of space and time usually come from satellitemeasurements and/or the application of bulk formulae. Bulk formulae are a parameterisationof fluxes in terms of variables that can be measured more easily, e.g., temperatures. Thesevariables are referred to as basic variables in this report (Table 1). Traditionally, they areobtained from ship observations, which have been collected in the COADS data set (Woodruffet al 1993), but nowadays also satellite measurements are used. The same bulk formulae thatare applied to measurements are also used in numerical models to calculate fluxes from thebasic model variables. Because most of the available flux values are based onparameterisations in terms of the basic variables, large parts of this report are devoted toparameterisation formulae and the measurement of those basic variables.1 - Introduction 4 1.4 The flux products assessed1.5 Layout of the reportReport of the Working Group on Air Sea Fluxes June, 20001.4 The flux products assessedDuring the first meeting the working group determined that the flux products whichwould be considered would be those which:(i) are readily available to all scientists either routinely or on request;(ii) are a gridded, derived surface flux or flux related field;(iii) cover major ocean basin scales and upwards;(iv) are adequately documented.The Working Group has assembled a catalogue of flux products which meet thesecriteria (Appendix C). However, given the limited time and resources, it has only provedpracticable to consider a subset of these products. Nevertheless this subset includes the mainrecent global flux field products (Table 2). Other products are included in the Catalogue ofFlux products (see Appendix B) but are not considered in the Report, for example because theyare new products that could not be evaluated before the report was finalised (e.g. J-OFURO,Kubota 1999).Table 1. Summary of the Basic Flux-Related VariablesSea surface temperature (SST) WavesSea surface salinity (SSS) CloudinessAir temperature PrecipitationAir Humidity (specific humidity, or mixingratio)River inflowWind (speed and direction) Sea IceTable 2. Summary of the flux products considered in this report.Type Product Referencein situ UWM (COADS) da Silva et al. (1994)in situ SOC (COADS) Josey et al. (1999)in situ IFM (COADS) Lindau (2000)model ECMWF: ERA15 Gibson et al. (1997)model NCEP/NCAR Re-analysis 1 Kalnay et al. (1996)model NCEP/NCAR Re-analysis 2 Kanamitsu et al. (2000)model GEOS1 Schubert et al. (1995)Satellite HOAPS Schulz et al. (1997)Sat. Rad. Various US productsSat. Rad. New Japanese productsCombined Residual Trenberth & Solomon (1994)1.5 Layout of the reportThe different requirements for flux data sets will be summarised in Chapter 2. In manycases the requirements include knowledge of the variability of the fluxes. Indeed, the space-time variability of the fluxes not only defines a set of accuracy requirements, it also impacts thesampling requirements, and the accuracy to which the fluxes can be determined. While1 - Introduction 5 1.5 Layout of the reportReport of the Working Group on Air Sea Fluxes June, 2000detailed treatment of flux variability was considered beyond the remit of the present workinggroup, Chapter 3 gives a summary of the state of knowledge of flux variability. Thecharacteristics of the data sources for either flux values, or values of flux related variables, willbe considered in Chapter 4. We will then summarise the state of the art with respect to Directflux measurement (Chapter 5). However most flux fields are based on parameterisationformula and these will be considered at some length for the radiative fluxes (Chapter 6) andturbulent fluxes (Chapter 7). Sources of error in the flux determinations, such as the samplingdensity, are considered in Chapter 8. Chapter 9 discusses methods of evaluating the variousavailable products for fluxes, and flux related variables. The state of the art for determiningthe basic variables will then be reviewed (Chapter 10). The results of the evaluation of the fluxproducts are given in Chapter 11. Finally the conclusions (Chapter 12) will include suggestionsfor future flux related activities. The working group membership is listed in Appendix A,Appendix B describes the catalogue of flux products , Appendix C lists the acronyms used inthe report.6Report of the Working Group on Air Sea Fluxes June, 20002 - Requirements for surface fluxes 7 2.1 Introduction2.2 NWP systemsReport of the Working Group on Air Sea Fluxes June, 20002 THE REQUIREMENTS FOR SURFACE FLUX DATA SETS.2.1 IntroductionThere are many requirements for surface flux values, for example, operationalmodelling, nowcasting/forecasting; climate change studies and diagnostic studies;atmospheric and oceanic synoptic scale case studies; shelf and coastal seas studies; oceanic,climate and earth observing systems; biogenic cycles, wave forecasting, marine engineering,etc. To consider the requirements of all of these applications would be a large task andprobably not helpful. However, the types of surface flux fields considered by the WorkingGroup are large scale, non-real time, documented products. The main requirements for thesetypes of flux fields are those for climate models: atmospheric General Circulation Models(GCM's), oceanic GCM's, and, for verifying coupled atmosphere ocean GCM's.This Chapter will discuss the flux requirements for atmospheric and ocean GCM's on aglobal scale, and also the requirements for some specific applications: regional applications andstudies, wave models, and sea ice modelling. The needs of Global Climate Models andClimate Change Studies will also be discussed. Finally an attempt will be made to summariseall these different requirements for surface flux information.2.2 NWP systems—assimilation and forecasting2.2.1 IntroductionAtmospheric computer models used in meteorology are based on the laws of physics asexpressed in the equations of fluid mechanics (e.g. see Kalnay, 1999). For both numericalweather prediction (NWP) and climate simulations they are integrated in time from an initialrealistic state of the atmosphere obtained through data assimilation. Climate simulations areintegrated long enough (typically months to years) so that the details of the initial conditions areunimportant and are generally run at lower resolution than NWP models. Climate simulationstend to focus on the atmospheric response to variations in boundary forcing fields; theirrequirements for air-sea flux fields are discussed elsewhere.NWP provides the basic guidance for modern weather forecasting on time scales fromseconds to years. Successful forecasting requires both realistic representations of atmosphericprocesses and accurate initial conditions from data assimilation. Early NWP models containedvery limited or no parameterisation of physical processes; modern NWP models includephysical parameterisations that attempt to reproduce all important atmospheric processes, allexchanges of radiation with space and all exchanges of momentum, water and energy with theearth’s surface (Pan, 1999). The increasing sophistication and accuracy of such physicalparameterisations has contributed to the tremendous improvement in the skill of weatherforecasts over the last 20 years (Kalnay et al., 1998).Data assimilation creates an initial atmospheric state for NWP (Zupanski and Kalnay,1999). It is designed to produce the atmospheric state that will yield the best subsequentforecast. In operational practice it is used continuously in data assimilation cycles. In eachcycle, usually every 6 hours, a new analysis is produced by a combination of observations and ashort-range model forecast from the previous analysis. The short-range model (first guess)forecast interpolates in space and time between observations and also serves as one of severalquality control checks on the observations. The observations used include:A. CONVENTIONAL1) rawinsondes providing vertical soundings of temperature, moisture and wind.2) aircraft observations of wind and temperature at flight level.3) surface observations of pressure, temperature and humidity from land stations, shipsand moored drifting buoys.2 - Requirements for surface fluxes 8 2.2 NWP systemsReport of the Working Group on Air Sea Fluxes June, 2000B. SATELLITE1) vertical retrievals of temperature and moisture generated by NOAA/ NESDIS1 fromTOVS vertical sounders on NOAA polar orbiters. Some data assimilation systems,including the NCEP and ECMWF operational systems and the ECMWF ERAreanalysis, now use the radiances observed by TOVS instead of the derived temperatureand moisture profiles.2) cloud-tracked winds from geostationary weather satellites.3) surface winds from scatterometer instruments on polar orbiters.4) surface wind speed from SSM/I .Research is underway on the assimilation of precipitation estimates from satellites andother sources. It is hoped that such assimilation would improve the tropical analysis,particularly the divergence of the wind field. Extensive experimentation with data assimilationsystems is now underway investigating the impact of different current and proposedobservations on numerical weather prediction. These experiments are designed to guide thedesign of the future observational system.The use in data assimilation of a first-guess model forecast acts to make the initialcondition of the atmosphere more compatible with the time integration of the model, althoughtwo significant errors can develop. One is ‘spin-up’ during the initial stages of a forecast,reflecting an imbalance between the initial conditions which are influenced by both the forecastmodel and observations and the atmosphere consistent with the forecast model’s physics anddynamics (White and Saha, 1999). This is often manifested as significant changes in thehydrological cycle during the first few hours or days of the forecast. The second error is climatedrift, a systematic tendency for atmospheric models to drift away from the actual atmosphericclimatology towards their own model climatology (Kalnay, 1999). Several papers documentexamples of these errors, including Heckley (1985), Arpe and Klinker (1986), Caplan and White(1989), Arpe (1989, 1991) and Klinker and Sardeshmukh (1987).One problem with NWP data assimilation has been the frequency of changes in theanalysis/forecast systems over the years. While the changes have produced greatly improvedweather forecasts, they have also caused changes in the analysed fields that can exceed andmask interannual variability. These changes are particularly noticeable in physical fields suchas precipitation and air-sea fluxes (Siefridt et al., 1999). To remedy this problem of apparentclimate changes due to data assimilation or model changes, several meteorological centres haveconducted extensive ‘reanalyses’ of past atmospheric data using a modern, frozenanalysis/forecast system (White and Saha, 1999). The climate change record in the reanalysescan still be contaminated by changes in the observing network over time, such as theintroduction of satellite observations in the 1970’s (White, 1999).2.2.2 The needs of data assimilation systems for flux fieldsModern data assimilation systems contain atmospheric models that produce flux fieldsdetermined by physical parameterisations from surface and near-surface model fields oftemperature, moisture and wind. These flux fields are usually generated at each model time-step and accumulated over the length of the first guess forecast or over some other forecastinterval. Thus the flux fields from data assimilation are usually from short-term forecasts.These flux fields and flux-related variables need to be carefully verified against independentestimates and observations.Assimilation systems combine observations with fields from the short-range forecastsand use the short-range forecasts as part of the assessment of the quality of the observations.Data assimilation groups need to know how well their analyses and short-range forecasts agree 1 a comprehensive list of acronyms used in the report can be found in Appendix C.2 - Requirements for surface fluxes 9 2.2 NWP systemsReport of the Working Group on Air Sea Fluxes June, 2000with the observations and whether their systems draw sufficiently from the information in theobservations. Quantitative estimates of the uncertainties in the observations are used in theanalysis; better estimates of observation error would be useful. Observations not used in thedata assimilation provide an independent check on the analyses. Quantitative estimates of theuncertainties in the independent observations are also useful in order to assess correctly howwell the data assimilation fields fit the independent observations.Independent flux estimates permit a check on the physical parameterisation used in theassimilation system. If the data going into a parameterisation are close to independentestimates, but the fluxes calculated by the parameterisation are not, this could reveal a need toupdate the parameterisation scheme. Comparison of different flux algorithms could help dataassimilation groups improve their parameterisation. Such a comparison of bulk aerodynamicalgorithms for computing sea surface fluxes has been carried out by Zeng et al. (1998) usingTOGA COARE and TAO data. The study led to a change in the thermal roughness length overthe ocean in the US National Centers for Environmental Prediction’s (NCEP) operational globalmodel.High quality observations and flux estimates from field experiments such as TOGACOARE can be especially valuable in evaluating the performance of a data assimilation system.Comparisons of the NCEP reanalysis to results from FIFE, a land surface field experiment overthe central United States, played an important role in developing a new boundary layerparameterisation that substantially improved the NCEP operational global model (Betts et al.,1996; Hong and Pan, 1996). Comparisons of surface fluxes from the European Centre forMedium-range Weather Forecasts (ECMWF) ERA reanalysis and NCEP reanalysis to surfacefluxes from TOGA COARE and other in-situ observational campaigns have also been made(Klinker et al., 1999; Moyer and Weller, 1997; Hendon and Shinoda, 1999). Recentdevelopment of single column versions of NWP models makes the comparison of modelphysics to field experiments easier and more complete. Uncertainty estimates need to beprovided. In data assimilation analysis/forecast systems all the physical parameterisationsinfluence each other; adjusting one physical parameterisation to fit one independent estimate orone field experiment too closely may distort other physical fields or the same physical fields inother locations.The purpose of operational data assimilation at NWP centres is to provide the initialcondition that gives the best forecast, not necessarily the initial condition that fits observationsmost closely nor the initial condition that makes maximum use of all available observations. Afew years ago the assimilation of satellite estimates of column-integrated precipitable water wasintroduced into the NCEP global operational analysis/forecast system. It was subsequentlyfound, however, that the use of this data led to excessive precipitation over the tropical oceansduring the first few days of the forecast and, in response, too little precipitation over thetropical continents. The artificial drought over tropical continents amplified with time due tothe interaction of precipitation and soil moisture. While there was some evidence of biases inthe satellite estimates, the main problem was believed to be incompatibility between the modelphysics and the satellite estimates of precipitable water. The use of satellite estimates ofprecipitable water in the NCEP global data assimilation was discontinued. Research on how touse these estimates and how to improve model physics continues at NCEP. This shows howclosely coupled data assimilation and model physics are in modern NWP. The assimilation ofnew data, such as satellite moisture fields or precipitation estimates, may require changes in thephysical parameterisations of the model.Operational NWP serves many users interested in many different phenomena anddifferent forecast lengths. Aviation users of global forecasts are interested in short-range (48 hror less) forecasts of upper level winds; marine meteorologists are interested in forecasts ofweather systems out to 5 days and near-surface winds and resulting ocean waves out to 2 days.Climate Prediction Center meteorologists at NCEP are concerned with forecasts from one weekto several seasons in advance. With many different user communities, NWP data assimilationand modelling groups may not focus on air-sea fluxes and may lack expertise in air-sea fluxes,2 - Requirements for surface fluxes 10 2.2 NWP systemsReport of the Working Group on Air Sea Fluxes June, 2000since personnel resources have not kept pace with increases in the complexity of NWP systems.The focus of the analysis and model developers may be on the effect of changes on the forecastskill of an atmospheric field and on atmospheric fields directly observed, fields whose accuracythey know and have confidence in. The developers may not know what independent estimatesof air-sea fluxes are available and how much confidence can be placed in the accuracy of theseindependent fields. Advice from experts on air-sea fluxes, such as this working group, would bevery useful.Operational NWP groups focus on forecast performance and usually on atmosphericfields. At NCEP a change in the operational analysis/forecast system, while beneficial in manyrespects, led to too warm low-level temperatures over the summertime Northern Hemispherecontinents. After complaints from operational forecasters, the parameterisation of sensible andlatent heat was changed and a parameterisation of atmospheric aerosols removed to lower thetemperatures; the warm bias was significantly reduced. Operationally, changes in physicalparameterisations are often made to improve observable atmospheric fields of importance toweather forecasters; their effect on the surface fluxes is not always as carefully assessed, partlybecause of a lack of confidence in independent estimates of the surface fluxes.The complexity of modern NWP analysis/forecast systems makes it difficult to establishwith confidence the exact cause of an error. NWP groups often adjust specificparameterisations and measure the success of that adjustment not by whether the parameterisedphysics is more realistic but by whether specific atmosphere fields (such as precipitation overspecific land regions, 500 hPa geopotential height, sea level pressure or winds) are betterforecast. This is a reflection not only of the perspective of NWP groups, but of the lack ofindependent estimates of atmospheric physics whose degree of accuracy is confidently knownby NWP groups. The problem with such adjustments is that if the true error is not found a newerror may be introduced. The new error may happen to offset an existing error, at least in thetest cases. If groups such as this working group can establish what independent estimates ofatmospheric physics exist and what their accuracy is, NWP groups could evaluate the effect ofchanges on the parameterised physics directly as well on the skill of forecasts of specificatmospheric fields.Data assimilation systems used in operational forecasting are constantly beingimproved, with substantial changes occurring annually or even more frequently. The effects ofthese changes on surface fluxes can be considerable. Independent estimates of fluxes could helpestablish whether these changes have a significant impact and whether they have positive,negative or neutral effects on the accuracy of fluxes. The accuracy of the independent estimateswould need to be established so that model developers could have confidence in the independentestimates and in how much uncertainty exists in the independent estimates.The use of independent estimates of atmospheric physics to assess operational NWPdata assimilation is limited by the requirement of timeliness. Many independent estimates ofatmospheric physics are based on observations taken one or more years before that have beencarefully processed. TOGA COARE atmospheric soundings, for example, have only recentlybeen corrected for moisture biases, more than 6 years after they were taken. NWP systemsundergo frequent changes, sometimes to adjust to changes in the observations. The operationaldata assimilation system may be designed specifically for current observations and not forprevious observation systems. A change in satellite data in early 1998, for example, requiredsignificant changes in the NCEP operational data assimilation system. A few months later thesechanges made it extremely difficult to test a higher resolution version of the operational dataassimilation/forecast system even on data from 1997. As a result, biases during the NorthernHemisphere summer were not uncovered until after the new system was implemented. At NCEPproposed changes to the operational system are tested for long periods on current observations;a comprehensive archive of output from the tests is available for the last 40 days. To be usefulin these tests, independent estimates of atmospheric physics must be available within a month ofobservation time.2 - Requirements for surface fluxes 11 2.2 NWP systemsReport of the Working Group on Air Sea Fluxes June, 2000Reanalysis efforts alleviate this problem. By processing several years of pastobservations with a frozen modern data assimilation system and making the results widelyavailable, reanalysis provides a much larger sample of the results of data assimilation. Theresults can be compared to a much larger range of independent estimates of atmospheric physicsfrom any time in the last 20 to 50 years by a much larger, more diverse group of scientists. Forexample, COADS based estimates of air-sea fluxes take considerable time to prepare and areavailable over many decades; they are more suited for comparison to air-sea fluxes fromreanalysis than from operational NWP. The reanalysis effort needs to be periodically repeated.The NCEP/NCAR reanalysis was performed with the operational model introduced in 1995.The NCEP/NCAR operational model has been changed several times since, limiting theapplicability of the results of reanalysis to model development. The reanalysis system has beenrun to test proposed changes to the operational system on past periods. The NCEP/NCARreanalysis system continues to be run on current data as the Climate Data Assimilation System(CDAS). Comparisons of CDAS to operational analyses and to tests of proposed changes canyield insight into whether known biases in the reanalysis are being reduced or increased.Global data assimilation systems currently provide air-sea fluxes every six hours,reflecting the length of their assimilation cycle. Users of surface fluxes have requested fieldsevery three hours. Many fields are output from the systems at intervals of 1 hour or less. Thecurrent resolution of operational global NWP systems ranges from approximately 40 km(ECMWF) to 210 km (NCEP forecasts beyond day 7). For complete verification and validation,independent air-sea flux fields should have the same spatial and temporal resolution.Independent estimates of air-sea flux fields at lower spatial and temporal resolutions are alsovery useful, as long as adequate information is given on the true resolution of the data and onany smoothing applied to ensure appropriate comparison.2.2.3 The needs of atmospheric forecast models for flux fieldsSince modern data assimilation uses the forecast model as an integral component, mostof the needs discussed in the last section also apply here. Short-range forecasts of physicalfields can suffer from the effects of ‘spin-up’. Independent estimates of physical fields of knownaccuracy could establish at what forecast lengths the most accurate estimates of physical fieldsoccur and thereby offer insight into the cause of the ‘spin-up’. Longer forecasts suffer fromsystematic model drifts. Independent estimates of physical fields could establish the real errorin the forecast physical fields, rather than the departure of the physical fields from theirestimated data assimilation values which have errors of their own.All NWP systems have problems, some common to all or most NWP systems and someunique to the particular system. Diagnosing and remedying these problems has led to majorimprovements in NWP systems. As an example, problems in the NCEP global model areemphasised here, reflecting the author’s experience with the NCEP model. NCEP's operationalglobal forecast model has problems maintaining the correct distribution of tropical precipitationin its forecasts. Tropical precipitation increases during the first 24 hours of model integration,then decreases in the convective maximum in the west equatorial Pacific. The diagnosis of thisproblem is complicated by the fact that different independent estimates of precipitation, bothbased on satellite data, give significantly different amounts of precipitation in the westernequatorial Pacific (see Sections 10.9.4 and 11.4.14). This makes it difficult to determine at whatforecast length the model’s precipitation is most correct.In the NCEP reanalysis, surface winds and stresses in the eastern equatorial Pacificwere too weak, even in the analysis phase (White, 1996b). The current NCEP global model hasstronger surface stress in the eastern Pacific; however, the equatorial surface stress in the easternPacific increases with forecast length. Accurate independent estimates of surface fields andfluxes (such as surface winds from the TOGA TAO buoys) have helped to identify theseproblems and can help identify which changes to the analysis/forecast system can reduce theseproblems.2 - Requirements for surface fluxes 12 2.2 NWP systemsReport of the Working Group on Air Sea Fluxes June, 2000Accurate tropical analyses and forecasts are important not only for tropical forecasts,including hurricanes, but for seasonal forecasts. Seasonal forecasting has received increasedemphasis in recent years at NCEP and elsewhere with successful forecasts of dramatic El Ninoand La Nina events in the Pacific and their impacts on the United States. Seasonal forecastingoften involves the use of an ocean data assimilation and forecast system coupled to theatmospheric model. As a result, at ECMWF, the effects of proposed changes in the operationalatmospheric model on air-sea fluxes (and the impact of these changes on the ocean model usedin seasonal forecasting) have been carefully examined in pre-implementation testing.Another problem in the NCEP global model is that the tropical upper tropospherictrough over the Caribbean is weakened significantly in the forecasts. This weakening causestoo favourable an environment for hurricanes in the global forecasts, complicating the work ofhurricane forecasters. This weakening of the tropical upper tropospheric trough may reflectproblems in maintaining the divergent flow in the tropics. Accurate independent estimates ofprecipitation and surface fluxes could help identify model problems in these fields and lead toimprovements in the divergent flow.Surface fluxes over the Gulf of Mexico and the Gulf Stream play a crucial role inaccurate analysis and forecasting of explosive United States east coast cyclogenesis, a majorwintertime concern for forecasters. Modern global models often have considerable success inforecasting such events, implying, as do comparisons with COADS-based fluxes (Section 11.4.2below), that the models’ surface fluxes are fairly accurate in these areas. In NCEP’s operationalmeso-scale model, a comparison of air-sea fluxes over the Gulf Stream with COADS-basedestimates led to changes in the model’s physics.Some years ago seasonal forecasters at NCEP found large differences in surfaceradiation between NCEP global model forecasts and independent estimates in the easternsubtropical North Pacific in regions of oceanic low-level stratus clouds. Due to excessiveradiative heating rates when cloudiness occurred in thin near-surface layers over land, the NCEPmodel did not permit low-level clouds anywhere, even when the vertical profiles of humidityand temperature over the subtropical oceans clearly suggested that low-level stratus cloudsshould be present. Simple changes in the model permitted the occurrence of oceanic stratusclouds and dramatic improvements in the surface radiative budget over the ocean. Morerecently, the parameterisation of low-level clouds were changed to reduce excessive low-levelclouds over the Arabian Sea; this change also affected low-level stratus clouds in the easternsubtropical oceans. Recent tests indicate that low-level stratus clouds can be significantlychanged by changes in the vertical and horizontal resolution of the model. Other operationaland reanalysis data assimilation systems have problems with stratus clouds (White, 1995; Whiteand da Silva, 1998; section 11.4.7 below).Comparison of surface net short-wave radiation from the NCEP/NCAR reanalysis toindependent estimates by da Silva and White (1996) led to the discovery of an error in theoceanic surface albedo. The error has been corrected in the operational NCEP global model andin the NCEP-2 reanalysis.2.2.4 Regional observing / nowcasting / forecasting systemsRegional operational systems are used for nowcasting and forecasting both themeteorological conditions at the ocean surface and also the dynamics of the upper layer of theocean. They are characterised by fine spatial and temporal resolution, increasing in the nearoffshore regions. Thus, for example, the EUROWAVE operational system for diagnosis andforecasting of waves in European coastal areas is running on a 5 km spatial resolution in the off-shore regions and at about 1/4 degree in the open sea. The French operational system fornowcasting of tides and tidal currents in the English channel uses 12 km spatial resolution inoperational mode, and twice finer resolution in non-operational mode. Operational models fornowcasting and forecasting of oil spills are running at 0.5 to 2 km resolution in the nearestvicinity to the source and at 5 to 10 km resolution in areas at least 50 km remote from thesource. The highest priority parameter required for such systems is wind speed. Since the2 - Requirements for surface fluxes 13 2.2 NWP systems2.3 Ocean GCM'sReport of the Working Group on Air Sea Fluxes June, 2000majority of operational models have a special focus on coastal regions, the wind fields requiredhave to be provisionally corrected for coastal biases. Further parameters which are veryimportant for these systems are SST and SSS. These are particularly used in oil spillforecasting for describing the interaction between the oil and sea water. Systems running in icecovered areas also require fine resolution sea ice extent data. Many operationalforecasting/nowcasting systems use measurements at buoys, these being usually implementedas part of the systems. In this context, a high priority is to provide reasonable estimates of thepossible biases between the buoy measurements and the VOS and satellite data assimilated bythe models over the larger area. At present, most of systems are focused on the use ofdownscaled operational analyses from global and regional meteorological centres together withsatellite observations. Thus, improvements in accuracy of these two sources of fluxes and flux-related variables, and increased resolution, would contribute considerably to the developmentof regional operational systems.2.3 Oceanic General Circulation Models (OGCM's)2.3.1 IntroductionThe ocean receives energy through the air-sea interface by exchange of momentum,which also represents a source of kinetic energy, and by exchanges of heat and water whichlocally modify the buoyancy of the fluid and so act as a source of potential energy generatinginternal pressure forces. Thus ocean models require an accurate representation of the surfaceflux fields to define their surface boundary conditions. However this alone is not sufficientbecause of systematic errors in ocean general circulation model (OGCM) solutions which oftenresult in large inconsistencies between the forcing fluxes and the model circulation. Forexample, mean currents in coarse resolution models are far too broad and slow, and if forcedwith observed surface heat fluxes they tend to produce unrealistic surface temperatures.Another difficult problem that ocean-atmosphere modellers are confronted with is theinteractive nature of air-sea exchanges. Variations in one fluid will force variations in the other,and this raises the question whether it is conceivable to drive one fluid without considering itscoupling with the other. Indeed, forcing an ocean model with specified surface fluxes generallyproduces SST's which are far from the observed sea surface temperature (Rosati and Miyakoda,1988). Therefore, it is not possible to force an OGCM without some kind of a parameterisationto account for the feedback to the atmosphere.This feedback issue is not relevant to the wind stress forcing, for which there is not adirect local feedback from the ocean circulation. However, the spatial resolution is of particularimportance to the momentum exchange since basin gyres are primarily driven by the wind stresscurl, a first order derivative of the wind components. For heat and freshwater, a commonOGCM boundary condition is to restore the model surface temperature and salinity to observedclimatological values (Cox and Bryan, 1984). The paradox here is that if the model tracer fieldsagree with observations, there is zero net tracer flux across the air-sea interface. In general,model solutions differ from observations both because they cannot 'keep up' with the changingobservations, and because advection introduces a systematic bias (Killworth et al., 2000).Several parameterisations of the feedback of the ocean to the atmosphere have beendeveloped in the past. It is not the purpose of this report to present an overview of the variousway of forcing ocean models. A recent review can be found in Barnier (1998). However, wepresent here some details of such parameterisations applied to the thermal forcing since they areuseful in determining which surface variables are required in the definition of the atmosphericforcing.2 - Requirements for surface fluxes 14 2.3 Ocean GCM'sReport of the Working Group on Air Sea Fluxes June, 20002.3.2 Parameterisation of the thermal feedbackA. THERMAL SURFACE BOUNDARY CONDITION.There are several ways to formulate the forcing of an OGCM based on the primitiveequations. A formulation which remains close to the physics of the air-sea interactions is theflux boundary condition. It is assumed that vertical gradient of the ocean temperature at thesurface is proportional to the flux across the interface. Thus the advection-diffusion equationfor the evolution with time (t) of the ocean temperature (T) is:() z F Diffusion AdvectiontTSOL + + =∂∂ (2.1)which includes a source term, FSOL(z), which represents the depth-dependent heating by the solarheat flux. FSOL(z) is commonly parameterised by an absorption law with depth (z) (Paulson andSimpson, 1977). The forcing of the ocean temperature by the non-solar heat flux QNS (theaddition of the net infra-red, latent and sensible heat fluxes) is introduced as a surface (z=0)boundary condition:()pwNSzv CQzTz K0 0 ρ= ∂∂= (2.2)where Kv is a mixing coefficient which depends on the depth z. Other parameters are the density(ρ0) and the specific heat (Cpw) of sea water. This type of formulation is commonly used formixed-layer models (Gaspar et al., 1990). The parameterisation of the ocean feedback usuallyoccurs through a model dependent definition of the non-solar heat flux QNS.B. FEEDBACK PARAMETERIZATION.There are two methods frequently used to overcome the physical inconsistency due tothe lack of air-sea feedbacks in the formulation of the atmospheric forcing of ocean models; amethod of flux correction, and a bulk forcing method.Flux Correction Method. This method is based on the pioneer work of Haney (1971) and Han(1984). More recent applications are those of Oberhuber (1988), and Barnier et al. (1995). Theunderlying idea is to express the feedback to the atmosphere as a flux correction which dependsupon the model SST. Therefore, at a given time step of the model integration, the non solarsurface heat flux, QNS(t), used in boundary condition (2.2) for the temperature is:Qt QQSSTSST SST t NS NSC NSCC ()limlimlim =+∂∂   ×− () ()(2.3)where:lim CNS Q = climatic estimate of the non-solar net heat fluxlim C SST = climatic estimate of the sea surface temperature used to estimate the air-sea fluxes) (t SST = instantaneous model sea surface temperaturelim CNSSSTQ∂∂= climatic estimate of the sensitivity of the non-solar heat flux to variations in SSTThe second term in the right-hand-side of (2.3) is a feedback term the effect of which isto prevent the model SST(t) drifting away from the climatic value used to estimate the forcingfluxes. Thus it is there to drive the model ocean toward a climatic state consistent with the air-sea fluxes. The sensitivity term SST QNS ∂ ∂ / can be estimated from atmospheric surfacevariables using a first order Taylor expansion of the bulk formulas (Barnier et al., 1995). Thisterm calculated from the surface fields provided by the ECMWF re-analysis is shown in Figure2.1.2 - Requirements for surface fluxes 15 2.3 Ocean GCM'sReport of the Working Group on Air Sea Fluxes June, 2000Figure 2.1: Sensitivity term SST QNS ∂ ∂ / (Wm-2K-1) estimated with atmospheric surfacevariables of the ECMWF re-analysis using the first order Taylor expansion of the bulk formulasproposed by Barnier et al. (1995).To apply this parameterisation of the thermal forcing requires estimates of the varioussurface heat fluxes and the SST. Therefore, this method allows flux estimates to be directly usedas components of the forcing; the feedback enters the surface boundary condition through thesensitivity term. This only needs to be estimated once, from a climatology of surface variables.Bulk Forcing Method. In this method, air-sea heat fluxes which enter the flux boundarycondition (2.2) are made model dependent by the direct use of the model-computed SST(t) inbulk formulations (see Large et al., 1997 for a detailed review of the bulk forcing method). Forexample, the latent heat loss at a given time QLAT(t) would be computed by the model accordingto the bulk formula (with the usual notations)2:() ) ( ) ( 10 t q q U C L t Q s a E E a LAT − = ρ (2.4)where qs(t) is the saturated specific humidity estimated with the SST predicted by the model attime t. All the other variables entering the calculation of (2.4) are obtained from a climatologyor from an analysis of surface variables. In addition, the sensible heat flux and longwaveradiation are calculated with the model SST. The application of this method does not require anestimate of every component of the net heat flux, but rather the knowledge of the net downwardradiation (shortwave and longwave), and of surface variables which enter the bulk formulas.Thus, the requirements for surface variables and air-sea fluxes are different for the FluxCorrection compared to the Bulk Forcing methods.2.3.3 Salinity ForcingThe case of salinity forcing is different from that of temperature. The salinity of surfacewaters vary because the ocean loses or gains water by evaporation, E, or precipitation, P, riverrunoff, R, and sea-ice production and melt, which causes a variation of the salt concentration inthe water. However, the total amount of dissolved salt remains constant. Because OGCMformulation generally assumes a constant volume, the forcing of the salinity equation is 2 see Chapter 7 for a detailed discussion of the bulk formulae.2 - Requirements for surface fluxes 16 2.3 Ocean GCM'sReport of the Working Group on Air Sea Fluxes June, 2000expressed as a virtual salinity flux which is proportional to the balance E-P-R (see Barnier,1998, for a detailed discussion). The formulation is then similar to the one used fortemperature, and a correction term is generally added in the form of a relaxation toward aclimatic estimate of the sea surface salinity (SSS). However, more OGCM's are now using afree surface formulation, and a more physical formulation of the salinity forcing could be used.This would consider the change in volume implied by the freshwater flux at the surface (Roulletand Madec, 2000). As yet, such a formulation presently is far from being used routinely, andadditional restoring to a climatological SSS is still included.2.3.4 Geophysical consistency of the ocean forcing functionThe section above discussed the two methods most used to overcome the physicalinconsistency between the model SST and the forcing fluxes which arise from the lack of air-seafeedback in the formulation of the forcing.However, when applying these methods, it is important to consider the geophysicallinks that exist between the various components of the atmospheric forcing. This is commonlyreferred to as the "geophysical consistency" of the model forcing function. Indeed, it isimportant that the inter-correlation existing between the wind field, the turbulent fluxes, theradiative fluxes, and evaporation and precipitation, are correctly represented. Inconsistenciesbetween the forcing variables may have a dramatic effect on the results of a simulation. This isespecially so since the feedback term, which also works to compensate for inaccuracies in theflux estimates, will generally not compensate for such inconsistencies. For example, in thewestern tropical Pacific the surface salinity of the ocean cannot be correctly calculated if theevaporation (air humidity and wind) and precipitation are not consistent. It is only recently thata bulk forcing which links heat and freshwater forcing through evaporation has been used in aglobal ocean simulation (Large et al., 1997). Therefore, when using flux estimates from varioussources (satellites, NWP analyses, COADS), geophysical correlation between the variouscomponents of the forcing should be verified.The analysis carried out by Garnier et al. (1998) on the 15-year flux climatologyobtained from the 6-hourly forecast ECMWF re-analysis provides an example of theinconsistencies which may arise. Their global budget calculation indicates that the oceanreceives an excess of heat of 8 Wm-2, despite a lack of incident short wave radiation in theintertropical zone which is only partly balanced by an excess of incoming short wave radiationat mid-latitudes. In the freshwater budget, evaporation globally dominates precipitation, and theocean loses, on annual mean, 1.4 Sv (1 Sv =106 m3s-1) of water (runoff estimates beingincluded). For the purpose of forcing an ocean model, corrections to the weak solar radiationreaching the surface in equatorial regions could be sought, but increasing this quantity wouldincrease the global imbalance of heat of 8 Wm-2. This could be compensated by an increase inlatent heat loss at latitudes where the analysis suggest it could be underestimated, but then theimbalance of the water cycle would be amplified. This contradiction illustrates the difficulty ofproducing a globally consistent forcing function.2.3.5 Forcing fieldsConsidering both flux correction and bulk forcing methods, and the fact thatgeophysical consistency of the forcing variables should be checked, the basic forcingcomponents of a prognostic OGCM include much more than the usual air-sea fluxes. A list ofthe useful ( if not always necessary) variables to define a forcing function of an OGCM is givenbelow.A. FLUXESThe net heat input by solar radiation; the non solar heat flux (the sum of the sensible, latent andinfra-red heat fluxes); the components of the wind stress vector; the wind stirring; theevaporation rate; the precipitation rate; the river runoff and sea-ice melt.2 - Requirements for surface fluxes 17 2.3 Ocean GCM'sReport of the Working Group on Air Sea Fluxes June, 2000B. OTHER SURFACE VARIABLESThe sea surface temperature; the sea surface salinity; the ocean albedo; the surface wind speed,air temperature and specific humidity at specified height; the mean sea level pressure, the cloudfraction, the sea-ice coverage and growth (or melting) rate. According to the forcing methodwhich is used, additional parameters, such as exchange coefficients and all variables enteringthe determination of the stability of the air column may also be necessary.C. RESOLUTION ISSUESThe requirements on air-sea flux resolution for modelling the different features of theocean vary according to the application and the beliefs of the users, from coarse resolutionglobal scale models run over centuries, to eddy resolving models run over a few decades. Formany applications in forthcoming years, monthly means on a one degree grid will be sufficient.However, the number of applications which have finer requirements is rapidly increasing asocean model resolution increases with available computer power, and these needs areemphasised here.Considering time scales, it is now well established that the characteristics of the surfacemixed layer are very sensitive to short-term fluctuations of the wind stress which set theamplitude of the vertical shear, and thus determine the intensity of the turbulent vertical mixing.The inertial frequency needs to be resolved here, and 3 hourly winds are recommended.Requirements for the heat and freshwater fluxes are not as stringent and six hourly to dailyvalues, which can presently be achieved by NWP models and geostationery satellites, arelikely to be acceptable.Considering spatial resolution, again the requirements for the wind are most critical, anda resolution of 50 km, which can already be achieved by scatterometer products, is a strongrequirement to obtain an accurate estimate of the wind stress curl, the primary forcing of oceangyres. Requirements for the heat and freshwater fluxes could be somewhat looser, but 50 kmresolution would allow a better resolution of the gradients across the major ocean currents.D. CONSTRAINTSThe various components of the forcing should meet to some degree of accuracy severalconstraints:- a global balance constraint- a global transport constraint- geophysical consistency2.3.6 DiscussionHaving discussed the forcing of ocean models with atmospheric variables, one maybriefly comment upon the feedback from ocean observations and ocean general circulationmodels on the validation of air-sea fluxes.Ocean hydrographic sections (WOCE) provide estimates of the meridional heattransport across ocean basins (Macdonald and Wunsch, 1996, Ganachaud, 1999), which can bechecked against other estimates (derived from satellites, numerical weather prediction centres,or COADS). However, large uncertainties (in excess of 0.2 PW) are still associated with thesetransport estimates.In specific cases (to be carefully controlled), the feedback term can provide valuableinformation on the consistency of the air-sea flux estimates used to force the model.Assimilation of ocean surface variables (sea surface height from altimetry and SST) inrelation to the heat content of the surface ocean may permit diagnosis of an estimate of the netheat flux at the ocean surface, independent of other estimates.Coastal oceanography is also concerned with air-sea fluxes. For such very localapplications where the diurnal cycle generally needs to be resolved, air-sea fluxes are of course2 - Requirements for surface fluxes 18 2.3 Ocean GCM's2.4 Wave modelsReport of the Working Group on Air Sea Fluxes June, 2000important, but the necessary resolution, in particular the high time sampling required for thewind field, are beyond the capabilities of the instrument systems used to observe the globalocean. Other means could be used, including dedicated instrumentation and atmosphericmodelling, since the studied areas are usually limited.Finally, it is clear that OGCM’s will always need to include some parameterisation ofthe mixed layer. Validation of mixed layer models requires very accurate estimates of the air-sea fluxes, combined with measurements of the turbulent fluxes and large scale flow in theupper ocean. Such complete data sets could only be obtained at a few given locations, andwould require specific experimental designs.2.3.7 ConclusionsWhile many applications still call for at least medium spatial (1 degree) and temporal(daily to monthly mean) resolution and global coverage, the demand for high temporal (3hourly) and fine spatial (50 km) resolution at global scale is rapidly increasing. Due to thesparseness of observations, these requirements can only be met by numerical or satelliteproducts. These products, however, have to be carefully checked and validated by comparisonwith in situ measurements. The specific requirements for development and validation of newparameterisations of mixing in the upper ocean are calling for local but very accurate fluxmeasurements.2.4 Wave ModelsOcean surface waves are generated by the wind blowing over the water. Wave modelsthus require the wind vector as input. Several studies (e.g., Cardone et al. 1995, Graber et al.1995, Sterl et al. 1998) have shown that the quality of the input wind is crucial for the quality ofthe modelled wave characteristics. Empirically it has been found that the significant waveheight for fully developed sea is proportional to the square of the wind speed (Sanders 1976,Janssen et al. 1984). Thus, although high waves are rarely fully developed, one may anticipatethat errors in the wind speed will be amplified by the wave model. As very high wind speedsare of limited temporal and spatial extend, a major aspect of "quality" turns out to be the spatialand temporal resolution of the driving wind field. For example:Cardone et al. (1995) used analysed wind fields from four Numerical WeatherPrediction Models and from a kinematic analysis to drive the WAM wave model (Komen et al.1994). They found that extreme wave heights were only correctly reproduced by the model ifthe driving wind contained wind-speed peaks of the correct magnitude.Graber et al. (1995) drove the same WAM model by wind fields that were artificiallydegraded by lowering the spatial and temporal resolution. They found that in regions of"slowly evolving, nearly linear features" a resolution of 1.5° and 6 hours was satisfactory, whilefronts and rapidly propagating jets required 0.5° and less than 3 hours resolution.Sterl et al. (1998) used the ERA winds to drive a global version of the WAM model.They found that with a resolution of 3°, mean wave heights were severely underestimated, andthat, even at a resolution of 1.5°, missed peaks in wind speed led to an underestimation ofextreme wave heights, thus corroborating the results of Graber et al. (1995).The conclusion to be drawn from these studies is thus that the resolution of the windfield for wave modelling studies should be of order of half a degree in space and roughly threehours in time. The spatial extent of the required fields depends, of course, on the desiredpurpose and can vary between regional and global.Two remarks should be added.1. The above estimates have been derived from climatological studies (Sterl et al. 1998) orfrom extratropical storms (Cardone et al 1995, Graber et al 1995). Due to their muchsmaller spatial extends, tropical storms (hurricanes) may need even higher resolution.E.g., a resolution of 0.25˚ in space is used in the case study of the WAMDI group (1988).2 - Requirements for surface fluxes 19 2.4 Wave Models2.5 Sea IceReport of the Working Group on Air Sea Fluxes June, 20002. From the fact that the forcing wind is so important for the quality of wave model results itfollows that for forecasting wave heights, the quality of the wind forecast is crucial.Improved wind forecasts will automatically lead to improved wave forecasts.2.5 Sea Ice in Ocean Models and Sea-Ice ModellingIce-covered and sub-polar oceans play a special role in the global ocean thermohalinecirculation - the vertical overturning of the world ocean. Whereas low-latitude thermohalineprocesses are controlled both by surface heating and by net evaporation, at polar latitudes theseprocesses are dominated by surface freshwater fluxes, that is, by net precipitation, and to alarger extent by sea ice melting and freezing; sea ice melt water has the same effect on the oceansurface as precipitation. In the northern hemisphere, large inter-annual variations of the exportof ice through the Fram Strait into the GIN (Greenland/Iceland/Norwegian) Seas are modelled(Häkkinen, 1993) and observed (Kwok and Rothrock, 1999). It is presently hypothesised thatabnormally high exports of sea ice in the 60's capped the Labrador Sea with fresh water andlimited the production of Labrador Sea Water (Lazier, 1980). This freshening event was calledthe Great Salinity Anomaly by Dickson et al. (1988). It appears that variations in freshwaterinput, both in the form of ice and somewhat less saline upper ocean water from the ArcticOcean, influence convective overturning and contribute to interannual variability in the NorthAtlantic circulation.In the southern hemisphere, the freezing and melting of sea ice controls the amount andlocation of deep and bottom water formation in the Weddell Sea and the Ross Sea which aremajor sources of bottom water of the world ocean. The appearance and disappearance over longperiods of polynyas are thought to force decadal variability in the properties of the flow ofAntarctic Bottom Waters into the North Atlantic. The seasonal melting of sea ice around theAntarctic is responsible for the low salinity of the Antarctic Intermediate Waters, and thesewater masses are found to intrude far into the North Atlantic with the upper branch of theAtlantic overturning cell. So, the importance of the freshwater forcing of the global oceancirculation in the form of input of sea ice is quite clear.We have seen in the previous section that some ocean models use as a surface boundarycondition the net surface heating and the net precipitation or ice growth or melt rate. Otherstreat separately the penetrating component of solar radiation. In this latter case, the modelrequires the ice concentration in order to compute how much radiation penetrates into the waterthrough open water "leads" (openings within the ice cover). Still others include a full ice modelwithin the ocean model. In this case, and for modelling sea ice separately from the ocean todetermine growth and melt rates as a function of space and time, the specification of globalocean surface fluxes takes on a different character over ice covered oceans. One can specify orobserve the momentum, radiative and turbulent fluxes at the top surface of the ice cover, but ofcourse then one needs to "solve" an ice model in order to translate these into fluxes to the mixedlayer of the ocean. Wind stress pushes the ice which has its own mass and resistance todeformation, and these conspire to apply a somewhat modified stress to the ocean surface. Thesame can be said of radiative fluxes; some of the flux goes directly into the ocean through leads,but most of it goes into the heat balance of the sea ice. The ice cover represents an isothermalboundary condition on the ocean, and turbulent heat fluxes at the ocean surface (and theunderside of the ice) are determined by the mixed layer and the vertical mixing at the base of themixed layer. The ice conducts heat away from the mixed layer, and ice melt (or growth) alsoabsorbs heat from (or deposits heat into) the mixed layer. In order to know these quantities, oneneeds an ice model.To force today's sea ice models, with or without an underlying ocean model, thefollowing fields are specified:❍ wind stress, usually taking a bulk drag coefficient as constant;❍ downwelling solar radiation (the surface albedo and reflected radiation beingdetermined within the ice model);2 - Requirements for surface fluxes 20 2.5 sea Ice2.6 Ocean data assimilationReport of the Working Group on Air Sea Fluxes June, 2000❍ downwelling longwave radiation (the outgoing being determined within the model bysurface temperature);❍ surface temperature being internal to the model;❍ the evaporation and latent heat flux, specified as a function of near-surface wind andhumidity (determined by the air temperature);❍ precipitation.These fields at the sea-ice surface, then, are the proper subject of this working group.To date, the practice has been to specify fluxes parametrically: downwelling solarradiation in terms of solar geometry and cloud fraction, downwelling longwave in terms ofsurface air temperature, cloud fraction, and all turbulent fluxes in terms of surface wind and airtemperature. It is fair to say that the subject of surface fluxes over sea ice has been taken prettymuch for granted by modellers, who have found that the mean annual cycle of sea ice can bemodelled satisfactorily with the fluxes specified parametrically years ago by Parkinson andWashington [1979) and have not had cause to examine these inputs much until recently. Nowthe focus of research is shifting more to interannual variability and climate change, and this hassparked recent interest in data on long-term variability of polar surface fluxes.A study of the sensitivity of an Arctic sea-ice model (Rothrock and Zhang, 1997) toerrors in radiative forcing has shown that an error of +10 Wm-2 in the downwelling shortwaveproduced an error of -20 cm in mean ice thickness in a full thermodynamic-dynamic model (and-60 cm in a radiative model with no ice dynamics). The same error in downwelling longwaveradiation produced an error of -50 cm in the full ice model (and -110 cm in the radiative model).The lesser sensitivity to shortwave was due to the high albedo that caused most of thedownwelling solar radiation to be reflected. The mean Arctic ice thickness is about 2 mpresently, so flux errors of only 10 Wm-2 cause a very significant error in modelling the sea icecover.2.6 Air-Sea Flux Fields and Ocean Data Assimilation2.6.1 IntroductionData assimilation (sometimes known as "ocean state estimation") is a very powerfultool which is now becoming the tool of choice for all operational centres as well as ongoing(e.g., WOCE AIMS) and future research programs (e.g., GODAE). As is the case for forwardocean models, ocean data assimilation (ODA) is dependent on accurate air-sea fluxes forforcing. However, unlike forward modelling, ODA requires fairly comprehensive errorestimates so data-model misfit feedback can be properly partitioned.2.6.2 Ocean Data AssimilationIn general terms, data assimilation techniques are employed to bring, in an extremelyefficient way, a model into consistency with the observations, which the forward model byitself predicts. From the resulting misfit between the model and the observations one candetermine which uncertain model parameter must be adjusted, and by how much, so as to bringthe forward computation into accord with the data. Changes in those fields - often referred to as"control'' terms - are determined as a best-fit in a least-squares sense over the full data period.Control terms are usually the model initial conditions, but can likewise include internal modelparameters such as friction coefficients and eddy tracer transfers. It is essential to realise that alladjustments of control parameters are enforced to stay in a dynamically acceptable range andthat the model equations are otherwise preserved.Since the observation-model misfits determine the control terms (in a statistical sense),it is critical to have error estimates of the observations in order to correctly partition the misfit2 - Requirements for surface fluxes 21 2.6 Ocean data assimilation2.7 Evaluation of Climate modelsReport of the Working Group on Air Sea Fluxes June, 2000information into each of the control terms. Additionally, estimates of the ocean circulationdepend critically on the quality of the meteorological (forcing) fields. But error estimates forsurface fluxes of momentum, heat and freshwater provided by NWP centres are generally notavailable. One way to deal with this situation is to incorporate flux error terms as controlparameters into the estimation process. Nevertheless, prior information about error structures isneeded to obtain a statistically consistent solution.Ocean observing systems are being rapidly developed for the purpose of monitoring theocean and to allow ocean prediction at global scale. These are the major objectives of importantinternational programmes such as the Global Ocean Observing System (GOOS) and the GlobalOcean Data Assimilation Experiment (GODAE). For these purposes, an ambitious ocean floatprogram is being implemented (ARGO). This plans to release more than 3000 profilinglagrangian floats (of P-ALACE and PROVOR types) which will sample the temperature andsalinity of the upper ocean with a space-time coverage never attained before. Assimilation ofthese data (with additional XBT, SST, and sea surface height observations) into a global modelshould permit estimation of the net heat flux into the ocean and its variation. The questionremains whether the accuracy of such estimates will approach the few Wm-2 required for climatestudies.Information about amplitudes, and spatial and temporal error covariances inmomentum, heat and freshwater flux fields, including those from operational centres such asNCEP and ECMWF are necessary. A concise review is needed of what the atmosphericscientists believe the error in meteorological centre analyses are. A summary of formalcomparisons among different centre estimates would also be valuable if error estimates cannotbe given.2.6.3 Example of Ocean Assimilation requirements - GODAEThe requirements which have been stated for GODAE are as follows. For SST,GODAE has a requirement for high-resolution global real-time products. A representativespatial resolution would be 5 to 10 km, and ideally the diurnal cycle should be resolved(implying 6 hour or better time resolution). The desired accuracy would be 0.1 to 0.2°C. If seasurface temperature is used as a strong constraint on the ocean models (that is a very small errorvalue is associated with it), then knowledge of the net downward shortwave radiation wouldcomplete the determination of the thermal boundary conditions. The latter is treated as aninternal heating source within the model. Such a product will require careful directmeasurements for validation; for delayed-mode products GODAE would be seeking anaccuracy of around 10-15 Wm-2.The availability of suitable surface wind stress products continues to be a major issuefor oceanography. Apart from the need for improved accuracy, modern ocean dataassimilation systems are now demanding estimates of the error in this fields; without them, theingested wind data cannot be balanced against other ingested data. Given the GODAE,emphasis on global, high-resolution, real-time products, the matter of how to define and obtaina suitable wind field is a matter for urgent debate.2.7 Evaluation of Climate ModelsWhen an Atmospheric General Circulation Model (AGCM) is constrained withprescribed SST's and sea-ice, there is no feedback from the ocean on the simulated atmosphere.If the AGCM is coupled to an Ocean General Circulation Model (OCGM) and a sea-ice model,air-sea feedbacks are present in the resulting (AOGCM) simulations. For this reason, coupledmodels are more suitable than AGCM's for studies involving long term variability and offer themost plausible means of predicting anthropogenic climate change. Realistic coupled modelsimulations depend critically on interactions between the atmosphere, ocean and sea-ice, via theair-sea fluxes of heat, momentum and moisture. An important motivation for a betterunderstanding of air-sea fluxes (and hence the effort of this Working Group) is the need for animproved means of evaluating air-sea fluxes simulated by coupled models.2 - Requirements for surface fluxes 22 2.7 Evaluation of Climate modelsReport of the Working Group on Air Sea Fluxes June, 2000Despite the lack of air-sea feedbacks, studying surface fluxes in AGCM's is ofteninstructive because many deficiencies are so serious that they can be readily identified even withthe use of prescribed SST's and sea-ice. Moreover, if the SST simulated by a coupled model isnot close to a realistic state, it is difficult to determine if deficient surface fluxes are a directresult of problems in the way fluxes are parameterised, or simply because the basic state of thesimulated climate is different from that observed. Detecting problems in coupled model surfacefluxes is thus perhaps best done early on in the simulation (before there is much drift in theSST's). However this is complicated by adjustments resulting from incompatibilities in theinitial states of the simulated atmosphere and ocean, even when they have been independently'spun up.' In short, it is useful to evaluate surface fluxes in both coupled and uncoupled climatemodels, even if the ultimate motivation is improved coupled models. Accurate observationally-based estimates of surface fluxes are fundamental in either case.To put the state of affairs in perspective, we take a first cursory look at AGCMsimulations recently made (1998-1999) as part of the on-going Atmospheric ModelIntercomparison Project (AMIP). Figure 2.1 shows the zonally averaged global ocean netshortwave radiation annual mean climatology of ten models. Also shown are sevenobservationally based estimates that are described and evaluated in this report. We see thatwhere the fluxes are largest (in low latitudes for the annual mean), there are discrepanciesamong models on the order of 50 Wm-2, and nearly as much among the differentobservationally based products. The situation for the latent heat flux is perhaps even moretroubling, as shown in Figure 2.2, although in this case there seems to be separation betweenthe models and observational products. Gleckler et al. (1994) demonstrated how common90S 60S 45S 30S EQ 30N 45N 60N 90NLatitude (area weighted)050100150200250300Watts/m**2AMIP ModelsSAT (Pinker)SAT (Staylor)SAT (Li)VOS (SOC)VOS (da Silva)RNL (ERA)RNL (NCEP)90S 60S 45S 30S EQ 30N 45N 60N 90NLatitude (area weighted)050100150200Watts/m**2AMIP ModelsVOS (SOC)VOS (da Silva)RNL (ERA)RNL (NCEP)g( )Figure 2.1 Zonal averages ofthe Global Ocean net SW fluxfrom reanalyses, observations(COADS and satellites) andAMIP2 (1998-99)Figure 2.2 Zonal averages ofthe Global Ocean latent heatflux from reanalyses,observations (COADS) andAMIP2 (1998-99)2 - Requirements for surface fluxes 23 2.7 Evaluation of Climate modelsReport of the Working Group on Air Sea Fluxes June, 2000deficiencies in modelling the cloud-radiative effects radically altered the implied oceanic heattransport (a common measure of evaluating an AGCM's suitability for coupling). While thiswas believed for some years to be the primary culprit for coupled model deficiencies, morerecently the implied ocean heat transport of the NCAR CCM3 has been shown to be equally assensitive to the parameterisation of deep convection (Hack, 1998), which principally altered thelatent heating. Clearly, the suitability of an AGCM for coupling is critically sensitive to allforms of surface heating, but especially to its surface net shortwave and latent heat flux.The need for "flux correction" has declined in the last several years (Covey et al. 1999)as many teams are having increasing success in reducing climate drift in their coupled models.Encouraging integrations of more than a hundred years with minimal drift have been made bythe Hadley Centre (Gordon et al. 2000) and NCAR (Boville and Gent, 1998). However a stablesimulation with a reasonable SST distribution does not guarantee that surface fluxes areproperly simulated. This can only be verified with suitable observations. In Figure 2.3, the 20year mean (years 60 to 80 of each simulation) net surface heat flux of four simulations takenfrom the Coupled Model Intercomparison Project are compared with four of the observationalproducts to be evaluated in this report. While there are encouraging similarities, once again wesee that even qualitatively there are substantial differences among the observations andmethods. The general impression is that the coupled models have large regions where there is arelatively small (less than 20 Wm-2) heat flux into the ocean. In comparison ERA shows oceancooling over many of these areas. The coupled models appear similar to the Residual methodresults. Compared to the COADS based climatologies they fall somewhere between the SOCclimatology and the tuned UWM climatology. Of course, if more of the ocean has net heating,to achieve a global heat balance the coupled models must have significantly greater cooling inthe regions where the ocean is loosing heat.Improving our understanding of, and the accuracy of estimates of, the seasonal cycle ofsurface fluxes is perhaps the most pressing need for the evaluation of climate models. We havejust demonstrated how state-of-the-art climate models still have difficulty in simulating manybasic features of this most basic climate variability. Examples include excessive shortwaveheating in marine stratus cloud regimes which remains a problem in AGCM's (and which is notapparent in the zonal means), and excessive evaporation with respect to climatologies in lowlatitudes. High quality estimates (i.e. with low and quantified uncertainty) of global oceanseasonal cycle climatologies of heat, momentum and freshwater are all desperately needed toguide further development of climate models.Estimates of interannual variability in as many areas as possible are also becomingincreasingly valuable in the evaluation process as coupled modellers strive to improve simulatedvariability in phenomenon such as ENSO, PNA and the NAO. In a recent study byTimmermann et al. (1999), it was clearly demonstrated that global coupled models that have'reasonable' climatologies are doing a poor job at capturing ENSO variability. While there aremany other measures of variability to evaluate models with, air-sea interactions are the linkbetween the atmosphere and ocean, and thus there will be an increasing need for reliableestimates of flux variability.Estimates of global ocean air-sea fluxes derived from in-situ observations, satellitemeasurements, atmospheric analysis, or potentially some combination of each can all be usefulin evaluating climate models. However, climate modellers want to know which is the singlebest (most accurate) observational data set, and we may not be able to answer that question withabsolute confidence. While identifying the strengths and weaknesses of these different productsis an important objective of this report, complex uncertainties with temporal-spatial variationsare inherent in each of the observationally based estimates and these are not well understood.As described in later sections of this report, the problems differ with each class of fluxestimates, and thus the 'best' estimate for the climate modeller may be application dependent.Developers of observationally based flux estimates are beginning to take advantage of the fewhigh quality direct measurements available for evaluating their methods. Climate modellers are2 - Requirements for surface fluxes 24 2.7 Evaluation of Climate modelsReport of the Working Group on Air Sea Fluxes June, 2000likely to consider this strategy less valuable, as they are more interested in how theirsimulations are fairing on the whole, that is they want maps!Figure 2.3. The climatological values of the net surface heat flux from four coupled models andfour other flux products. Shown are (from top, left) ARPEGE/OPAICE, HadCM3, ECHAM3 +LSG, NCAR-CSM, the SOC climatology, the ERA15 reanalysis, the tuned UWM/COADSclimatology, and the results from the Residual method (Trenberth and Solomon, 1994)2 - Requirements for surface fluxes 25 2.7 Evaluation of Climate models2.8 Climate Change StudiesReport of the Working Group on Air Sea Fluxes June, 2000A better quantitative understanding of our global ocean estimates of heat, momentumand freshwater fluxes is urgently needed for the evaluation of climate models. Accuracy oflong-term means and inter-annual variability with medium spatial resolution and globalcoverage is important. Wind stress, latent and sensible heat, short and long wave radiation,precipitation and evaporation, sea-ice extent and thickness, are all important for long termclimate simulations. Despite the difficulties, for the evaluation of coupled models the time isright for a comprehensive examination of state-of-the-art flux products. While it may be yearsbefore uncertainties in large scale fluxes can be reduced to a desired level (e.g. <10Wm-2 for thenet heat flux) it would be of tremendous benefit if a serious attempt is made to identify thestrengths and weaknesses of available products, and to the extent possible, quantify theiruncertainties (as this Report attempts to do).2.8 Climate change studiesOcean-atmosphere fluxes establish the link between ocean surface temperature changesand atmospheric circulation variability. On the other hand, they provide the mechanism of bywhich ocean variability is forced by the atmosphere. Thus accurate knowledge of the fluxvariability is extremely important for understanding climatological variations in the coupledocean-atmosphere system. Climate change studies are primarily based on the consideration ofgridded flux products with at least monthly temporal resolution. For these products therequirements can be divided into three groups: (1) accuracy requirements; (2) resolutionrequirements; (3) specific requirements with respect to the reliable description of fluxes in keyregions.With respect to accuracy, the most important requirement of the flux fields is reliablerepresentation of the temporal behaviour of the anomalies of fluxes, rather than closure of thebalance either for the globe or for a particular basin. Cayan (1992a-c) and Gulev (1995)stressed that consideration of anomalies avoids a number of errors and uncertainties inherent inconsideration of climatological means. On the other hand, variability patterns derived fromdifferent data sets are largely influenced by time dependent biases which result from changes inobservational techniques and sampling density (for the VOS data) and from the changes in themodel configurations and assimilation input (for NWP products).If we consider interannual and decadal-scale variability, sensible and latent heat are themain contributors to the net flux anomalies in mid- and high latitudes, explaining 70 to 90 % ofthe magnitude of the net flux anomalies (Cayan 1992a, Gulev 1995, Gulev et al. 2000). In thetropics and subtropics, the net flux anomalies are determined by both latent heat and SWradiation. Evaporation minus precipitation anomalies are largely influenced by precipitation inlow and high latitudes; in mid latitudes and subtropics, anomalies of evaporation are of similarimportance. Rough estimates of the typical magnitudes of the interannual to decadal midlatitudinal winter signals associated with the North-Atlantic Oscillation (NAO) and Pacific-North American Oscillation (PNA) are from 10 to 50 Wm-2 (Cayan 1992b,c, Iwasaka andWallace 1995, Tanimoto et al. 1997, Alexander and Scott 1997, Gulev et al. 2000). Thus anaccuracy of at least 10 to 20 Wm-2 is required for mid-latitudinal gridded anomalies of fluxes.This estimate does not have a direct link to the accuracy of the mean flux. In the tropics thetypical magnitude of the interannual variability, associated with the ENSO signal and theAtlantic subtropical dipole, is somewhat smaller, and we can take 7 to 10 Wm-2 as the accuracyrequirements for the tropical anomalies of latent heat and SW radiation.For secular changes (with respect to a period of about several decades), the largestobserved tendencies vary from 5 to 15 Wm-2 per decade for selected locations and from 1 to 5Wm-2 for ocean basins, implying a somewhat higher accuracy for the flux anomaly estimatesthan is needed for an adequate description of interannual variability. Note however, that allexisting estimates of secular climate changes in sea-air fluxes are influenced by the uncertaintiesinherent in the data used. Thus any given estimates of required accuracy should be considered2 - Requirements for surface fluxes 26 2.8 Climate Change StudiesReport of the Working Group on Air Sea Fluxes June, 2000as very tentative. This is particularly important with regard to anomalies of wind speed and thewind stress products used to force ocean models. Wind speed based on Voluntary ObservingShip (VOS) observations may be affected by trends which are artificial in nature, resulting fromhistorical changes in observational techniques (such as the relative contribution of Beaufortestimates and anemometer measurements, changing ship size and macrostructure, etc. -Peterson and Hasse, 1987, Cardone et al. 1990, Lindau et al. 1990, Isemer 1995, Kent andTaylor 1997, Gulev 1999). NWP wind (and stress) products can be also influenced by time-dependent biases due to the impact of VOS and other data on the data assimilation. Changes ofwind speed of around 1 ms-1 can result, for moderate and strong winds, in an apparent 20 to25% increase of wind stress, and even larger effects on the mixing parameters (for instance, u*3)used in ocean modelling. Time dependent biases inherent in the wind fields can strongly affectevaporation estimates, leading to trends in latent heat flux of up to 10 Wm-2 per decade or evenmore. Therefore, accurate evaluation of wind fields free from time dependent biases, would beof great importance for climate research.The required spatial resolution of the surface flux fields for climate studies isdetermined by the necessity to resolve the main variability patterns. Although multi-year meanclimatologies are available currently at 1˚ latitude x longitude grid resolution (da Silva et al.1994, Josey et al. 1999) the true spatial resolution is worse, typically by a factor of 3 or more(Kent et al., 2000). To require 1˚ resolution for monthly anomalies would therefore beunreasonable, since it is even more difficult to meet the accuracy requirements for very fineresolution on these time scales. Moreover, taking into account that linear matrix operations,based on SVD and EOF analysis, still constitute the core of most of climate analysistechniques, it is important to avoid continuous gaps in the anomaly fields. On the other hand,the 5˚ resolution used in the studies of Cayan (1992b,c) and Iwasaka and Wallace (1995) maylead to an inadequate description of a number of key-processes in mid- and high latitudes; inparticular those associated with flux variability in the vicinity of SST fronts and sea ice margins.It is probable that the optimal spatial resolution for monthly anomalies of the fluxes is around2˚. That would fit the resolution of the best, present, coupled models but would be hard toachieve from observational data. Furthermore, some key-regions associated with mechanismswhich are crucially important for the description of climate variability require finer resolution ofthe anomalies of the flux fields, and high accuracy. The most remarkable examples are theLabrador Sea and GIN Sea regions which are responsible for deep convection driven primarilyby the surface heat and fresh water fluxes. Convection events may occur within quite shortperiods of around several days, so that both, monthly temporal and 2˚ spatial resolutions can betoo coarse for adequate description of the climatic variability of the sea-air fluxes.Studies of climate variability in ocean-atmosphere flux fields require accuratedescription of the sea ice extent. In the presence of ice, the fluxes between the ocean and theatmosphere are changed considerably compared to open water conditions (section 2.5).Moreover, the flux between the ice surface to the air and the flux lost by the ocean are differentin the presence of ice, and dependant on the ice thickness. In addition the degree of ice coverstrongly affects the ocean-atmosphere fluxes in the vicinity of the ice margins, where thehighest local sea-air temperature gradients may be observed. It is known that the ice cover haspronounced decadal scale variability (Hilmer et al. 1998). If this is not taken into account (thatis if flux producers do not have a time-dependent ice coverage and use a climatological icemask), large artefacts will affect the computed anomalies of the fluxes.To summarise, climate studies have some important specific requirements with regardto flux products, relating primarily to the reliability of the anomalies of fluxes. These may notnecessarily be properly represented by even the best flux climatologies. In this context,satellite fluxes (when they reach continuity on climatological time scales) will definitely serveas an important data source for reliable anomalies of the fluxes, being expected to have thesmallest time-dependent biases.2 - Requirements for surface fluxes 27 2.8 Climate Change Studies2.9 ConclusionsReport of the Working Group on Air Sea Fluxes June, 20002.9 ConclusionsFor NWP models there is clearly a need to continue the observations needed forassimilation into the model. However beyond that, the requirement is for independentestimates of the basic variables and of the fluxes for verification of the model physics. Suchestimates must be associated with a realistic error assessment and specification of the trueresolution of the data. For regional models the requirement is similar but with increasedemphasis on high space/time resolution which at present may only be achieved by using models(although in future high time resolution satellite products may become available).For OGCM's high resolution forcing fields are needed. For example 3 hourly winds arerequired to simulate the shear in the mixed layer, and a 50km resolution is sought for the windstress curl. A useful development for OGCM's would be air-sea interaction experiments wheremeasurements of the fluxes in both atmosphere and ocean are obtained and the ocean mixedlayer measurements allow budget closure. Although difficult, such experiments are necessaryfor parameterisation development.For wave models, high resolution in space and time is increasingly found to beimportant, and, for wave forecasting, high quality forecast winds are vital. Time resolutionon the order 2 to 4 hours is presently sought and NWP and reanalysis centres are being asked for3 hourly fields.For sea ice modelling resolution of the flux fields on synoptic scales is adequate: dailyand 50 km resolution. The mean flux values must be known to high accuracy; biases of a fewWm-2 can change mean ice thickness substantially. The fraction of open water needs to beknown to order 1%.For climate variability studies the fluxes need to be known to a few Wm-2 accuracy butthe requirements on spatial resolution can be relaxed; a factor of two coarser than meanclimatological fields would generally be adequate. However there are certain vital areas wherehigher resolution may be required, for example the Labrador Sea. For such areas it is alsoimportant to allow for the variability of ice cover; climatological ice masks are not sufficient.For variability studies a long time series of observations is vital. At present such studies arebased on the ship data and we must recognise that there are some areas of the ocean,particularly the Southern Ocean, where we really have little idea of what the variability is.Even in better sampled regions such as the Kuroshio there is scope for further studies.Climate modellers need a reliable data set to compare with - both basic variables andsurface fluxes. Climate models tend to be very sensitive to the surface fluxes which are neededto within 10 Wm-2 . They also need information with regard to sensitivity, for example, howdo the fluxes change with changes in the fields of the basic variables?In summary, for many purposes there is a need for high space time resolution which atpresent can only be obtained from models (although new satellite products show promise for thefuture). The models need high quality verification data of known resolution and errorcharacteristics, and also improved representation of the basic physics of air-sea fluxes. Thus itwill be argued in this report that the flux products from various sources are complementaryrather than being in competition, and that the ensemble of air-sea flux estimates that theyrepresent cover the time and space scales that ocean science studies are investigating.28Report of the Working Group on Air Sea Fluxes June, 20003 - Space-time variability of the fluxes 29 3.1 Introduction3.2 Synoptic and mesoscale processesReport of the Working Group on Air Sea Fluxes June, 20003 SPACE-TIME VARIABILITY OF THE FLUXES3.1 IntroductionThe surface air-sea fluxes link oceanic and atmospheric processes, and knowledge oftheir variability on different space-time scales is crucial for understanding ocean-atmosphereinteraction. Accurate quantitative description of the variability of atmosphere-ocean energyexchange is important for process studies and for the development of all types of oceanic andatmospheric models. In addition, accurate knowledge of sea-air flux variability can help toimprove the flux fields themselves, since it indicates the accuracy which is required to quantifythat variability.In some respects, determination of the flux variability may be easier than determiningthe mean flux values; systematic errors are of less importance, and the main spatial scales oflonger term variability tend to be large. Similarly, numerical weather models tend to performbetter with regard to determining the variability than they do in determining the mean. This isprobably because the models are designed to forecast the weather - the day to day variations inclimate. However despite these considerations, and the undoubted importance of surface fluxvariability, the Working Group considered that detailed assessment of the variability of fluxproducts could not be accomplished at this time. Thus, in this chapter, a brief discussion offlux variability will be presented. The aim will be to set the context of the flux field evaluationspresented in future chapters rather than to present a complete description of the characteristicsof air-sea flux variability. A thorough assessment of the variability characteristics of availableflux products must remain an important priority for further studies, perhaps by a future workinggroup.3.2 Synoptic and mesoscale processes3.2.1 Mid to High latitude variabilityA. INTRODUCTIONIn the mid and high latitudes, synoptic and mesoscale variability of the sea-air fluxes isprimarily determined, on the one hand by the atmospheric synoptic scale transients and theirassociated high frequency variability, and on the other hand, by mid-latitudinalinhomogeneities of sea surface temperature. Thus SST fronts, together with coastal andmarginal ice zones, generate sharp spatial gradients of surface properties which vary little onthe time scales of atmospheric synoptic variability. In other words, mid latitudinal synopticscale ocean-atmosphere energy exchange is characterised by a highly non-stationaryatmosphere and a strongly inhomogeneous ocean. The contrast is considerable; atmosphericsynoptic variability has spatial scales several times larger than the ocean surfaceinhomogeneities, and ocean temporal variability on synoptic space scales is several timesslower than the atmospheric synoptic time scales.B. MAGNITUDE OF FLUX VARIABILITYThe typical temporal time scale of mid-latitudinal atmospheric variability is from 1 to 7days. Atmospheric parameters in different parts of propagating mid-latitudinal cyclones arehighly variable. For instance, in winter time, the air temperature in cyclones over the north-west Atlantic and north-west Pacific can vary from 15˚C in the warm sector to strongly negativetemperatures behind the cold front. Each winter, the monthly record of wind speed in mid-latitudes shows a range from calm conditions to gale force winds. Thus, mid-latitudinalsensible and latent heat fluxes vary enormously, from heat input into the ocean to very highrates of ocean cooling. As an example, direct measurements during the winter season in theLabrador Sea (The LabSea Group 1998) showed variations of sensible flux between weakocean warming, of order several tens Wm-2, to 600 to 800 Wm-2 cooling during a 22 dayperiod.3 - Space-time variability of the fluxes 30 3.2 Synoptic and mesoscale processesReport of the Working Group on Air Sea Fluxes June, 2000Extremely high variability of mid-latitudinal fluxes occurs in cold and warm airoutbreaks, associated with the propagation of cyclones over SST fronts or ice margin areas.This variability can be further enhanced in rapidly intensifying cyclones (“meteorologicalbombs” - Rogers and Bosart, 1986), which are characterised by extremely high winds andtemperature gradients. Thus, in 12 hours during the development of a rapidly intensifyingcyclone in the CASP experiment, Yau and Jean (1989) reported changes of sensible heat fluxfrom -70 Wm-2 (ocean warming) to 500 Wm-2 (ocean cooling), and changes in the latent heatflux from 30 to 1100 Wm-2. Neiman et al. (1990) reported about 1000 Wm-2 spatial change inthe latent flux between the warm and cold sectors of an extra-tropical cyclone propagated alongthe North American coast. Gulev and Tonkacheev (1996) demonstrated that during a cold airoutbreak latent heat fluxes over the Gulf Stream front increased from 20 Wm-2 to 500 Wm-2 in adistance of 12 km. During the GALE experiment, Bane and Osgood (1989) measured similarspatial gradients of sensible and latent heat fluxes during a cold air outbreak over the GulfStream. While the Gulf Stream and Kuroshio are major features, less pronounced frontalzones in the mid-latitudinal and subtropical ocean can be associated with strong local anomaliesof surface fluxes (quantified in such experiments as FASINEX and SEMAPHORE).Temperature gradients at the FASINEX front in the subtropics of about 3˚C per 20 km wereassociated with surface latent flux changes from 150 to 300 Wm-2 (Friehe et al. 1991, Halliwelland Cornillon 1990). Moreover, local anomalies of the turbulent fluxes showed spatialstructures with typical horizontal scales of 30 to 60 km.C. EFFECTS ON OCEAN AND ATMOSPHEREThe synoptic variability of the fluxes in mid latitudes plays an important role in oceandynamics and atmospheric synoptic variability. High sensible and latent fluxes from the oceanto the atmosphere, together with strong winds, are responsible for cooling and mixing in theLabrador Sea, Greenland-Iceland-Norway Sea, and in some areas of the Southern Ocean Thedeep convection events which result are crucially important for the thermohaline circulation ofthe ocean. These events are associated with flux anomalies on daily to weekly time scales (theLabSea Group 1998), and their description requires accurate knowledge of energy exchange atthe ocean surface on those time scales. In the mid-latitudinal open ocean, and in thesubtropics, synoptic and meso-scale sea-air flux variability contributes to ventilation andconvergence processes in the ocean. These may result in SST and mixed layer temperaturechanges, and so form long-lived anomalies of upper ocean temperature.Synoptic variability of mid-latitudinal sea-air fluxes strongly affects the developmentand propagation of atmospheric synoptic systems, modifying low-level static stability andcontributing to local vorticity anomalies. High extremes of the synoptic scale fluxes in midlatitudes can be directly responsible for the intensification and even local generation of marinecyclones. In particular, most rapidly intensifying cyclones with deepening rates more than 1mb per hour are considered to be directly associated with the highly variable sea-air interactionprocesses in mid latitudes. Strong spatial gradients of the sea-air fluxes in the vicinity of SSTfrontal zones can diabatically modify extra-tropical cyclones and thus affect their life cycle(Neiman et al. 1990).D. MEASURING FLUX VARIABILITYAccurate quantitative description of synoptic and meso-scale variability of the ocean-atmosphere fluxes cannot be done on the basis of Voluntary Observing Ship (VOS) data, sincethe sampling is generally insufficient for this task. Ocean weather ship (OWS) data provide avaluable source of information for the analysis of short-period flux variability, if only in a fewlocations. These data have been analysed in a number of studies (e.g. Josey et al. 1995, Gulev1997a) which quantified synoptic scale variability in the surface fluxes. In this context datafrom NDBC and JMA buoys located in mid latitudes are also a valuable data source.It should be noted that, for a number of key synoptic and meso-scale processes such asexchange in the vicinity of sharp SST fronts and ice margin zones, the use of standard bulk3 - Space-time variability of the fluxes 31 3.2 Synoptic and mesoscale processes3.3 Climate VariabilityReport of the Working Group on Air Sea Fluxes June, 2000formulae (Chapter 7) may not be valid due to the very strong non-stationarity and intensivelocal advection effects. Thus new experiments (like FASINEX, GALE, ERICA, CASP, andLabSea) are very desirable to provide direct measurements of the fluxes in regions of stronggradients of surface temperature and highly variable atmospheric processes. The highresolution flux fields now available from reanalyses also give an excellent opportunity forglobal-scale analysis of synoptic variability in the ocean-atmosphere fluxes. Satellite data alsomay be used. For example, Miller and Katsaros (1992) demonstrated the possibility of usingSSM/I data for the diagnosis of surface latent heat fluxes in a rapidly deepening marine cyclonein the Atlantic. Even if the absolute flux values derived from satellites are biased, remotelysensed fluxes may realistically depict the space-time variability on synoptic and mesoscales.3.2.2 Variability in the tropicsAlthough synoptic and mesoscale sea-air flux variability in the tropics has smallermagnitude in comparison to mid- and high latitudes, it may play an important role in some keyprocesses in the tropical boundary layer. Large synoptic variations of tropical evaporation canaffect cumulus convection, contributing to the instability above the 850 mb level. Note alsothat in the tropics, the diurnal cycle in the sea-air fluxes becomes important, especially undercalm conditions and considerable insolation (Slingo and Delecluse 1999, Weller and Anderson1996). This enhancement of convection, and the reduction of the magnitude of the diurnalcycle, is responsible for the transition of tropical convection regimes from primarilycontinental to marine type. These effects were studied explicitly in the TOGA-COAREexperiment (Slingo and Delecluse 1999). In general, TOGA-COARE contributed considerablyto our knowledge of synoptic and mesoscale sea-air flux variability in the tropics particularlythrough the extended buoy observational program. It has been found that tropical synopticvariability can be characterised by 2-3 days dominant time scales. Weller et al. (1999) showedthat large variations in sea-air fluxes are associated with a family of convective systems ofdifferent origin. Mesoscale convection can result in 60 and 250 Wm-2 peaks in the sensible andlatent heat fluxes respectively. Peaks associated with the sub-mesoscale systems are somewhatsmaller, but also pronounced (20 and 150 Wm-2). Esbensen and McPhaden (1996) analysedTAO moorings and IMET buoy data in the TOGA-COARE experiment and found meso-scalevariability in the surface heat fluxes, with characteristic ranges of 0-50 Wm-2 and 0-300 Wm-2respectively for sensible and latent heat.3.3 Climate variability3.3.1 IntroductionClimate variability covers time scales from several years to centuries. Although thenumber of studies dealing with climate variability in sea-air interaction processes is quite large,these studies are (to date) primarily based on the consideration of anomalies of SST andassociated changes in the atmospheric circulation characteristics. There are very few studieswhich directly consider the variability of the ocean-atmosphere fluxes. However, it is throughthe exchange of heat, moisture and momentum that the atmosphere interacts with the ocean.Thus it is not SST itself, but these energy fluxes to and from the ocean, that actually force theatmospheric circulation and, at the same time, control the ocean temperature. Reviewing oneof the latest results on the mid-latitudinal links between SST and atmospheric circulation(Rodwell et al. 1999), Kushnir (1999) argues that we need “to understand the mechanisms bywhich the atmosphere ‘feels’ the SST anomalies”. Although it is obvious that sea-air fluxesprovide more valuable information on these mechanisms in comparison with say, SST,researchers tend to avoid consideration of the climate variability of the fluxes. This may beexplained by the following reasons: (i) flux fields contain more uncertainties and errors(including time dependent biases) than individual variables; (ii) flux fields are characterised byworse sampling in comparison to say SST, and only shorter time series in well sampled oceanbasins are available. Nevertheless, some results for a number of basins have been obtainedduring the past several years.3 - Space-time variability of the fluxes 32 3.3 Climate VariabilityReport of the Working Group on Air Sea Fluxes June, 20003.3.2 Detection of climate trendsThe best studied ocean basins are the North Atlantic and the North Pacific, since theseare characterised by better sampling. Bunker (1982) computed linear trends in themeteorological variables and sea-air fluxes over the Atlantic Ocean from 1948 to 1972 on thebasis of the Bunker (1976) data set. He found significant positive tendencies in winds andevaporation in the tropics and mid latitudes. However, in the 1980's and 1990's, there hasbeen considerable debate on the reliability of wind trends derived from VOS data (Ramage1987, Peterson and Hasse 1987, Cardone et al. 1990, Lindau et al. 1990, Ward 1992, Bigg1993, Isemer 1995, Ward and Hoskins 1996, Gulev 1999). Changes with time of the fractionof anemometer measurements, growing ship size and other reasons were considered as possiblesources of unrealistically high (0.1 to 0.5 ms-1 per decade) trends in the wind speed derivedfrom VOS data. This makes all estimates of secular tendencies in the turbulent fluxesquestionable, since any wind speed bias propagates into the heat flux results through the bulkformulae. Thus, Wu and Li (1995) found a significant increase in evaporation in a number ofthe World Ocean basins in the decade of the 1980's, the same period which showed a sharpincrease in wind speed in comparison to earlier decades. Trends in the sea-air fluxes can alsobe affected by time-dependent biases in variables other than wind. For example, the changingfraction of SST bucket measurements and engine intake readings could result in an artificialSST trend.Thus, at present, estimation of secular trends in the fluxes is more uncertain comparedto other scales of climate variability, and time dependent biases in the basic meteorologicalquantities constitute the main contribution to this uncertainty. Any future efforts in this areashould include corrections to the observations (as recommended for example by Kent et al.,1993b; Josey et al., 1999). However, such corrections require that additional information beavailable. Since that information is not available for every observation, the sampling errorsmay be considerably increased, and this can also affect the estimates of trends. Now that thereanalyses have become available for periods of several decades, there is an alternativeopportunity to estimate secular changes in the flux fields. However, although the performanceof the models has been kept frozen for the reanalyses, the assimilation input unavoidablychanged with time. Thus, estimates of secular changes from the reanalyses may also beseriously influenced by the changes in the data assimilation input. White (1999) reported thatthe significant increase of satellite data available in the late 1970's could result in artefacts inestimated secular tendencies in SST and precipitation, especially in the Southern Hemisphere(where the relative role of satellite data in the total amount of observations is very large). Inaddition, time dependent biases in the COADS data may propagate into the reanalyses fluxproducts resulting in additional uncertainty of the reanalyses-based trend estimates. Inspectionof linear trends in evaporation and net flux, derived from the VOS measurements and fromNCEP/NCAR reanalysis, give comparable trend estimates for well sampled areas (e.g., upwardtendency of about 15 Wm-2 per decade in the North Atlantic mid latitudes and negative trendsmatching 10 Wm-2 per decade in the Gulf Stream area in the subtropics). However in otherareas there are strong disagreements. In the tropics the VOS fluxes indicate significantly largepositive trends, in disagreement with the NCEP reanalysis fluxes. In high latitudes (e.g. theLabrador Sea) the NCEP reanalysis fluxes demonstrate strongly positive secular changes whichare not supported by estimates based on VOS observations.Satellite data sets are already becoming comparable in time span with the reanalyses.In future these will give another alternative data source for estimating secular tendencies, whichmight reasonably be considered less biased in comparison to VOS and NWP fluxes. In thissense, satellite data have an advantage because, even if there are biases in the flux products,these should not be of time-dependent nature and thus should not affect estimates of seculartendencies. As an example of the application of these data, White (1999) reported that evenfor the period from late 1970's to late 1990's (which is considered to have more homogeneousdata assimilation input) precipitation trends derived from the NCEP/NCAR reanalysis andsatellite data set of Xie and Arkin (1997) have different signs.3 - Space-time variability of the fluxes 33 3.3 Climate VariabilityReport of the Working Group on Air Sea Fluxes June, 20003.3.3 Interannual to decadal scale variabilityA. MID AND HIGH LATITUDES.We can expect better progress to be achieved when interannual to decadal scalevariability is analysed, since the uncertainties present in the mean fields and time dependentbiases can be avoided. Cayan (1992a-c) used COADS MSTG products to compute the sea-airflux anomalies for the period 1946-1986 in the North Atlantic and North Pacific and toinvestigate the links between the flux variability patterns and associated changes in the SST andatmospheric circulation. Iwasaka and Wallace (1995) and later Tanimoto et al. (1997) madesimilar studies of the variability in the ocean-atmosphere fluxes derived from COADS data.All these studies emphasised that the major portion of variance in the surface net fluxes in theNorth Atlantic and North Pacific is explained by dipole patterns made by subtropical andsubpolar centres of action. These patterns mirror corresponding SST patterns on interannual todecadal time scale, and can be linked to the sea level pressure patterns associated with theNorth Atlantic Oscillation (NAO) in the North Atlantic and with the North Pacific Oscillationand Pacific-North American pattern in the North Pacific. Cayan (1992a) found a typical winteranomaly magnitude of about 30 to 50 Wm-2 in the mid-latitudinal North Atlantic and asomewhat smaller anomaly in the North Pacific. Sensible and latent fluxes were primarilyresponsible for these mid latitudinal anomalies in the net flux.Although the mid latitudinal variability patterns in the flux fields reported by differentauthors are quite comparable to each other, careful inspection of the results shows that thespatial features and the flux behaviour in time (usually described in terms of the leadingempirical orthogonal functions, EOF's and the corresponding principal components) showdifferences in different flux products. Whether these observed differences are significantdepends on the scientific task considered. For describing the surface flux signal affecting thelarge scale atmospheric circulation (represented by sea level pressure or 500 mb heightpatterns), one can be satisfied with similarity of the time series of principal componentscorresponding to the first EOF, which typically accounts for 20 to 35% of the variance. Onthe other hand, analysis of relationships between the surface flux variability and behaviour ofthe mid-latitudinal storm tracks requires consideration of the flux patterns associated with thehigher order EOF's. These can reflect differences between products, and may not necessarilybe clearly separated one from another because of the errors and uncertainties which again canbe different in different data sets. From the view point of forcing the general circulation of theocean by surface fluxes, even the apparent comparability of the first EOF's in different fluxproducts has to be viewed with caution. For example, for the ocean circulation in the NorthAtlantic, it is crucially important whether the local flux anomalies in the Labrador Sea,primarily caused by sensible flux, are depicted explicitly. If we consider, for example, theCayan (1992a) pattern, the Labrador Sea will be characterised by weak anomalies. These arecorrelated with the anomalies in the centre of the subpolar gyre, which may be partly generatedby the spatial interpolation procedures during periods of poor sampling in the Labrador Sea.However, the NCEP/NCAR and ECMWF reanalyses show a pattern characterised by stronganomalies explicitly located over the Labrador Sea and not centred on the subpolar gyre ingeneral (Gulev et al. 2000). Exactly this pattern was also found recently by Alexander andScott (1997) who studied surface flux variability from the atmospheric GCM simulations. Thetime behaviour of these patterns is highly correlated, however only one of the two productscan provide realistic production of Labrador Sea water.Recently Gulev et al. (2000) analysed interannual variability in VOS products derivedby using different calculation schemes in comparison to the NWP fluxes taken from theNCEP/NCAR reanalysis. First, the EOF's of the VOS fluxes derived using differentparameterisations were computed. They were very comparable to each other. The use ofdifferent schemes resulted, as expected, in different magnitudes for the anomalies, but did notaffect the estimates of the long-term tendencies and interannual variability. In order to achievefull comparability of the spatial patterns, the anomalies of fluxes derived from different3 - Space-time variability of the fluxes 34 3.3 Climate VariabilityReport of the Working Group on Air Sea Fluxes June, 2000schemes were projected on the EOF's of a reference product - for example, the Large and Pond(1982) scheme for the turbulent fluxes. The correlation between the temporal behaviour of theprojected fluxes (i.e. the quasi-principal components) and the principal components taken asreference, showed that different parameterisations did not lead to any qualitative differences inthe temporal behaviour of the flux anomalies. The correlation between different projectionswas close to one and always over the 0.98 limit. On the other hand, the standard deviation ofthe variability obtained using different parameterisations was quite different, and the selectionof which scheme to apply was important in order to estimate the forcing magnitude (forexample for model forcing fields).This is illustrated in Figure 3.1 (Gulev et al. 2000). Figure 3.1b shows the first EOF'sof the net heat flux derived from fluxes computed using individual COADS reports and theobservation corrections and parameterisations recommended by Josey et al. (1999) - except thatfor turbulent heat fluxes the Large and Pond (1982) scheme was used instead of Smith (1988).Also shown (Figure 3.1a) are those from the NCEP/NCAR Reanalysis data for the period from1958 to 1997. On the first glance, at least in mid latitudes, the patterns are quite comparableand indicate a north-south dipole made by centres of action in the subpolar gyre and in the GulfStream area. But if we consider the NCEP/NCAR fluxes, the northern centre of action iscentred exactly on the Labrador Sea, and for the other products we have somewhat moresmooth picture with the local maximum located east of Newfoundland. More comparability isachieved in the southern centre of action.The observed difference between the VOS and NCEP/NCAR can be influenced by inadequatesampling in the VOS data set, which has a climatological dependence and can affect both long-term trends and interannual variability. Figure 3.1c shows the first EOF in the modifiedNCEP/NCAR net flux, which was derived from the original 6-hourly snap-shots by matchingthem to the dates and hours of the COADS reports used for computing the VOS fluxes.Remarkably enough, this “undersampled” NWP product demonstrates a spatial pattern which isvery similar to the original VOS net flux. In this case, the importance of good sampling inorder to describe variability justifies the use of COADS MSTG products (see also Section10.12), at least for investigating climate variations in the flux fields. Figure 3.1d shows thefirst EOF in the fluxes derived from the enhanced COADS MSTG, which were taken astrimmed statistics of the basic variables; the same parameterisations as for the VOS-type ofdata were applied on a monthly basis. In comparison to the NCEP fluxes, the variability overthe Labrador Sea is more similar in amplitude when derived from the MSTG rather than fromthe VOS. The reason is that the MSTG product is less influenced by undersampling comparedto VOS products computed on an individual basis. This suggests that MSTG can besuccessfully used for climate studies, especially if some ad-hoc corrections are applied toremove, at least partially, the time dependent biases inherent in the MSTG. Figure 3.2 showsthe time behaviour of the 1st normalised principal components of different net fluxes incomparison to the NAO index. The time coefficients for all four products are highly correlatedwith each other and show quite impressive correlation with the NAO index.B. TROPICSThe interannual and decadal variability of the fluxes for tropical oceans is significantlyinfluenced by variations in the radiative fluxes in addition to variations of the turbulent fluxes,and this considerably increases the uncertainty of the variability estimates. Moreover, themagnitude of flux anomalies in the tropics is smaller in comparison to mid latitudes. Also,sampling frequency in the tropics is lower than in mid latitudes; fortunately the smallersynoptic variability in these regions does not require the same sampling as in mid latitudes foradequate description of the anomalies (Weare and Strub 1981). If we consider the tropicalAtlantic Ocean, we can expect that the flux variability should reflect the dominant modeassociated with the Atlantic subtropical dipole, characterised by a pronounced decadal cycle inthe SST anomalies (Mehta 1998). In the tropical Pacific the dominant variability is associatedwith El Niño events. Although the fluxes are crucially important for understanding the El Niño3 - Space-time variability of the fluxes 35 3.3 Climate VariabilityReport of the Working Group on Air Sea Fluxes June, 2000 Figure 3.1 First EOF of the net heat flux derived from (a) (top left) NCEP/NCAR reanalysis;(b) (top right) COADS ship reports; (c) (bottom left) the reanalysis sampled at COADS reportpositions; (d) (bottom right) using COADS MSTG data.55 60 65 70 75 80 85 90 95Y E A R S-4-3-2-10123NAO index-4-3-2-101231st normalized PC of the net fluxNCEP (R=0.85)COADS/ind (R=0.76)NCEP/us (R=0.71)COADS/MSTG (R=0.85)Figure 3.2 Time series of the NAO index (red line) and of the first normalised principalcomponent of the net heat flux from the analyses shown in Figure 3.1. (NCEP/NCARreanalysis; NCEP/us is sampled at ship data positions; Individual COADS reports; andCOADS MSTG data.)3 - Space-time variability of the fluxes 36 3.3 Climate Variability3.4 SummaryReport of the Working Group on Air Sea Fluxes June, 2000life cycle on an interseasonal scale, there is no indication that they can serve better than SSTas predictors on longer time scales. Variability in the Indian Ocean is also poorly quantified.Jones et al. (1995) made an EOF analysis of 30-year surface fluxes over the Indian Ocean,using COADS, and found increasing latent heat flux from the 1960's to 1980's ranging fromseveral Wm-2 to several tens of Wm-2.C. SEASONAL VARIABILITYThe interannual variability of flux fields should be considered in the context of seasonalvariability since this affects the observed climate changes. Summer flux anomalies can beprimarily driven by net flux components different from those which are crucially importantduring wintertime. In particular, radiative fluxes may play a more important role during thewarm season. Tanimoto et al. (1997) noted differences in the summer and winter variabilitypatterns in the mid-latitudinal North Pacific. However, this problem is still has to be fullyexplored.3.4 SummaryTo summarise, our knowledge of the climatic variability of ocean-atmosphere fluxes isstill poor. Results obtained over the last several years have suggested that studies of thevariability of the fluxes can potentially tell us much about the surface climate signals ondifferent time scales. However in practice the signals which have been derived have notproved significantly more valuable for understanding climate variability compared to thoseobtained by studying the variability of basic variables such as SST. Nevertheless, developmentand improvement of different flux products, available from VOS, NWP and satellites, mayimprove this situation in the immediate future.4 - Data Sources 37 4.1 Introduction4.2 in situ data sourcesReport of the Working Group on Air Sea Fluxes June, 20004 DATA SOURCES FOR FLUXES AND RELATED VARIABLES4.1 IntroductionThis Chapter will consider the characteristics of the different sources of data for thefluxes and flux related variables. Each have their advantages and disadvantages. The mainsources of in situ data are from ships and buoys. Merchant ships in the Voluntary ObservingShip programme are the traditional source of flux estimates providing weather observationsfrom which the fluxes can be estimated. However the sampling is restricted to shipping lanesand many parts of the ocean are rarely observed. Likewise, intensive field campaigns byresearch ships (e.g. TOGA-COARE) are too restricted in both sampling time and space, tocontribute significantly to the global flux database; however, the resulting high-qualitymeasurements of fluxes and related basic variables are invaluable for verification of systemswith broader coverage. Satellites provide globally distributed data but for a restricted range ofvariables. Models can be used to assimilate the various information available, and produceregular gridded fields of flux values. There is continuous effort, using results from the intensivefield studies referred to above, to improve the performance of model parameterisations andhence the accuracy of the fluxes. Flux values can also be estimated from what are effectivelybudget methods, the "residual technique" and assimilation into ocean models. Finally in thisChapter the data sets for determining the fluxes in sea ice regions will be discussed.4.2 in situ data sources4.2.1 Voluntary Observing ShipsOur present understanding of the climate over the global ocean (e.g. Budyko, 1963;Esbensen and Kushnir, 1981; Oberhuber, 1988; da Silva et al., 1994; Josey et al. 1999) isbased on the meteorological reports from the Voluntary Observing Ships (VOS) of the WorldWeather Watch. These merchant ship observations constitute the only quasi-global data setwhich extends over a number of decades. In addition, the range of observations is such that ithas been possible to develop parameterisation formulae with which to calculate all the requiredfluxes.A major disadvantage of the ship observations is that adequate coverage is obtainedonly in areas that are well covered by shipping lanes (Figure 4.1). For example, in July thereare very few observations which enable a latent heat flux estimate to be made south of 40°S.In contrast the North Atlantic and North Pacific are well covered by observations. Inevitably thecharacteristics of the ship observations (biases, precision etc.) have changed with time. Therehave been, and will continue to be, changes in instrumentation, for example, sea surfacetemperature buckets (of varying types) are increasingly being replaced by engine room intakereadings. The increasing use of anemometers to estimate the wind may results in errors due toairflow disturbance and errors in calculating the true wind velocity. However the major changehas been in the size and type of the ships with modern ships typically larger, faster, andtravelling on different routes compared to those in past decades. In recent years ourunderstanding of the resulting biases in these observations has increased significantly (Chapter10 ) allowing the possibility both of applying correction schemes and implementing improvedobservational methods.Because there are about 7000 VOS, with ships continually leaving the programme andnew ships being recruited, the instrumentation provided by the Meteorological Agencies hasgenerally been basic and inexpensive. Kent and Taylor (1991) described a subset of 46Voluntary Observing Ships (VOS) operating in the North Atlantic, and the meteorologicalinstrumentation with which they were equipped. Typical of this subset would be a containervessel of about 210m length, travelling at about 9 ms-1, and loaded with cargo to about 10m to20m above the main deck. As an instrument platform, this represents a large object capable ofsignificantly altering the environment to which the meteorological sensors are exposed. Seatemperatures were measured near the surface using a SST bucket, or, more likely, at depths of4 - Data Sources 38 4.2 in situ data sourcesReport of the Working Group on Air Sea Fluxes June, 20003m to 9m using engine intake or hull contact sensors. Air temperature and humidityobservations were taken at about 20m to 30m above the sea using a thermometer screen or handheld psychrometer. If carried, the anemometer was mounted at about 30m to 35m above sealevel, but, in the North Atlantic, two-thirds of the ships reported visually estimated winds.−80−60−40−20020406080Latitude60 120 180 −120 −60 0Longitude0251025100250Figure 4.1Global distribution of the number of latent heat flux estimates per 1˚ x 1˚ square forthe period 1980 - 1993. White indicates that there were no observations over the entire periodconsidered (from Josey et al. 1999)For some time there have been suggestions that improved instrumentation might beplaced on a subset of the VOS e.g. (Taylor, 1984). An example is the ImprovedMeteorological System, IMET (Hosom et al., 1995) developed at Woods Hole OceanographicInstitution (WHOI) which has been installed on a number of the U.S. Research Vessels and isnow being placed on U. S. VOS. IMET uses sensors chosen for accuracy, reliability, and theirability to stay in calibration during unattended operation. The present set of IMET modulesincludes wind speed/direction, air temperature, sea surface temperature, relative humidity,precipitation, incoming shortwave radiation, incoming longwave radiation, and barometricpressure. A data set of automatically recorded meteorological data has been assembled fromthe research ships operating during the WOCE Hydrographic Programme (1991 - 1998). Theseships used IMET or similar systems. Often these ships were away from the main shippinglanes, so these data are potentially of value for flux verification (see Section 9.2.3).Recently there has been an initiative by the World Weather Watch to establish a VOSClimate Project. The initial aim would be to assemble a set of chosen VOS and to documentcarefully the instrumentation in use. Data from these ships would undergo detailed analysis inorder to estimate any biases on a ship by ship basis. The Working Group considered that suchan initiative was to be encouraged since, in time, the resulting data set will form an importantasset for verification of both basic variables and flux estimates.4.2.2 Ocean Weather ShipsThe array of Ocean Weather Ships was originally subsidised by the commercialaviation sector, and maintained at different sites for varying periods during the years 1945 to1989 (Table 4.1 and Figure 4.2). Apart from surface and upper air meteorologicalmeasurements, the weather ships provided, during various periods, time series of surfaceradiation budget, surface waves, ocean profiles, and other variables. Valuable climate datawere obtained, for example the long time series of data from ocean station Papa. However the4 - Data Sources 39 4.2 in situ data sourcesReport of the Working Group on Air Sea Fluxes June, 2000weather ships were implemented for weather forecasting rather than for climate observationsand this has implications with regard to the accuracy of the data. Indeed, it should not beconsidered that the Weather Ship data were immune from systematic errors. For example,Isemer (1994) demonstrates that deriving a long time series of consistent weather ship windobservations is not a trivial task given the many changes in ship type and observing practise.Nevertheless the weather ships form a valuable data resource where a long time series of data isrequired from specific sites.LongitudeMTXNPBEADCILJRKFigure 4.2 Distribution of Weather Ship StationsTable 4.1 Summary of the Weather ship sites (from Isemer, 1994)Position start end Country Position start end CountryA 62N 33W 4/45 4/74 US,F,N,NL,UKL 57N 20W 7/75 12/89 F,NL,UKB 56.5N 51W 1/46 4/74 US M 66N 2E 1/49 12/89 F,UKC 52.5N 35,5W 11/45 12/89 US,USSRR 47N 17W 7/75 12/85 FD 44N 41W 1/46 6/73 US N 30N 140W 6/46 4/74 USE 35N 48W 9.49 6/73 US P 59N 145W 10/49 6/81 US,CI 59N 19W 9/47 6/75 NL,UK T 29N 135E 9/48 10/81 JJ 52.5N 20W 8/47 6/75 NL,UK V 34N 164E 3/55 1/72 USK 45N 16W 6/49 6/75 F,NL,UK4.2.3 BuoysBuoy data does not allow global estimation of the surface fluxes; however buoys are agood source of verification data for other observing systems. Many countries now operatemoored coastal buoy arrays, for example the NDBC (Gilhousen, 1998) and AES (Axys, 1996)buoys off North America, and the ODBS buoys off Japan. Taking as an example the NDBCbuoys, these range in type from the very large 12m and 10m discus designs, through the 6mNomad buoy to the 3m Discus buoy. Over 20 buoy locations have been maintained in both theAtlantic and Pacific with most time series dating from the mid 1970's, or early 1980's, to the4 - Data Sources 40 4.2 in situ data sourcesReport of the Working Group on Air Sea Fluxes June, 2000present. In addition to standard surface meteorological data, spectral wave data and currentprofiles are available from some locations. Although primarily established for weatherforecasting and now-casting purposes, these buoys have also proved valuable for thecalibration of remotely sensed data from satellites.Operational buoy arrays have begun to be established away from coasts in the openocean. Foremost of these was the TOGA TAO array (McPhaden et al., 1998). A majorresponsibility for the western part of the array is now to be assumed by the TRITONprogramme (Kuroda et al., 1999) and a similar array (PIRATA) is being implemented in theTropical Atlantic (Servain et al., 1998). The relatively simple and inexpensive AutonomousTemperature Line Acquisition System (ATLAS) buoys which are used for these arrays measurea basic set of the meteorological variables. In contrast "Flux buoys" have been developed,principally by a group at WHOI as part of the IMET programme, to measure all the variablesrequired to estimate the heat, momentum and radiative fluxes. They have been used in theSubduction experiment in the North Atlantic (Moyer and Weller, 1997), in the west Pacificduring the TOGA COARE experiment (Weller and Anderson, 1996), in the Arabian Sea(Weller et al., 1998) and elsewhere. These buoys are a valuable source of data for verifyingflux estimates. Equipped with fast response sensors, research buoys can also be used todetermine the transfer coefficients.LongitudeIMET Operational TAO/Triton, PirataFigure 4.3 Positions of Meteorological buoy arrays (past and present). (circles: standardmeteorological buoys; triangles: TAO/Triton and PIRATA arrays; black diamonds: Flux buoysites(IMET) ). Note that not all of these buoys are still in operation.Drifting buoys are deployed in significant numbers and are well distributed over theglobal ocean. However they measure a too restricted range of variables to allow fluxestimation. Sea temperature is normally measured, air pressure may be measured, and, onsome buoys, wind speed may also be estimated.4.2.4 Platforms, Rigs, etc.Fixed platforms, drilling rigs, etc. generally tend to be poor instrument platforms.Their large size is likely to result in poor instrument exposure. Instruments, such asanemometers, are often mounted several tens of metres above the sea. If the platform isinvolved in oil or gas production then there may be strict controls on the type of instrumentationthat can be installed and the facilities for access for maintenance. However, because some4 - Data Sources 41 4.2 in situ data sources4.3 Satellite data sourcesReport of the Working Group on Air Sea Fluxes June, 2000platforms report meteorological observations on a very frequent basis it may be necessary toensure that potentially biased observations from a platform do not dominate the climatology ofa region. This is also true of coastal observation stations, such as lighthouses, where the datamay be significantly influenced by the position of the platform.An exception to these negative judgements are platforms designed or implementedspecifically for meteorological observation, however these are very few in number andconfined to coastal regions.4.3 Satellite Data Sources4.3.1 IntroductionMany algorithms have been developed deriving basic state variables like SST,atmospheric surface temperature, wind speed, atmospheric humidity, cloud liquid water, rainrate, longwave and shortwave radiation etc. from satellite data using instruments measuring atdifferent wavelengths. Whereas the radiative fluxes at the surface are derived by measuringradiative fluxes at the top of the atmosphere and using a radiative transfer model (Chapter 6),turbulent fluxes are estimated from the basic state variables using bulk formulae (Chapter 7).Today most of the basic state variables necessary for estimating surface fluxes can be derivedfrom satellite data. These quantities may be directly derived using radar backscatter or fromvisible, infrared or microwave brightness temperatures. The exception is atmospheric surfaceair temperature. This variable is important to estimate sensible heat flux, and the transfercoefficients within the bulk formulae, but estimation from satellite data is still troublesome.Previously, some of these retrievals have been used to derive flux climatologies, howevermostly they have only been used to derive exemplary fields of the basic state variables overshort time periods. However, there has been a remarkable development of new algorithmswhich is gradually changing the situation.In this section the major instruments used for flux determination which are presentlyorbiting are introduced. The orbit and instrument characteristics of the different satellites areidentified and discussed. Major demands on the instruments utilised are that they must deliveraccurate and relatively stable measurements over a long period. This is only attainable by usingon-board calibration, applying algorithms that consider the ageing of the particular radiometer,or by applying ongoing calibration with high quality in situ data. A further requirement, forinstruments on polar orbiters, is that the swath width must be large enough (over 1000 km) tosample the earth's surface during a couple of days.4.3.2 Non geostationary satellitesThe classical instruments that meet the above requirements are the Advanced VeryHigh Resolution Radiometer (AVHRR) and the Special Sensor Microwave/Imager (SSM/I).Measurements of sea surface temperature are best performed in the infrared range where the seasurface has a high emissivity. On the other hand, this high emissivity dominates the signal andprevents the extraction of wind speed or boundary layer water vapour content with highaccuracy from infrared measurements. For these parameters the microwave spectral range ismuch better suited since the ocean surface emissivity is relatively small in the microwave rangecompared to the infrared. A disadvantage of these considerations was that, before the TropicalRainfall Measuring Mission (TRMM), it was not possible to obtain energy fluxes frominstruments installed on one satellite. Thus, any estimate of a parameterised quantity like latentheat flux will suffer from being derived from estimates of basic variables obtained fromdifferent satellites at different times. Fortunately, in general it is expected that this error isrelatively small since the sea surface temperature obtained from AVHRR data varies relativelyslowly compared to the atmospheric variables obtained from SSM/I measurements. To derivefields of wind speed and rainfall, active instruments like scatterometers and radars may also beused4 - Data Sources 42 4.3 Satellite data sourcesReport of the Working Group on Air Sea Fluxes June, 2000A. AVHRRThe AVHRR instrument has been flown on the NOAA satellite series since 1979 andused to construct global fields of sea surface temperature, for example the optimallyinterpolated SST dataset of Reynolds (Reynolds and Smith, 1994) or the NOAA/NASA OceansPathfinder Sea Surface Temperature dataset (Brown et al. 1993). Flight periods for thedifferent NOAA satellites are shown in Table 4.2.The AVHRR instrument measures in five channels, two in the solar spectral range, onein the near infrared, and two located within the infrared atmospheric window. The infraredwindow channels are best suited for estimates of sea surface temperatures in cloud free cases.Despite the broad swath of approximately 3000 km, a complete coverage of the Earth surface isonly achieved within one or two weeks depending on the actual cloud coverage. Someproblematic regions, with almost persistent cloud coverage, are the extended stratocumulusfields to the west of Africa, the ITCZ, and Arctic and Antarctic regions during winter.Therefore the quality of an estimation of sea surface temperature is highly dependent on thequality of the implemented aerosol and cloud detection scheme. A description of errorsoccurring in sea surface temperature, due to the different quality of cloud detection during dayand night, can be found in Reynolds (1993). If measurements of sea surface temperature arehampered for weeks by persistent cloudiness (or long periods of darkness as over polar regions)the estimates might become biased due to the more difficult cloud detection in the infraredrange. What consequences this has for the combination of AVHRR and SSM/I has not beenfully explored.Table 4.2: Temporal coverage of AVHRR (lighter shading = partial coverage of the year)78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99TIROS-NNOAA-6NOAA-7NOAA-8NOAA-9NOAA-10NOAA-11NOAA-12NOAA-14B. ATSRThe Along-Track Scanning Radiometer (ATSR – Zavody et al., 1994) is a visible andinfrared radiometer with a built-in calibration system. It provides views of the sea surface attwo different incidence angles and hence has the potential to estimate atmospheric transmissioneffects. In comparison with buoy data, rms scatter better than ±0.25°C has been claimed (seesection 10.2.3). However the recent workshop on SST (Arkin, 1999) considered that the ATSRhad not yet fully fulfilled its promise for a number of reasons. There had been a failure of onechannel on the first instrument (flown on ERS-1) and problems with the cloud clearancealgorithm. Because the ATSR was not part of the core payload for the satellites the data werenot widely available in operational mode and this had limited their exploitation. A furtherlimitation is that the ATSR has a narrow swath, only 500km, and therefore provides manyfewer cloud-clear views of the sea surface compared to AVHRR.4 - Data Sources 43 4.3 Satellite data sourcesReport of the Working Group on Air Sea Fluxes June, 2000C. SSM/IOne of the most advanced sensors for monitoring flux related atmospheric variablesand ice cover from space is the SSM/I aboard the satellites of the Defense MeteorologicalSatellite Program (DMSP). The SSM/I is a passive microwave radiometer measuring emittedradiation from the earth’s surface and the atmosphere at four frequencies located at 19.35,22.235, 37.0, and 85.5 GHz at two polarisations (with the exception of 22 GHz which is onlymeasured at vertical polarisation). The SSM/I instrument scans conically at a constant earthincidence angle of 53.1° resulting in a swath width of nearly 1400 km, only half of that of theAVHRR. However, cloud coverage is less of a problem within the microwave spectral range,so the determination of flux related variables is only hindered in cases where rain occurs.SSM/I data are sampled every 25 km (A-Scan) at the five lower frequencies and every12.5 km (B-Scan) at 85.5 GHz. Most of the retrieval schemes rely upon the A-scan data anddon’t consider the effects of different ground resolutions at different frequencies. A moredetailed description of the instrument can be found in Hollinger et al. (1987).SSM/I's have been flown on different DMSP satellites since July 1987 as shown inTable 4.3. These satellites have different orbits resulting in different local observing times andthis can have implications for the construction of a satellite-derived climatology (see section8.6). If the data from only one satellite is used (as has been the case to date) sampling errorscan result, at least in all quantities that have a distinct diurnal cycle, for example, precipitation.D. TRMMThe Tropical Rainfall Measuring Mission (TRMM) satellite, dedicated to performingmeasurements relevant to tropical precipitation and the resulting latent heat release, waslaunched in November 1997. It is a low latitude, precessing satellite carrying five instrumentsof which three are suitable for the precipitation mission. These are the TRMM MicrowaveImager (TMI), the Visible and Infrared Scanner (VIRS), and the Precipitation Radar (PR). TheTMI is an instrument similar to the SSM/I but with key differences: an additional channel withvertical and horizontal polarisation at 10.7 GHz, and a shifted water vapour channel at 21.3GHz instead of 22.235 GHz used for the SSM/I which is the centre of the water vapourabsorption line). The latter change should avoid the signal saturation at high atmosphericwater vapour contents found within SSM/I measurements. The VIRS has the samewavelengths and bandwidths as the AVHRR (see above). The major differences betweenVIRS and AVHRR are that the VIRS has a post launch calibration unit and a largerinstantaneous field of view at nadir of 2.11 km (compared to 1.1 km for the AVHRR). The PRis an active system working at 13.8 GHz that provides, for the first time, information on thevertical distribution of rainfall. Since the TMI and VIRS are very similar to SSM/I andAVHRR, in addition to measuring precipitation they may be used to obtain most of flux relatedatmospheric variables including SST using almost the same algorithms developed for the lattersensors. A comprehensive description of the different features of all three instruments can befound in Kummerow et al. (1998).Table 4.3: Temporal coverage of SSM/I (lighter shading indicates partial coverage of the year)87 88 89 90 91 92 93 94 95 96 97 98 99F-8F-10F-11F-13F-14F-154 - Data Sources 44 4.3 Satellite data sourcesReport of the Working Group on Air Sea Fluxes June, 2000Due to the orbit characteristics, TRMM measurements are only available within ±40°latitude so no global datasets can be derived. In addition, TMI, VIRS, and PR have verydifferent swath widths ranging from 215 km for the PR, 720 km for the VIRS, and 759 km forthe TMI, respectively. Thus more time is needed to achieve full coverage compared to theSSM/I and AVHRR. In coming years it is expected that TRMM and SSM/I, together with thenew Advanced Scanning Microwave Radiometer (AMSR), will be used to improve andcontinue satellite-derived products.E. TOVSThe TIROS Operational Vertical Sounder (TOVS) consists of two components. TheHigh-resolution Infrared Radiation Sounder (HIRS) is the infrared part with 19 channelsbetween 3.7 and 15 µm and a single day-time cloud-detection band in the visible spectraldomain. The HIRS is the main vertical sounding component with sixteen temperature andthree water vapour channels. Additionally there is the Microwave Sounding Unit (MSU) withfor channels arranged between 50.31 and 57.95 GHz. These channels are used for soundingtemperature profiles with coarser vertical and horizontal resolution but the ability to obtain datain cloudy cases.The TOVS package has been flown on the NOAA satellite series, being used to inverttemperature and humidity profiles for assimilation into NWP models. Temperature andhumidity from the lowest levels of the HIRS derived profiles could be useful for fluxcalculations but so far have not been used for that purpose. Over ice surface, TOVS data havebeen used to determine the drag coefficients as well as cloud fractions (see Section 10.11.2).F. SCATTEROMETERS (ERS-1,2; NSCAT; QUIKSCAT)Satellite borne scatterometers have the unique capability of returning estimates of windspeed and direction over the ice-free zones of the global oceans. This is achieved throughtransmitting microwave signals aimed off-nadir and analyzing backscattered energy from theocean surface. The magnitude of the backscattered energy indicates wind magnitude whilenumerous looks at the same ocean surface location provides the wind direction information.The wind direction information is critical for determining ocean-relevant forcings such as windstress curl. Additionally, wind divergence is an important diagnostic variable indicative ofboth ocean and atmospheric processes, and can be calculated only with directional information.Since the first such system was launched aboard SeaSat (1978), there have beensignificant advances in technology and instrument design to improve the quality of the windsfrom these instruments. The European spacecraft ERS-1 (replaced by ERS-2) has providedcontinuous wind measurements over the ocean since 1991. In its scatterometer mode, the ERSplatforms provide wind vectors from a single but relatively narrow (500-km) swath with windsprovided every 50-km within the swath. Near coastal regions and in ice zones thescatterometer was turned off to facilitate SAR imaging thus limiting wind vector coverage.The NSCAT mission (Oct 96 - June 1997) featured dual swaths of 600-km width each with 25-km grid spacing for the wind retrievals. Continuous scanning over the globe resulted in windvectors reported over more than 90% of the world’s ice-free oceans every two days. A specialissue of JGR-Oceans (May 1999 Volume 104, No. C5) contains a number of papers describingthe NSCAT mission and scientific results. The next mission, QuikSCAT, was launched inJune 1999 and features a radially scanning scatterometer providing a single 1800-km swath ofwind vector data covering 90% of the earth’s oceans every day.4.3.3 Geostationary satellitesGeostationary satellites like GOES-E, GOES-W, GMS, and METEOSAT typicallyscan the earth using three bands in the visible, infrared window, and water vapour absorptionband at 0.5 – 0.9 µm, 10.5 - 12.5 µm, and 5.7 – 7.1 µm, respectively. These data have beenused in combination with data from microwave polar orbiting instruments to enhance thesampling rate - one of the largest error sources for rainfall measurements from space. Since4 - Data Sources 45 4.4 NWP and reanalysisReport of the Working Group on Air Sea Fluxes June, 2000rainfall estimates from geostationary satellites are inaccurate (because they rely on cloud toptemperature) they have been calibrated with the more accurate microwave estimates to developrainfall products like GPCP or CMAP where the sampling is only improved within the field ofview of the geostationary satellites (see Section 10.9).4.4 NWP and Reanalyses4.4.1 IntroductionIf one was to design a system to produce the best estimates of air-sea fluxes, the systemwould use all available observations of the atmosphere and ocean, their exchanges of radiationwith space and the exchanges of energy, water and momentum between the atmosphere and theearth’s surface. It would include the effect of data from different observation times and fill ingaps in the data by using the laws of physics as expressed in the equations of fluid mechanics tointerpolate in time and space. The fields actually observed, and not derived fields, would beused; thus radiances observed by satellites would be used rather than derived temperature andmoisture fields to ensure consistency in physical parameterisation. This is a data assimilationsystem.Such a system would not be identical to a data assimilation system designed fornumerical weather prediction, but might well be a modified version of an operational NWP dataassimilation system. As discussed in Section 2.2, data assimilation for NWP is designed toproduce the best forecasts, not necessarily the most complete or the most realistic depiction ofthe atmosphere. NWP has important timeliness requirements. Data assimilation for the mostrealistic estimates of air-sea fluxes could have a longer data cut-off time and in fact mightchoose to use observations occurring after the analysis time, using a computer model tointerpolate both forward and backward in time. Observations could be used even if theirprocessing took significant time and they had little effect on weather forecasts. More completeand complex assimilation of satellite measurements could be done than is desirable inoperational NWP. The selection and improvement of parameterisations would be designed togive the more realistic and complete depiction of the atmosphere and its boundary conditionsrather than the best forecast. The system would benefit from a close association with anoperational NWP data assimilation effort.Such a system should not provide the only estimates of air-sea fluxes. Global estimatesof precipitation by Xie and Arkin (1996) (CMAP) include separate estimates of precipitationfrom each source they use - rain gauges, different satellite methods and data assimilation. Asimilar range of estimates for air-sea fluxes could give valuable insight both into the uncertaintyof the estimates and also the primary sources of information for the best estimate.Today, data assimilation for operational NWP produce complete global analyses of theatmosphere and its interactions with space and with the earth’s surface every few hours on aregular grid at fairly high spatial resolution (from 40 to 105 km). These analyses use largeamounts of observations from a wide variety of sources and combine them with highly accurateshort-range forecasts. The analyses are widely distributed a few hours after observation timeand are critically scrutinised by many diverse users, including marine forecasters. Operationaldata assimilation and NWP modelling groups are made aware every day of the limitations oftheir analyses and forecasts; they are constantly seeking to improve all aspects of their analysesand forecasts. Data assimilation systems and NWP models have greatly improved over the lasttwo decades, but the physical parameterisations used are still relatively crude and the physicalfields produced by analysis/forecasts systems need to be critically examined to see how closewe are to the future system described above.4.4.2 Fluxes from operational data assimilationA. INTRODUCTIONPhysical fields from operational global data assimilation systems, such as surface fluxesof moisture, energy and momentum, are generally accumulated during the short-range six-hour4 - Data Sources 46 4.4 NWP and reanalysisReport of the Working Group on Air Sea Fluxes June, 2000“first guess” forecast and during longer-range forecasts as well. Meteorological fields such asnear-surface winds, temperature and moisture are analysed from a combination of theobservations and the first guess forecast and are available at 6 hour intervals as analyses and as6 hour forecasts. Users of fluxes from data assimilation have requested fields every three hoursand global NWP systems may well move to produce analyses every three hours in the nearfuture. Special files for individual points can be prepared in which data is output every hour ofthe forecast. Model errors can affect the analysed meteorological fields as well as the physicalfields through the first guess forecast, especially in data-scarce regions over the oceans. White(1995) compared precipitation and surface fluxes from four operational global NWPanalysis/forecast systems: NCEP, ECMWF, the United Kingdom Meteorological Office(UKMO) and the Japanese Meteorological Agency (JMA). Section 11.5 of this report describesa Working Group on Numerical Experimentation (WGNE) proposal to conduct a moreextensive comparison of surface fluxes of operational global NWP systems and to establish anongoing centre for archiving and comparing such fluxes.There are many operational NWP systems. Pan (1999) describes the parameterisationof subgrid-scale processes in atmospheric models. Phillips (1994) describes the atmosphericmodels of several centres. This section considers NCEP and ECMWF as examples ofoperational systems.B. THE NCEP OPERATIONAL GLOBAL MODELFormulation of the Model: The home page of the NCEP global modelling branch(http://sgi62.wwb.noaa.gov:8080/research/global2.html) contains information on theformulation and performance of the NCEP operational global analysis/forecast system. Adescription of the NCEP global analysis/forecast system can be found in Kanamitsu (1989). Asan example of an operational global model, the present operational NCEP global model issummarised below (http://sgi62.wwb.noaa.gov:8080/research/mrf.html):a) T170 spectral model (equivalent to a horizontal resolution of 75 km).b) 42 vertical unequally spaced sigma levels. For a surface pressure of 1000 hPa, its lowestlevel is about 996 hPa.c) physics calculated on a Gaussian grid of 512 by 256, approximately 1˚ resolution;d) Shortwave radiation is based on work by Chou (1992). Surface solar absorption isdetermined from the surface albedo. Ocean surface albedo depends on zenith angle.e) Longwave radiation follows Fels and Schwarzkopf (1975), Schwarzkopf and Fels (1991).Long-wave emissivity is prescribed to be unity for all surfaces.f) Cloud fractions are diagnosed from the relative humidity based on RTNEPHobservations.g) Over the ocean the Charnock (1955) formula is used to update the momentum roughnessfrom the estimated surface stress. Thermal roughness is based on TOGA COAREobservations (Zeng et al., 1998).h) The lowest model layer is assumed to be the surface layer (sigma=0.996) and the Monin-Obukhov similarity profile relationship is applied to obtain the surface stress and sensibleand latent heat fluxes. The formulation was based on Miyakoda and Sirutis (1996) andhas been modified by P. Long in the very stable and very unstable situations. A bulkaerodynamic formula is used to calculate the fluxes once the turbulent exchangecoefficients are obtained.i) Boundary layer turbulent mixing is a bulk Richardson number based non-local mixingscheme (Troen and Mahrt, 1986; Hong and Pan, 1996). Free atmosphere verticaldiffusion uses a local mixing scheme.j) Shallow convection by Tiedtke (1983).k) Deep convection is a simplified Arakawa-Schubert scheme originally developed by Grell(1993) and modified by Pan and Wu (1995) and Hong and Pan (1996).4 - Data Sources 47 4.4 NWP and reanalysisReport of the Working Group on Air Sea Fluxes June, 2000l) Large-scale precipitation is caused by super-saturation removal and by convectiveadjustment when the sounding is conditionally unstable and supersaturated. Falling rainis evaporated based on Kessler (1969).Formulation of the operational analysis: The analysis seeks to obtain the best fit tothe observations and to the six-hour background or first guess forecast (Parrish and Derber,1992; Derber et al., 1991; http://sgi62.wwb.noaa.gov:8080/RTPUB). The analysis is performedas a series of iterative problems in the model’s vertical coordinate system and uses thefollowing observations:a) rawinsondes and dropwindsondes—winds, temperatures, specific humidity, surfacepressureb) aircraft reports—winds and temperaturesc) satellite cloud tracked windsd) surface marine observations—winds, temperatures, specific humidity, surface pressuree) surface land observations—specific humidity, surface pressuref) SSM/I wind speeds over waterg) Australian sea level pressure bogush) satellite microwave and infrared radiancesi) hurricane bogus windsj) profiler windsk) ERS-2 wind speeds and directions over waterl) SBUV ozone profilesObservational error statistics vary with each observation type and can vary withlocation. They contain both instrument error and representativeness error. Background errorstatistics are used to weight the first guess field and are nearly homogeneous around a latitudeband.C. THE ECMWF OPERATIONAL SYSTEMECMWF currently uses a spectral T319 (about 40 km) model with 60 levels in thevertical. It uses a 4D variational (4DVAR) analysis and processes raw TOVS/ATOVS radiancedata. A fully coupled wave model produces surface fluxes dependent on sea state. It currentlyblacklists ship humidity data; ship wind data makes use of WMO tables for height assignment.A seasonal forecast system, based on a coupled integration of a T63 and 31 level (as ofOct. 1998) version of the ECMWF atmospheric model and the HOPE ocean model, is rundaily. However the model drift tends to be comparable in magnitude to the seasonal anomalybeing forecast. Tests of revised physical parameterisations appeared to reduce the drift in SST.Beljaars (1997) described the parameterisation of atmosphere-ocean exchange in theECMWF model. Klinker (1997) examined the ECMWF model’s surface fluxes. Siefridt et al.(1999) examined the effect of parameterisation changes of ECMWF operational fluxes during1986-95. ECMWF publications can be obtained from the ECMWF Library, also seehttp://www.ecmwf.int/pressroom/publications.html .4.4.3 Fluxes from reanalysesA. INTRODUCTIONIn reanalysis a frozen NWP analysis forecast system is used to process data overseveral years of past data. The advantages of a reanalysis over operational NWP dataassimilation is that it offers several years of fields from an unchanging system and that thefields can be more easily obtained. The disadvantage is that by the time the complete reanalysisrecord is available the system used is lower resolution and uses older, less developed physicalparameterisations than the latest operational NWP systems. Several years of results areavailable from four reanalyses:a) ERA15 - ECMWF’s reanalysis: 1979-1993.4 - Data Sources 48 4.4 NWP and reanalysisReport of the Working Group on Air Sea Fluxes June, 2000b) GEOS1 - Goddard Earth Observing System March :1980-Nov. 1993 (Data AssimilationOffice (DAO), Goddard Space Flight Centre (GSFC), National Aeronautics and SpaceAdministration (NASA))c) NCEP1 - NCEP/NCAR reanalysis: 1948-present (ongoing as Climate Data AssimilationSystem (CDAS))d) NCEP2 - A rerun of the NCEP/NCAR reanalysis introducing more up-to-date physicsand correcting known errors in NCEP-1 (also known as the NCEP/AMIP-II DOEReanalysis): 1979-Jan. 1996 (as of Nov. 30, 1999). Plans are to continue this up to thepresent. Years before 1979 may also be done.An earlier pilot reanalysis was run by the Centre for Ocean-Land-Atmosphere Studies (COLA).It covered the period April 5, 1982 to Nov. 30, 1983 and is documented athttp://grads.iges.org/res/proj5/proj5.html.The NCEP-1 reanalysis is currently being run on current data as the Climate DataAssimilation System (CDAS). The Climate Prediction Centre at NCEP is currently consideringwhether to run NCEP-1 or NCEP-2 or both as the operational CDAS. ECMWF is preparing torun a new reanalysis (ERA-40) at higher resolution and with improved parameterisation from1958 to the present. GEOS has spent several years improving its system from what was usedin GEOS-1 and plans to do reanalysis again in the future.A workshop on the GEOS-1 reanalysis was held in March 1995 (Schubert and Rood,1995). The 1st WCRP International Conference on Reanalyses was held in Oct. 1997.Proceedings of the conference are available as WCRP Report 104. The 2nd InternationalConference on Reanalyses was held in Aug. 1999 and the proceedings are now available(WCRP, 2000).B. NCEP1 SYSTEM FORMULATIONKalnay et al. (1996) and Kistler et al. (2000) describe the NCEP1 reanalysis and furtherinformation is available from a number of web sites (Table 4.4).Table 4.4 Web site addresses for the NCEP/NCAR reanalysisContents URLOfficial sites for NCEP/NCARreanalysishttp://wesley.wwb.noaa.gov/reanlysis.htmlhttp://www.cdc.noaa.gov/cdc/reanalysisReanalysis problems http://wesley.wwb.noaa.gov/reanalysis.html#problemhttp://www.cdc.noaa.gov/cdc/reanalysis/problems.shtmlReanalysis studies and problem reportsby Robert Kistler (technical manager ofthe NCEP/NCAR reanalysis)http://lnx21.wwb.noaa.gov/Examples of the observation and forecasterrors used in the reanalysis.http://lnx21.wwb.noaa.gov/oberr/reanl-obs.htmlhttp://lnx21.wwb.noaa.gov/oberr/fcsterr.htmlThe NCEP/NCAR reanalysis analysis/forecast system was identical to the NCEP globaloperational model implemented in Jan. 1995, except that it had a horizontal resolution of T62(about 210 km). The vertical resolution was 28 sigma levels, the same as the currentoperational model. The physical parameterisations were done on a Gaussian grid of 192 x 94,roughly 2o x 2o latitude. The radiation grid was a Gaussian grid of 128 x 62. A time step of 20minutes was used for computing dynamics and physics, except that the full atmosphericradiation was calculated every 3 hours with corrections at every time step for diurnal variationsin short wave fluxes and in surface upward long wave flux. Mean orographic heights wereused. The radiation used in reanalysis was an older short wave radiation (Lacis and Hansen,4 - Data Sources 49 4.4 NWP and reanalysisReport of the Working Group on Air Sea Fluxes June, 20001974) than that used in current operations. An incorrect, too high, surface albedo was usedover the oceans. An older boundary layer model, than that currently used in operations, wasused. The equation for roughness length for heat in the NCEP bulk flux algorithm used in thereanalysis has been found to be inappropriate under strong wind conditions and to overestimatelatent heat flux (Zeng et al., 1998). It has been changed in the operational model.The NCEP1 and NCEP2 reanalyses used the following data (Ebisuzaki et al., 1998):a) radiosondes, dropsondes, pibalsb) conventional aircraft windsc) ACARS aircraft winds and temperaturesd) Marine winds, temperature, specific humidity in surface layere) Land surface pressuref) Satellite cloud-track windsg) NESDIS temperature retrievalsh) PAOBSi) SST and snow-cover analysesThe following data was not used:a) precipitationb) radar, profilersc) SSM/I winds and precipitable waterd) Land winds, temperature, specific humidity in surface layere) Most cloud informationf) Radiances from satelliteg) Satellite humidity estimatesh) Soil/albedo/snow depth.The analysis system used was the spectral statistical interpolation (SSI or 3Dvariational) analysis (Parrish and Derber, 1992; Derber et al., 1991). Nonlinear normal-modeinitialisation was not needed. Additional details can be found in Kalnay et al., 1996.Several problems have been found in the reanalysis. Among them are:a) Bogus sea-level pressure observations generated by Australian analysts over the SouthernHemisphere oceans during 1979-92 were misplaced by 180 degrees. (Reruns of 1979and Nov.-Dec, 1992 corrected this.) The problem has little effect north of 40S and littleeffects on monthly means. The error is comparable to the difference between ECMWFand NCEP analyses.b) The initial attempt to use high-density vertical soundings over the TOGA COARE regionfrom Nov. 92 to Feb. 93 included a failure to convert temperature to virtual temperatureand resulted in a cold bias over the TOGA COARE region. The problem has beencorrected, the period rerun, and the archives corrected, but the annual CD-ROM's for1992 and 1993 include this error for the 4 months. The effects are limited to the TOGACOARE region and are strongest near the surface.c) The snow cover for 1973 was used for 1974-94 as well.d) Due to an incorrect parameterisation of the horizontal diffusion of moisture, snow overland tended to fall in valleys.e) Discontinuities in radiation fluxes at the International Date Line due to cloud tuning werediscovered.In other instances, portions of observational data sets did not make it into the NCEP1reanalysis, such as poorly encoded surface observations between 1948 and 1967 andradiosondes in particular regions in particular periods (http://lnx21.wwb.noaa.gov/). Acomparison of ERA and NCEP/NCAR reanalyses for a few months revealed that eachcontained observations not in the other. The two sets of observations are being merged.4 - Data Sources 50 4.4 NWP and reanalysisReport of the Working Group on Air Sea Fluxes June, 2000While the data assimilation system was not changed during the reanalysis (except forsome very minor modifications after 1986-89 were processed), the data did change dramaticallyduring the 50 years, as can be seen in Figure 4.4. Satellite temperature soundings becameavailable in significant numbers in 1979, the major change in the data record in the last 50years, and had a noticeable effect on atmospheric temperatures (Fiorino, 2000). An increase indata in the late 1960's is also evident, as is an increase in the late 1990's due to an increase insatellite winds. In recent years conventional observations have declined, as can be seen infigure 4.5. The number of ship reports increased until the late 1960's and decreased from thelate 1980's to 1994. Data from the TAO buoy array in the tropical Pacific in the 1990's isincluded. White (1999) examined global trends in precipitation and air-sea temperature contrastthat could be related to changes in the observational network. It should be noted that the four-dimensional data assimilation systems used in reanalysis can transport information from datarich to data sparse regions; therefore observations can influence regions outside the 2.5o boxthey were taken in. Ebisuzaki and Kistler (2000) examined the effect of changes in data onreanalysis by comparing fields from NCEP1 and NCEP2 to a rerun of 1998 without PAOBS,aircraft or satellite data.Figure 4.4. Zonally averagednumber of all types ofobservations available to theNCEP/NCAR reanalysis per2.5o latitude-longitude box permonth from 1946 to 1998. Atwelve-month running mean hasbeen applied. Shading indicatesmore than sixty observations permonth.Figure 4.5: Zonally averagednumber of ship reports per 2.5obox per month. Oceanic buoysare included.4 - Data Sources 51 4.4 NWP and reanalysisReport of the Working Group on Air Sea Fluxes June, 2000C. NCEP2 SYSTEM FORMULATIONThe NCEP2 reanalysis is documented at http://wesley.wwb.noaa.gov/reanalysis2/index.html. Papers on the NCEP2 reanalysis were presented at the 2nd International Conferenceon Reanalyses. Kanamitsu et al. (2000) gave an overview of NCEP2 . Hnilo et al. (2000)compared NCEP1 and NCEP2. NCEP2 was done with the same data and the same vertical andhorizontal resolution as NCEP1. It corrected the five mistakes listed above as well as the oceansurface albedo. The following model changes were introduced:a) assimilation of five-day mean estimated rainfall from Xie and Arkin to adjust soilmoistureb) elimination of a nudging of deep layer soil moisture to climatologyc) a smoother orography than NCEP1d) new non-local boundary layer parameterisation of Hong and Pan (1996)e) a change to the convective parameterisationf) a fix to cloud-top cooling in radiationg) updated cloud parameterisation and RH-cloud relationshiph) radiation code run every hour and on full Gaussian gridi) improved desert albedoj) improved sea-ice SST fieldsNCEP2 did not change the roughness length for heat as suggested by Zeng et al. (1998).D. ERA15 SYSTEM FORMULATIONAn introduction to the ECMWF reanalysis can be found at http://www.ecmwf.int/research/era/index.html. Reports from the ERA15 reanalysis are available by anonymous ftp atwww.ecmwf.int in the /pub/era/era15 directory. Gibson et al. (1997) described the ERA15reanalysis system, including initial experiments they did. They tested the effects of a newprognostic cloud parameterisation (Jakob 1994; Teidtke 1993) for one year of data assimilationtogether with changes in ocean surface albedo and shallow convection. Compared to an olderdiagnostic cloud parameterisation similar to the one used in the NCEP reanalyses, the newphysics produced quite different surface energy fluxes. It produced more low clouds over theoceanic storm tracks and near the equator and reduced stratus over the Arctic, allimprovements, but decreased low-level stratus over upwelling ocean currents, adisadvantageous impact. The new scheme increased downward longwave radiation into theocean by 20-30 Watts/m2 in the latitudes of the storm tracks, decreased short wave radiationinto the ocean at low latitudes, and decreased evaporation locally by as much as 30 Watts/m2.The changes, used in the full reanalysis, increased energy input into the ocean in the tropicsand over the high latitude storm tracks. These results indicate the sensitivity of reanalyses tochanges in the physical parameterisation.The ERA-15 data assimilation system included:a) spectral T106 resolution with 31 vertical hybrid levels (Gaussian grid resolution 1.125o orabout 125 km)b) Intermittent statistical (optimum interpolation) analysis with 6 hour cyclingc) One-dimensional variational (1D-Var) physical retrieval of TOVS cloud clearedradiances below 100 hPa.d) NESDIS humidity retrieval below 300 hPa over oceanse) Diabatic, non-linear normal mode initialisationf) 3-dimensional semi-Lagrangian advectiong) Prognostic equations for cloud water and ice content and cloud cover. Clouds tuned toISCCP clouds.h) ECMWF operational radiation parameterisation with prescribed concentration of aerosolsvarying geographically (Morcrette, 1991)i) ECMWF operational planetary boundary layer parameterisation based on similarityj) Mass flux convection scheme by Tiedtke (1989)4 - Data Sources 52 4.4 NWP and reanalysisReport of the Working Group on Air Sea Fluxes June, 2000k) Mean orography with a parameterisation of sub-grid scale orographyKallberg (1997) examined the 15-year mean climate of the reanalysis. He documentedthe sensitivity of precipitation and surface fluxes to “spin-up” during the first 24 hours offorecasts in the ERA15 reanalysis. He also found evaporation over the ocean to be verysensitive to the observations. Near surface winds observed by ships were generally strongerthan either analysed or forecast winds; near surface winds reported by isolated island stations,affected by local effects and not representative of the surrounding oceans, were generallyweaker than the analysed and forecast winds. These mismatches generated large increases inevaporation during the first 24 hours of forecasting, locally up to 40 Watts/m2 (the NCEP1Reanalysis also found suspicious features near isolated island stations; experiments did not finda solution to the problem). ECMWF concluded that the fluxes from 12-24 hour forecasts werebetter than the fluxes from 0-6 hour forecasts and recommended the use of the former.ERA15 assimilated TOVS cloud-cleared radiances from a long series of NOAAsatellites that suffer from the effects of drift and sudden changes in instruments. In spite ofextensive monitoring, quality control and bias correction, the use of radiances resulted insudden jumps in the time series of analysed temperatures and humidities in the lowertroposphere, especially in lower latitudes. The worst jump occurred in Nov. 1986 with anunrealistic large-scale warming and moistening of the Inter-Tropical Convergence Zone(Gibson et al., 1997; Fiorino, 2000).E. ERA40 SYSTEM FORMULATIONPlans for the next ERA40 reanalysis were discussed by Uppala et al. (2000) at the 2ndInternational Conference on Reanalyses. ERA40 is still under development. Current plans areto process data from mid-1957 to 2001. In addition to the data in ERA15, this reanalysis willinclude:a) conventional ship and upper air data before 1979 from NCAR,b) declassified U. S. Navy data;c) full VTPR and TOVS 1b data from 1972 on;d) SSM/I radiances;e) ERS-1 and ERS-2 data;f) updated COADS;g) new SST data from UKMO and NCEP with compatible sea ice.The projected production system will usea) spectral T 159 resolution with 60 hybrid vertical levels;b) 3 dimensional variational (3D-VAR) analysis incorporating physical retrieval of satelliteradiance data;c) coupling with a 1.5˚ wave model to produce ocean surface fluxes. Analysis of ERS wavealtimeter data will be included. The fluxes will be sea-state dependent. (Janssen andViterbo, 1996);d) the Integrated Forecast System (IFS) version of the ECMWF forecast model;e) varying carbon dioxide and aerosols;f) assimilation of ship winds at 25 m rather than 10 m.Experiments with the proposed system indicate fewer problems with the near-surfacewinds over the open ocean and islands, a weaker and slower “spin-up” and more rainfall overthe northern hemisphere storm tracks.F. GEOS1 SYSTEM FORMULATIONOverviews of the NASA/GSFC DAO GEOS1 reanalysis can be found in Schubert et al.(1995) and at a number of web sites (Table 4.5) Higgins et al. (1996) compares the NCEP1and GEOS1 reanalyses for the period 1985-93. Bony et al. (1997) compared the two reanalysesover the tropical oceans.4 - Data Sources 53 4.4 NWP and reanalysis4.5 Residual Methods4.6 Ocean data assimilationReport of the Working Group on Air Sea Fluxes June, 2000The GEOS1 data assimilation system is described in Takacs et al. (1994) andPfaendtner et al. (1995). It employed an optimal interpolation (OI) analysis scheme at ahorizontal resolution of 2o latitude by 2.5o longitude at 20 sigma levels. It did not includeinitialisation and used an Incremental Analysis Update (IAU) procedure by which a fraction ofthe data increment are added each time step during a rerunning of the forecast (Bloom et al.,1996).The radiation parameterisations follow those of Harshvardhan et al. (1987); a relaxedArakawa-Schubert scheme (Moorthi and Suarez, 1992) is used for deep convection. Theplanetary boundary layer is explicitly resolved in a 2 to 4 layer region. Wind, temperature andhumidity profiles, in an extended surface layer up to 150 m thick, and the turbulent fluxes atthe surface are obtained from Monin-Obukhov similarity theory by selecting similarityfunctions that approach the convective limit for unstable profiles and agree with observationsfor very stable profiles. Surface roughness lengths are taken as a function of surface stressover water (Schubert et al., 1995).The SST data set used in GEOS1 used monthly mean blended sea surface temperatureanalyses of Reynolds (1988) and Reynolds and Marsico (1993); NCEP used the improvedweekly global optimum interpolation SST analyses of Reynolds and Smith (1994). The majordifferences between the SST analyses are in regions of high SST gradients.Table 4.5 Web site addresses for the NASA/GSFC DAO GEOS1 reanalysisContents URLOverviews of the GEOS1 reanalysis http://dao.gsfc.nasa.gov/experiments/assim54A.htmlhttp://www.cdc.noaa.gov/cdc/reanalysisTechnical reports on the GEOS-1reanalysishttp://dao.gsfc.nasa.gov/subpages/tech-reports.htmlStrengths and weaknesses of GEOS1 http://dao.gsfc.nasa.gov/experiments/assim54A/assim_prod_evalutaion.html (sic)Problems http://dao.gsfc.nasa.gov/experiments/DATA_PROBLEMS.html.4.5 Residual methods (TOA + model divergences)In the residual method (Trenberth and Solomon 1994; Keith 1995) the top of theatmosphere radiation budget and the divergence of the atmospheric energy transport from annumerical model is used to infer the net surface heat flux. Used with the atmospheric moisturebudget the net freshwater flux can also be obtained (Trenberth and Guillemot 1998), althoughthe accuracy is crucially dependent on the calculated wind and moisture divergence. A majorlimitation of this method is the lack of estimates of the individual fluxes and the reliance onatmospheric model data, thus restricting use for model verification.4.6 Ocean Data assimilationInverse techniques such as ocean data assimilation can also provide estimates of thesurface fluxes of heat, momentum, and freshwater. The approach is straightforward. Oneconstrains (in a least-squares sense) an ocean model (admittedly imperfect or is missingphysical parameterisations of ocean processes) to observed temperatures, salinity, or othersuitable observations (which have their own errors possibly specified in the inversemethodology) using the flux fields as dependent model parameter fields. Such an approachwas demonstrated using a relatively simple tropical Pacific ocean model forced byclimatological winds and SST (Yu and O'Brien, 1995). The resulting monthly net heat fluxfields had features similar to existing in-situ based climatologies outside the equatorial zone4 - Data Sources 54 4.7 Fluxes over Sea IceReport of the Working Group on Air Sea Fluxes June, 2000except the magnitude is not as strong. Similarly, for the Atlantic, seasonally varying oceantemperature and salinity data provided necessary information to estimate surface heat andfreshwater flux estimates (Yu and Malonotte-Rizzoli, 1998). Estimates of surface fluxesproduced by these inverse methods will improve as ocean modelling evolves.4.7 Data Sources for Fluxes and Flux-Related Variables over Sea IceLike other uninhabited and barely frequented areas of the globe, the ice-covered oceanspresent special problems for observing global phenomena and for estimating global fields. Thesituations in both polar regions have similarities: a few permanent manned or unmannedstations provide sparse, continual data, but large areas are sampled directly only occasionally.Interpolation is asked to provide information that man did not have the resources to observe.Winds are specified from pressure fields which in the Arctic have had the benefit since1979 of a network of drifting buoys, but in the Antarctic have only sporadic buoy data. In theArctic the buoy and station surface pressure data are assimilated into the NCEP and ECMWFreanalysis fields, making them the best gridded pressure fields. In the Antarctic there is nostanding network of buoys, and the reanalysis pressure fields are of lower accuracy but still thebest available. Similarly, air temperature fields are recently available from buoys in the Arctic(for example the IABP/POLES 2-m air temperature data set (Rigor et al. 2000)), and again relyon more sparse data in the Antarctic. The surface air temperature fields in the reanalyses are notbased on surface temperature observations, but use only the first level temperatures fromradiosondes, and model physics to estimate surface temperature.In addition to drifting buoys, an important Arctic data set on surface fluxes, cloudamounts, winds, air-surface temperature differences, snow depths, and radiosondes has beenprovided by decades of Russian "North Pole" manned drifted stations. Data from these stationshave been assembled into long-term climatologies of monthly means (the Arctic GlobalRadiation (AGR-1 and AGR-2) Data Sets. There have been a number of drifting camps (e.g.,Arlis, T-3, AIDJEX, CEAREX, LEADEX, and presently SHEBA) and ship expeditions(Polarstern cruises, and the Arctic Cross-Section), all with observations crucial toparameterising and validating large-scale estimates of surface fluxes.Satellite data sets are just coming into use. Satellites provide indirect observations ofsurface variables and fluxes, and much effort goes into trying to make good use of these data.New satellite-derived data products promise good accuracy, better spatial resolution and theresolution of interannual variability. These include the Surface Radiation Balance estimatesfrom ERBE and AVHRR, the ISCCP cloud and albedo data and radiative fluxes derived fromthem, and Polar Pathfinder data sets from AVHRR and from TOVS.And of course, global numerical weather prediction models provide fields of manydesired variables, some of which are of more value than others. Surface wind and flux fieldsare available from both the NCEP and ECMWF reanalyses.As for data on the sea ice state, the most comprehensive record is that of gridded sea iceconcentration at both poles are available from 1978 to present, from SMMR and SSM/I passivemicrowave sensors. Substantial records are available from scatterometers (Ezraty and Cavanié,1999), ERS-1 and -2 (1991 - 1999), NSCAT (9 months) and QuikSCAT (beginning September1999), but these are not yet presented as data sets of sea ice variables. Synthetic aperture radarhas been used to track ice and infer the ice thickness distribution for ice up to 2 m thick and forridged ice. These data are produced for the Arctic Ocean by the RADARSAT GeophysicalProcessor System (RGPS) and will eventually span several years from 1996 on.5 - Direct Flux Observations 55 5.1 Introduction5.2 Radiative FluxesReport of the Working Group on Air Sea Fluxes June, 20005 DIRECT FLUX OBSERVATIONS - STATE OF THE ART5.1 IntroductionIn general, direct measurements of the air-sea fluxes are too few to contribute directlyto the calculation of large scale flux fields. Rather they are used for developing, calibrating,and verifying the parameterisation formula used to estimate the fluxes from the basic variables.The accuracy of the direct flux determinations thus represents a limit for the accuracy of theindirect estimates. Methods of direct measurement will therefore be reviewed in this Chapterfor the radiative fluxes, turbulent fluxes, and precipitation.5.2 Radiative fluxes5.2.1 IntroductionThe radiant energy flux to and from the ocean surface comprises the solar (orshortwave) component in the wavelength band 0.3 to 3 µm, and the terrestrial (longwave orinfrared) component from 3 to around 50 µm. A good illustration of the sources whichcontribute to each of these wavebands is given in Figure 1 of Katsaros (1990). For directmeasurement of the radiative fluxes at sea, the best compromise between the competing needsof accuracy and practicality is provided by instruments which have been in existence for aroundhalf a century, with little modification over that time; namely the pyranometer for shortwaveradiation and the pyrgeometer for longwave. The two instruments are similar in form, bothmeasuring the radiation flux through a hemispherical dome of appropriate spectraltransmissivity. Both experience particular problems in use at sea, contamination of the domesby salt, motion of the ship or buoy and, because it is impossible to screen the sensing element,vulnerability to the many sources of electromagnetic interference aboard ships (e.g. Lind andKatsaros, 1986). Perversely, to avoid shadowing of the instruments they usually have to belocated at the highest place on the ship along with the radio antennae.Only the top of the range instruments from the various manufacturers are potentiallycapable of providing radiation data to the accuracy required to close an ocean mixed layerbudget, or to validate coupled models or remotely sensed products. Their use and performancewill be discussed in this section. However, instruments of lesser quality are available, and oftenused when cost is a factor. More accurate devices, such as the broadband Normal IncidendencePyrheliometer (NIP), are sometimes used as calibration standards for working instruments, butfor reasons given below are generally unsuitable for use at sea.At the same time, the situation is changing rapidly with the introduction of newknowledge and technology, and the urgent needs of climate research. Post et al. (1998)describe instruments, several at prototype or research stage of development, which weredeployed together during a NOAA-sponsored Combined Sensor Program cruise to the centraland western Pacific ocean in March-April 1996. They included sophisticated radiationinstruments, such as the Fourier Transform Infrared Instrument (FTIR) and AtmosphericEmitted Radiance Interferometer (AERI), both of which measure infrared spectra at highresolution over a broad spectral range, and a sun-tracking photometer to obtain solar flux atseveral filtered visible and infrared wavelengths. The technologies developed to date mostlyserve to retrieve variables that can be used to compute fluxes with a radiative transfer code,rather than to observe the fluxes themselves. For example, the humidity field from AERI can beused as one of the inputs needed to compute downwelling LW flux, and the aerosol spectraloptical depth from a photometer can be used in the computation of the downwelling broadbandSW insolation. Comprehensive suites of sophisticated and well-maintained radiationinstruments are already installed operationally in various climatically important parts of theworld (e.g. Pacific islands, Alaska), at some of the Atmospheric Radiation Measurement5 - Direct Flux Observations 56 5.2 Radiative FluxesReport of the Working Group on Air Sea Fluxes June, 2000(ARM) program’s Cloud and Radiation Testbed (CART) sites, and it is likely that ruggedised,affordable versions of such sensors will be in fairly routine use within the next decade.Determination of radiative components in very high latitudes presents particularproblems, partly due to the effects of low temperatures on the instruments, but also because ofspatial inhomogeneity of the surface. Sea ice, snow and open leads greatly affect radiativeexchange through spatial variability of surface albedo and by selectively insulating the coldatmosphere from relatively warmer water below the ice. To further complicate the issue, it isnot usually meaningful to discuss the radiation fields in polar regions without simultaneousconsideration of clouds and the various cloud-surface feedbacks. A comprehensive overview ofcloud and radiation characteristics in the Arctic, together with discussion of the boundary layerfeatures, is given by Curry et al. (1996). In the following two sections, which deal separatelywith the general features of shortwave and longwave radiation, and in section 6.5 on albedo, weinclude comment on the specific problems presented by sea ice.5.2.2 Shortwave radiationDownwelling shortwave (SW) radiation at the surface (referred to as shortwaveirradiance) has a component due to the direct solar beam, and a diffuse component scatteredfrom atmospheric constituents and reflected from clouds. Upwelling shortwave radiation comesfrom reflection at the surface and the re-emergence of radiation backscattered as it is absorbedthrough the upper ocean (together referred to as exitance). The terms exitance and irradianceimply hemispheric, broadband radiation, and for the shortwave their ratio is the surface albedo,discussed more fully in section 6.5.Both irradiance and exitance can be measured with the pyranometer, facing eitherskyward or towards the sea surface. Modern pyranometers are still based on the Moll-Gorczynski design (Moll 1923) in which radiation falls on a blackened horizontal receivingsurface bonded to a thermopile and protected by two concentric precision hemispheric glassdomes. The most important factors affecting the accuracy of these instruments are; reliabilityand stability of calibration, dome temperature effects, cosine response and detector temperaturestability. Manufacturers claim temperature stability of 1% over the range –20°C to +40°C, andthe same figure for cosine response between 0º and 70º solar zenith angle. The reliability oftransfer of the World Radiation Reference (WRR) calibration standard to working instrumentsis estimated to be 1%. Combined, these sources of error give a basic accuracy for theinstrument as 2%, the figure usually quoted. However, Bush et al. (2000) have demonstratedthat variability in the temperature of the dome used to shield the detector can affect theobservation by several Wm-2. The most common manifestation of the impact of dometemperature on the pyranometer is the “night offset”; negative values (typically 1-10 Wm-2 andvariable with time) for the SW insolation at night. In the ARM Enhanced ShortwaveExperiment (ARESE) a first order correction for the dome effect was made by adjusting thediffuse component of the daytime insolation by approximately 5 Wm-2 , being largest in clearskies (Alberta and Charlock 1999).Another source of error, particular to pyranometers used at sea, is caused by theplatform motion. For correct measurement the receiving surface must be horizontal, but bothships and buoys can roll through several degrees, or take on a systematic lean caused by windforce or poor trim. For given platform angle the severity of the error depends on severalfactors; cloudiness, latitude, season and time of day. Katsaros and DeVault (1986) calculatethat it can be as large as 10-20% in the daily average for clear skies poleward of 45° in winter ifthe instrument has a 10° tilt. This is an extreme example, and they point out that the error isconsiderably less at lower latitudes and that there can be compensating errors, which they havenot accounted for, when averages are taken over a whole day. MacWhorter and Weller (1991)also consider the problem. Their experiments simulating symmetrical rocking of the platform,without any mean tilt, show that for 10° amplitude the largest error was 2.1% (and for 20°,5.1%; 30°, 7.8%; 40°, 12%). These figures were for zenith angle of 51° and suggest that forlow latitudes and modest seas the effect of platform roll may not be too serious. Even at mid-5 - Direct Flux Observations 57 5.2 Radiative FluxesReport of the Working Group on Air Sea Fluxes June, 2000latitudes, more severe seas causing greater roll tend to be associated with bad weather andcloudiness, reducing the absolute irradiance (which is also mostly diffuse and therefore lesssusceptible to tilt error). However, as MacWhorter and Weller (1991) show, a persistent tilteven of only 5° will produce errors between 5% and 16%. Even if the record of ship or buoymotion and all other factors were available, it would be a daunting task to correct the radiationmeasurement a postieri. Both Katsaros and DeVault (1986) and MacWhorter and Weller(1991) recommend that pyranometers be set in gimbals.During TOGA-COARE a field comparison between several pyranometers on ships,aircraft and a mooring on a clear day, revealed systematic differences up to 3% for the first-class instruments and 8% for one of lower quality, well outside manufacturer’s specification.The cause was assumed to be calibration error, subsequently confirmed by laboratoryintercalibrations. The instrument’s calibration can change through deterioration of the receptorsurface (flaking of the paint for example) or of the glass dome (scratching), or some electricalfault within the thermopile system. Manufacturers recommend re-calibration once a yearagainst a WRR standard but hitherto this seems to have been seldom observed. Given theCOARE experience, it seems prudent to calibrate before and after each field deployment, and ifpossible to intercompare instruments in the field.The issues described above highlight the extreme difficulties of making accurateshortwave measurements at sea, compared with over land. The WCRP has established stringentprocedures to apply at each continuous observing site to comply with the Baseline SurfaceRadiation Network (BSRN) standards for climate monitoring (Gilgen et al., 1995). Formeasurement of the direct beam these call for the deployment of an NIP, which requires a suntracking system; rigorous calibration procedures are employed for measurement of the directbeam at stations meeting BSRN protocol. The diffuse component is obtained with a shadedpyranometer; however, there are at present virtually no routine measurements of the diffusecomponent of downwelling SW insolation whose calibration protocols are sufficiently rigorousfor clear sky conditions.Hence, the only measurement system with the prospect of meeting WCRP observingstandards for long-term monitoring of SW insolation at sea would be one deployed on a rigidplatform. Neither the NIP sun tracker nor the diffuse system are compatible with unsteadyplatforms such as ships and moorings. Over land, or on a fixed platform at sea, the diffusecomponent of short-wave radiation is measured by fitting the pyranometer with a “shadow-band”, set to shield the sensing element from the direct solar beam as it tracks across the sky.The position of the band is changed manually to accommodate the variation of solar elevationthrough the annual cycle. Floating platforms obviously preclude the use of a fixed shadow-band.Nevertheless, the needs of the modelling community to distinguish between the directand diffuse components, and to parameterise appropriately, has stimulated efforts to improvethe situation. Recently, measurements of the diffuse component have been made fromshipboard using a rotating shadow-band which, whatever the relative position of the sun, casts ashadow on the radiation sensor once per revolution. A fast response sensing element, such as aphotocell, is required and the diffuse signal determined from the time series and approximatetiming. The signal produced by the shadow-band is unambiguous under clear skies, but inconditions of broken cloud can be difficult to recognise. Measurement accuracy iscompromised by the poorer quality sensor, although a reference measurement of the directcomponent is usually provided by a high-quality pyranometer alongside. Descriptions of suchinstruments are given by Reynolds (1998) and on the ARM web site1..The effect of the above sources of error in terms of absolute energy exchange dependson the latitude and season. Table 5.1 shows the daily averages of downwelling irradiance 1 :http://www.arm.gov/docs/instruments/static/mfrsr.html (mfrsr refers to multi-filter rotatingshadow-band radiometer).5 - Direct Flux Observations 58 5.2 Radiative FluxesReport of the Working Group on Air Sea Fluxes June, 2000observed during field experiments at various latitudes. In terms of the basic 2% accuracy of thepyranometer, instrumental error is seen to be less than 7 Wm-2 on daily average, even in thetropics where shortwave irradiance is a dominant component of air-sea energy transfer.However, the dome effect, tilt error and poor calibration can seriously degrade this.Furthermore, at noon on a clear day irradiance in the tropics often reaches 1000 Wm-2, andinstrumental uncertainty alone increases to 20 Wm-2. Such an uncertainty could beunacceptable when trying to model processes in the oceanic and atmospheric mixed layers onthe diurnal timescale. Two initiatives by the NASA-Langley group will improve the accuracyof measurement of the marine SW radiation field; Haeffelin et al. (2000) have begun a programto study the physics of the pyranometer; a BSRN-quality measurement site has been establishedat Chesapeake Lighthouse (36.9N, 75.71W).Table 5.1 values for the daily average downwelling shortwave irradiance observed during fourfield experiments.Experiment Dates Lat. SW (Wm-2 ) ReferenceJASIN July-Sept. 1978, 60ºN; 40 to 270 (Lind et al., 1984);STREX November 1980, 50ºN; 10 to 60 (Lind and Katsaros, 1986);FLIP February 1974, 35ºN; 65 to 169 (Simpson and Paulson, 1979);COARE Nov.1992-Feb.’93, 2ºS; 63 to 330 (Fairall et al., 1996a).While the role of air-sea transfer in forcing global climate has long been recognised intropical regions, the importance and vulnerability of polar regions has been acknowledged onlyrelatively recently. This has led to increased effort to measure fluxes in situ in support of sea-ice modelling and the validation of remotely sensed products. Some intensive measurementexpeditions to the arctic, and pointers to data sets are given in section 4.7. The recent SurfaceHeat Budget of the Arctic Ocean (SHEBA) field experiment2 involved a year-long deployment,from October 1997, of a comprehensively instrumented ice camp within the perennial ArcticOcean ice pack. Radiation instruments were included, to augment those on the nearby ARMCART site3 .Simplicity and availability result in the pyranometer being used as the front-lineinstrument for measurement of shortwave irradiance in most ocean environments, including theice-prone waters of the Arctic and Antarctic. However, these regions present three instrumentaluncertainties additional to those listed above; whether the instrument calibration holds intemperatures below those specified by the manufacturer; cosine response degrades for solarzenith angles greater than 70°; and frost formation on the dome. The first two problems do notappear to have received much attention, but the third is alleviated in the most recentdeployments by blowing air, sometimes heated, across the dome.Within the Arctic and Antarctic circles (latitudes > 66.5° N and S) shortwave irradianceis either continuous or completely absent for significant periods of the year. There are areas ofvery high albedo, but variable in space and time. Shine (1984) shows that ignoring multiplereflections between these high albedo surfaces and clouds can lead to errors in shortwavesurface flux of 30% over bare ice and 50% over new snow. Although the solar zenith angle isalways large, the magnitude of the surface flux is by no means trivial, the average observed forJune being around 300 Wm-2. Pinto and Curry (1997) point out that this compares withmodelled values ranging from 85 to 185 Wm-2, ascribed to over-prediction of cloud. They note 2 http://sheba.apl.washington.edu/about/about.html3 http://www.arm.gov/docs/sites/nsa/nsaaao.html5 - Direct Flux Observations 59 5.2 Radiative FluxesReport of the Working Group on Air Sea Fluxes June, 2000that these models may embody parameterisations for clouds and radiative processes which weredeveloped for regions with quite different climatic conditions from those at the poles. Theunique features of the shortwave radiation environment in very high latitudes cause problems ofsimulation and emphasise the need for high-quality validation datasets.5.2.3 Longwave radiation.Downwelling longwave radiation (longwave irradiance) originates from the emissionby atmospheric gases (mainly water vapour, carbon dioxide and ozone), aerosols and clouds.The major component of longwave exitance is emission from the sea surface which depends onthe surface emissivity and the skin temperature, augmented by a small contribution due toreflection of the longwave irradiance. Longwave absorption and emission take place in aboutthe top 0.5 mm of water, depending on wavelength.The pyrgeometer is of similar construction to the pyranometer, but the single dome ismade from silicon or similar material transparent to the longwave band, coated on the insidewith an interference filter to block shortwave radiation. The longwave irradiance passingthrough the dome, which we wish to determine, is only one component of the thermal balanceof the thermopile. The remaining components come from various parts of the instrument (e.g.Fairall et al. (1998) - Figure 1). To isolate the geophysical component, Q↓lw , the manufacturersprovide an equation derived by Albrecht et al. (1974):QV sT B T T lw pc c d ↓= + + − /( ) εσ σ44 4 (5.1)where V is the thermopile output, s the radiometer sensitivity (the calibration factor displayedon the side of the instrument, usually µV/Wm-2), σ the Stefan-Boltzmann constant, B is the ratioof emissivity to transmissivity of the dome material, and Tc and Td the respective case and dometemperatures (K). Modern instruments are provided with thermistors in the dome and case toenable equation (5.1) to be calculated. εp is the emissivity of the thermopile surface which is, infact, included incorrectly in this equation (see below).For completeness, we note that the most common pyrgeometer in use by geophysicists(the Eppley precision infrared radiometer, or PIR), includes a battery-powered resistancenetwork which generates a voltage in series with the thermopile that is the electrical analogue ofthe second term in (5.1). Thus, providing the observer is prepared to ignore the third (domeheating) term or estimate it using a scheme such as that of Alados-Arboledas et al. (1988),longwave irradiance may be recorded with a single output signal, as from the pyranometer.This procedure obviously trades accuracy for simplicity of operation; this is discussed furtherbelow.The performance of pyrgeometers, and particularly the Eppley PIR, has come underclose scrutiny recently. Philipona et al. (1995) evaluated the thermal balance of the instrumentand developed a more complete version of (5.1), retaining three parameters to be determinedempirically. To obtain the highest measurement accuracy, they stressed the need to characteriseindividual instruments and they describe an apparatus designed to obtain the pyrgeometercalibration factor and their three empirical parameters, one of which is closely equivalent to Bin (5.1). They also measured significant temperature gradients over the dome, and suggest thata single dome thermistor is inadequate to specify Td .Fairall et al. (1998) also re-examined the thermal balance of the pyrgeometer anddiscovered that the factor εp appears erroneously in (5.1). The reason is that Albrecht et al.(1974) make the approximation εp = 1 (εp is usually quoted as 0.98), but do so inconsistently.Since the second term of (5.1) is usually dominant, the error can be significant. For theCOARE data, where Tc was around 300K, it is easy to see that the correction increases thecalculated longwave irradiance by about 9 Wm-2. Fairall et al. (1998) used a radiative transfermodel with atmospheric soundings launched from the ship in clear-sky conditions, to calculatelongwave irradiance at the surface; the results agreed with simultaneous pyrgeometermeasurements within 2 Wm-2 in the mean, supporting their revision of (5.1). Persson et al.(1997) calculate the impact on various climatically important parameters, of removing the5 - Direct Flux Observations 60 5.2 Radiative FluxesReport of the Working Group on Air Sea Fluxes June, 2000erroneous εp, for example the disappearance of an apparent diurnal cycle of sensible heat fluxtransfer coefficient.Like the thermopile calibration factor, the dome heating correction factor, B, isinstrument-specific and must be determined empirically, yet a value is not normally providedby the manufacturer. Values from about 2.5 to 4.5 are in common use, their origin usuallyobscure. The best published determinations are probably those by Philipona et al. (1995), usingtheir new calibration system, who obtain values for different instruments ranging from 3.15 to4.27. Russell et al. (1999b) intercompared five Eppley PIR's during SHEBA and reduced theuncertainty in downwelling longwave by mean-normalising values of s and B for eachinstrument. Their adjusted values for B span a very similar range to those quoted above. Thesignificance of this uncertainty can be tested with a rather severe example from the cruise to theequatorial Indian ocean described by Godfrey et al. (1999). Around midday on August 26,1005 Wm-2 of solar radiation fell on the pyrgeometer, and Td - Tc was 2°C, so adopting anominal B=3.5, the dome heating term was -46 Wm-2 from a total longwave irradiance of 407Wm-2. Using B=3.15 the term becomes -42 Wm-2, an uncertainty of 4 Wm-2 or 1%.In practice, scatter in the apparent values of B may be due to shortwave leakagethrough the dome filter. Dickey et al. (1994) added such a term to (5.1):QV sT B T TR s lw pc c d down ↓= + + − − /( ) εσ σ λ44 4(5.2)where λ is a shortwave leakage factor and Rsdown the downward shortwave radiation. Pascal andJosey (2000) showed that this shortwave leakage effect was significant with λ varying frominstrument to instrument. There was some indication that instruments with smaller leakagefactors had larger values for B and vice versa. They found that the differential heating of theradiometer was primarily caused by the incident shortwave radiation, modified slightly by thecooling effect of airflow over the dome.Over the SHEBA sea-ice, Russell et al. (1999a) report frosting of the domes ofpyrgeometers and discovered that an ice coating makes the PIR more sensitive to dometemperature, leading in clear conditions to an overestimate of downwelling longwave flux by upto 50 Wm-2. They found that ventilation and 10-25 Watts of heating were needed to keep thedomes ice-free. Similar differences between ventilated and non-ventilated pyrgeometers werereported by Guest (1998) working in the Antarctic. Guest (1998) compares his longwaveobservations with the predictions of several empirical formulae, similar to the work of Key etal. (1996) for the Arctic, and draws attention to dissimilarities in the processes of ice formation,longwave radiation and cloud effect in the north and south polar regions. However, bothGuest’s (1998) observations and the (Arctic) satellite analysis by Curry et al. (1996) show thatdownwelling longwave radiation dominates the surface radiation balance, increasing fromaround 200 Wm-2 in winter to 280 Wm-2 in summer. For March and April, Russell et al.(1999b) report around 260 Wm-2 for cloudy conditions but considerably less (~160 Wm-2) underclear skies, very similar to those of Guest (1998 - Figure 4).Philipona et al. (1995) conclude that pyrgeometer accuracy can be ±2 Wm-2 withcareful calibration and operation. The error analysis by Fairall et al. (1998) indicates that thebasic precision of the instrument is about 1.5% of the total longwave irradiance or about 5 Wm-2. They also find that using the battery-compensation circuit leads to errors of at least 5% or 20Wm-2. This is unacceptable in many applications nowadays, and also unnecessary. Fairall et al.(1998) discovered that the limitations of the battery-compensated system are well-known toradiation specialists, but not among ocean and atmospheric scientists who acquire pyrgeometerswithout appreciating the subtleties of their design, and therefore fail to take full advantage oftheir capability. To do so, however, entails logging three signals and performing regularcalibration checks. For data sets where the separate temperatures have not been logged, Pascaland Josey (2000), suggest that correction is still possible if the factors B and λ are known forthe specific radiometer and the available data set contains values for the incident solar radiationand wind speed.5 - Direct Flux Observations 61 5.2 Radiative Fluxes5.3 Turbulent FluxesReport of the Working Group on Air Sea Fluxes June, 2000Calibrating facilities for infrared instruments are more complicated and therefore lesscommon than those for solar radiation, and require careful technique. Fairall et al. (1998)recommend field intercomparisons if several instruments are available (e.g. Russell et al.1999a,b). They also suggest that calculations from a modern radiative transfer model (RTM),using on-site atmospheric soundings in clear-sky conditions, would provide an absolutereference for longwave irradiance within about 2 Wm-2; Miskolczi (1994) comes to a similarconclusion (aspects of RTM's are discussed in Section 6.3). RTM's are not as rigorouslyvalidated for cloudy sky conditions, however. The calculation of the downwelling longwaveflux under a cloud requires knowledge of both cloud base height and emissivity. Cloud baseheight may be measured with active systems such as a MicroPulse lidar (Spinhirne, 1993) or acloud profiling radar, although an infrared radiometer (such as the pyrgeometer) would still beneeded to estimate cloud emissivity. The requirement for downward LW under cloudy skiesargues for continued deployment of pyrgeometers on research ships.The pyrgeometer can be faced downward to measure longwave exitance, but for eithera ship or buoy would need to be mounted at the end of a fairly long boom to exclude theplatform itself from the field of view. There are obviously practical problems to thisarrangement, and it is preferable and probably more accurate, to measure sea temperature anduse the Stefan-Bolzmann law, remembering to include the small component of longwavereflected from the sea surface. Then:QT Q lw s lw ↑= + − ↓ εσ ε4 1 () (5.3)where ε is the emissivity of the sea surface, and Ts the skin temperature of the water, as wouldbe measured by an infrared radiometer. This immediately raises a problem, because accurateIR radiometers are not usually available, and the sea temperature measured at some depth maybe considerably different from Ts (see Section 10.2).5.3 Turbulent fluxes5.3.1 The Eddy correlation methodIn a turbulent atmosphere, the instantaneous vertical transport of some atmosphericconstituent is given by the product of its concentration, C, with the vertical wind velocity, W. Itis customary to write these quantities in terms of their mean values over some convenientperiod (10 minutes to 1 hour), plus their instantaneous fluctuation from that mean. Thus,Ccc =+ ′ and Www =+ ′; multiplying and performing the usual Reynolds averaging wehave, for the vertical flux of constituent C:FW Cw cw c c == + ′′ (5.4)When the site is horizontally uniform, and atmospheric conditions steady over the averagingperiod, it is expected that w is zero, so that the flux is given by the last term of (5.4).Measurement of the fluctuating quantitiesw′and c′ with fast-response sensors, and calculatingthe covariance, is therefore a direct way of obtaining the fluxes. The covariance must embracea frequency range from the turbulence dissipation scale (see below) to a period long enough toinclude all flux-carrying wavelengths. In practice this entails recording continuous time-seriesat a resolution of 10-20 Hz. See Friehe et al. (1991) for a discussion of this, and some practicalaspects of the flux calculation.Referred to as the eddy-correlation method, rapid advances in the development of sonicanemometers and fast-response temperature and humidity sensors have made measurement ofcovariance fluxes over land routine for almost two decades. However, measurements at sea arecomplicated by contamination of the complex sensors by salt and sea spray, by ship motion,and air-flow distortion. Eddy-flux equipment has been operated from bottom mounted towersand research platforms, but necessarily these are shallow water sites. Aboard ship, the need tolocate turbulence sensors well clear of obstructions is even more critical than with other5 - Direct Flux Observations 62 5.3 Turbulent FluxesReport of the Working Group on Air Sea Fluxes June, 2000instruments, because distortion to the wind flow and wakes from even small objects upstreamcan significantly modify the turbulent field. More importantly, pitching and rolling of the shipusually dominates the observed wind signals, and the 3-dimensional motion of the wind sensorrelative to the surface must be recorded and subtracted post-facto from the recorded windsignals (Edson et al. 1998).Nevertheless, it’s value as a direct method of flux determination to validate indirectmethods such as the bulk and inertial dissipation (see below), has spurred increased effort toovercome these inherent difficulties. Following the pioneering work of Fujitani (1985),instruments and computational methods have been improved, and several investigators reportsuccessful motion-corrected covariance fluxes from ships (Tsukamoto and Ishida 1995; Fairallet al. 1996a; Fairall et al. 1997; Edson et al. 1998). The greatest difficulty arises in the case ofmomentum flux, or stress τρ = ′′ uw where the spurious horizontal and vertical wind signals inw′ and u′ are themselves correlated. The sensible and latent heat fluxes,HC w p = ′′ ρθ and EL w q = ′′ ρ respectively,are less susceptible to slight errors in the motion correction, because the scalar concentrationsare only linked to the movement of the sensor as it pitches through the vertical gradients oftemperature and humidity, which are generally small at sensor height.In the context of a 10 Wm-2 heat flux accuracy goal as specified for TOGA COARE,the small correction to the fluxes of water vapour (and other trace gases) described by Webb etal. (1980) may be significant. They point out that; “If the heat flux is upwards (positive) thenrising parcels of air are on average warmer than descending parcels, so on the assumption ofzero mean vertical mass flow of air there must exist a small mean upward velocitycomponent.”. In this case the first term on the right hand side of (5.4) may not be negligible.This vertical velocity is given by;T w q q w w / ) 61 . 1 1 ( 61 . 1 θ′ ′ + + ′ ′ = (5.5)and the consequent correction to the latent heat flux is:q w L EWebb ρ = (5.6)Over the COARE 4-month Intensive Observation Period, the “Webb” effect averaged 4 Wm-2.During strong wind periods, with large sensible and latent heat fluxes, Webb corrections oforder 20 Wm-2 were observed.Although much progress has been made with the measurement of eddy-fluxes fromships in the open ocean, and the effort continues to make such measurements more common(Fairall et al. 1997; Edson et al. 1998), it is likely that their role will remain as a research orvalidation tool for some time to come. However, the so-called inertial dissipation technique,which employs essentially the same fast-response wind, temperature and water vapour sensorsas the eddy-correlation method, may be more readily developed as an operational facility(Fairall et al. 1990, Yelland and Taylor 1996).5.3.2 The inertial dissipation methodA. MOMENTUM FLUXThe inertial dissipation method has been favoured for use on ships for two mainreasons. The turbulence measurements required can be made at frequencies (a few Hz) abovethose affected by the motion of the ship, and the implied short spatial scale of the turbulencereduces the errors due to airflow distortion. However since various assumptions are necessaryto determine the fluxes, the inertial dissipation method is less direct than the use of eddycorrelation. The underlying theory will be outlined using, as an example, the momentum flux.5 - Direct Flux Observations 63 5.3 Turbulent FluxesReport of the Working Group on Air Sea Fluxes June, 2000Based on the Kolmogorov hypothesis, the power spectral density, Suu(n), of the down-stream wind component, u, can, in the inertial subrange, be related to the dissipation rate εvia the wave number, n:Sn K n uu() =− ε23 53 //(5.7)where K is the one-dimensional Kolmogorov constant. Henjes (1998) has shown that (5.7) isvalid for use in the inertial dissipation method provided the spectrum of the turbulence ishomogeneous and isotropic for wave numbers in the inertial subrange. Using Taylor’shypothesis, that is assuming “frozen” turbulence, (5.7) becomes:Sf K f U uu rel () = ()− επ23 53 232// //(5.8)where Urel is the wind speed as measured by the anemometer, and f is the measurementfrequency. Hence the dissipation rate can be obtained by calculating the mean value of f5/3Suu(f)over an appropriate frequency range.The wind stress is derived from the dissipation rate using the turbulent kinetic energybudget (see for example (Busch, 1972)) which for steady state, horizontally homogeneousturbulence, can be written as:uuzgwTTzwezwpvv*'' ''' ' ' 2 1 ∂∂∂∂ρ∂∂ε +−+ =(5.9)orP + B - Dt + Dp = ε (5.10)where primes indicate fluctuations and < > indicates mean quantities. In (5.10) P is themechanical production, B the buoyant production, Dt and Dp are the vertical divergence of theturbulent transport and pressure transport terms, and ε is the dissipation. Equation (5.9) can bemade dimensionless by multiplying by the Monin-Obukhov surface layer scaling parameterkz u / *3 giving:φφ φεφε mt pzLkzu−−+= =*3(5.11)where the Obukhov length, L, is:LuTgk T wvv=−()*''30 (5.12)and each of the dimensionless profiles φm, φt and φp are expected to be universal functions ofζ ( = z/L). Equation (5.11) defines the dimensionless dissipation function φε. If the terms onthe left-hand side of (5.11) are known (or equivalently the form of φζ ε() ), the friction velocitycan be evaluated from an estimate of the dissipation. Thus :ufS fKUkzzLfurel mD*/ /()(/ )=    −− ()12 132πφφ(5.13)Unfortunately, the exact forms of φζ m() , φζ t() and φζ p() are not well known(Fairall and Larsen, 1986), and previous authors have made various assumptions as to theirmagnitude for turbulence over the sea. The form of φζ m() will be discussed in Section 7.2;with regard to the vertical divergence terms, Large (1979) argued that the available evidencesuggested thatφφ tp ≈− (5.14)This leads to a balance between dissipation and the sum of mechanical and buoyant production:5 - Direct Flux Observations 64 5.3 Turbulent Fluxes5.4 Precipitation FluxReport of the Working Group on Air Sea Fluxes June, 2000φφ ε =−  mzL (5.15)However, other authors suggest that there is an imbalance between production and dissipation.Thus:φφ φ ε =− −  mDzL (5.16)Deacon (1988) argued that the value of the Kolmogorov constant used in the inertial dissipationmethod is not the true value, but rather an effective value which corrects for the φD term atneutral stability. Edson et al. (1991) and Edson and Fairall (1998) have estimated that thiscorrection might be of order 10 to 20%. By evaluating the friction velocity to wind speedrelationship for different stability ranges Yelland & Taylor (1996) and Dupuis et al. (1997)evaluated the variation of the apparent imbalance term with ζ. However Taylor and Yelland(2000a) have shown that much of that apparent imbalance was mainly caused by random errorsin the data which caused errors in the calculated stability. The implication is that the truemagnitude of any imbalance may well be smaller than previously estimated, thus giving moreconfidence in results from the inertial dissipation method.As formulated above, the inertial dissipation technique requires assumptions withregard to the values of the von Karman constant, k, the Kolmogorov constant K, and the formof the dimensionless functions φm(ζ) and φ ε(ζ). These constants and functions are notindependent and Fairall and Larsen (1986) have suggested a different formalisation of theinertial dissipation method in terms of turbulence structure functions. As implemented byEdson et al. (1991), this requires determination of φm(ζ) and the dimensionless structurefunction parameter fm(ζ) but does not require an explicit Kolmogorov constant value. We havedescribed the above formulation because it follows that of Large (1979) and Large and Pond(1981,1982) which represent the main verification of the inertial dissipation method over theopen ocean.B. SCALAR FLUXESFor other variables the inertial dissipation method consists of determining the spectrallevel of the fluctuations in the inertial subrange and then solving the budget equation for thevariance of that quantity. Thus:wK fS ffS fuNu''()()/* γβφφε γγ=122(4.17)where γ signifies any passive scalar and φγ N , β are respectively the dissipation function andKolmogorov constant pertaining to γ.5.4 Precipitation Flux5.4.1 IntroductionThe determination of precipitation flux over the oceans is a critical requirement forstudies of the hydrological cycle, ocean stratification and mixing, and for the development ofcoupled ocean-atmosphere GCM's. However, accurate measurement of rainfall at sea presentsdifficulties in two respects; there are measurement (instrumental) problems, and samplingproblems. Rainfall, particularly in storms associated with tropical convection, is perhaps the"patchiest" of all meteorological variables. For most applications, over land or sea, single pointobservations of rainfall are generally less relevant than area-averaged values or spatialcharacteristics. Whereas over land these quantities may be derived from an appropriately-spaced network of surface raingauges, at sea both spatial and temporal sampling by5 - Direct Flux Observations 65 5.4 Precipitation FluxReport of the Working Group on Air Sea Fluxes June, 2000conventional means is usually quite inadequate, even during intensive measurement campaigns(see Section 10.9.1). Remote sensing can provide areal coverage, by ship or aircraft mountedradars on the local scale, or from satellites in the longer term and globally. The sensors andrecovery algorithms should, however, be verified by reliable ground-truth measurements.Results of different algorithm intercomparison projects (see Section 10.9.4) have shown thatthis is rather difficult because the in situ and radar measurements are generally notrepresentative enough to serve as a verification data source.For the TRMM satellite (see Section 4.3.2) an intensive validation experiment(KWAJEX) was conducted around the island of Kwajalein (Marshall Islands) from July toSeptember 19994. Ground-truth instruments were deployed on the island, on aircraft and ship-borne. When available, the results of this experiment will add to our knowledge of theperformance of various instruments in common use to determine precipitation at sea.Development in this area has been much needed, because, as will be discussed next, there aresignificant problems with using conventional funnel rain-gauges on ships.5.4.2 in situ rainfall using Funnel raingauges.A. INTRODUCTIONThe use of conventional rain-collecting instruments, designed for land use, results inuncertainties which are of the same order of magnitude as the mean precipitation estimates.Hasse et al. (1998) stressed two main sources of the uncertainties of precipitation measurementsusing traditional land rain gauges at ships and buoys: (i) the effect of the flow around the ship’soverall structure which can lead to undercatch or overcatch depending on the location of gauge;(ii) the effect of the flow in the close vicinity of the rain-gauge, which tends to carry the rainabove the orifice of the gauge and leads to a wind speed dependent undercatch. Corrections ofmeasurements for the wind speed, and proper installation of rain gauges, can slightly improvethe accuracy, but do not resolve the problem in general (Olbrueck 1979, Ruprecht 1993, Hasseet al. 1998). There is also the problem that conventional tipping bucket raingauges do notwork on board ships or moorings because of the platform motion.B. FUNNEL DESIGNA serious source of error is loss of catch due to the deflection of raindrops away fromthe opening of funnel-type gauges during strong winds. This phenomenon is well-known infunnel gauges used over land. It was first documented by Heberden (1769) who observed thedifference between rainfall measured in the garden of Westminster Abbey, and that measuredon the roof. Jevons (1861) explained the problem analytically and Koschmeider (1934) deviseda simple empirical wind-speed dependent correction. In about 1980 the WMO Commission forInstruments and Methods of Observation (CIMO) initiated a series of workshops and amonitoring program designed to standardise international practice in the form and exposure ofoperational gauges (WMO 1985, 1989). A recent contribution to this effort is described byYang et al. (1998). Folland (1988) developed a theory for loss of catch based on the pattern offluid flow past cylindrical obstacles, and involving droplet size and rainrate. The lighter therainfall and the smaller the drop, the more likely will it be swept past the funnel instead ofdropping in. Folland adopted the drop size distribution (DSD) of Best (1950) rather than thebetter-known DSD of Marshall and Palmer (1948) on the grounds that the latter overestimatedthe number of small drops. Folland’s model agrees fairly well with field observations, butcovers rainrates from 0.1 to only 10 mm hr-1 which may be adequate for most rainfall over landin Europe, but is at the low end of rainfall at sea and far short of tropical storms. With a 10 ms-1wind and 10 mm hr-1 rainrate, the model predicts about 10% loss. But other empirical studieshint at the possibility of much larger effects; 5-80% loss (Kurtyka, 1953), 50% loss for 1-2mmdrops at 12 ms-1 in a wind tunnel (Mueller and Kidder, 1972), annual loss 20% and“considerably greater” with taller gauges (Rodda and Smith, 1986).According to Folland’s (1988) model, for the large drop sizes and high rainrates of atropical storm loss of catch would be negligible. However, the problem is much more severe on5 - Direct Flux Observations 66 5.4 Precipitation FluxReport of the Working Group on Air Sea Fluxes June, 2000ships because, to avoid obstructions, raingauges are usually mounted aloft where winds can beaugmented by the ship’s speed. Further, the wind flow distortion is over the entire vessel, notjust the gauge itself. The problem has been investigated in respect of the COARE rainfall data,and extended to studies of measurement variability at various locations on the ship duringsubsequent cruises. This will be discussed in more detail below, but here we simply observethat even in heavy rainstorms, raindrops were seen to be deflected upward away from thefunnel in some locations. For funnel gauges mounted in more favourable positions,Koschmeider’s (1934) empirical wind-speed dependent correction, brought the siphon rainfallvalues close to the corrected optical raingauge values in the COARE, MCTEX and Indian oceancruises (Bradley, pers. comm.; Godfrey et al. 1999).Hasse et al. (1998) have designed a rain gauge for use on ships specifically toovercome the wind loss problem, using a "dual collector" arrangement. The top funnel is ofsimilar shape to the “champagne glass” design recommended by Folland (1988), and feeds to acalibrated orifice from which drops are counted. Rain blown against the side of the instrumentis trapped, funnelled down into a separate collector, and its contribution weighted according towind speed measured by a co-located anemometer. This instrument has been tested for someyears on land against standard gauges, and on ships against an optical disdrometer achieving anaccuracy of about 3% for 6-hourly sums. (Grossklaus et al. 1998). Tests continue on variousships, including a 2-year operational pilot project on ship lines in the Baltic Sea. Itsperformance must be judged eventually against a consensus of experience with otherinstruments, all of which carry some measure of uncertainty when used at sea. Taking intoaccount the relatively low cost of the gauge and computer interface for data storage (inoperational design mode), its operational use on ships would appear feasible.C. MEASUREMENT METHODThe most common type of funnel gauge used aboard ships and moorings is the siphongauge. This has a reservoir which steadily fills with rainfall to its capacity, when it siphonsautomatically and starts filling again. Some electronic system (e.g. capacitance) is used to keeptrack of the level of water in the gauge, and provide an analogue output; a commonspecification is capacity equivalent to 50 mm rainfall with resolution 0.01 mm. The siphonsystem is relatively, but not completely, independent of ship motion. There is also loss ofrainfall registration while the instrument is siphoning; since this point is frequently reachedduring a period of particularly heavy rainfall, the loss can be significant.5.4.3 Other methods of in situ measurementA. OPTICAL RAINGAUGESThe most common optical raingauge (ORG) in use was developed partly to address theproblems of measurement in the remote, marine environment. It has no moving parts, is robustand completely sealed, and provides an analogue output which is a simple function of therainrate. These ORG's measure rainrate directly by detecting the intensity of scintillationscaused by raindrops passing through a partially coherent beam of infrared radiation. Theinstrument, and the theoretical basis of its optical scintillation system are described by Wang etal. (1978, 1979) and Crosby (1994). Some ORG's were provided by the NASA-TRMM officeto ships and moorings participating in TOGA-COARE, in support of the TRMM satellitevalidation program.At the 1994 Toulouse COARE data workshop (Chinman et al. 1995, page 26),disagreement between various rainfall estimates was found to be worse than a factor of two,which led to a comprehensive study of rainfall measurement. The methods under scrutiny, andtheir Intensive Observation Phase (IOP) average rainfall estimates were reported at a COAREFlux Group workshop (Bradley and Weller 1995a, page 14). Leaving aside the broad range ofsatellite estimates, these results seemed to form two groups; the radars, atmospheric budgetsand siphon gauges supporting an IOP-average rainfall of around 5-6 mm day –1 with the ORG'sindicating about twice that value. Being a relatively new and untried instrument, the ORG was5 - Direct Flux Observations 67 5.4 Precipitation FluxReport of the Working Group on Air Sea Fluxes June, 2000suspected of overestimating relative to the more conventional systems. Suggested defects wereerrors in calibration, sensitivity to ship vibration, and inappropriate response to the intensity andDSD of tropical rainstorms.All of these possibilities were studied on three of the COARE ORG's, using an artificialrain facility (or rain-tower) in which a uniform distribution of raindrops fell to terminalvelocity, and rainrate could be varied over the range 20-200 mm hr -1. An ORG was subjectedto quite severe vibration in the rain tower, but no spurious signals appeared either with orwithout rainfall. Neither was any dependence on DSD found, agreeing with the conclusions ofNystuen et al. (1996). The calibration slopes of all three ORG's tested agreed with the factoryspecification and was linear over the entire range of rainrate, but for two of the three there was aconstant offset of a few mm hr –1. This could lead to large percentage errors at low rain rates.The only other defect found was an imperfect cosine response, such that the verticalcomponent of rain falling through the ORG beam is overestimated. This can be a significanteffect on board ship, where the relative rainfall angles are accentuated by the ship’s speed.Given relative wind speed and direction measured on the ship, this can be allowed for; thecorrection lowers the originally reported COARE ORG rain rates, typically by 15%. ORGrainrates were used to obtain closure of the freshwater budget over the R/V Wecoma COAREcruise area by Feng et al. (1998) and in the tropical Indian Ocean by Godfrey et al. (1999),providing further support for their performance. The test ORG's have been deployed on theship and a drifting buoy during several cruises of R/V Franklin, and recently on R/V R.H.Brown.B. DISDROMETERSThe primary purpose of a disdrometer is the measurement of drop size, and drop sizedistribution in rainfall. Automated designs may also serve to record rainrate continuously and,by integration, total rainfall. Among some precipitation investigators the disdrometer isregarded as the reference instrument. However Post et al. (1998) comment that disdrometerswere used during the Combined Sensor Program cruise and appeared to work satisfactorily, butthat after the cruise, serious concerns about calibration and linearity raised doubts that any ofthe rain data could be used quantitatively.The most usual disdrometer is an acoustic device (effectively a microphone) whichconverts the sound of impact of raindrops hitting a sensor surface into an electrical signalrelated to the size of the drop (Joss and Waldvogel, 1967). One commercial instrument has asensor area of 50 cm2 and sorts the signals into 20 drop size categories covering the range 0.3 to5.5 mm diameter. Improved calibration boundaries by Sheppard (1990) and Sheppard and Joe(1994) improved its response to rainfall which contained a high proportion of large drops. Notsurprisingly, the instrument is sensitive to ambient acoustical noise, including noise from rainhitting nearby surfaces, so special measures have to be taken to isolate the instrument (Nysteunet al.,1996). For the TRMM project, improved impact disdrometers are being developedfollowing the design of Roland (1976).Optical disdrometers, which measure the light extinction by raindrops falling through asensitive volume, have been described by Bradley and Stow (1974), Illingworth and Stevens(1987) and Grossklaus et al. (1998). The latter, designed particularly for use at sea, has ahigher 128-bin resolution. The incidence of multiple drops and grazing incidence on thesensitive volume are considered. Rainrates are determined from droplet spectra by assumingterminal velocity according to drop size. The resolution is 0.05 mm diameter.C. ACOUSTIC METHODSAnother acoustical method of rainfall measurement at sea uses the underwater soundproduced by raindrops impacting on the surface. Rainfall produces a unique acoustic signalwhich may be readily distinguished from other common sound signals (breaking waves,biological sources, etc.), and is typically louder by orders of magnitude than these other sources(Nystuen, 1996). Acoustic signatures have been identified for different raindrop sizes (Medwin5 - Direct Flux Observations 68 5.4 Precipitation FluxReport of the Working Group on Air Sea Fluxes June, 2000et al., 1992), enabling estimation of DSD within the rain and hence rainrate. An "AcousticRain Gauge" employing these principles has been described by Nystuen (1999), and deployedon ocean moorings during SCSMEX and on the TOGA-TAO array (Nystuen et al., 2000).6 - Parameterisation of radiation 69 6.1 Introduction6.2 Radiative fluxes from ship dataReport of the Working Group on Air Sea Fluxes June, 20006 PARAMETRISATION OF RADIATIVE FLUXES6.1 IntroductionShortwave (SW) is considered as radiation whose source is the sun. Longwave (LW) isconsidered as radiation whose source is the emission of thermal infrared by the atmosphere andsurface. SW (LW) is generally below (above) 3 µm in wavelength; the maximum overlap atthe surface is well below 10 Wm-2. LW forcing to the climate system by concentrations of tracegases can be computed more confidently than it can be directly measured; the trace gases canbe measured accurately and their spectroscopic characteristics are fairly well known.Unfortunately the exception is water vapour – which is the most important radiatively. SWforcing to the climate system by aerosols (direct scattering, absorption, and some thermalemission; indirect through clouds) and changes in land use (surface albedo) is uncertain andcannot be measured in a straightforward manner; theoretical calculations of SW forcing couldbe validated by comparing a time history of computed and measured fluxes (provided that themeasurements are sufficiently stable). Because at present the computed and measured SWfluxes do not match, the uncertainty in SW forcing is a significant problem for IPCCassessments of anthropogenic impacts on climate.6.2 Radiative fluxes from ship data6.2.1 Parameterisation of SW fluxBecause the radiation community has generally considered that acceptable accuracywould not be obtained, merchant ships are not equipped to measure the incoming shortwaveradiation. Instead the insolation must be estimated from information on the ship's position andthe cloud information visually estimated by the ship's officer. Such an estimate has to beconsidered relatively crude and it might be argued that the deployment of pyranometers (andpyrgeometers) on at least a subset of the VOS would be advantageous.In general, the short-wave radiation flux on the sea surface may be parameterised as:Q↓sw =Qt TF (6.1)where Qt is the SW radiation at the top of the atmosphere, equal to Qt =S0 cos h, where S0 is thesolar constant, and h is solar altitude. TF represents the transmission factor of the atmosphereand has to be parameterised in terms of the cloud cover and thermodynamic parameters of theatmosphere. There are few parameterisations which use this very general approach (see forexample the reviews of Lind et al., 1984; Dobson and Smith 1988). However, the accuracy ofthis approach is not very high because it requires consideration of the radiation transfer in thewhole atmospheric column. Parameterisations based on ship data are usually poorly determinedbecause of the very complicated and uncertain dependency of the transmission factor on thesurface parameters available from marine data. To avoid this very large uncertainty, it is morehelpful to parse the transmission factor into two terms. One represents the modification ofshort-wave radiation under clear sky conditions (astronomy, temperature, humidity, andaerosols are the main agents of these modification). The other is the cloud modification of theclear sky radiation. In this case, the general formula for the short-wave radiation becomes:Q↓sw = Q0 F(n, T, h) (6.2)where Q0 is clear sky solar radiation at sea surface, F(n, T, h) is the empirical function of thefractional cloud cover n, air temperature T, and solar altitude h (in general case). Clear sky solarradiation is assumed to be a function of the astronomy and of the transmission for the clear skyatmosphere. In most schemes, these two factors are parameterised through the purelyastronomical characteristics (latitude and solar altitude) and empirical coefficients whichaccount for the atmospheric air transparency under clear skies (Seckel and Beaudry 1973; Lumb1964; and others). There are only a few parameterisations which directly include surfaceatmospheric parameters into the clear sky radiation formula. Thus, Malevskii et al. (1992)suggested to use for Q0 the parameterisation:6 - Parameterisation of radiation 70 6.2 Radiative fluxes from ship dataReport of the Working Group on Air Sea Fluxes June, 2000Qcd0 = (sinh) (6.3)where c and d are empirical coefficients, which depend on the atmospheric transmission for theoptical mass number 2P2. P2 itself is a function of atmospheric surface humidity, althoughMalevskii et al. (1992) also gives formulae using air temperature, when humidity measurementsare not available.Quantitative estimation of the cloud reduction factor F is given in many works,reviewed by for instance Dobson and Smith (1988). The most frequently usedparameterisations are the result of a compromise between the complexity of the radiationtransfer in the cloudy atmosphere and the availability of data to describe this complexity. Thusit is obvious that a universal parameterisation of the cloud modification of radiation should bebased on the consideration of cloud types and heights. Against that it is often considered thatthe only reliable parameter in the VOS data is the amount of cloud cover. Attempting to fit thelimited data set of direct measurements using this parameter obviously leads to a dependency ofthe fits on the region and time of the year, and results in the different parameterisationssuggested by different authors. As a good example, Dobson and Smith (1988) foundpronounced differences fitting their formulae using the data from OWS Papa and Sable Island.However for this flux there is a further potential problem; most ship's report at most 4 times perday and the most reports occur at the standard reporting hour closest to local noon. To avoidintroducing bias, the Reed (1977) formulae for the daily mean net shortwave flux QSW uses thecalculated monthly mean values for the fractional cloud cover n, and the noon solar elevation indegrees, φ. Hence:QQ c n sw sw n =− + () − ()0 1 0 0019 1 . φα (6.4)where cn = 0.62 the cloud attenuation factor, and α the albedo (Payne 1972). Qsw0 is theshortwave insolation at the surface under clear skies (Seckel and Beaudry 1973). The Reedformula (6.4) should only be used for 03 1 . ≤≤ n , and QQ sw sw =0 should be used for n < 03 .(Gilman and Garrett, 1994).Being based only on the mean monthly estimated cloud amount, (6.4) is a crude methodof estimation. However it was recommended by the comparative studies of Frouin et al.(1988) and Dobson and Smith (1988) who found a site dependant long term bias of -1 to +12Wm-2 and a monthly mean rms error of about 8 Wm-2. In contrast, using comparisons withdata from several air-sea interaction experiments, Katsaros (1990) and Godfrey et al. (1991)found the Reed formula to be biased high by about 20Wm-2.Malevskii et al. (1992) suggested formulae for the use of the low and total cloud coveras available from the VOS reports (although the quality of the low cloud cover estimates can bequestioned). Their two level scheme uses the low cloud amount and total cloud amount, whiletheir one level scheme just uses the total cloud amount. Intercomparison of theseparameterisations with Reed (1977) and Dobson and Smith (1988) as well as with instrumentalmeasurements shows that the two-level Malevskii et al. (1992) scheme gives the least biases atleast in the mid latitudes (Niekamp 1992).An important problem of the validation of the SW schemes is that the most ofcontinuous instrumental measurements were undertaken in mid latitudes. However the tropicalcloudiness is characterised by very different transmission characteristics. An effect in highlatitudes, still poorly understood, is the secondary reflection of radiation from the cloudmargins under low declinations and small cloud cover. Note here, that the Dobson and Smith(1988) and Malevskii et al. (1992) schemes assume the possibility for the cloud “reduction”coefficients to be greater than 1, partially accounting for this effect.We should note that the atmospheric radiation community generally avoids the use ofexpressions such as (6.4). This is because the optical depth (optical thickness) in (6.4) isimplicitly constant. In a formal radiative transfer model (RTM - Section 6.3) the perturbationto TOA albedo and surface insolation induced by overcast cloud (n =1 in 6.4) over a dark ocean6 - Parameterisation of radiation 71 6.2 Radiative fluxes from ship data6.3 Radiative Transfer ModelsReport of the Working Group on Air Sea Fluxes June, 2000can vary widely, depending primarily on the optical depth of the cloud. Thus, a thin veil ofcirrus may increase the albedo by 0.10, while thick altostratus may increase the albedo by 0.60.For similar reasons, remote sensing of cloud cover n and cloud optical depth with satellite dataare equally challenging problems. Nevertheless, expressions such as (6.4) will continue to beuseful for some applications, since they allow changes in the surface insolation and TOA albedoto be simply estimated from changes in cloud cover n noted by routine visual observation.One of the more difficult issues faced by the RTM community is indirect aerosolforcing: the changes to cloud optical depth and reflectivity that are caused by increases in theconcentration of cloud condensation nuclei (CCN). A group of radiation experts (Hanen et al.,1998) has used historical records of cloud cover n over the US in this century to estimate thechange in radiative forcing (i.e., the output of an RTM using optical depth as input) due toincreases in CCN.6.2.2 Parameterisation of LW fluxAs for short wave radiation, the incoming longwave radiation is not measured but mustbe estimated from the visually estimated cloud amount. Other parameters are the airtemperature and humidity and, for determining the net longwave flux, the sea temperature. Forexample (Clark et al., 1974):QT en T T T LW S S s a =− − + − εσ λ εσ40 5 2 3 03 9 00 5 1 4 (. . ) ( ) ( ).(6.5)where ε (= 0.98) is the spectrally integrated emittance, σ (= 5.67 x 10-8 Wm-2K-4) the Stefan-Boltzmann constant, e the water vapour pressure, n the fractional cloud cover, and Ta and Ts arethe air and sea temperatures in K. The cloud cover coefficient λ varies with latitude. Equation(6.5) is essentially a simple parameterisation of the RTM's for clear sky conditions (Section6.3).The latitude-based adjustment λ accounts for the variation of cloud based height andemissivity, both of which may vary enormously within a latitude zone. Thus it is perhaps notsurprising that the global validity of (6.5) and similar formulae has recently been questioned.For the Mediterranean, Gilman and Garrett (1994) found that this class of formulaeunderestimated Qlw by about 17 Wm-2 and, based on Mediterranean data, Bignami et al. (1995)proposed a new formulae which, for mid-latitude regions, gives Qlw values some 25 Wm-2greater than (6.5) (see Section 9.4.3B). In contrast the review of Katsaros (1990) found that(6.5) underestimated the mean by only 5.5 Wm-2 and comparisons with ship measurementsfrom the Atlantic and Southern Ocean (Josey et al. 1997) have shown agreement typically towithin 5 Wm-2 on timescales of order one month. In the tropical western Pacific, Godfrey et al.(1991) found (6.5) to be the best of four such formulae, showing no systematic bias comparedwith pyrgeometer measurements. 6.3 Radiative Transfer Models - RTM’s6.3.1 Longwave radiative flux (LW)In a cloud-free atmosphere, the net longwave flux density at any height may becalculated from an atmospheric temperature profile, with knowledge of the thermal absorptionand emission characteristics of the atmospheric gaseous constituents, via an appropriateradiative transfer model, RTM (see e.g. Paltridge and Platt 1976, chapter 7). RTM's require asinput the vertical concentration profiles of the atmospheric gases, the most important forinfrared transfer being water vapour, carbon dioxide and ozone. Most exact are the Line-By-Line Radiative Transfer Models (LBLRTM) which, as their name implies, compute transfer ofeach constituent for each emission and absorption spectral line at many levels throughout theprofile. Their computational burden is therefore large, which makes them unsuitable for routineuse in numerical models Over the years therefore, many broadband RTM's have beendeveloped, increasing in accuracy and efficiency with improved parameterisations and increasedcomputer power. Such models are widely applied in climate modelling, and in flux retrievalfrom direct and remotely sensed atmospheric variables.6 - Parameterisation of radiation 72 6.3 Radiative Transfer ModelsReport of the Working Group on Air Sea Fluxes June, 2000Longwave irradiance at the surface may thus be obtained by integrating the calculationsfrom an RTM through the atmosphere. Dutton (1993) used LOWTRAN7 (Kneizys et al. 1988)to determine longwave irradiance from two radiosonde launches per day at each of four sitesspread from the South Pole to the tropics, over the course of a year, selecting the clear-skyoccasions. His purpose was comparison with simultaneous pyrgeometer readings, and theyagreed overall within 5 Wm-2. Dutton (1993) was careful to stress that the result demonstratedinternal consistency of the two methods, and claimed absolute accuracy for neither, but his workillustrated convincingly the potential value of RTM's for obtaining accurate global flux fieldsfrom routine meteorological observations.Miskolczi (1994) also used an RTM to compare with pyrgeometer measurements and,as noted in Section 5.2.3, Fairall et al. (1998) used the Rapid Radiative Transfer Model (RRTM)developed for the Atmospheric Radiation Measurement Program (ARM) science team byMlawer et al. (1997) for comparison with the Moana Wave pyrgeometer measurements. Thisparticular model allows for the use of up to 35 molecular species, defaulting to the seven mostimportant, H2O, CO2, O3, N2O, CO, CH4 and NO. Mlawer et al. (1997) verified RRTM againsta LBLRTM (Clough et al. 1992), and relied on LBLRTM verifications against measurement.They claim that RRTM and LBLRTM longwave calculations differ by less than 1.0 Wm-2 at anyaltitude and, based on a large number of validations with well specified water vapour profiles(the SPECTRE data), that LBLRTM has an accuracy of 2 Wm-2.The parameterisations used in RTM's steadily improve as new observational technologyprovides better validation data. For example, in collaboration with COARE, the ARM programoperated the Pilot Radiation Observation Experiment (PROBE) which was equipped with aFourier Transform Infrared Radiometer (FTIR, Shaw et al. 1991) (see section 5.2.1). One of theprimary goals of PROBE was to compare radiance spectra measured with the FTIR with thepredictions of a LBLRTM based on radiosonde observations of temperature, water vapour andpressure. In the water vapour window region (830 to 1250 cm-1), the calculations were lowerthan measurements by about 5%, which enabled revision of the water vapour continuumformulation used in the model (Clough 1995). Post et al. (1998, their Figure 10) illustrate theeffect of this improvement. Tobin (1999) notes that recent refinements to the parameterisationof the water vapour continuum (Clough et al., 1992) improve accuracy by 1-2 Wm-2. However,the continuum itself is not rigorously understood in terms of basic physics.The computation of LW flux with the best high spectral resolution codes under clearconditions is at an advanced state. Further advances will depend upon developments in sensortechnology in the far infrared (wavelengths exceeding ~ 20µm). Accurate computation of LWunder clear skies requires a treatment of surface emissivity, to account for scattering as well asabsorption and emission. Some rapid codes (e.g., Fu and Liou, 1993) include this. For cloudysky conditions, however, RTM's are not well validated. The calculation of the downwellinglongwave flux under a cloud requires knowledge of both cloud base height and emissivity.Cloud base height may be measured with active systems such as a MicroPulse lidar (Spinhirne,1993) or a cloud profiling radar, although an infrared radiometer (such as the pyrgeometer)would still be needed to estimate cloud emissivity. RTM's designed for GCM calculations (e.g.,Morcrette, 1991; Chou et al., 1995a; Fu and Liou, 1993) are compared with higher spectralresolution codes in the Intercomparison of Radiation Codes in Climate Models (ICRCCM,Ellingson et al., 1991; Ellingson and Fouquart, 1991).6.3.2 Shortwave radiative flux (SW)Knowledge of atmospheric scattering is essential for shortwave RTM's under both clearand cloudy conditions. Recent studies with SW RTM's under clear skies have indicatedsignificant discrepancies when compared with measurements (e.g., Wild et al. 1995; Charlockand Alberta, 1996); computed insolation values are too high, suggesting that the real atmosphereabsorbs more than is predicted by theory. A thorough study by ARM (Kato et al., 1997) hasdemonstrated that under clear skies, the discrepancy is in the diffuse component. Zender et al.(1997) used alternate radiometers (Valero et al., 1982) and do not regard their excess in6 - Parameterisation of radiation 73 6.3 Radiative Transfer ModelsReport of the Working Group on Air Sea Fluxes June, 2000computed versus observed flux as “significant”, although their discrepancies are of order 10Wm-2. One recent study (Wild, 1999) found no discrepancy for clear skies at Payerne; butpersonal communication with one of the authors revealed that a “model” aerosol profile wasemployed. The aforementioned ARM studies used measured spectral optical depth. Someocean studies (Chou and Zhao, 1997; Waliser et al., 1996) have also reported no discrepanciesfor clear skies, but their measurements were made with unshaded pyranometers. Similarly,many measurements of surface shortwave irradiance (i.e., most of those in the Global EnergyBalance Archive (GEBA), have been made with pyranometers only. Under clear skies, theerrors with a pyranometer can be quite large (DeLuisi et al., 1991); for the most accurate results,the correct procedure is measurement of the direct beam with a normal incidence pyrheliometer(NIP) and the diffuse component with a shaded pyranometer (see Section 5.2.2). A strictmeasurement and calibration protocol has been developed under WCRP to guide the BaselineSurface Radiation Network (BSRN, Gilgen et al., 1995). At present, no ship-borne systemsubscribes to the BSRN protocol.The importance of such a protocol is illustrated with the following results from thecomprehensive ARM Program (Stokes and Schwartz, 1994) in Oklahoma under clear skies inOctober, 1995. ARM measurements for aerosols, cloud screening, soundings, and otherparameters were thorough.Table 6.1. Results from the Atmospheric Radiation Measurement (ARM) ProgramMean incoming at TOA for SW 794 Wm-2 Surface SW Insolation: Pyranometer only 549 Wm-2 NIP + shaded pyranometer 571 Wm-2 RAMS (Valero radiometer) 585 Wm-2 Theory (Fu-Liou code) 617 Wm-2The theoretical calculation with the Fu-Liou code is 32 Wm-2 larger than themeasurement with the sophisticated RAMS radiometer (Pilewskie and Valero, 1995). A similarbias has been found by many teams in ARM, with most of the discrepancy in the diffusecomponent. The NIP + shaded pyranometer set-up approximates BSRN protocol; the NIP hasalready been corrected by over 2% following special cavity radiometer measurements(Michalsky et al., 1997). The measurement with the pyranometer only (which would be typicalfor an ocean measurement but without the additional errors listed in Section 5.2.2) is the outlier;the bias depends upon the angular distribution of the radiation and is not constant. There areseveral possible reasons for this. Taking the NIP + diffuse measurement as a calibrationreference, it may be that the particular pyranometer here was poorly calibrated by themanufacturer, or had changed subsequently. In Section 5.2.2 we discuss this question and notethat systematic differences of 3% were found among first-class pyranometers used in COARE.GCM's use fast RTM's to compute SW under clear and cloudy skies. Well regardedGCM-type codes include Morcrette (1991), Chou et al. (1995a), and Fu and Liou (1993). It iswell known that faster codes, which employ techniques such as 2-stream, delta-Eddington, and4-stream, are not as accurate as the higher order plane parallel codes such as discrete ordinate(Stamnes et al. 1988) and adding-doubling; errors of several per cent can be expected (Liou,1992). But few clouds are plane parallel. With finite geometry, more computationallyintensive routines like Monte Carlo and the ingenious spherical harmonics discrete ordinate(Evans, 1998) are more appropriate for estimating an instantaneous flux. Finite geometry isappropriate for some LW applications, too (e.g. Takara and Ellingson, 2000). Finite cloudgeometry often has a dramatic effect on the albedo at the top of the atmosphere (TOA) and thetransmission to the surface; Stephens and Greenwald, (1991) have pointed out that cloudmacrostructure (i.e. finite geometry) can be more important than cloud microphysics. But the6 - Parameterisation of radiation 74 6.3 Radiative Transfer ModelsReport of the Working Group on Air Sea Fluxes June, 2000surprising general result is that when the TOA albedos of plane parallel and finite cloud RTM'sagree, the surface insolation in the RTM's often differs by only a few Wm-2. While finite cloud(2-D or 3-D) geometry can either increase or decrease the atmospheric absorption relative toplane parallel for particular cases, ensemble means generally show a small effect - at least inRTM's. Cloud finite geometry appears to be much more critical for the retrieval of the physicaland optical properties of clouds than for the computation of time-mean radiative flux.But if theoretical 3-D and 1-D clouds with the same albedos also have approximatelythe same atmospheric absorption, do 3-D effects account for the strong “anomalous SWabsorption by clouds” which has been reported? The existence of such absorption is a matter ofdebate. For example, Cess et al. (1995), Ramanathan et al. (1995), and Pilewskie and Valero(1995) have found significant discrepancies between computed and measured SW radiation forcloudy skies, as was noted earlier by Stephens and Tsay (1990). From Cess et al. (1995), wewould infer that if a plane-parallel (GCM) code produced an albedo at the TOA that matchedobservations, it should have a global mean error of approximately 25 Wm-2 for the atmosphere(and hence also for the surface). Over Oklahoma, matched aircraft observations by Valero et al.(1997; 2000) found a strongly absorbing cloud, corroborating the finding of Pilewskie andValero (1995) in the Pacific Ocean; both studies used the broadband RAMS instrument, ratherthan Eppley or Kipp and Zonen radiometers, and were based on a few hours of measurement.The brief aircraft-based results of Valero et al. (1992) have been seconded (Charlock et al.,1998) with narrow band (GOES-8) satellite data (Minnis et al., 1995) but only for short periods;when the domain is extended to several weeks, observed absorption by the clear and cloudyatmospheres both exceed theoretical values by approximately the same amount (i.e. there is alittle anomalous absorption – but in both clear and cloudy skies). Cess et al. (1995) andRamanathan et al. (1995) used longer records of broadband Earth Radiation Budget Experiment,ERBE, data (which is more reliable than GOES) at the TOA and broadband radiometers at thesurface to infer anomalous SW absorption by clouds; most of their surface observations,however, were not based on separate measurements of the direct (NIP) and diffuse (shadedpyranometer) components, as recommended by BSRN. An intensive study (Charlock et al.,1999) with the newly released broadband CERES data and separate direct and diffuse surfacemeasurements at a score of ARM Southern Great Plains (SGP) sites for January-August 1998found no support for the level of cloud forcing to atmospheric absorption reported by Cess et al.(1995) and Ramanathan et al. (1995). A similar study by Trischencko et al. (1999) also foundno support for anomalous absorption by clouds over long time periods in either ERBE, CERES,or ScaRaB (Scanner for Radiation Budget) data that was matched with surface data.6.3.3 COARE atmospheric soundingsClearly, however good its internal physics and parameterisations, the accuracy withwhich an RTM can deliver radiative fluxes depends directly on the reliability of the atmosphericsoundings used as input. Following COARE, several research groups, came to suspect that themajority of COARE soundings were in error, in particular that the humidities seemed about 1 gkg-1 too low (Bradley and Weller 1997; Zipser and Johnson 1998). This was initially based onanomalous north-south gradients of humidity and Convective Available Potential Energy(CAPE), which did not correspond with any observed surface meteorology or SST gradient.Other signals appeared to reinforce this view; a step-function was evident in humidity time-series when the brand of radiosonde was changed; budget calculations based on the soundingspredicted that some visibly rainy regions ought to have been cloud-free; during thepyrgeometer/RRTM comparisons described in Section 5.2.3, only two sets of independentsoundings known to be reliable could be used, while the others clearly underestimated thelongwave irradiance. Working with the radiosonde manufacturers, NCAR-ATD have found thesource of the problem: calibration drift due to contamination of the radiosonde by its packagingmaterial. A time-dependent correction algorithm has been developed (Cole and Miller 1999),and a schedule has been worked out for recalculation of the entire COARE soundings dataset.Guichard et al. (2000) confirm that the impact of this bias on the calculation of convectiveavailable potential energy (CAPE) in the tropics can be quite large. Meanwhile, ECMWF and6 - Parameterisation of radiation 75 6.3 Radiative Transfer Models6.4 Remote Sensing for RadiationReport of the Working Group on Air Sea Fluxes June, 2000NCEP have postponed their planned high-resolution reanalysis of the COARE period pendingthis process. The consequences may well reach beyond COARE into other areas of climateresearch, reanalysis and data assimilation, which make use of historical atmospheric soundings.The impacts of these humidity biases are likely to be enormous for numerical weatherprediction models, which attempt to predict wind fields modulated by convection, responding tothe observed thermodynamic profile. The observed errors would seriously distort theconvection field in space and time, and produce unrealistic ocean-atmosphere coupling. Anincorrect moisture field would certainly affect the dynamics of cloud-resolving models. Theimpact would also be of first order for the calculation of moisture budgets, particularly duringlight rainfall periods. However, the effect on longwave irradiance computed from RRTM usingthe COARE data (see Section 5.2.3) was not large; with humidity in the lowest 300m of theprofile increased by the full 1g kg-1, downwelling longwave was increased by only 1.5 Wm-2.This calculation was for a tropical atmosphere, of course; in a drier atmosphere, the sameincrease of specific humidity would have a larger effect on the flux.A similar problem has also recently been identified by ARM scientists with theirCLASS radiosondes. They find that the new generation of high technology sensors, such as themicrowave radiometer, can be used to detect radiosonde problems. At the ARM Southern GreatPlains Central Facility, data from a Microwave Water Substance Radiometer (MWSR) is nowused to scale radiosonde moisture profiles.6.4 Radiative Fluxes by Remote Sensing6.4.1 SW and LW fluxes from TOA radiancesThe determination of the surface radiation budget (SRB) from satellite data involvesmeasuring the radiative fluxes at the top of the atmosphere (TOA) and accounting for the effectsof the atmosphere using a radiative transfer model (RTM). An obvious problem is allowing forthe effects of clouds. It has proved easier to model the scattering and absorption of SWradiation than to model the effects of clouds on the LW radiation. The problem for LW is thatthe surface budget is dependent on the height of the lowest cloud, a quantity not easilydetermined by satellite. As a result there are significantly more products for surface SW thanthere are for LW.The different algorithms differ in the cloud information used and the sophistication ofthe RTM. While various methods have been proposed that use the narrow band (visible)satellite radiances directly together with an RTM (e.g., Tarpley, 1979; Gautier et al., 1980;Dedieu et al., 1987; Stuhlmann et al., 1990; Pinker and Laszlo, 1992) no extensive datasetshave been produced. Most data sets are based on algorithms which have used cloudinformation from ISCCP. These cloud data have been retrieved from the narrow band satelliteradiances using a method based on a radiative transfer model. Examples are the SW productsfrom GEWEX SRB-1 (Whitlock et al., 1995), and Bishop et al. (1997), and the SW and LWproducts of Rossow and Zhang (1995), Gupta et al., (1999). While each of these products hasused the ISCCP C1 data (280 by 280 km equivalent area grid boxes; 2.5 by 2.5 degree at theequator; number of grid boxes changing with latitude), the GEWEX SRB Version 2 will bebased on ISCCP DX (pixel scale) data with correspondingly high resolution.The goal of the CERES SRB is to reduce the error in the GEWEX SRB by a factor oftwo. In addition to the CERES broadband scanning instruments, the CERES data will beprocessed with simultaneous cloud imager data (VIRS on TRMM and MODIS on EOS-AM1);3-hourly GEOS 2-3 meteorology (Schubert et al., 1995); and 3-hourly operationalgeostationary satellite data. SRB retrievals will be validated and released 2-3 years after launch.Gridded products (1x1 degree) will eventually be available 3-hourly, daily, and monthly.Instantaneous SRB at the individual “footprints” of the broadband CERES scanner will typicallyhave diameters of 10-50 km. One set of CERES retrievals of the SRB will be based on fast“surface-only” algorithms for SW (Li and Leighton, 1993) and LW (Inamdar and Ramanathan,6 - Parameterisation of radiation 76 6.4 Remote Sensing for RadiationReport of the Working Group on Air Sea Fluxes June, 20001997; Gupta et al. 1992); a second set will be based on “full-physics” radiative transfer (Fu andLiou, 1993), balancing CERES broadband TOA measurements (Charlock et al., 1997).More details of each of these various satellite based radiation products will be given inSection 11.3.3 where the products are evaluated.6.4.2 Longwave Net Flux from a combination of AVHRR and SSM/IThis section describes a method to compute the longwave net flux, RL, at the sea surfacefrom a combination of AVHRR-derived sea surface temperature and SSM/I-derivedatmospheric back radiation. For sea surface temperature TS:RR T LL S =−↓ εε σ4 (6.6)where ε is the spectrally integrated surface emissivity (which is close to 0.98, Gardashov et al.,1988) and σ is the Stefan-Boltzmann constant (see also equation 5.3).Schlüssel et al. (1995) described a retrieval scheme of the atmospheric back radiationunder clear and cloudy conditions from SSM/I measurements using three channels at 22, 37, and85 GHz. This retrieval is based on the following consideration: about 80 % of the downwellingirradiance is emitted from the lower 500 m of the atmosphere (Schmetz, 1989) and is mostlyemitted in the water vapour bands near 6.3 µm and above 20 µm, and to a lesser extent in theCO2 band at 15 µm. As shown by Schulz et al. (1993) the SSM/I measurements containinformation on the boundary layer water vapour content. Schlüssel et al. (1995) conclude thatthe irradiance emerging from the lower 500 m should be proportional to the product of the meanlayer temperature and the absorption by the columnar water-vapour content of this layer, sincethe emitted flux density is proportional to the fourth power of the temperature according to theStefan-Boltzmann law. Radiative transport calculations then showed that functions f1 and f2 arein fact closely correlated:fT T T T f Tw T e v h v h S l Swl (,) ( ) ( , ) ( ) 22 37 22437424 1 =− ∝ =−− σσ (6.7)where T22v and T37h are the brightness temperatures for the 22Ghz vertically polarised and 37GHz horizontally polarised channels respectively.The RMS error for the retrieval scheme has been theoretically estimated to be 30 Wm-2.It has been partly verified using simultaneously obtained pyrgeometer measurements,radiosonde data, and cloud observations from the TOGA-COARE and CEPEX experiments.The radiosonde data and the cloud observations have been used within radiative transfersimulations as another means for comparison. Considering hourly means, Schanz andSchlüssel (1997) stated a bias of -3 Wm-2 and a standard deviation of 14 Wm-2 for thecomparison of pyrgeometer measurements and satellite retrieval and a bias of +3 Wm-2 and thesame standard deviation for the comparison of radiative transfer simulations with satelliteretrieval. For monthly averages a RMS error of around 10 Wm-2 is expected.Combining the back-radiation retrieval with AVHRR derived sea surface temperatureusing equation (6.6) gives the net flux at the sea surface. This scheme has been used withinHOAPS (Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data) to retrievelongwave net fluxes with the same sampling as evaporation and precipitation.An alternative method estimating net longwave fluxes RL directly from SSM/Imeasurements using neural networks has been published by Liu et al. (1997). The neuralnetwork technique has been used to better account for the non linearity between RL and themeasured brightness temperatures. Validation attempts were restricted to 5 clear sky and 14cloudy cases during the International Cirrus Experiment (ICE) in October 1989 over the NorthSea. The comparison of the technique with pyrgeometer measurements revealed small errorsfor the clear sky cases but a large standard error (24 Wm-2) for the cloudy cases. Liu et al.(1997) claimed that this error is probably caused by the spatial inhomogeneity of clouds in thefield of view of the SSM/I. However a likely further source of error was the use by Liu et al.(1997) of an algorithm which excluded the 85 GHz channels (due to the malfunctioning of these6 - Parameterisation of radiation 77 6.4 Remote Sensing for Radiation6.5 Surface AlbedoReport of the Working Group on Air Sea Fluxes June, 2000channels during 1989). However those channels are the most sensitive SSM/I channels to cloudparticles and act as main predictors for the cloud signal in the Schlüssel et al. (1995) algorithm.A comparison of the monthly mean of RL over the Atlantic for October 1989 reveals thebest agreement with climatological values from Lindau (2000), but large discrepancies with theIsemer and Hasse (1985) climatology, and also with an ISCCP derived dataset (Darnell et al.,1992), especially in regions with quasi-persistent maritime stratocumulus over the Atlantic westof the African coast. It can be argued that factors such as the solar breakup of stratocumulusduring the day, which is not considered in ISCCP, account for a certain amount of thisdifference.6.5 Surface Albedo6.5.1 Ocean Surface AlbedoAs mentioned in Section 5.2.2, sea surface albedo is the ratio of shortwave exitance toirradiance. The broadband albedo can be measured with a pair of pyranometers, one facingupward and the other downward, but as with upwelling longwave the latter must be mounted ona boom so that it does not “see” the platform. This presents obvious difficulties for ships on theopen ocean. The seminal study of sea surface albedo is that of Payne (1972), who madecomprehensive measurements from a platform in Buzzards Bay, MA (41°N), expressing theresults in terms of only two parameters, solar altitude and atmospheric transmittance. The latteris the ratio of solar irradiance actually measured at the surface to that incident at the TOA,which can be simply calculated from knowledge of the solar constant, date, time and location(Paltridge and Platt 1976). Solar transmittance is affected by absorption or scattering fromatmospheric constituents, mainly water vapour, ozone, aerosols and clouds. Thus, Payne’s(1972) parameterisation actually relates to the varying ratio of diffuse to direct shortwaveradiation. The Fresnel laws predict (and common observation confirms) that reflectivity at awater surface increases toward glancing angles of incidence. Generally speaking then, for highsolar altitude and clear skies the albedo is small, but any increase in the diffuse component dueto cloudiness will reduce the average angle of incidence and increase the albedo. For low solaraltitude, the addition of cloudiness has the opposite effect.Katsaros et al. (1985) confirmed Payne’s albedo results during GATE at 7°N andJASIN at 60°N (both during summer), and their Figure 1 provides an excellent illustration of theeffects of diffuse radiation, solar altitude and surface roughness on surface albedo. Albedo isexpected to depend on wind speed through changing sea roughness, but Payne (1972) showedtheoretically that the effect is small excepting for solar altitudes less than 30° with a clear sky.Within this restricted regime, Katsaros et al. (1985) combined Payne’s observations with theirown, and those of Simpson and Paulson (1979), to estimate that albedo decreased by 3.6% perms-1 for winds less than 12 ms-1. At wind speeds above about 15 ms-1, albedo increases due tothe presence of whitecaps (Monahan and O’Muircheartaigh 1987).Payne’s (1972) empirical relationships are widely used in ocean-atmosphere studies,and to parameterise broadband albedo in numerical models. They were confirmed over the deepocean with direct albedo measurements from a boom mounted forward of the bow of R/VFranklin over 20 days during COARE in the vicinity of 2°S. To avoid undue bias from the verylarge albedos at low sun elevations, daily average values were calculated as the ratio of totalupwelling to downwelling radiant energy. For the typical conditions of predominantly lightwinds and relatively smooth seas, a constant albedo of 0.058 was found to apply to dailyaveraged air-sea heat flux with negligible error over using the diurnally variable values. This isthe value incorporated into the COARE bulk flux algorithm (Section 7.4.1).As the broadband albedo of the sea is a ratio of upwelling to downwelling flux, it is notan appropriate input for the spectrally resolved theoretical radiative transfer calculations madein GCM's or satellite-based retrieval algorithms. Rather, a radiative transfer model uses aspectral reflectance as an input, and the broadband albedo is determined from the ratio of itsoutput fluxes. For example, the CERES retrievals of surface fluxes use the Fu and Liou (1993)6 - Parameterisation of radiation 78 6.5 Surface Albedo6.6 SummaryReport of the Working Group on Air Sea Fluxes June, 2000code and assume, following Bowker et al. (1985) and Payne (1972), spectral reflectances fordiffuse SW: 0.0878 (0.2 to 0.7 µm), 0.0505 (0.7 to 1.3 µm), and 0.0163 (above 1.3 µm).Under clear conditions, these are adjusted with solar zenith angle. A more rigorous treatmentof the spectral bi-directional reflectance function (BRDF) of the sea has been developed by Hu(2000, pers comm.) using the Cox-Munk wind-speed wave distribution and a modified discreteordinates scheme with 100 streams; this is to be validated at the Chesapeake Lighthouse withmeasurements of upwelling spectral radiance.Using data from aircraft-mounted instruments during the JASIN experiment (flight level50m and solar altitude about 50°), Katsaros et al. (1985) determined the sea surface albedo inthree spectral bands. In the shortest waveband (0.28 to 0.53 µm) albedo was almostindependent of cloudiness at around 0.07, whereas in the remaining visible (0.53 to 0.70 µm)and near-infrared (0.70 to 2.8 µm) bands albedo increased with cloudiness. Observations thatthe broadband surface albedo under clouds exceeds the clear-sky value are widely reported.There are two main reasons; the change to the diffuse component referred to above, and theselective absorption of solar radiation by clouds at wavelengths greater than about 0.7 µm(Lindsay and Rothrock, 1994; Curry et al., 1996).6.5.2 Albedo over sea iceThe variability of albedo, including its spectral dependence, take on particularsignificance at high latitudes. Lindsay and Rothrock (1994) tabulate values of albedo withinpack ice ranging from 0.06 for smooth water, through 0.32 – 0.68 for various forms of meltingice, to 0.87 for fresh snow. The effective albedo over a given region will depend on the relativeareas of each type of surface present. At these predominantly large solar zenith angles theproportion of ice to water is particularly critical. It creates obvious problems for bothrepresentative measurement and modelling, leading Lindsay and Rothrock (1994) to devise anequivalent “potential open water fraction” based on surface temperature and albedo. Drawingon a large number of previous studies, Ebert and Curry (1993) developed a surface albedoparameterisation which considers both spectral variation and solar zenith angle dependence.They find that the annual cycle of Arctic ocean broadband surface albedo ranges from about0.48 during summer to 0.83 in winter.Several authors (e.g. Shine, 1984; Lindsay and Rothrock,1994: Curry et al, 1996)emphasise the particular importance over high albedo surfaces of multiple reflections betweenthe surface and cloud base, which contribute substantially to the downward flux. Shine (1984)presents a calculation method which takes account of this and finds that, for reasonable cloudthickness, neglecting multiple reflections can incur errors in the surface net flux of 30% overbare ice and 50% over new snow. On the other hand, neglecting spectral variability of albedoseems to generate errors no greater than 1.5% in surface flux. Pinto and Curry (1997) note thatmultiply reflected shortwave radiation from the high albedo surface will change the heating ratewithin the cloud layer, as well as modifying the flux at the surface itself.6.6 Summary - Parameterisation of Radiative fluxesIn summary, parameterisations of both SW and LW radiation result in wide range ofestimates of radiative fluxes. More in-situ observations of radiative fluxes are needed indifferent regions, especially in the tropics to verify the parameterisations. Although cloudcorrection is the most uncertain point of SW parameterisations developed for use with VOSdata, large uncertainties also result from the formulation of clear sky radiation. Use of two-level cloud cover can improve parameterisations of SW radiation, but there are still relativelyfew VOS reports which provide both total and low level cloud cover information. LW radiationestimates computed with different parameterisations largely depend on the data used to produceparticular formula. These data are affected by regional variations in cloud conditions.7 - Parameterisation of Turbulent Fluxes 79 7.1 Introduction7.2 Traditional Bulk FormulaReport of the Working Group on Air Sea Fluxes June, 20007 PARAMETERISATION OF TURBULENT FLUXES7.1 IntroductionThe parameterisation formulae are used for determining the turbulent fluxes from thebasic variables such as wind speed, air temperature, etc. The same "bulk formulae" areapplicable whether the basic variables have been obtained by in situ measurement or by remotesensing, or have been calculated by a numerical atmospheric model. Thus these formulae arefundamental to flux estimation and therefore they will be discussed in some detail in thisChapter.Until recently there was general agreement as to the form of the bulk formulae which,at the simplest, could be written: F ∝ uz Cz (∆sz) where F is the flux, uz is the wind speedmeasured at height z, and ∆sz is the difference in value of the appropriate basic variable betweenheight z and the sea surface. Written in this form, most of the physical processes have beenparameterised by the transfer coefficient C z which will vary with height, and stability (andpossibly wind speed and other factors). Since the form of the stability correction is thought tobe reasonably well known, Cz is normally represented by C10n, the 10m neutral value (defined asthat which would give the same flux given measurements at 10m height obtained under neutralatmospheric stability). Depending on which flux is to be calculated (and the state of ourknowledge), C10n may be taken to be a constant value, to have a simple wind speeddependence, or to have a more complicated variation with sea state and other factors.While the standard bulk formula is designed to model turbulent transfers near a roughsurface, in recent years experiments such as TOGA COARE have resulted in formulae whichattempt to also take into account other physical processes. Now many versions of the formulaeexist and new formulae are constantly being developed. At the same time, many models havefor many years used the older versions of the formulae, partly because "that is what they havealways used" and there is the possibility that other processes in the model may have becometuned to the resulting flux values.To try to bring some order to the present situation, in this Chapter we will first describethe derivation of a "traditional" bulk formula for the surface fluxes. We will then summarise thepresent state of knowledge with regard to the roughness lengths (which determine the transfercoefficients). Recent algorithms for the bulk formula will then be described. Although many ofthe modifications have attempted to deal with low wind phenomena, these new formulae arebeing applied more generally. Finally we will consider transfers in strong wind conditions.7.2 Example of traditional "Bulk Aerodynamic" formulaeThe aim of the stability dependent bulk formulae is to calculate the air-sea flux ofmomentum, sensible, or latent heat from mean values of the meteorological variables (windspeed u, air temperature t, and humidity q) measured at some height z above the surface,together with values of the sea surface temperature, ts. Although often considered to be purelyempirical, the formulae used can be derived from the dimensionless profile equations (whichcan themselves be derived using dimensional considerations and "similarity theory", see forexample Geernaert (1990) for a more detailed treatment):kzuuzm*∂∂φ<>=(7.1a)kzttzt*∂∂φ<>=(7.1b)kzqqzq*∂∂φ<>=(7.1c)7 - Parameterisation of Turbulent Fluxes 80 7.2 Traditional Bulk FormulaReport of the Working Group on Air Sea Fluxes June, 2000where k is the von Karman constant, and the dimensionless profiles φx are each functions of thestability parameter ζ (= z/L ). Here L is the Monin-Obukhov length, defined by (e.g. Stull,1988 p.180):LTugk w Tvv=*''3(7.2)where Tv is the virtual temperature of the air. The dimensionless profile functions have beendetermined experimentally. The correct form is critically dependent on the assumed value for k(Frenzen and Vogel, 1994). Edson et al. (1991) review several formulations for φm(z/L),modified where necessary on the assumption k = 0.4. The best performing formulae could besummarised by:φαβm zL =− () ()− 1/ ζ < 0 (7.3a)φγ m zL =+() 1/ ζ > 0 (7.3b)where values of α ranging from 16 to 28 all fitted the data to a similar degree and a value α = 20was chosen because it gave the best overall fit with γ = 8. In most formulations β is 1/4 butsome authors have suggested β = 1/3 as theoretically more consistent. For temperature andhumidity (referred to by many authors as the scalar variables), it will be assumed that:φφφ tqm ==2ζ < 0 (7.4a)φφφ tqm == ζ > 0 (7.4b)where the φt relationship follows Paulson (1970). The assumption that φq = φ t seemsreasonable but is based on very little evidence. In (7.1) the scale variables u*, t* and q* aredefined, using dimensional reasoning, by reference to the surface fluxes:uu w * ''2 ==τρ (7.5a)ut twHcp** '' ==ρ (7.5b)uq qwELv** '' ==ρ (7.5c)Remembering that we need the formulae to allow the fluxes to be determined frommeasurements at some height z, (7.1) is integrated between the surface and the measurementheight to give:uuukzzzum =+   −   00* ln Ψ(7.6a)tttkzzztott =+   −   0* ln Ψ(7.6b)qqqkzzzqoqq =+   − 0* ln Ψ(7.6c)where uo, to, and qo are the surface values and the stratification functions Ψi represent thestability corrections to the profiles and are the integrals of the dimensionless profiles φi (Paulson,1970):7 - Parameterisation of Turbulent Fluxes 81 7.3 Determining the Transfer CoefficientsReport of the Working Group on Air Sea Fluxes June, 2000Ψmmmm =+    ++    −+−−−− 21212221211 ln ln tanφφφπζ < 0 (7.7a)Ψmm =− 1 φ ζ > 0 (7.7b)ΨΨ tqt ==+   −2121lnφζ < 0 (7.7c)ΨΨ tq t == − 1 φ ζ > 0 (7.7d)Note that in neutral stability φi = 1 and Ψi.=0 Using Equation 7.6(a-c) to substitute for u*, t* andq* in 7.5 (a-c) we can derive the bulk formulae:uC uu Dz *202=− () (7.8a)ut C u u t t Hz z **=− () − () 00 (7.8b)uq C u u q q Ez z **=− () − () 00 (7.8c)which define the transfer coefficients as the expressions:CkzzDum =   −  −202ln Ψ(7.9a)CkzzzzHtttum =   −     −  −−20101ln ln ΨΨ(7.9b)CkzzzzEqqqum =   −   −  − −20101ln ln ΨΨ (7.9c)From (7.9) it can be seen that the 10m neutral drag coefficient is:CkzDn 1020210=    −ln(7.10a)which emphasises that the drag coefficient is directly related to the roughness length; the twoare interchangeable. The transfer coefficients for heat and water vapour (the Stanton and Daltonnumbers, respectively) depend on both the momentum roughness length and the heat andmoisture roughness lengths:CkzzHnt102010110 10=        −−ln ln(7.10b)CkzzEnq102010110 10=      − −ln ln (7.10c)7.3 Determining the Transfer Coefficients7.3.1 Traditional bulk formulaeA. INTRODUCTIONThe above discussion suggests that the transfer coefficients could be determined byobtaining, in some way, the various roughness lengths (7.10). However in the traditionalapproach the transfer coefficients have been obtained by measuring the surface fluxes, using oneof several techniques, together with measurement of the mean physical variables required (7.8).7 - Parameterisation of Turbulent Fluxes 82 7.3 Determining the Transfer CoefficientsReport of the Working Group on Air Sea Fluxes June, 2000Early work on air-sea exchange tended to focus on the momentum transfer betweenatmosphere and ocean. Synoptic methods, such as the slope of the surface or the geostrophicdeparture of the wind field, were used to estimate the wind stress on the ocean and obtain a dragcoefficient. Later, application of classical boundary-layer laws relating the momentum flux (orstress) to the vertical wind gradient (e.g. (7.6a)), allowed a drag coefficient to be generatedentirely from measurements of the vertical wind profile. Semi-empirical expressions for thediabatic flux-profile relationships established over land sites (e.g. Businger et al. 1971) enabledcorrection of the measured drag coefficient to its equivalent neutral value, and also to a standardreference height of 10m above the water surface, as set out in the previous section.Experimental studies on wind/wave interaction and the ocean drag coefficient proliferated, sothat by the time Garratt (1977) published his comprehensive review of the subject, he was ableto quote five previous reviews between 1951 and 1969 (his Table 1). Garratt (1977) goes on tolist seventeen determinations of CD10n between 1967 and 1975, most of which obtained the stressby the direct method, covariance (see Section 5.3.1).For the scalar (sensible and latent heat) fluxes, Pond et al (1974) examined results forCH10n and CE10n determined from profile measurements, while Friehe and Schmitt (1976)compiled data from nine experiments all using covariance fluxes. However, it was the much-quoted comparison of ten common bulk transfer coefficient schemes by Blanc (1985) whichdrew attention to the substantial differences between these schemes, and quantified theconsequent uncertainty in flux estimates. This review also highlighted the limited wind speedrange over which many of the schemes had been verified. There were virtually nodeterminations above 14 ms-1, where exchange of heat and moisture begin to be complicated byspray droplets, or below 4 ms-1, a common situation in the climatically important region of thetropical oceans. Improved parameterisation of the fluxes in both of these regimes was badlyneeded for numerical climate models.Some of the more frequently used results from recent traditional determinations of theexchange coefficients are discussed briefly below. It is common practice to plot the neutralexchange coefficients against the 10m wind, as Blanc (1985) does, although the possibly ofdependence on other factors should be borne in mind.B. WIND STRESSFor wind stress, the drag coefficient values used in many past studies were thoseobtained by Large and Pond (1981, 1982) or Smith (1980, 1988). Although these studies usedboth eddy correlation and inertial dissipation stress data, the bulk of the Large and Pond resultswere obtained with the latter technique (see Section 5.3.2). For 10m wind speeds u10 between 3and 10 ms-1 they found a constant value (103CD10n = 1.12 ± 0.2), and for u10 between 10 and 25ms-1 suggest a linear increase given by (103CD10n = 0.49 + 0.065u10). Other schemes suggest alinear relation over the entire range of validity; Garratt (1977) gives (103CD10n = 0.75 + 0.067u10)valid from 4-21 ms-1 and Smith (1980) gives (103 CD10n = 0.61 + 0.063u10) from 6-22 ms-1(Figure 7.1).These upper wind speed limits are supported by very few observations, and are bestregarded as extrapolations from the lower wind regime where data is more plentiful and morereliable. However Yelland et al. (1998) used a large data set from the Southern Ocean,obtained using the inertial dissipation technique, to effectively confirm the Smith (1980) eddycorrelation derived formula for open ocean wind stress for wind speeds between about 6 and 20ms-1. Above 20 ms-1 the Smith (1980) formula possibly underestimates the wind stress, theSmith (1988) formulation (a reinterpretation of the 1980 results based on the Charnock formula,and presented in tabular form) under-estimates significantly at higher wind speeds.As the wind decreases below about 2 ms-1, Smith (1988) suggested that CD10n wouldincrease due to viscous effects. However, the significantly greater CD10n values for windsbelow about 5 ms-1 predicted by Wu (1994) appear to be a better fit to the data of Bradley et al.(1991) and Yelland and Taylor (1996) (however see discussion in section 7.4.2B).7 - Parameterisation of Turbulent Fluxes 83 7.3 Determining the Transfer CoefficientsReport of the Working Group on Air Sea Fluxes June, 2000Figure 7.1 Examples forthe Drag Coefficient, CD10n,plotted as a function ofwind speed, U10n 25 20 15 10 5 00.51.01.52.02.53.0Large & Pond 1982Smith 1980Smith 1988Yelland et al. 1998Smith 1992u10n (m/s)CD10n (x1000)C. SENSIBLE AND LATENT HEATUnlike the drag coefficient, traditional estimates of CE10n and CH10n over the ocean tendto support a fairly constant value over a wide range of wind speed. Instrumentation problems,partly caused by exposure to the salt environment, have limited the number of reliabledeterminations and there is still at least 10% uncertainty in their values and hence in the derivedfluxes. Friehe and Schmitt (1976) recommend a constant Dalton number (103 CE10n =1.32 ±0.07) on the basis of several turbulence-based datasets where the highest wind speed was 4 ms-1.After a critical assessment of previous studies, Smith (1989) also suggested a constant“consensus” value (103 CE10n =1.2 ± 0.1) for winds between 4 and 14 ms-1. The HumidityEXchange Over the Sea (HEXOS) experiment results (DeCosmo et al., 1996) also suggest anear constant value (any increase being less than 15 to 20%) with (103CE10n , = 1.12 ± 0.24 ) forwinds up to 18 ms-1.For the Stanton number, Friehe and Schmitt (1976) obtained slightly different values forunstable and stable conditions (103 CH10n = 0.97 and 0.86 respectively), and proposed that thedissimilarity with their Dalton number was partially an effect of the classical ratio of the thermalto species diffusivities (Prandtl/Schmidt numbers), CE/CH = 1.16. Smith (1988) suggested 103CH10n = 1.0 which is consistent with this ratio and his value of CE10n . In contrast DeCosmo etal. (1995) suggest that ( CE10n ≈ CH10n) for the high wind HEXOS results, to the accuracy of theirdetermination.Given that the drag coefficient, and hence the roughness length, increases with windspeed, a constant value for the Dalton number, implies that the humidity roughness lengthdecreases with increasing wind speed (see (7.10b,c)). In explanation, Liu et al. (1979)suggested that, whereas momentum is transferred by pressure differences as well as bymolecular forces, heat and moisture are transferred only by molecular diffusion which becomesless efficient as the roughness increases and sheltering occurs between the wave troughs.For the low wind speed regime, the transfer coefficients, CE10n and CH10n, have beenfound to increase as the wind decreases below about 2 ms-1 (Bradley et al., 1991) as predictedby Liu et al. (1979) (see section 7.3.2C). 7.3.2 New bulk formulaeA INTRODUCTIONThe remaining uncertainty in the specification of transfer coefficients, and theconsequent limited accuracy with which fluxes could be calculated from bulk formulae, becameunacceptable in the context of climate models and particularly in attempts to couple ocean and7 - Parameterisation of Turbulent Fluxes 84 7.3 Determining the Transfer CoefficientsReport of the Working Group on Air Sea Fluxes June, 2000atmospheric models. Along with improved instruments and further experimental studies, newbulk formulae have been developed aimed at incorporating better physical descriptions of thetransfer process. These newer bulk formulae differ from the traditional approach with regard tothe transfer coefficients in several ways. Typically, the momentum roughness length, zo , isobtained from a formula which takes sea state into account, and the roughness lengths fortemperature and humidity are calculated from zo by considering the transfer of heat through theinterfacial boundary layers, so-called “surface renewal theory” (Liu and Businger 1975;Brutsaert 1975). They also seek to extend the wind speed range over which the algorithm isapplicable, particularly into the climatically important low-wind, convective regime. In thisrespect, improved convective flux/gradient relationships have been incorporated, and stepstaken to overcome the fundamental singularity in the bulk formula as the wind speed approacheszero.B MOMENTUM ROUGHNESS LENGTH, Z0The aerodynamic roughness length, zo , appears as the integration constant in (7.6a); itis the height at which the wind velocity extrapolates to zero on the logarithmic wind profileunder neutral conditions. Considerable efforts have been made in the disciplines of fluidmechanics, engineering and micrometeorology to relate zo to the geometrical properties ofvarious natural and fabricated surfaces and some empirical generalities have emerged (e.g.Figure 4.1 of Garratt 1992). Over most rigid land surfaces zo may be obtained from the neutralwind profile (and its value is nowadays cross-checked by measuring all the other quantities in(7.6a)). Panofsky and Dutton (1984) give values of zo for various surfaces, from which we notethat the sea is very “smooth”. The associated small velocity gradient make both zo and u*extremely difficult to measure accurately using the profile method, especially from shipboard.Furthermore, the sea surface geometry and roughness alter continuously with varyingwindspeed. It has therefore become common practice to specify the roughness lengthindependently, drawing on the methods of classical fluid dynamics (Schlichting, 1968, ChapterXX).Kraus and Businger (1994, Chapter 5) describe in detail the application of this work tothe air-sea interface, so we discuss only the basic issues here as they relate to current fluxalgorithms. It is common to characterise the surface and the wind flow regime in terms of theroughness Reynolds number:Ru z r = * / 0 ν (7.11)where ν is the kinematic viscosity of air. When Rr < 0.13, aerodynamically “smooth” flow existswhen the roughness elements lie entirely within the viscous sublayer at the surface. Theconcept of a viscosity dependent transfer regime is important in the context of surface renewaldiscussed below. In smooth flow, classical experiments show that:zu 0 01 1 = ./ * ν (7.12)With increasing windspeed the flow becomes aerodynamically “rough” when Rr> 2.5,as the stress is dominated by pressure and viscous forces around the surface roughness elements.In this regime over a rigid surface, zo (and CDz) is independent of windspeed, but a water surfaceresponds to the varying wind stress continuously by changing its roughness. Charnock (1955)argued for proportionality of the two quantities, and on dimensional grounds proposed a relationfor rough flow over the sea:zu g c 02 =α * / (7.13)where αc is referred to as Charnock’s constant, for which values between 0.010 and 0.035 cantypically be found in the literature (Garratt 1992, Table 4.1). Fairall et al. (1996a) presentvalues of αc as a function of Rr and conclude that the “best” value to use in the COARE bulkflux algorithm (see below) was 0.011. However in the open ocean, the near linear increase ofCD10n with wind speed (Yelland et al. 1998) implies that αc increases to around 0.018 at about20 ms-1 (Hare et al. 1999).7 - Parameterisation of Turbulent Fluxes 85 7.3 Determining the Transfer CoefficientsReport of the Working Group on Air Sea Fluxes June, 2000Roughly speaking, flow over the ocean is smooth below a 10 m windspeed of about 2ms-1 and fully rough above 8 ms-1; between these limits there is a transition regime. Smith(1988) therefore proposed:zuguc 02011 =+ αν **.(7.14)valid for the entire smooth to rough flow regime, a scheme which is being increasingly adoptedin bulk flux algorithms (Fairall et al. 1996a; Zeng et al. 1998). Kraus and Businger (1994,figure 5.3) illustrate the form of this relationship.The wide range of empirical values found for αc have led to suggestions that it is not aconstant, but dependent on what may loosely be called “sea state”. The complicated topographyof the ocean surface depends strongly on the interaction between wind and waves, which hasbeen an active field of study and controversy for the past half-century. Claims have been madefor relationships between the drag coefficient (or equivalently z0) and various wavecharacteristics and influences. For example, several sets of observations over shallow watersites appear to give higher drag coefficient values than those over the open ocean (Geernaert1990, Figure 8). This has prompted modification of Charnock’s relationship to make αcdependent on wave parameters, particularly the wave age: wc u ap = / * (7.15)where cp is the phase speed of the dominant waves at the peak of the locally wind-driven wavespectrum (Melville 1977, Nordeng 1991, Donelan et al. 1993). The notion is that youngerwaves, developing as a result of limited coastal fetch, limited duration, or steepening due toshallowing depth, travel slower than the wind and absorb more momentum, therefore appearingto be rougher.Wu (1968) rationalised Charnock’s expression as an “equation of state characterisingequilibrium interaction between the wind and waves with gravity waves as the roughnesselements”, and suggested that capillary waves contribute significantly to the stress atintermediate wind speeds, implying a dependence on surface tension and viscosity of the water.On this basis Wu (1968) proposed an expression for z0 due to capillary waves, and subsequently(Wu 1980) a further refinement to the Charnock relationship for separating flow behind “shortwaves superimposed on the dominant waves in the ocean”.During the HEXOS experiment near the coast of Holland, Smith et al. (1992) found therelationship:guwza2*048 . 0=(7.16)effectively modifying the Charnock “constant” to accommodate the range from very youngwaves (wa ≈ 5 ; α c = 0.096) to mature waves (wa ≈ 30 ; αc = 0.016). Komen et al. (1998)reviewed various formulations for the Charnock parameter and concluded that, of thoseavailable the HEXOS relationship (7.16) had the advantage of fitting data sets ranging from theopen ocean to the short fetch Lake Ontario data of Donelan (1982). However reanalyses of theHEXOS data by Janssen (1997) and Oost (1998) questioned the validity of (7.16). Forexample, Oost (1998) suggested that at least some of the enhanced stress values were caused byshoaling waves. When those data were excluded, Equation (7.16) was valid only for theyoungest waves (wa < 13). Similarly, Yelland (2000) found little difference in stress values forthe open North Atlantic and the region of the HEXOS experiment. However data collectedfrom the shallower North Sea regions did show higher stress values.Bourassa et al.(1999) proposed an air-sea interaction model which, for zo, used the root-sum-of-squares of Wu’s (1968) capillary wave parameterisation and the wave-age modifiedCharnock expression of Smith et al. (1992):7 - Parameterisation of Turbulent Fluxes 86 7.3 Determining the Transfer CoefficientsReport of the Working Group on Air Sea Fluxes June, 2000zbu wugcwga0 222212048=+ () (.)**/βσρβ(7.17)Here σ is surface tension, ρw the water density, b is Wu’s parameter (modified by Bourassa etal.), and βc, βg are weights for capillary and gravity waves respectively, based on physicalfactors. Capillary waves are the dominant roughness for windspeed < 7 ms-1 and short gravitywaves for wind > 7 ms-1. Other aspects of the bulk flux model of Bourassa et al. (1999) will bediscussed below (section 7.4.2B).Wave age is not necessarily a function of fetch alone; in the open ocean a suddenchange in windspeed due to passage of a frontal system, for example, can produce a non-equilibrium wave/stress situation. Indeed, the effects of wave age as predicted by (7.16) can besignificant. For example Gulev and Hasse (1998) estimated sea state dependent wind stress, bycomputing wave age from the wave periods reported by Voluntary Observing Ships. They usedthe sea period, arguing that, by definition, swell is not related to the local wind, and that it isthe interaction of the local wind with wind sea that determines the part of the wind stress causedby waves. Computing the drag coefficient from (7.16) they found differences between wave-age-based and traditional estimates to be quite pronounced in the mid latitudes and tropics, withwave induced stress contributing up to 20 % of the total stress estimate in wintertime.The occasionally large CD10n values predicted by wave age based formulae have ledYelland et al. (1998) to question whether the roughness depends on wave age. Their largedataset obtained during three cruises to the Southern Ocean showed no evidence for wave-agedependence of the wind stress nor of the high values predicted for storms. Furthermore, using abuoy mounted sonic anemometer system, Taylor et al. (1999b) found no variation of roughnessfor wave ages from 10 to over 30. Janssen (1999) suggested that this was because the datawere obtained using the inertial dissipation method, but this was disputed by Taylor andYelland (2000b) who emphasised that Janssen's arguments, if true, would have led to anenhanced wave age dependence in their data set. Examining the published data from a numberof field experiments, Taylor and Yelland (2000c) proposed a different roughness lengthformulation:zHAHLoSSpB=   (7.18)where Hs and Lp were defined as the significant wave height and peak wavelength for thecombined sea and swell spectrum. The suggested values for the coefficients were A = 1200, B= 4.5. The character of the CD10n values derived from (7.18) are significantly different fromthose predicted by wave age based formulae. Short duration or fetch do not result inenhancement of the roughness. Thus the CD10n values predicted for a pure wind sea always havea similar relationship to the wind speed. At most wind speeds these pure wind sea values werehigher than is observed in the open ocean. However the typical mean open ocean values werepredicted when (7.18) was applied to wave measurements which were dominated by mixedwind sea and swell. Thus Taylor and Yelland (2000c) claimed that (7.18) successfully predictedthe observed values of CD10n for the open ocean, coastal seas, lakes, and even wind-waveflumes. This is a wider range of conditions than those for which wave age based formulae havebeen found to hold. However they were unable to predict the large CD10n values observed byDonelan (1982) at very short fetch on Lake Ontario and questioned whether the upwindroughness over land had affected these data.In summary the present position is that, despite decades of work on wind-waveinteractions, the parameterisation of z0 in terms of sea state is still a matter of considerablecontroversy. A problem is that, for the wave age based formulae to be valid, there must be awave age dependence of the short waves (wavelength ≈ 10cm). However, at present, there isno direct proof for or against this (Komen et al. 1998). This uncertainty in z0 also affects ourknowledge of CE and CH.7 - Parameterisation of Turbulent Fluxes 87 7.3 Determining the Transfer CoefficientsReport of the Working Group on Air Sea Fluxes June, 2000C SURFACE RENEWAL THEORYAnalogous to the momentum roughness length, surface length scales for the scalarquantities appear in the equations involving temperature and humidity (Section 7.2). Themodel of Liu et al. (1979) was one of the first to deal with air-sea transfer in the low wind-speedregime, and to derive the scalar roughness lengths from the principles of “surface renewaltheory”. This theory was introduced many years ago in chemical engineering applications, andapplied to the air-sea interface by Liu and Businger (1975) and independently by Brutsaert(1975). Here we will briefly outline the development leading to the Liu et al. (1979) bulkalgorithm (hereafter LKB).Whereas the atmospheric (and oceanic) surface boundary layer transports heat, massand momentum to the interface by turbulent motions, at the surface itself there exists aninterfacial layer of order 1 mm thick, in which molecular diffusion plays a significant role in thetransport. The idea behind surface renewal theory is that, across this interfacial layer, smalleddies of air transfer heat randomly and intermittently between the “bulk” turbulent fluid, oftemperature Tb, and the surface itself which therefore warms or cools by conduction from theeddies. The temperature gradient and the surface heat flux are determined by the heatconduction equation:22zTtT∂∂=∂∂κ(7.19)where κ is the thermal diffusivity. The solution for initial condition T(t=0) = Tb = constant, andsurface temperature T(z=0) = Ts = constant (Carslaw and Jaeger, 1959) gives the surface heatflux :2 / 1 0 ) () () ( - t) H(0,tT T CzTCb s pz p πκκρκρ−=∂∂= =(7.20)Liu and Businger (1975) define a distribution function to describe the areal fraction ofeddies which have been in contact with the surface for time t, and assume a characteristic time,tc, for which an eddy remains in contact with the surface before breaking away. For constant Tsand a random distribution of contact duration they determine the time-averaged temperatureprofile in the interfacial layer as:  − =−−2 / 1 ) (exp 1) () (c s bstzT TT Tκ (7.21)and average heat flux:2 / 1 ) () (cb s ptT T CHκκρ −=(7.22)For the scaling length (κtc)1/2 , LKB substitute δT, the thickness of a layer in which the sameheat flux and temperature difference would be maintained solely by molecular diffusion.Similar relations to (7.21) and (7.22) can be derived for moisture and momentumtransport:QQQQzQQE tsbs QQs b c−−=− − = − = 112 exp( ); ( )/ ( )/δδε ρ ε (7.23a)UUUUzUU tsbs UUs b c−−=− − = − = 112 exp( ); ( )/ ( )/δδν ρ τν (7.23b)where ε is the molecular diffusivity of water vapour and ν the kinematic viscosity. With thescale variables, u* , t* , q* as defined in (7.5), the above relations can be written:)] / exp( 1 [ / ) ( * * κ S zu S t T T s − − = − (7.24a)7 - Parameterisation of Turbulent Fluxes 88 7.3 Determining the Transfer CoefficientsReport of the Working Group on Air Sea Fluxes June, 2000)] / exp( 1 [ / ) ( * * ε D zu D q Q Q s − − = − (7.24b))] / exp( 1 [ / ) ( * * ν C zu C u U U s − − = − (7.24c)where “sublayer parameters”, sometimes referred to as inverse interfacial Stanton and Daltonnumbers (Brutsaert 1982) are given by:κ δ / / ) ( * * u t T T S T s b = − = (7.25a)ε δ / / ) ( * * u q Q Q D Q s b = − =(7.25b)and the inverse interfacial drag coefficient:ν δ / / ) ( * * u u U U C U s b = − = (7.25c)Brutsaert (1975) suggested that the renewal time scale tc be proportional to the time scale ofKolmogorov eddies:tz u co = (/ ) */ ν312 (7.26)which, combined with the relations for sublayer thickness (7.21) indicates that the sublayerparameters can be expressed in terms of the roughness Reynolds number (7.11)), the Prandtlnumber, Pr (= ν/κ) and the Schmidt number, Sc (= ν/ε).4 / 12 / 1 4 / 12 / 1 4 / 1 PrGRr CSc GRr DGRr S===(7.27)where G is a proportionality constant determined empirically by LKB as 9.3.LKB match the exponential sublayer profiles (7.24) smoothly with the slope of the outerturbulent logarithmic profiles (7.6) via a transition layer. This leads to a relationship betweenthe scalar roughness lengths (z0T ,z0Q) and the momentum roughness length via the roughnessReynolds number. As they show, the resulting continuous temperature and velocity profilesagree well with laboratory and field experiments; the profile for moisture (and other scalarquantities) is assumed similar to temperature. LKB (their Figs. 6 and 7) illustrate their modelfor the roughness scalar lengths compared with a small number of field measurements, andparameterise the relationship in the form:RzuaR totrb ==*ν (7.28a)RzucR qoqrd ==*ν(7.28b)where the values a, b, c and d were determined from field experiments by LKB and given intabular form.Brutsaert (1982) also matched the sublayer and logarithmic profiles, but assumeddifferent sublayer constants and velocity behaviour. This leads to significant differencesbetween his model and that of LKB, as illustrated by Fairall et al. (1996a) for the neutralmoisture exchange coefficients and for the functional dependence of Rq and R r. Othervariations on the application of surface renewal theory will be discussed below in the context ofrecent bulk flux models.The physics and thermodynamics of the interfacial sublayers applies at both sides of theinterface. Soloviev and Schlüssel (1994, 1996) have developed models of ocean-atmospheregas transfer, applying surface renewal theory to the sublayer on the ocean side of the interface,otherwise referred to as the cool skin. They explore the sources of convective instability whichcontrol the sweeping and breaking away of the turbulent eddies. In this case, both heat andsalinity fluxes play a role; Soloviev and Schlüssel (1996) point out that under low wind speedconditions, solar heating can damp the convective instability, increasing the renewal time andreducing gas transfer across the sublayer.7 - Parameterisation of Turbulent Fluxes 89 7.4 Light to Moderate windsReport of the Working Group on Air Sea Fluxes June, 20007.4 Bulk formulae for light to moderate wind conditions7.4.1 The TOGA COARE algorithmA. BACKGROUNDProcess studies such as GATE, COARE, ASTEX, HEXOS etc. are closely focussedexperiments designed to describe, improve our knowledge of, and to quantify certain aspects ofthe ocean-atmosphere climate system. While they may be limited in extent and scope in globalterms, they nevertheless provide the basic relationships and parameterisations of physicalprocesses needed within the broader framework of global atmospheric and oceanic studies, andof climate models. Thus, within the overall aims of TOGA, TOGA-COARE was designed toanswer specific questions about energy exchange, convective processes and scale interactions inthe west Pacific warm pool (Webster and Lukas 1992). Various considerations led to therequirement that the net air-sea energy exchange be specified to an accuracy of about 10 Wm-2of which the allowable uncertainty in the turbulent flux components would be 6-7 Wm-2 . Thisleads to limits on permitted errors in the mean variables used to calculate bulk fluxes, windspeed = 0.2 ms-1, sea and air temperatures Ts , Ta = 0.2 K, specific humidity q = 0.2 g kg-1,assuming that they are independent.It is likely that the COARE surface observations have experienced closer scrutiny andmore rigorous quality control than any previous similar data set. Details are given in the variousFlux Group workshop reports (Bradley and Weller 1995a, b; 1997; Bradley et al. 1997) (also onthe COARE Data Users web site: http://www.ncdc.noaa.gov/coare/ ). These observations havebeen used as a benchmark to develop and validate various models of the tropical ocean-atmosphere climate system, including remote sensing and surface flux bulk algorithms. TheTOGA-COARE algorithm was developed early in the post-experiment analysis, by Fairall et al.(1996a), to provide common code for use by the COARE research communityB. ALGORITHM HISTORYThe algorithm was based on the model of Liu et al. (1979, LKB), to take account of thelight wind, strongly convective conditions commonly found over tropical oceans. Someadditional physics has been incorporated and, on the basis of data from COARE and otherrecent field programmes, some empirical constants have been adjusted. Version 1.0 wasreleased in November 1993, and included various modifications to the basic LKB code.Following Smith (1988) the momentum roughness length (and hence Cd10n) was specified as thesum of the Charnock formula and a smooth flow limit; i.e. (7.14), with the value αc = 0.011evaluated from the Moana Wave direct flux measurements. The interfacial specific humiditywas calculated from the saturated vapour pressure at sea surface temperature, appropriate to asalinity of 34 psu; i.e. reduced by 2% relative to the saturated vapour pressure over fresh waterat the same temperature (Kraus and Businger 1994).Like LKB the Monin-Obukhov stability length, L (7.2), was calculated using virtualpotential temperature, defined in full here to emphasise the point that the contribution ofmoisture to buoyancy is usually of first order over tropical oceans:[] ) 61 . 0 ) 61 . 0 1 () 61 . 0 1 (* *2*Tq q t gku q TL+ ++= (7.29)where the scaling parameters, u* , t* , q* , were defined in. (7.5). However, the dimensionlessprofile functions were given a form that asymptotically approached the proper convective limitas wind speed goes to zero (Garratt 1992). In this “free convection” regime, the dimensionlessscalar profile functions are expected to follow a (z/L)-1/3 dependence. The form: []3 / 1 ) / ( 1− − = L z y γ (7.30)was adopted for both velocity and scalar dimensionless profiles, with the empirical constant γ =12.87, which leads to stratification functions of the form,7 - Parameterisation of Turbulent Fluxes 90 7.4 Light to Moderate windsReport of the Working Group on Air Sea Fluxes June, 20003 31 2arctan 331ln 5 . 12 πψ +  +−  + +=y y yc (7.31)As stability approaches neutral conditions, the function was blended to a standard Kansas type(7.7) (Businger et al. 1971). For stable conditions, the Kansas form ) / ( 7 . 4 L z − = ψ , whichfollows from (7.3b) with γ = 4.7, is used for both momentum and scalar profiles.Following Godfrey and Beljaars (1991), the wind speed in the bulk expression was augmentedby a gustiness velocity, Wg, such that the wind speed, SU , becomes:Suu WUW Uxy g g =++ = +22 2 2 2 (7.32)where ux and uy are the mean wind components (i.e., magnitude of the mean wind vector) andWg is proportional to the convective scaling velocity, W* (Deardorff 1970):WgTHCTELzpVi * .3 061 =+ ρρ. (7.33)where zi is the depth of the convective boundary layer. Thus Wg = β W* , and a value β = 1.2was adopted on the basis of the Moana Wave direct flux measurements in COARE. Areasonable default value for the depth of the marine boundary layer in the tropics is zi = 600m.The major modification for Version 2.0 (August 1994) was the inclusion of optionalmodels for the cool skin temperature effect and the diurnal thermocline to correct bulk watertemperatures to true SST. The cool skin model was based on Saunders (1967) with amodification to include the effects of buoyancy flux. This indicates a cool skin of about 0.3˚Kduring the night. During the day the cool skin may be reduced or apparently eliminated entirelyby solar heating in the upper layer of the ocean (Soloviev and Schlüssel 1994). A warm layermodel was added to correct bulk water temperature measurements made at some depth, zb. Theidea is that ship intake and buoy temperature sensors at a metre or so depth are unable to resolvethe diurnal warm layer which commonly forms in the upper tropical oceans under light windconditions. This model was based on a simplified scaling version of the Price, Weller andPinkel (1986) mixing model. If daytime solar heating is sufficient, a stable near-surface layer isformed causing the surface temperature to increase. Linear profiles of temperature and currentare assumed. The depth is determined by a critical Richardson number and the profile ofabsorption of solar energy in the water (Paulson and Simpson 1981). The physics of both thecool skin and warm layer models are described in Fairall et al. (1996b)Under very light wind conditions, peak solar warming as great as 4˚K is produced witha warm layer depth of about 0.25 m. Once the warm layer forms, its depth and intensity aredetermined by integrating the accumulated momentum and heat input in the layer. Thus, thismodel requires a complete time series of data throughout the diurnal cycle; appropriate timeresolution is from ten minutes to one hour. Both the cool skin and warm layer models can bedisabled if true skin surface temperature is available (e.g. from aircraft or satellites with IRradiometers).Calculation of fluxes of momentum (Caldwell and Elliott, 1971) and sensible heat dueto rainfall were included, with raindrops assumed to be 0.2˚K cooler than the surface wet-bulbtemperature (Gosnell, Fairall and Webster, 1995). The so-called Webb correction to latent heatflux, which arises from the requirement that the net dry mass flux be zero (Webb et al., 1980), isalso calculated in the algorithm.The last major modifications to the algorithm were made at the COARE Air-SeaInteraction (Flux) Group Workshop in Honolulu, 2-4 August 1995 (Bradley and Weller, 1995b).Transfer coefficients were adjusted by six percent to give better average agreement withcovariance latent heat fluxes from several COARE ships. At the Woods Hole workshop, 9-11October 1996 (Bradley and Weller, 1997) it was agreed that no further development would beattempted to the community version of the COARE Bulk Flux Algorithm, and that a version7 - Parameterisation of Turbulent Fluxes 91 7.4 Light to Moderate windsReport of the Working Group on Air Sea Fluxes June, 20002.5b bulk algorithm "package" would be made available, consisting of the Fortran source codeand a test data set. This was released at a meeting of the Flux Group at NCAR, 14-16 May1997 (Bradley et al., 1997)1.The formalism of the COARE algorithm is fully described in Fairall et al. (1996a). Ithas been used with success in subsequent field observations from ships and aircraft, both inequatorial and mid-latitude regions; a sample list of publications in which the COAREalgorithm has been used appears in Bradley et al. (1997). During an ocean mixed layer heatbudget experiment by R/V Franklin in the Indian ocean in 1996, using the COARE algorithmfor the sensible and latent heat fluxes, closure was obtained to within the equivalent of about 3Wm-2 over a 7-day period (Godfrey et al. 1999).The major limitation of the COARE algorithm is that it has not been verified aboveabout 12 ms-1 windspeed. In it’s published form there are two factors which could impede itsuse in numerical models. Although the iteration around exchange coefficients, stability andfluxes normally converges within 5 iterations, except for very stable conditions, the code stillretains the original 20 loops. Also, the use of complete functional forms for parameters such asg, LV and ν, and the inclusion of all minor flux corrections in pursuit of the COARE 10 Wm-2accuracy goal, may be regarded as superfluous at this stage.The authors have released2 for trial a new version 2.6 (Bradley et al. 2000) whichaddresses these limitations and introduces other recent analytical developments, as follows:1. The empirical constants in the convective portion of the profile functions have beenchanged for improved matching to direct profile observations (Grachev et al., 2000)2. The Kansas stable profile functions have been replaced by those from Beljaars andHoltslag (1991) based on new profile data taken over the Arctic ice cap.3. A fixed value of the Charnock parameter (αc= 0.011) has been replaced by aformulation with a simple wind-speed dependence above 10 ms-1 based on data fromYelland and Taylor (1996) and Hare et al. (1999).4. The Liu et al. (1979) scalar roughness relationship [fx(Rr)] has been replaced with asimple analytic form which fits both the COARE and HEXMAX data bases.5. The stability iteration loop has been reduced from 20 to 3 using bulk Richardsonnumber parameterisation for an improved first guess (Grachev and Fairall, 1997).6. The latent heat flux has been reformulated in terms of mixing ratio instead of watervapour density to eliminate the need for a Webb et al. (1980) correction.7. Gustiness velocity has been expanded to include the effects of mesoscale variabilityassociated with convective precipitation using the approach of Jabouille et al. (1995) orRedelsperger et al. (2000).For a later release, two options are being considered to make the COARE algorithmmore useful for applications to more limited data sets and for implementation in numericalmodels. The first is the addition of simple parameterisations to furnish downward IR and/orsolar radiative fluxes when they are not available by direct measurement (e.g., COADS data).These would be standard parameterisations available in the literature (see Josey et al. 1999 forexamples or refer to Section 6.2). The second is the addition of a parameterised form of thewarm layer effect, probably based on Webster et al. (1996). This has the great advantage that 1 Available at: http://www.ncdc.noaa.gov/coare/catalog/data/atmosphere_large_scale/bulk.htmlor by ftp from the NCAR site:ftp://ncardata.ucar.edu/datasets/ds606.1/flux_algorithm/2 The new algorithm is available at ftp://ftp.etl.noaa.gov/pub/et7/users/cwf/bulkalg/7 - Parameterisation of Turbulent Fluxes 92 7.4 Light to Moderate windsReport of the Working Group on Air Sea Fluxes June, 2000the fluxes would be a pure function of a single record of data, rather than an integral of theprevious time series. A major addition is being considered to account for the effects of waveson the fluxes. This would allow the algorithm to be applied in coastal/shallow water areas suchas much of the Gulf of Mexico. The new model from Taylor and Yelland (2000c) is a primarycandidate with the surface roughness parameterised in terms of the significant wave height andthe peak wavelength (or, equivalently the wave period) both of which are observable fromsatellites.7.4.2 Other AlgorithmsA. ZENG, ZHAO AND DICKINSON (1998)The algorithm described by Zeng et al. (1998, ZZD) takes essentially the same path asthe COARE algorithm, but uses different stability functions for the velocity and scalar profiles,and a different form for the scalar roughness lengths. The various empirical constants are givenvalues which ensure reasonable agreement with observation over the wind speed range 0 -18ms-1. The observations used are the COARE Moana Wave dataset for low to moderate windspeeds (0-12 ms-1) and the HEXOS data up to 18 ms-1.Like Fairall et al. (1996a), ZZD use the Kansas expressions for near-neutralatmospheric stability and the stable case (with γ = 5 in (7.3b)) but, as stability increases beyondz/L = 1, the expression of Holtslag et al. (1990) is adopted :L z q t m / 5 + = = = φ φ φ (7.34)which prevents φ from becoming too large in very stable conditions. For convective conditionsZZD use the expressions of Kader and Yaglom (1990, see their Figure 1):φφ φ mt q kzLand kzL=−  == − −07 0923134313..////(7.35)which lead to simpler integrals than those given in (7.30) and which are therefore more suitablefor incorporation into numerical models. ZZD match the near-neutral to convective forms at z/L= -1.574 for momentum and z/L = -0.465 for the scalars.They also use the functional form of Smith (1988) for the momentum roughness length,but their analysis of the Moana Wave COARE data suggests a value for the Charnock constantαc = 0.013. For the scalar roughness lengths they use the expression given by Brutsaert (1982):ln ln/ zzzzbR r bTQ00001142 =   =+ (7.36)where the constants determined empirically by ZZD (b1 = -2.57 and b2 = 2.67) differ slightlyfrom those given by Brutsaert (1982). This is partly because they are optimised to provide acontinuous fit to both the COARE and HEXOS datasets.Other similarities with the COARE algorithm include allowance for the 2% reduction insaturated specific humidity over sea water, which ZZD find decreases latent heat flux by 20% at14 ms-1, and the “gustiness” correction at low wind speeds. However, they adopt a value β =1.0, rather than the value 1.2 which Fairall et al. (1996a) prefer. ZZD does not include thecontributions to sensible heat and momentum due to rainfall, nor models for the cool skin anddiurnal thermocline, so input data for the algorithm must be either measured or derived skinsurface temperature. Zeng et al. (1999) have developed an hourly dataset from the Pacificocean TOGA-TAO mooring array for use with their algorithm, which includes such a derivedsurface skin temperature field.ZZD compare their algorithm with COARE 2.5b, and with those embodied in severalresearch and operational numerical climate models, NCAR’s CCM3, the ECMWF forecastmodel, the NCEP medium range forecast model and the GEOS DAS model.7 - Parameterisation of Turbulent Fluxes 93 7.4 Light to Moderate windsReport of the Working Group on Air Sea Fluxes June, 2000B. BOURASSA-VINCENT-WOOD (1999) FLUX AND SEA STATE MODELThe main innovation of the Bourassa et al. (1999, BVW) model is a scheme to relateroughness lengths (z0 , and by implication the neutral exchange coefficients) to the variousconstituents of “sea state”, particularly drawing the distinction between the effects of capillarywaves and gravity waves. BVW also note that, particularly under low to moderate windconditions with swell, the wind, stress and current directions are not necessarily parallel, andtheir algorithm allows the cross-wind component of stress to be calculated. BWV contend thatwith the co-existence of these different wave types, and interactions between them, it is unclearwhat the proper coordinate frame of reference should be for the wind and water velocities, andfor z0, and question whether the surface current is the appropriate reference velocity (see Section10.6.2). They argue that wind parallel to the crests of swell will not “feel” the swell asroughness, citing the observations of Donelan et al. (1997), who measured much larger dragcoefficients in the presence of counter and cross-swells, than would be expected over a purewind sea.BVW distinguish between aerodynamically smooth and rough regimes with a Smith(1988) type roughness length expression (c.f. 7.14) :zsusbusugccwgg 0 22221201 1=+ + ννβσρβ.() ( )***/(7.37).Each term includes a binary switch, sx , to exclude any surface type which is not contributing tothe roughness at that time. Thus for smooth flow sν =1 and sc = sg = 0, the criterion for whichis that the calculated phase speed, cp , for the dominant wave be less than the minimum phasespeed for surface waves:4 / 1min ) / 4 ( w p g c ρ σ = (7.38)which is approximately 0.23 ms-1. Above this windspeed surface waves are assumed to exist,and sν becomes zero. This is the basis for the BVW claim to distinguish betweenaerodynamically smooth and rough flow. Capillary waves appear above a windspeed U10 ≈ 1.8ms-1, referred to as the “capillary cutoff”. BVW re-evaluate Wu’s (1968) parameter, b, on thepremise that Wu failed to take account of a zero-displacement correction when using the profilemethod for z0 and u*, and Wu (1994) failed to correct for the increase in drag coefficient for thelow wind, strongly unstable regime of the Bradley et al. (1991) observations which he used toverify his value of b for field conditions. Their re-evaluation reduces b from Wu’s 0.18 to 0.06,which agrees with their mean value of 0.05 using the wave age and phase speed data fromNOAA’s San Clemente Ocean Probing Experiment (SCOPE) on R/P FLIP (Kropfli andClifford, 1994).The weighting parameters, βc and βg , account for the two effects referred to above, namely thecorrect frame of reference for velocities and z0 and the non-parallel wind and stress vectors. Notmuch detail of the structure of these weights is provided, although two examples are given; thecorrection to u0 (7.8a) for the velocity of capillary waves superimposed on a gravity wave field,and for gravity waves propagating in different directions relative to the wind. Then :) / exp( * u c U p c c κ β − = (7.39)for simplicity not written here in the vector form used by BVW. The shift in velocity, Uc , is anempirically determined fraction of the orbital speed (Uorb = πHs/Tp), where the significant waveheight, Hs , and the wave period, Tp , are estimated from the “3/2”relationship of Toba et al.(1990) :()2 / 3 2 / 1*) ( p s T gu B H = (7.40)with B = 0.62. For the gravity wave roughness length zog, BVW use the weighting βg=0.48/(cP/u*) (the HEXOS result, see equation 7.16) rather than the form which may be a betterphysical representation :7 - Parameterisation of Turbulent Fluxes 94 7.4 Light to Moderate windsReport of the Working Group on Air Sea Fluxes June, 2000βg = exp(-κΨcP/u*) (7.41) because of the lack of data to determine the scaling parameter Ψ. This shortcoming in thegravity wave parameterisation leads to an overestimation of stresses for rising seas, and a verysmall underestimation of fluxes for falling seas (e.g. those examined by Fairall et al. (1996c)).However data from a directional Waverider and a buoy mounted anemometer (Dobson et al.1999; Taylor et al., 1999b) showed that the BVW model significantly overestimated stress forconditions of growing seas, with the larger errors occurring for U10 > 12 ms-1 (which presumablycorresponds to larger departures from local equilibrium).BVW go on to express the wind profile, roughness length components, and wave age invector form, so that their working form of (7.37) is written in terms of parallel and crosswindcomponents of stress. For aerodynamically smooth or rough flow, the BVW scalar roughnesslengths are adopted from the forms of Brutsaert (1982, Section 7.3.2C) :*0*062 . 0;40 . 0uzuz Q Tν ν= = (7.42)and, citing Smith (1988, 1989) apply them to both smooth and rough flow conditions.For the profile stability functions, BVW depart from tradition in respect of both stableand unstable forms. We detail these here because Clayson et al. (1996), whose algorithm isdescribed in the next section, use the same functions. Beljaars and Holtslag (1991) point outthat the Webb (1970) stable relation (ψψ ψ mtq zL === − 5/ see (7.3b, 7.4b)) is fairlyconsistent with most data for 0 < z/L < 0.5 , but produced unrealistic results when used in aweather forecast model (Louis 1979). On the basis of several sets of very stable data theypropose the following :−=+ −   −   + ψ m azLbzLcddzLbcdexp (7.43)and−= −= +   +−   −   +− ψψ tqazLbzLcddzLbcd12313/2exp (7.44)valid over the range 0 < z/L < ~10 , with a = 1 , b = 0.667 , c = 5 and d = 0.35 in bothexpressions.In place of the standard Kansas expressions for the unstable case, BVW and Clayson etal (1996) adopt the expressions of Benoit (1977) :[] −=++++ +−−− ψζζζζζζ m n l() ( )() ( )tan ( ) tan ( )02022211011112 (7.45a)−= −=++   ψψλλtq n 2110 l (7.45b)whereζζ =− =− (/ ) ;( / )// 11 6 11 6140014 zL z L (7.46)andλλ =− =− (/ ) ;(/ )// 11 6 11 612012 zL zL (7.47)which have the correct analytical limit as z/L tends to zero and are stable numerically. BVWadopt the “gustiness” correction for low wind speeds, using the same value of β = 1.25 as in theCOARE algorithm.There are few open ocean data for low to moderate wind speed, including wavemeasurements, with which the BVW model can be compared. The model results show minima7 - Parameterisation of Turbulent Fluxes 95 7.4 Light to Moderate windsReport of the Working Group on Air Sea Fluxes June, 2000in the neutral drag coefficient (CDN) for windspeed just below the capillary cutoff (~1.8 ms-1).For aerodynamically rough surfaces there is a local minimum in CDN(U10) for cP/u* = 5. This isqualitatively in accord with measurements by Bradley et al. (1991), Dupuis et al. (1997) and acomposite result by Large et al. (1995). In other words, if capillary waves exist, below 4 ms-1the drag coefficients are much larger than those for an aerodynamically smooth surface. On theother hand, Grachev et al. (1998) have suggested that the traditional stability formulae overestimate the drag coefficient at wind speeds below about 2 ms-1 and Taylor and Yelland (2000a)suggest that many low wind speed data may be biased high. These studies suggest that theminimum drag coefficient occurs at lower wind speeds, possibly at 3 ms-1 as predicted by Smith(1988).The BVW comparison of modelled stress with the bulk of the COARE observations isnot unreasonable, most of the serious disagreements being underestimates on the part of themodel. The SCOPE observations are nearly all underestimated. BVW speculate that this is dueto lack of wave information to put in the model, and the probability of large angles between thewind direction and that of wave propagation. The model predicts that the stress (and dragcoefficient) are least when these directions are parallel, in accord with the findings of Donelanet al. (1997).The BVW model predicts that the influence of capillary waves on surface fluxes is verysignificant for winds less than 7 ms-1 down to the capillary “cutoff” around 2 ms-1. On the basisof the Franklin (Bradley et al, 1991) low windspeed observations they calculate that, near thecutoff, capillary waves contribute about 10 Wm-2 to the latent heat flux compared with only 1Wm-2 due to “gustiness”. The model flux predictions are compared with data from SCOPE,COARE (R/V Moana Wave) and R/V Franklin, with and without capillary waves in the model.Inclusion of capillary waves increased the mean modelled fluxes of momentum (by 0.004 Nm-2), sensible heat (by 1 Wm-2) and latent heat (by 6 Wm-2); and in all cases, the modelled resultswere closer to observation. BVW argue that the overall effect of capillary waves is three to sixtimes that of gustiness, and is therefore much more important than convective processes.Unfortunately, the comprehensive dataset including meteorological variables, air-seafluxes and the various sea state parameters which could validate this algorithm are rare. Evenobservations of the angle between the wind and direction of wave propagation are uncommon.While the anisotropic z0 aspect is qualitatively consistent with the observations of Donelan et al.(1997) and appears to be of reasonable magnitude, this aspect of the model has yet to bequantitatively validated.C. CLAYSON, FAIRALL AND CURRY (1996) FLUX ALGORITHMThe Clayson, Fairall and Curry algorithm (Clayson et al. 1996, CFC) is based onsurface renewal theory (Brutsaert 1982) as set out in section 7.3.2.C, following the generalpattern established by LKB and Fairall et al. (1996a). The main differences from theseantecedents are: a new surface renewal time-scale parameterisation, the BWV sea state surfaceroughness model, derivation of the scalar roughness lengths solely from surface renewal theory,and a new skin surface temperature model.Following Wick (1995) CFC identify Brutsaert’s renewal time-scale (7.26) as a shear-driven timescale, and define a convective time-scale also:t ConstCgQcwpN*/. =νρα12 (7.47)where α is the thermal expansion of sea water and QN is the sum of the turbulent and radiativefluxes. The shear-driven and convective forms are combined and the contribution of eachdetermined by a Richardson number criterion. This composite time-scale is then used to formexpressions for the interfacial Dalton and Stanton numbers after the method of Brutsaert (1975).7 - Parameterisation of Turbulent Fluxes 96 7.4 Light to Moderate windsReport of the Working Group on Air Sea Fluxes June, 2000For z0 , CFC employ the parameterisation developed by BVW, but with somedifferences. The capillary and gravity wave terms are simply added (not root-mean -squared)but without any weighting, and there is a clear distinction between smooth and rough flow onthe same basis as BVW:zbuwugfor u w czufor u w co roughwaapo smooth ap()*** min()** min.,.,=+ >=<σρν22 04 801 1(7.48)CFC use a value of Wu’s b for capillary waves of 0.019. i.e. smaller even than theBVW value. Referring back to BVW (their Fig. 2), we find that this is the value found initiallyin their re-evaluation of Wu’s data before applying the frame of reference correction. In otherwords, CFC adopt the simplest form of the BVW parameterisation, ignoring the features whichaccount for anisotropic wind/wave propagation directions and frame of reference adjustment.Unlike BVW, they do not include a “gustiness” term, relying on their capillary waveparameterisation and surface renewal theory to obtain correct fluxes at low wind speeds.The turbulent boundary layer profiles used by CFC are those of Beljaars and Holtslag(1991) for stable conditions, and Benoit (1977) for unstable conditions, with the interfaciallayers of height, h, matched to these assuming continuity of T and q as before. Algebra leads toBrutsaert-like expressions for the scalar roughness lengths:zz kuuS tzz kuu ScDaot ohoq oh =−   =−   expPr; exp**1100 (7.49)where Pr and Sc are the turbulent Prandtl and Schmidt numbers (0.71 and 0.60 respectively),and St0 and Da0 the interfacial Stanton and Dalton numbers. CFC take (uh/u*) = 5 after Brutsaert(1975). They have a system of eight equations which they solve iteratively for Da0, St0, u*, t*,q*, z0, ,zoT and z0Q. Because this algorithm was developed with the calculation of fluxes fromsatellite data in mind, they incorporate a model of the ocean cool skin (Wick et al. 1996) toreduce bulk SST observations to true surface skin temperature, and adjust surface humidity forthe 2% reduction of vapour pressure over saline water. However, they do not correct SST forthe diurnal thermocline; the data set which they use for verification (Fairall et al. 1996a) has seatemperature measured at 5 cm depth, so only the cool skin adjustment is needed. However,lack of a warm layer model would be of concern in the tropics particularly, with bulk watertemperatures measured at customary depths on a ship’s hull.D. ZHANG AND MCPHADEN (1995)Zhang and McPhaden (1995, ZM) studied the relationship between SST and latent heatflux in the equatorial Pacific ocean, using data from the TOGA-TAO moored array. Indeveloping a flux algorithm for this region, they faced the same difficulties as the previousmodels described here, namely the need to take account of occasionally very low wind, stronglyconvective conditions. Their approach was to incorporate the minimum of complexityconsistent with this, and we describe their model here briefly because it represents possibly thesimplest of its type and is computationally economical, and because it was included in thecomparison by Chang and Grossman (1999) referred to in Section 7.4.2F.ZM employ the Kansas forms (Businger et al. 1971) for the stability-dependent wind,temperature and moisture profiles (7.6), and the Paulson (1970) integral expressions for the Ψ-functions (7.7). For the value of α in (7.3a) they use 16, and for γ in (7.3b) they use a value of7. They adopt the Smith (1988) Charnock plus smooth form for the momentum roughnesslength (7.14), but set the temperature and moisture roughness lengths equal at a constant 2 x 10-5 m (Geernaert 1987).7 - Parameterisation of Turbulent Fluxes 97 7.4 Light to Moderate windsReport of the Working Group on Air Sea Fluxes June, 2000E. BELJAARS (1994)Beljaars (1994) has developed a parameterisation for the case of free convection in theatmosphere, for use with bulk transfer models. It is based on the idea that normal Monin-Obukhov similarity theory can be used in the surface layer without modification for freeconvection, except for inclusion of the convective scaling velocity, w*, as an extra windcomponent in the bulk equation. This derives from the analysis of Godfrey and Beljaars (1991),and is given by (7.32) and (7.33) as used in the COARE algorithm.Beljaars (1994) follows the standard development set out in Section 7.2, leading to theusual form of the bulk exchange coefficients (7.9). The “Kansas” expressions are used for thedimensionless profiles, with the Paulson (1970) integrals for the stability functions, ψm, ψt,q.These do not have the 1/3-power law dependence expected for free convection. However,Beljaars (1994) argues that the behaviour of the stability functions for large −z/L is not toocritical because vertical gradients are small in the well-mixed regime, and contribute little to theair-surface velocity, temperature or moisture difference.The problem is simplified by writing asymptotic forms for the profile stabilityfunctions, leading to expressions for the stress and heat flux in terms of z0/L, z0t/L, βw* and (t0 −tm) the temperature difference between the surface and the mixed layer; i.e. they areindependent of height, z. Beljaars (1994) shows that the resulting heat flux transfer coefficientis strongly dependent on roughness length, indicating that the surface is still being “felt” in thefree convection regime, through coupling between convective motions and the rough surface.Over a smooth surface, Beljaars (1994) adopts the classic smooth-flow form (7.12) for z0, andthe same expressions as BVW for the scalar roughness lengths, (7.41) from Brutsaert (1982).He compares his parameterisation with convective data taken over land by Stull (1994), and onthe basis of large eddy simulation data by Sykes et al. (1993), estimates the empirical constant,β, to be about 1.2, close to the value measured over the western Pacific by Fairall et al. (1996a)during COARE.For air-sea transfer over the ocean, Beljaars (1994) uses the Smith (1988) interpolation(7.14) for z0 with a Charnock constant of 0.018, and applies the Brutsaert (1982) smooth-flowscalar roughness length parameterisation to both smooth and rough flow. His justification forthis is tentative, on the grounds that empirical evidence points to an almost constant moisturetransfer coefficient over the entire low to high wind speed regime (Smith 1989; DeCosmo et al.1996). Approaching zero wind speed, his transfer coefficients for both heat and moisture followquite well the increasing trend shown by the low wind data of Bradley et al. (1991).F. STULL (1994)A quite different approach from the foregoing models is the so-called convectivetransport theory (CTT) by Stull (1994), specifically aimed at prescribing the surface fluxes inthe limit of free convection. Unlike Beljaars (1994), Fairall et al. (1996a), Zeng et al. (1998),etc., who deal with the approach to zero wind speed by introducing a horizontal “gustiness”velocity based on the convective scale w*, Stull (1994) focuses on the role of convective plumes(thermals) in transporting heat and mass vertically from the surface into the mixed layer.Like Beljaars (1994), Stull (1994) notes that a crucial factor controlling the turbulentfluxes at the surface is the total difference in velocity, temperature or moisture concentration(i.e. ∆u, ∆t and ∆q) between the surface and the atmospheric mixed layer. This total is the sumof the individual differences across the three layers discussed above, the interfacial layer, thelogarithmic surface layer and the mixed layer. The lowest two layers respond fairly rapidly tochanges, whereas thermal circulations are relatively slow. Stull (1994) suggests that thisseparation of time-scales creates a reservoir of air in the surface layer which has been modifiedby the surface, and that the removal of this air and its transport into the mixed layer by thermalsis the bottleneck in the system. Thus, Stull (1994) argues that, apart from their role inmaintaining the reservoir of air, processes in the lower two layers can be ignored, and thattransport by convective plumes is the governing process.7 - Parameterisation of Turbulent Fluxes 98 7.4 Light to Moderate windsReport of the Working Group on Air Sea Fluxes June, 2000Against this background, Stull (1994) goes on to parameterise the surface fluxes interms of mean mixed layer variables rather than surface-layer variables. This leads to thesurprising proposition that the surface fluxes should be independent of surface roughness, incontrast with the Beljaars (1994) result. Furthermore, provided the mixed layer is deep enoughthat variables may be defined in a mid-region well above the surface layer, Stull (1994) suggeststhat its depth and the shape of the surface layer profiles are irrelevant also.Stull (1994) writes the vertical transport of an entity, χ, across an interface as theproduct of a vertical transport velocity, wT, and the concentration difference, ′′ = ww T χχ ∆ .This is in the form of the usual bulk transport equation where vertical transport is caused byshear-driven turbulent eddies in the surface layer; i.e. wT appears in place of CD∆u. By analogyStull (1994) proposes that, in free convection, thermal eddies transport air aloft with a transportvelocity which scales with w* :wC w T = ** (7.50)where C* is a mixed-layer transport coefficient to be determined empirically. He thereforewrites equations for the surface fluxes in the usual form, but in terms of mixed layer variables:uC w uwt C w twq C w qDHH** *’’**’’**2 ===∆∆∆(7.51)where C*D and C*H are mixed layer transport coefficients for momentum and scalarsrespectively. (To properly include all buoyancy effects, Stull (1994) works throughout withvirtual potential temperature (see (7.29)), and also expresses moisture as the mixing ratio. Herewe describe his formalism, but for simplicity and consistency with previous discussion, retain tand q).Because the fluxes appear on the RHS of (7.51) through w*, Stull (1994) converts theseequations to explicit form by defining a buoyancy temperature difference:∆∆ ∆ tt q t q Bs s ≈+ + (.) . 1 061 061 (7.52)and a buoyancy velocity scale:wgtzt Bi B ≡    ∆1/2(7.53)which as Stull (1994) points out, unlike w* does not contain the turbulent fluxes. Finally hewrites a set of equations which form the mathematical basis for the application of convectivetransport theory:wt b w twt b w twq b w qub w uvs H B Bvs H B BHBDB’’’’’’*’====∆∆∆∆2(7.54)where bC HH () */ =32 and bC C DD H () **/ =12are simplified convective transport coefficients forscalars and momentum, respectively. These must be determined experimentally, and Stull(1994) quotes values: bD = (1.83 ± 0.60) × 10-3 ; bH = (5.0 ± 1.9) × 10-4 obtained from the BLX83experiment in Oklahoma (Stull and Eloranta, 1984).The theory is tested against the independent Koorin experiment over land in Australia(Clarke and Brook, 1979). The agreement between predicted and observed surface stress isgood on average, but very scattered; the plot of kinematic heat flux has much less scatter, but apronounced difference in slope and an offset. Stull (1994) argues that the overall agreement7 - Parameterisation of Turbulent Fluxes 99 7.4 Light to Moderate winds7.5 Moderate to high windsReport of the Working Group on Air Sea Fluxes June, 2000between transport coefficients measured in Oklahoma and Australia confirms that neitherCoriolis parameter nor Ekman layer depth are relevant for bulk transfer in free convection. Forthe continuity from low to high wind speed conditions, Stull (1994) proposes linearsuperposition of the convective transport and bulk aerodynamic formulas, suggesting that in thesurface layer free convection takes over when z/L < -3.Sorbjan (1997) argues that the results embodied in CTT can be obtained within theframework of Monin-Obukhov similarity theory, and suggests that the lack of dependence onsurface roughness and surface layer processes could lead to errors in situations other than thoseunder which CTT was tested. Stull (1997) disagrees, pointing out that Sorbjan’s analysis isbased on surface layer, rather than mixed layer equations, and that his definitions of mixed layerto surface temperature difference and of transport coefficients are not equivalent to those inCTT. Fairall et al. (1996a) also suggest that as the mean vector wind approaches zero, agustiness velocity, w* , appearing in the standard bulk equation will yield a result equivalent tothe Stull (1994) scaling theory. Chang and Grossman (1999) included CTT in their comparisonof several algorithm’s using COARE data, but needed to “tune” the coefficients of CTT for useover the tropical ocean.7.5 Transfers in moderate and high windsThere is now a fairly large body of reliable momentum flux measurements at windspeeds in excess of 12 ms-1 (Yelland and Taylor, 1996; Yelland et al., 1998; Hare et al., 1999).These show that the drag coefficient increases steadily (and more or less linearly) to around 25ms-1. However, the fluxes of heat and water vapour have not yet been measured in the field forwinds over 20 ms-1. In fact, the most widely quoted dataset for the high wind regime, from theHEXOS experiment (DeCosmo et al. 1996), contains fewer than a dozen data points above 16ms-1. Furthermore, the HEXOS site was over water only 15m deep. More flux data in highwinds over the open ocean are badly needed.The evaporation of spray might be expected to modify the heat fluxes at high windspeeds. Thus Andreas (1992) and Andreas et al. (1995) suggested that, whereas the turbulentfluxes are proportional to wind speed, spray production should increase at roughly the thirdpower of the wind speed. This implies that for wind speeds up to 15 m/s the spray contributesno more than 10% of the total heat flux. At 20m/s and above the spray contribution would be ofsimilar magnitude to the surface turbulent flux. However consideration of the feedback on theparameterised surface evaporation due to changes in the mean surface humidity profiles causedby the evaporating spray was not fully included. This may substantially modify the estimatedflux (Katsaros and de Leeuw, 1994, Andreas, 1994).7.6 Summary of bulk algorithmsIn the introduction to this chapter we outlined the traditional concept of bulk fluxformulae, particularly drawing attention to the notion that many of the physical processesinvolved in air-sea transfer of heat or momentum became embedded within a transfer coefficientfor the particular entity. The formalism commonly used to construct these transfer coefficients isgiven in section 7.2. The best-known algorithm employing this methodology is probably that ofLarge and Pond (1981,1982) who supported their analysis with some direct (covariance) fluxdata which in turn validated their much more extensive inertial dissipation data set.Nevertheless, while transfer coefficients were known to be a function of wind speed, height andatmospheric stability, more often than not the available test data justified use of only a constantvalue for the scalar fluxes, or a simple wind-speed dependant value for stress.Such simple expressions have become inadequate for several reasons, mostly connectedwith the burgeoning requirements of climate research, and recognition of the sensitivity ofnumerical climate models to small changes in air-sea flux values, particularly when attemptingto couple ocean and atmospheric GCM's. Initially, the focus was on the climatically importanttropical oceans where bulk flux theory was inadequate to deal with the prevailing low winds and7 - Parameterisation of Turbulent Fluxes 100 7.6 SummaryReport of the Working Group on Air Sea Fluxes June, 2000strong convection. Attention was directed toward an understanding of transfer mechanisms atthe surface itself, including development of surface renewal theory, and considerableexperimental effort was made to obtain supporting data in this regime. Most modern bulkalgorithms for the tropical regions are based on the pioneering work of Liu et al. (1979), and thedata obtained during the 1992-93 TOGA-COARE experiment. The basis of this approach isgiven in section 6.3.2. In a somewhat curious twist, the exchange coefficients have nowbecome embedded in the analysis, which is an iterative process between the fluxes and thesurface roughness, and are often only calculated post facto for diagnostic purposes. This isindicative of the connection between stress and wave state formulated in its simple form byCharnock (1955), and now receiving closer attention.We have described several recent bulk flux algorithms in some detail. The list is notexhaustive or even possibly up to date, because air-sea flux determination by a variety of meansis a growth industry responding to the immediate needs of forecasting and climate research.Those described have in general been developed to serve some particular area of this research.Common features tend to be corrections for thermal stability, exchange processes in light windsand sophisticated representation of surface roughness lengths for velocity, temperature andhumidity. Differences between algorithms tend to lie in the choice of parameterisations, forgustiness, for surface renewal parameters (timescale, thickness), and for the roughness lengths.Direct measurements of most of these parameters over the ocean are virtually non-existent.Similarly, direct covariance flux data sets against which to test the various algorithms are verylimited, although some well-validated inertial dissipation measurements are available.For research purposes, perhaps the most widely used of the modern bulk algorithms isthe so-called COARE algorithm (Fairall et al., 1996a), developed specifically as a commonresource for COARE scientists. Originally, it adhered strictly to the LKB model as regardsstability corrections and scalar roughness parameters, but gradually introduced modifications forgustiness, convection and surface roughness which were actually derived from shipboardobservations during COARE. It was scaled against direct (covariance) measurements of thelatent heat flux. Thus there were no arbitrary assumptions. However, from the perspective ofmodellers it was inefficient because of long iterative loops in the code, one of which includedcorrections to the measured SST for cool skin and the diurnal thermocline. At the same time,being structured around the basic physical processes involved, the results of new theory andfresh data are readily incorporated. With much additional direct flux data available in bothtropics and mid-latitudes since COARE, an improved version of the "COARE" algorithm is nowavailable, including more efficient computation.The algorithm by Zeng et al. (1998) is designed to suit the needs of numerical modelcode, being a simplified version of the COARE algorithm. It is tuned to the same data set as theCOARE algorithm for low to moderate wind speeds (the Moana Wave COARE observations byFairall et al., 1996a). However, there is very little of this data with wind speeds in excess of 12m/s, so for higher wind speeds Zeng et al. (1998) extend their algorithm using the HEXOS dataof DeCosmo et al. (1996). This is the best-known of flux data sets at stronger winds, but shouldbe used with caution, because these are shallow water coastal measurements, and the authorsshow that they do not conform to open ocean exchange coefficients in the overlapping windspeed range.The Bourassa et al. (1999, BVW) bulk model is notable for its attempt to relate surfaceroughness lengths, and hence the exchange coefficients, to various aspects of sea state, swell,gravity waves and capillary waves. They draw on, and extend, published representations ofwave structure in their analysis, but adequate validating data is not yet available to test thepotential of this algorithm. The Clayson et al. (1996) algorithm, developed to obtain surfacefluxes from satellite observations, make use of the BVW sea state model, but otherwise followthe surface renewal theory and stability correction path.Two of the algorithms which we describe depart from the conventional approach to freeconvection, by Fairall et al. (1996a) or Zeng et al. (1998) for example. Beljaars (1994)7 - Parameterisation of Turbulent Fluxes 101 7.6 SummaryReport of the Working Group on Air Sea Fluxes June, 2000approaches the problem from the perspective of the numerical modeller seeking economicalsolutions without violating physical reality. He argues that normal Monin-Obhukov similaritycan be used in the surface layer, provided a convective scaling velocity (gustiness) is included inthe bulk equation and that, in the well mixed region, the behaviour of stability functions is nottoo critical in any case. His analysis suggests that the surface influences the free convectionregime through coupling between convective motions and the surface roughness. Stull (1994)takes a similar approach in his convective transport theory, that transport into the mixed layer byconvective plumes is the dominant process, but his analysis leads to the opposite conclusionfrom Beljaars (1994). By parameterising the surface fluxes in terms of mixed layer, rather thansurface layer variables, Stull (1994) finds that turbulent exchange is independent of surfaceconditions.With such a variety of advanced bulk flux algorithms available, it is obviously temptingto intercompare their flux output using a common set of basic input data, to determine which isthe "best". Using COARE and TAO mooring data, Zeng et al. (1998) have compared several ofthe algorithms described here and show that, up to around 12 m/s, the performance of thealgorithms is very similar. Since they derive from essentially the same analysis as given in theabove sections, the only difference will be due to the different choice of parameters orfunctions. As expected, these differences become more prominent at higher wind speeds, butuncertainty about the quality of the test data at these speeds makes judgement difficult. Zeng etal. (1998) also consider the parameterisations employed in several numerical models. Theyconclude that these are seriously defective, and recommend substitution with their ownalgorithm. As indicated in section (4.4.3B), the Zeng et al. (1998) algorithm is now used in theNCEP operational model, achieving some improvement. It is probably fair to comment thatalmost any of the modern algorithms, with reasonable roughness parameterisations and stabilitycorrection, would have considerably improved the performance of the models.Chang and Grossman (1999) have also compared several algorithms, using the MoanaWave covariance flux measurements from COARE as reference. Adopting a particular set ofcriteria to constitute agreement, they find that most of the algorithms predict latent heat fluxwithin 8% of the observed value. The predictions for sensible heat and momentum are morevariable. The authors identify the departure from measured values as “a windspeed dependentbias in the model due to the surface flux parameterisation”, but don’t examine this in moredetail. It would have been helpful to identify the sources of disagreement, as a guide to thesensitivity and relative importance of various parameters, and to the accuracy with which theyneed to be determined. To be constructive, any further comparisons between algorithms shouldconsider not just the output values, but the consequences of alternative choices of parametersand internal physics.The main shortcoming in the area of algorithm development is not so much a dearth ofnew parameterisations, as a supply of reliable directly measured flux data at high wind speedsand higher latitudes. Reference data from tropical regions is becoming relatively plentiful.Since TOGA-COARE, considerable additional low to moderate windspeed direct flux data hasbeen assembled by the NOAA/ETL group, in tropical and mid-latitudes. As a result, betterrelationships between momentum and scalar roughness lengths than those used by LKB (1979)and Fairall et al. (1996a) have been established, and incorporated into the COARE algorithm.At the same time, advanced forms of the profile functions and a more accurate first estimate ofthe atmospheric stability have enabled convergence of the flux-roughness loop within 3iterations, making the algorithm more attractive for numerical modelling and remote sensingapplications.102Report of the Working Group on Air Sea Fluxes June, 20008 - Random and Sampling Errors 103 8.1 Introduction8.2 Random errors and samplingReport of the Working Group on Air Sea Fluxes June, 20008 RANDOM AND SAMPLING ERRORS IN FLUX FIELDS.8.1 IntroductionThis Chapter will consider some aspects of determining the error characteristics of fluxfields. Random errors, and the implications for the required sampling density will beconsidered in Section 8.2. Systematic errors will be only briefly discussed in Section 8.3; morediscussion with respect to the parameterisation formulae was given in sections 6 and 7.Methods of determining the systematic errors, and error estimates for the various products arediscussed in sections 9 through 11. A potential error source for flux fields depends on themethod chosen for averaging the fluxes. The use of averages of the basic variables in theparameterisation formulae (rather than averaging individual flux estimates) was originallyadopted for computational efficiency, however it also makes fuller use of the available data incases where all the needed basic variables are not available with each observation. Asdiscussed in Section 8.4, this advantage must be weighed against the possibility of introducingbiases into the flux estimates due to neglecting the correlations between the basic variables.The problem of combining the estimates of the random and systematic errors, toprovide the overall error estimate, will be discussed in Section 8.4. The calculation of fluxfields involves smoothing with interpolation and extrapolation of the flux estimates. Asexamples, the effects of a commonly used method, successive correction, and the advantagesof a more sophisticated method, Kriging, will be discussed in Section 8.5.Although more generally applicable, most of the discussion in sections 8.2 to 8.5 hasbeen developed with regard to the application to ship data. Therefore the particular samplingproblems associated with satellite data will be specifically discussed in Section 8.6.8.2 Random errors and sampling density8.2.1 IntroductionThe sampling density is an important problem with regard to the reliability of ocean-atmosphere flux fields derived from VOS and satellite observations. With respect to individualvariables, the sampling problem can be formulated in terms of the similarity of probabilitydensity distributions of meteorological quantities, computed both from the real observations andfrom some kind of “ideally sampled” observations for the selected space boxes and timeperiods. The latter are typically one month periods for most climatologies, although forinstance Legler (1991) studied importance of sampling for 5-day wind and temperature means.Since the distributions of most of the directly observed quantities are not Gaussian, or at leastmay be not Gaussian in particular regions, the significance of sampling biases is regionallydependent. Although sampling biases are usually considered with respect to temporalinhomogeneity of sampling, for some particular regions spatial inhomogeneity of samplingmay be as important as temporal. This is especially important for VOS observations which areusually sampled along the major ship routes. For some regions the arrangement of sampleswithin the n by n degree box may seriously affect the monthly means of both the individualvariables and the fluxes even though the overall number of observations may be adequate.Morrissey and Maliekal (1995) found that a linear network, which aligned along the major axisof the spatial correlation function, provided standard errors in wind speed estimates three tofive times higher than a random network. This problem is serious only for spatial boxesgreater than 3-4 degrees, because the ship routes form relatively wide tracks of about 100 to 200km width.When the fluxes of heat, moisture and momentum are computed from individualvariables using strongly non-linear parameterisations, even small sampling errors in individualobservations can lead to significant sampling biases in space-time averaged fluxes. Weare andStrub (1981) studied sampling errors in the tropical surface heat fluxes, computed from VOSobservations, and found eleven individual observations per month to be adequate for monthly8 - Random and Sampling Errors 104 8.2 Random errors and samplingReport of the Working Group on Air Sea Fluxes June, 2000mean flux estimates. However in the mid and high latitudes, this limit can be several timeshigher due to the necessity to sample extreme conditions associated with very high sea-airfluxes. These can seriously affect monthly means. Thus, Cayan (1992a) and Legler (1991)found 50 to 100 samples per month to be required for unbiased flux computations for a 5 degreebox.Climatologies of fluxes derived from VOS observations in COADS and similararchives are characterised by adequate sampling in much of the North Atlantic and NorthPacific but to be strongly affected by under sampling in the southern hemisphere. However,even in such well sampled areas as the North Atlantic, there are some regions which are poorlysampled. Such areas may also be characterised by extreme flux values of crucial importancefor adequate description of regional sea-air exchange processes. A good example is theLabrador Sea, known as the source of Labrador intermediate waters which are formed by theextreme sea-air exchange processes in that limited region (LabSea Group 1998). From the1950's to the early 1970's this region was represented by regular observations at OWS B.However, since 1973 when OWS B was stopped, only a few observations per month wereavailable. Yet this period, the 1970's - 1990's, was characterised by several dramatic deep-convection events which are therefore poorly represented in the VOS observations.The flux products which have become recently available from numerical weatherprediction systems are regularly sampled with 6-hourly temporal resolution. These allow thesampling errors in the VOS fluxes to be estimated. Gulev et al. (2000) simulated the samplingfrequency of COADS using the NCEP/NCAR 6-hourly fields, and found that both climatemeans and interannual variability patterns may be seriously affected by the poor sampling. Itwas found that the absolute value of the sampling error over the North Atlantic domain waswithin a range of ± 5 Wm-2. Thus the meridional heat transport estimates were not seriouslyaffected by the under sampling. However, locally the effects in such areas as the Labrador Seawere large, possibly reaching 50 to 60 Wm-2 for climatological means.Further assessment of the possible impact of under sampling in the VOS observationsshould be made using the regular instrumental observations at buoys, OWS, and other in-situplatforms. However it will be shown in the next section that, if it is the accuracy of, say, amonthly mean that is required, then that is simply determined from the variance of thecalculated monthly means. It is only if natural variability is to be distinguished from variabilitydue to sampling errors that a more sophisticated analysis is required.8.2.2 Errors and Variability of Monthly Mean FieldsMonthly mean fields of the air-sea fluxes are affected by two kinds of uncertainties:systematic and random errors. In general the first influence the mean, the latter the variance ofthe calculated fields. The wind stress is an exception from this rule. Since the squared windspeed occurs in this parameter, random errors are not cancelled out by averaging.Consequently, they affect not only the variance, but also the mean. The principal aim is todiscriminate the error variance from the true natural variability, and, of course, to determine andeliminate the systematic errors.Random errors are completely unavoidable, but good-natured compared to systematicerrors. Several statistical techniques are available to quantify them and to eliminate their meaneffects at least. Random observation errors of the basic synoptic parameters, such as windspeed and temperature, propagate through the applied bulk formulae and influence the resultingflux fields. Due to random errors the true variance is increased. For long-time means, inregions with a high number of observations, the effect of random errors remains small.However, if not only the long-term mean annual cycle is considered but also the interannualvariability of individual monthly means, the data density even in the central North Atlantic isnot sufficient to neglect the effects of random errors.Before discussing problems connected with variability and error variance, the differentrelevant variances should be clearly defined. The total temporal variance at a fixed point is:8 - Random and Sampling Errors 105 8.2 Random errors and samplingReport of the Working Group on Air Sea Fluxes June, 20001121 1 mxx iinkN k−− ()= =∑ ∑ (8.1)where m is the total number of observations and x the total mean, xi denotes the individualvalues, N is the number of months and nk the number of observations in the k-th month.Obviously, the total number m can be obtained by:mn kkN==∑1(8.2)and monthly means xk are defined as:xnx kkiink==∑11(8.3)The total variance can be split up into the total apparent variance and the error varianceof the total mean:1111121 1221 1 1 1 mxxmxxmmxx iinkNiiinkNinkN k k k−− () =− () +− ()− ()= == = = =∑ ∑∑ ∑ ∑ ∑ (8.4)The second righthand term in (8.4), the error variance of the total mean, is mostly very small, ifthe total number of observations is large enough. The first righthand term in (8.4), the totalapparent variance, consists again of two parts: the apparent extra-monthly variance and theapparent intra-monthly variance:111 21 11221 1 mxxmnx xmxx iinkNkkNki kinkN k k− () =− () +− ()= == = =∑ ∑∑ ∑ ∑ (8.5)The apparent extra-monthly variance gives a mean value for the interannual variabilityweighted by the numbers of observations per month. The intra-monthly variance gives ameasure for the mean short time variability within the months. We added the expression'apparent', because the two terms are not equal to the true variability, but both are affected byrandom errors, in other words, true natural variability is mixed with the error variance. In thenext step we will try to separate these two kinds of variance. That will be quite simple for theextra-monthly but more difficult for the intra-monthly variance. Fortunately, we are mostlyinterested in those outer quantities: the monthly means and their errors and variability.The apparent intra-monthly variance of (8.5) can be split up into:1111121 11 1221 1 mxxmnnxxmnnnxx ikinkNkk kNinikkkkikinkN kk k− () =−− () −− ()− ()= == = = =∑ ∑∑ ∑ ∑ ∑ (8.6)In order to interpret the decomposition carried out in (8.6), we have to consider thefollowing: the mean error variance of a sample's mean is given by the variance of the sampledivided by the number of independent observations. Regarding ship observations, the demandof independence is obviously fulfilled, one of the great advantages of such data sets. Thus, theerror variance of an individual monthly mean is given by the inner variance of that monthdivided by the number of observations. A weighted average over all months gives then anestimate of the mean error variance of monthly means. The second righthand term in (8.6) justdescribes these errors. Subtracting this error variance from the apparent extra-monthlyvariance finally gives an estimate of the true natural variability.The first righthand term in (8.6) gives the weighted average of intra-monthly variancewhich is still affected by observation errors. For this expression the separation of the natural8 - Random and Sampling Errors 106 8.2 Random errors and samplingReport of the Working Group on Air Sea Fluxes June, 2000variability from errors is possible following Lindau (1995a). Pairs of simultaneous individualobservations are considered. The idea is that natural variability increases with increasingdistance, while errors are independent of separation because of their randomness. The squareddifference between both observations is plotted as a function of distance between the locations.A linear fit gives an idea of the potential value at distance zero where no natural variability butonly error variance remains. In Figure 8.1, to give an example, the mean random error of VOSobserved wind speed is determined by this technique. For zero distance a variance of 23.5(ms-1)2 is obtained, representing twice the error variance of merchant ships. By repeating thesame procedure with pairs of VOS-OWS observations, the error of OWS's can be concluded.Since 11.75 (ms-1)2 is allotted to the merchant ships, 5.4 (ms-1)2 remain for the OWS. But wehave to keep in mind that this procedure has to be applied only when individual observationsare under consideration, which is only necessary when sub-monthly processes are investigated.Figure 8.1 Determination ofindividual error variance. Meansquared wind speed differencesfrom VOS-OWS pairs (shaded)are compared to VOS-VOS pairs(total columns).In contrast to the rather arduous technique to reveal the mean individual observationerror, the situation for extra-monthly errors is quite simple: the intra-monthly variance gives ameasure of the error of the monthly mean. It is not relevant whether the intra-monthly varianceis caused by observation errors or natural variability. The following consideration may help toillustrate that: even with a time series of perfect quality we are not able to derive the trueaverage during the considered month, because of the disturbing natural variability. The higherthis variability is, the higher is the uncertainty of the mean, when a constant number ofobservations is available. An additional random error of the instrument just increases the intra-monthly variance and consequently the error of the monthly mean. Thus, if we are dealing withmonthly means, the error of a particular month is easy to determine: just divide the intra-monthly variance by the number of observations. The overall effect of several months is thengiven by a weighted average of all such monthly errors as indicated in the second righthandterm in (8.6). As mentioned above, for the estimation of the true extra-monthly variability thisvariance has to be subtracted from the apparent extra-monthly variance.Summarising the above discussion, the total variance can be decomposed into:111121 121 1 mxxmmxx iinkNiinkN kk−− () =− ()− ()= == =∑ ∑∑ ∑ (8.7)+− ()=∑112mnx x kkNk (8.8)8 - Random and Sampling Errors 107 8.2 Random errors and samplingReport of the Working Group on Air Sea Fluxes June, 2000−− ()− ()= =∑ ∑1121 1 mnnnxx kkkikinkN k(8.9)+−− ()==∑∑11 112mnnxx kk kNinikk(8.10)The four constituents are (8.7) error of the total mean, (8.8) apparent extra-monthly variance,(8.9) mean error of monthly means, (8.10) intra-monthly variance. In order to give an ideaabout the magnitude of these quantities, the variances of COADS derived latent heat flux forJanuary are given in Figure 8.2 - 8.5. For these calculations only monthly 1o by 1o grid boxeswith at least 2 observations can be taken into account, since errors should be definable, and 2 ofsuch months should exist, to define the extra-monthly variance. These restrictions diminish thedata base especially in the South Atlantic.In the most parts of the Atlantic the error of the total mean is negligibly small (Figure8.2). All other three components (Figures 8.3 - 8.5) reach their maxima in the Gulf Streamregion, while the South Atlantic shows generally remarkable low variances. Comparing thedifferent kinds of variance, it is not surprising that the intra-monthly (Figure 8.5) dominates: byaveraging over a period of one month, the main part of variance will be cancelled out, so thatthe majority of the total amount is found inside the months and only a minor part remains forthe extra-monthly variability. The prevailing of the inner variance is even increased by theeffects of random errors. They are perceptible nearly entirely as an additional part of the intra-monthly variance, while only a small portion, depending on the number of observations,contributes to the error variance of monthly means. However, the intra-monthly variance isobviously not dominated by errors. Errors increase the intra-monthly variance by an amountwhich is equal to the mean individual observation error. In contrast to the extra-monthly case,the size of error effects does not depend on the number of observations so that a spatiallyhomogeneous offset can be expected. Thus, the strong regional differences in intra-monthlyvariance are an indication for the dominance of natural variability.However, the intra-monthly variance plays a minor role in our considerations, and isonly used for the determination of the errors of our main topic, the monthly means. Theircharacteristics are given in Figure 8.3 and Figure 8.4. The apparent extra-monthly variancedoes not only reflect the true natural variability but overestimates the variance due to theincluded errors. These are depicted in Figure 8.4. The first striking feature is that themagnitude of the error variance is comparable to the apparent extra-monthly variance itself.Thus, the overestimation by the raw data is considerable. Subtracting the error from theapparent extra-monthly variance (not explicitly shown) reveals that the true extra-monthlyvariability is extremely small in the South Atlantic.The strongest errors occur in the Gulf Stream, although this region has a remarkablehigh observation density. Obviously, the very strong internal variability is responsible for thehigh uncertainty. On the other hand, low errors are not only found in the shipping lanes butalso in the South Atlantic, where an extraordinary low intra-monthly variabilityovercompensates the effects of sparse data coverage. Therefore, only few observations areenough to give relative accurate monthly means in that region.One of the grid points with most observations in the central North Atlantic isconsidered in more detail (Figure 8.6). Error effects in such well covered regions are expectedto be minimal. The latent heat flux at the selected point has a mean value of 116 Wm-2, with aconsiderable extra-monthly variance. Additionally, the errors of monthly means are plotted,which depend on the intra-monthly variance and the number of observations. It becomes clearthat the information about the errors is absolutely necessary, since otherwise we are not able tointerpret such variability. In most months the error bar passes over the total mean line,indicating a reliability of less than 68% for a significant difference from the total mean. Totalvariance of the shown time series is about 1300 W2m-4. More than 60% of this is not due to8 - Random and Sampling Errors 108 8.2 Random errors and samplingReport of the Working Group on Air Sea Fluxes June, 2000natural variability but due to errors. Remembering that such circumstances prevail in one ofthe best sampled regions of the world ocean, we realise the outstanding importance of errors.Figure 8.2 Error of the total means of LHF forJanuary, from COADS 1940-1979.Figure 8.3 As fig.8.2, but the apparent extramonthly varianceFigure 8.4. Error variance of monthly mean. Figure 8.5 Intra monthly variance.8 - Random and Sampling Errors 109 8.2 Random errors and sampling8.3 Systematic ErrorsReport of the Working Group on Air Sea Fluxes June, 2000Figure 8.6. Example for the apparent extra variability and the error variance of monthly meansfor a 1o by 1o grid box in the North Atlantic (29.5o E, 36.51o N)8.3 Systematic Errors8.3.1 IntroductionSystematic errors occur in the measurement or estimate of the basic flux observables.They are also a fact of life within the parameterisations themselves, particularly for climateconditions that differ from those in which the parameterisation was developed. When sucherrors can be identified it is possible to correct their effects, but generally these errors aredifficult to isolate. The key difference between systematic and random errors is that the overallsystematic error is not dependent on the number of observations used to compute the flux.8.3.2 Example of a parameterisation errorA possible misapplication of the Reed (1977) formula for shortwave radiation serves asan illustration for a systematic error due to parameterisation. The formula provides mean dailyradiation using as astronomical input only the noon sun altitude. Obviously, such a crudeparameterisation of clear sky radiation is only possible for constant latitudes. Consequently, theapplication of Reed's formula in high latitudes where long day lengths during summercompensate the relative low sun altitude would cause considerable systematic errors. For windstress, to give another example, possible sources of systematic error are a mean underestimateof CD due to sea state effects or biases in the directly observed wind speed.8.3.3 Determining errors in VOS reportsAs an example of the determination of systematic errors in observational data considerthe accuracy of data from the VOS. As for any systematic errors, determining the typical biasesin the meteorological reports from merchant ships is very difficult. The accuracy depends notonly on the calibration of the instruments, but their siting, the characteristics of the ship, andthe way in which the observations were taken. One attempt to determine the systematic errorsin ship data was the Voluntary Observing Ship Special Observing Programme - North Atlantic(VSOP-NA) project (Kent et al., 1993a). The method used was to choose a subset of 46 VOS,document the instrumentation used on each (Kent & Taylor, 1991), and ask the ships' officersto include extra information with each report. Since the ships would rarely pass close to oneanother, the output from an atmospheric forecast model was used as a common standard forcomparison. The results were then analysed according to instrument type and exposure, shipsize and nationality, and other factors. By this means relative biases could be determined.For example it was found that SST values from engine intake thermometers were biased warmcompared to other methods (Kent et al. 1993a), and that daytime air temperatures were toowarm due to solar heating (Kent et al. 1993b). It could be shown that the dew point8 - Random and Sampling Errors 110 8.3 Systematic ErrorsReport of the Working Group on Air Sea Fluxes June, 2000temperature was not biased by the latter error (Kent and Taylor, 1996) but, compared toaspirated psychrometer readings, the dew point was biased high when obtained from fixedthermometer screens. The VSOP-NA results suggested that correction of measured winds toallow for the height of the anemometer was advantageous. Following the VSOP-NA, laterwork by Kent and Taylor (1997) suggested that, for visually estimated winds, the form of theBeaufort scale devised by Lindau (1995a) gave best agreement with the anemometer winds.A specific problem which affects VOS reports, and which can lead to errors which aresystematic rather than random in nature, is errors in the reported ships' positions. About 2 to3% of the VOS weather reports in COADS can be identified as having incorrect positioninformation. Typically the position is incorrect by 10° or is in the wrong quadrant. Often thesedata exist in COADS as duplicates, one report having the correct position (Lander andMorrissey, 1987). Position errors are detected in operational forecast centres by trackingindividual ships, but this is rarely done for climate studies. However position errors arepotentially very serious because the ship might be falsely placed away from the shipping lanesin a data sparse region where the false report may be given undue weight. For example inJanuary, 1984, ship reports from near Iceland appeared as a group of erroneous duplicates in theCOADS data set, positioned near Antarctica.8.3.4 Method of averaging fluxesA particular problem connected with the issue of sampling, is the difference betweenthe averaged fluxes computed from individual observations and from observations which havebeen previously averaged in space and time. Again this has the potential to introducesystematic errors.The restricted computer power available in the past led to many "classical" flux fieldsbeing calculated from averaged observations rather than individual samples. Hence thisproblem has become known as the "sampling" versus "classical" difference in the flux fields.The present level of computer power and data availability allows fluxes to be calculated fromindividual observations - the sampling approach. However, reliable estimates of "samplingminus classical" differences for different flux components are still important for comparativeassessments of historical data sets which are frequently only available as monthly or seasonalgrids. Moreover, climate studies still use monthly averaged meteorological quantities to derivelong-term series of sea-air fluxes. The problem is also important where fluxes are derived fromsatellite data since, in many cases, some variant of the "classical" approach must be used.Comparisons of sampling and classical estimates of fluxes are given in many articles(e.g. Esbensen and Reynolds 1981, Hanava and Toba 1987, Ledvina et al. 1993, Gulev 1994,1997b, Josey et al. 1995, Staneva et al. 1995, Zhang 1995, Esbensen and McPhaden 1996).Different authors report various "sampling minus classical" differences. Thus, for the sensibleand latent heat fluxes, it is generally believed that sampling estimates are slightly higher thanthose computed using the classical method (by about 5 to 15 percent). Gulev (1994) andStaneva et al. (1995) reported larger sampling to classical ratios. Such errors will occur inregions where stronger winds are correlated with cold, dry air and large heat loss from theocean. In contrast, Josey et al. (1995) found that sampling estimates for the latent flux may besmaller than classical estimates in the North East Atlantic due to negative correlations betweenthe wind speed and humidity gradients. In this region, the stronger winds were oftenassociated with the moist, warm air in the warm sectors of mid-latitude depressions.The "sampling minus classical" differences will also strongly depend on the schemesused for determining the bulk transfer coefficients (Gulev, 1997b). For example, the Liu et al.(1979) scheme provides negative correlations between wind speed and the sensible and latentheat transfer coefficients for moderate and strong winds. The Smith (1988) parameterisation isalso characterised by slightly negative correlations between wind speed and the coefficients forunstable conditions. Thus, estimates of "sampling minus classical" differences for these8 - Random and Sampling Errors 111 8.3 Systematic Errors8.4 Combining errorsReport of the Working Group on Air Sea Fluxes June, 2000schemes will be different compared to parameterisations which assume coefficient values whichincrease with wind speed.8.3.5 Further discussionOther examples of systematic errors in meteorological observations and their correctionare described in Section 11.2.4. Systematic errors associated with parameterisations werediscussed throughout Sections 5 and 6. Efforts to determine the total effect of the random andsystematic errors on the total uncertainty is summarised in the next section.8.4 Combining ErrorsSections 8.1-8.3 have concentrated on the quantification of random and systematicerrors. To further assess confidence in our observational estimates, ultimately we need to knowthe combined effect on the observational uncertainty of all random and systematic errors.Gleckler and Weare (1995, 1997) attempted to formulate a strategy for combining all errorsassociated with VOS type flux estimates. They made use of simple sampling theory which isbriefly summarised here: considering a field F, which is a function of x and y, to first order theuncertainty estimate for a single measurement of x and y is:2 2 22222 y x xy y x F yFxFyFxFσ σ ρ σ σ σ   ∂∂∂∂+   ∂∂+ ∂∂= (8.10)where σx and σy are the combined random and systematic uncertainties in x and y, and ρxy is thecorrelation between the error in x and y. If x and y are independent, they are uncorrelated andthe third term in the above equation vanishes. If they are fully dependent (ρxy= 1), then there isa maximum propagation of uncertainty in σF. In the case of anticorrelaton (ρxy ≤ 1), or thepartial derivatives with respect to F are negative, the correlation term will act to reduce theuncertainty in F.To estimate uncertainties from a collection of measurements of each observable, thanthe first RHS term can be expanded to:) (2,22,222N fxFxFxFran x xyx x x σ σ σ ∂∂+ ∂∂⇒ ∂∂(8.11)where σx,sys and σx,ran are respectively estimates of systematic and random uncertainties in x.Random uncertainties are reduced by some function of the number of available observations, N.This simple treatment for the combination of uncertainties was generalised by Glecklerand Weare and applied to the same bulk parameterisations (and data) as used by Oberhuber(1988). Estimates of random and systematic errors for each basic observable and theparameterisations themselves were based on previous research, and were assumed to beconstant in space and time. With the assumptions of a valid sampling theory and accurateuncertainty estimates, Gleckler and Weare were able to construct spatial maps estimating therelative importance of each uncertainty associated with the various heat flux terms. Spatial lagcorrelations were used to estimate the degree of spatial dependency from which random errorscould be further reduced as zonal means.Since the effort of Gleckler and Weare, several possible improvements to their methodhave been identified. First, there have been advancements with the parameterisationsthemselves in the subsequent work of da Silva et al. (1994) and Josey et al. (1999).Additionally, the estimation of random error reduction needs to be generalised to that outlinedin Section 8.2.2, as Gleckler and Weare assumed temporal inhomogeneities to be nearly to benearly Gaussian. But perhaps most importantly, significant refinements have been made tosome of the uncertainty estimates used (by Gleckler and Weare) such as in the surface wind.But in fact the situation is likely to be more complex than merely updating error estimates, asJosey et al. (1999) have demonstrated, perhaps not surprisingly, that such errors are likely to8 - Random and Sampling Errors 112 8.4 Combining errors8.5 Mapping ErrorsReport of the Working Group on Air Sea Fluxes June, 2000vary substantially in both space and time. Thus any revisit of the method applied by Glecklerand Weare should take into account spatio-temporal variations in systematic error of basicobservables and parameterisations. Determining just what these variations are poses asignificant challenge, and adds further argument to the plea for additional buoy deployments.There has been progress in combining errors and estimating their relative importance.Quantification of the propagation of error estimates into total uncertainties should be an integralpart of future flux estimates derived from VOS. Such efforts should help to further establishconfidence limits and assist us in determining how to improve them. Clearly, further work isneeded in this area.8.5 Mapping errors8.5.1 IntroductionErrors in flux fields may be caused by the need to smooth, interpolate and extrapolatethe data to generate global fields. If not done correctly, significant errors may be introducedinto the analysis. The "successive correction" method will be discussed as an example of afrequently used technique that is likely to introduce errors into a flux field. An alternativemethod will then be described, the "Kriging" technique, which is computationally moreexpensive, but which has the advantage of providing an error estimate for each part of the field.8.5.2 Successive correctionSuccessive correction has been used in a number of studies to produce fields ofgeophysical variables (e.g. Barnes, 1964; Levitus, 1982; 1994). It has the advantage,compared to more sophisticated methods such as optimal smoothing (Kaplan et al. 1998),Kalman filtering, or optimal interpolation, that it does not require information about thetemporal and spatial structure of the data. In particular, the recent surface flux climatologies ofda Silva et al. (1994) and Josey et al. (1998; 1999) were compiled using this method. Datawithin different radii of influence are weighted by distance and averaged in successive passes ofthe smoothing process. The radii used by Levitus and da Silva were 1541, 1211, 881 and 771km. Josey et al. used 1541, 1211, 771 and 331 km, so this latter data set will contain structureon smaller scales than those of Levitus and da Silva.Kent et al. (2000) have investigated the effects on a data set of smoothing by successivecorrection. The resulting spatial resolution was estimated using a distribution of ship reportsfrom a sample month. Although the same characteristic radii were used over the whole globe,the resulting resolution was shown to be spatially variable depending on the local samplingdensity. Thus in data sparse regions large month to month variability of the resolution couldoccur with changes in the distribution of the ship tracks. The Josey et al. (1999) climatology,which was gridded at 1°, was shown to have a typical resolution of 3°. The mean resolution(using SST for January 1980 as an example) was 4.0 ° latitude by 5.8 ° longitude. Themaximum zonal spread of information was 50° and the maximum meridional spread was 19°.This reflected the choice of a zonal mean as the background field. Thus the resolution wassignificantly worse than the grid might suggest and information was smeared appreciabledistances into data sparse areas. An obvious example of the latter effect was the wind speed inthe Southern Ocean in winter. Since there were no data from much of the Southern Ocean,lighter winds observed further north are extrapolated southwards. As a result the climatologyshows significantly lighter winds than, for example, were measured by satellite scatterometersin that region.Kent et al. (2000) also showed that the successive correction procedure was notsuccessful in removing all of the noise in data sparse regions. Furthermore, the well-definedintermonthly variability in the main shipping lanes, where there are many observations, wasdegraded by the influence of poorer quality data in the surrounding regions. This typicallyincreased the intermonthly variability estimates in the shipping lanes by a factor of two. Incontrast, the reduction of intermonthly variability in highly variable regions such as the Gulf8 - Random and Sampling Errors 113 8.5 Mapping ErrorsReport of the Working Group on Air Sea Fluxes June, 2000Stream by up to a factor of six, was greater than could be accounted for by noise in theindividual ship reports. This reduction was due to the removal of small-scale variability by thesmoothing process. Removal of coherent and persistent small-scale variability changed thetemporal and spatial characteristics of the data. Kent et al. (2000) concluded that smoothing bysuccessive correction, although commonly used, is poorly suited to such spatiallyinhomogenous data as those from the merchant ships.Considering the impact of the successive correction smoothing on variability analysisusing empirical orthogonal functions (EOF's), Kent et al. (2000) found little effect for the mostsignificant modes of variability identified in the Gulf Stream region. This was because thelarge-scale variability occurs in the highest order modes. However, too large a fraction of thetotal variance explained was ascribed to these large-scale modes of variability. If raw datawere used in the EOF analysis, variability with small spatial scales was more likely to besignificant. Finally, little significance should be given to EOF modes with spatial scalessimilar to the size of gaps between shipping lanes; a distance which varies from region toregion.8.5.3 Kriging of monthly means and error estimationA. INTRODUCTIONOn a global scale, monthly means represent the shortest time resolution where shipobservations provide reasonable results. However, even on that relative long time scale, datadensity begins to degrade drastically for most regions of the world ocean. In this situation theerror variance of the observations begins to dominate. Therefore, monthly maps of the raw datashow large unrealistic scatter which has to be eliminated. Many smoothing techniques exist,leading in each case to an acceptable appearance of the maps. But often these techniques areused in a somewhat arbitrary manner by repeating them over again until the result lookssatisfactory. Clearly, we cannot be sure that such results are optimal.It should be mentioned here, that a widely used technique for eliminating error effects,that is searching for the best bi-linear or higher order 'surface', has disadvantages. Such fittingof polynomials is used to discriminate the errors from the true natural part of the total variance.The spatial variability of the fitted 'surface' is considered to be true. Any additional variance,that is deviations of the data points from the fitted polynomial, is identified as observation error.However, polynomials of higher order are able to follow the structure of the data better andinclude more variance, so that the data appears to be more accurate. Which order ofpolynomial is appropriate is not exactly definable, so that the resulting error estimates are notentirely objective.In Section 8.2.2 we pointed out that it is absolutely necessary to provide not only themonthly fields of air-sea fluxes but also the error estimates of each field, because in mostregions of the world ocean the magnitude of error variance is comparable to the naturalvariability. Without knowledge of the errors the interpretation of variability is not possible.Kriging techniques provide both optimum fields of the considered parameters togetherwith an error map. Therefore these techniques are especially suitable for the inhomogenouslydistributed ship data set. In addition, it is possible to produce complete fields of values even inareas with a very low data density. Without an explicit knowledge of the errors, these regionsare often left as blank areas on flux maps because the errors appear to be irresponsibly large.Kriging provides an error estimate for each point so that users may decide whether uncertainvalues should be omitted. Experience shows that many users would fill gappy data on theirown, so that the optimal value, even if very uncertain, is better than blanks.B. PRINCIPLESIn principle Kriging can be regarded as a prediction of a value x0 at a location P0 whereno measurement has been carried out, by using information from measurements in thesurrounding region, at Pi. That means the following expression has to be solved:8 - Random and Sampling Errors 114 8.5 Mapping ErrorsReport of the Working Group on Air Sea Fluxes June, 2000xx x iinii 01=− ()=∑λ∆ (8.12)where x0 is the value which has to be predicted. xi denotes the available measurements and ∆xitheir errors. The task is to determine the weights λi. Obviously, a solution is not possible forone single case. But if a time series of m measurements at each location Pi is available, areasonable requirement is that the mean squared deviation between predictions and truth isminimal:tmiinii xx x==∑∑ ( −− () ) =1012 λ∆ min (8.13)Differentiating (8.13) leads to the following expression, where, for the sake of clarity, thetemporal summation is abbreviated by brackets [].:[] [ ] [] [][] [] [ ] [][] [] [] [ ]xx x x xx xxxx xx x x xxxx xx xx x xnnnn n n n n n11 1 1 12 121 22 2 2 21212+++•∆∆∆∆∆∆LLMM M MLMλλλ =[][][]xxxxxx n01020M(8.14)Thus, a linear set of equations results, containing the covariance matrix between the data pointsPi with the error variances on its diagonal, and a vector giving the covariance between thepredicting point P0 and the locations Pi where measurements are available. If these quantitiesare known, the solution of the above set of equations leads, via the determination of the weightsλi, to the optimal prediction at P0.Obviously, the minimised expression in (8.13) is equal to the error of the predictedvalue, often called as Kriging error. Transformation of that expression leads to:xx xx xx xx oo i oiininijjnij iiinii [] − [] + [] + []== = = ∑∑ ∑ ∑ 211 1 1λλ λ λ λ (8.15)The first term of the transformed Kriging error denotes the variance at P0. If the calculationsare carried out with normalised anomaliesxxxnormix=−σ(8.16)which is advantageous for many reasons: [x0x0] is equal to unity. That means if no informationat all is available, the error is equal to the interannual variability. This is reasonable becausewithout any information we cannot do more than predicting the long-time mean of this location.The error of such a crude procedure is easy to realise. If we predict for every month of a timeseries just the total mean, the error defined by the mean squared deviation between truth andprediction is obviously the intermonthly variance itself. This fact is represented by the firstterm in (8.15). A further conclusion of the above discussion is that the maximum error formonthly means is the intermonthly variability of the considered location.The second term in expression (8.15) contains the covariances between the predictingpoint P0 and the data points Pi, and represents that part of the Kriging error which can be called'information'. Its negative sign indicates that the error decreases with increasing information.But information of the data points Pi are not independent from each other, as two points inclose vicinity carry more or less the same information. That is what the third term representswhich we can call 'redundance'. Covariances between the data points increase the error.Obviously the fourth term describes the effects of individual errors for the data points used.8 - Random and Sampling Errors 115 8.5 Mapping ErrorsReport of the Working Group on Air Sea Fluxes June, 2000C. NUMERICAL IMPLEMENTATION AND PRESELECTIONIf a fixed number of n observations is preselected, the Kriging technique is easy toapply. By solving only a single set of linear equations (8.14) the optimum value at thepredicting point is obtained. That is of course only optimal under the condition that thepreselected data points should exclusively be used. However, the use of an additional datapoint Pn+1 could improve the result. On the other hand, also omitting some data points of highindividual error and redundance may lead to an improvement. Thus, a kind of meta optimumhas to be searched for.The amount of information carried by a certain measurement does not only depend onits accuracy and its distance to the predicting point, but also on its redundancy in relation to theother points already used (8.15). That makes an a priori order of rank concerning the qualityof data points impossible. Thus, beginning with the trivial prediction, the long-term mean, wehave to select always that data point explaining for the moment the most variability at P0. Asmore and more points are included, the Kriging error decreases. The meta optimum is found,when no additional point is able to diminish the Kriging error under the condition of a positiveweight λ+1.However, as outlined the method is arduous and time consuming. As mentioned abovea preselection of a fixed number of observations would provide only the optimum result for thatspecific combination, but we are searching for the meta optimum. Nevertheless, a kind of metapreselection is necessary in any case. Considering only the Atlantic, on a spatial resolution of1o by 1o, about 10,000 possible data points are involved. Solving a huge number of 10,000 by10,000 matrices becomes a problem even for modern computers. On the other hand the metapreselection should not be too petty in order not to risk missing the meta optimum. Dealingwith 100 data points in the surrounding of the predicting point P0 is therefore realistic. Thedetermination of the second data point (the first is trivial) requires solving 99 2-by-2 matrices,that of the third one 98 3-by-3 matrices, and so on. These are still rather extensive calculations.When arriving at, say 58 43-by-43 matrices, where the meta optimum may be finally found,we have to be aware that only one single monthly mean of a single parameter in one specificyear at one point in the world ocean is determined.A substantial reduction of computing time is attainable, when the potential change ofthe Kriging error for an additionally included data point Pn+1 is considered. Instead of solvingseveral matrices the potential change of Kriging can be directly computed from:−+ + + ++ +=++ + + + + ∑ 22 10 1 1111211 1 1 λλ λ λ nn niiinnn n n n n xx xx x x x x [] [] [ ] [ ] ∆∆ (8.17)The first two terms are giving information and redundance gain, respectively. The last twodescribe natural and error variance for the potential new data point Pn+1. The point with thehighest potential Kriging error reduction is actually included, and the exact new Kriging error isobtained by solving only one single (n+1) by (n+1) matrix. Following the above describedtechnique, optimum monthly mean fields, always paired with their error fields, are obtainedwith an acceptable expenditure of time.D. MEANING OF THE SUM OF WEIGHTSSpatial gradients in long-term mean fields are not treatable with correlation techniques.This becomes obvious by considering the following example. In the extreme case twoneighbouring points with different total means may well have a correlation up to 1. In that casewe would be able to predict exactly the monthly anomalies of one point from the other, but ofcourse not the supposed difference between their long-term mean, because correlations do notcontain any information about the averages at both locations. That is the main reason for theuse of anomalies instead of absolute values.Dealing with anomalies, a constraint for the sum of weights:8 - Random and Sampling Errors 116 8.5 Mapping ErrorsReport of the Working Group on Air Sea Fluxes June, 2000−+ + + ++ +=++ + + + + ∑ 22 10 1 1111211 1 1 λλ λ λ nn niiinnn n n n n xx xx x x x x [] [] [ ] [ ] ∆∆(8.18)is not necessary, moreover, it would not be useful. To illustrate that, the trivial case of Krigingwith only one available data point may be considered. The minimising condition of (8.13) issimplified to:xx xtm01 1 112−+ () () ==∑ λ∆ min (8.19)with the solution:λ10111 1 1=+[][] [ ]xxxx x x ∆∆(8.20)In the denominator the total variance at P1 appears, split into the natural and the error variance.The numerator shows the covariance between the predicting and the data point. Analogue tothe slope of a regression line, the entire fraction is equal to the relative variance at thepredicting point, which is explained by the time series at P1. Because of the errors at P1, P0 isin any case not completely predictable. Consequently, the variance at P0 is underestimated, aknown and unavoidable feature of regression techniques. Thus, when Kriging techniques areapplied to anomalies, the sum of weights can obviously be interpreted as the sum of explainedvariance. Values less than 1 do not indicate a bias, as for absolute values, but a reduction ofvariance to the explainable portion, which is indeed reasonable.E. ESTIMATION OF THE TRUE COVARIANCE FROM REAL DATAAccording to (8.14), a prerequisite for the application of the discussed Krigingtechnique is the knowledge of the spatial covariance matrix. Since complete time series for allpoints in the world ocean, as conceived in (8.13), are obviously not available, mean covarianceshave to be derived.Firstly, the covariance of two time series strongly depends on the distance between theobservation places. For many applications it is therefore sufficient to assume a constantcovariance for a given distance. However, on global scale anisotropy has to be taken intoaccount, additionally. Secondly, spatial covariance strongly depends on the considered timescale: the prediction of the annual anomaly at a certain location is indeed possible withmeasurements from a far distant station, while short-term variability is connected only overshort distances. Since we are aiming at the correlation length of monthly means, covariancesshould of course be calculated on the base of monthly means, which show much largercorrelation lengths than individual observations representing a quasi instantaneous snap shot.Thus, the average covariance of monthly means is calculated separately for eachparameter as a function of distance and direction. Due to the spatial averaging, the covariancesof different regions are lumped together. Therefore it is advantageous to normalise theanomalies according to (8.16). Otherwise regions of high variability would dominate theresults. The normalisation allows to take into account adequately the information from lowvariability regions, such as the tropics.An exponential fit over the covariances, which due to the normalisation are convertedinto correlations, conserves the directional information:ra exaya =−+0212222(8.21)where r denotes the correlation, x and y the zonal and meridional distances, respectively.8 - Random and Sampling Errors 117 8.5 Mapping ErrorsReport of the Working Group on Air Sea Fluxes June, 2000After the derivation of the coefficients ai in (8.21), we are able to describe thecorrelation as a pure function of the position. However, the coefficients ai are derived by usingreal observations, which are of course affected by errors. But for the intended Krigingtechnique information about the natural covariance on the one hand and the error variance onthe other hand is needed separately. Therefore, we have to consider to which extent theobserved correlation, given in (8.21), is diminished compared to the actually sought true naturalcorrelation.For error affected anomalies the correlation between two points is:rxx xxxx xxerr =+ () + () ()+ ()+ ()∑∑ ∑11 22112222∆∆∆∆(8.22)Assuming that errors ∆x1 and ∆x2 are random, the above equation can be transformed to:rxxxx x x xx x xerr =+ () + ()∑∑ ∑∑ ∑1211 1 1 22 2 2 ∆∆ ∆∆(8.23)On the other hand, the true natural correlation, unaffected by errors, is:rxxxx xxtrue = ∑∑ ∑1211 22(8.24)The quotient of both is equal to:rrxx xxxx x x xx x xerrtrue=+ () + ()∑ ∑∑ ∑∑ ∑11 2211 1 1 22 2 2 ∆∆ ∆∆(8.25)which is equal to the quotient of the true variance, excluding errors, and the total variance,including errors. This becomes clearer when, for the moment, spatially homogenous variancesare assumed, so that (8.25) is simplified to:rrxxxx x xerrtrue=+∑∑ ∑1111 1 1 ∆∆ (8.26)Due to errors, the observed correlation is reduced by the above given expression.Since errors do not dependent on distance, it is a reduction by a constant factor. Themagnitude of this factor is given by a0 in (8.21), because at distance zero the true correlation isequal to 1, so that here the observed correlation becomes equal to the reduction factor. Thus,the true correlation is obtained by a simple division of (8.21) by the factor a0:rexaya =−+212222(8.27)Besides the true spatial correlation, given in (8.27), the individual error variance of theused data points has to be known for solving the Kriging (8.14). As discussed in section 8.2.2,the error of an individual monthly mean is obtained by dividing the intra-monthly variance ofthat particular month by the number of available observations. For months with only a singleobservation the intra-monthly variance is not attainable. In order not to lose the information ofsuch months, the error variance is assumed to be as high as the total variance at this location.Applying the above described technique, monthly fields of the air-sea fluxes arederived. Figure 8.7 shows an example for the latent heat in January 1979, while thecorresponding errors are given in Figure 8.8. In the region south of 40oS the errors areincreasing up to values of 1, which is the theoretical maximum. As mentioned above, a8 - Random and Sampling Errors 118 8.5 Mapping Errors8.6 Satellite sampling errorsReport of the Working Group on Air Sea Fluxes June, 2000normalised error of 1 indicates that no information is available at all. In this case the error isequal to the interannual variability. However, since the interannual variability is extremely lowin the South Atlantic, the absolute error in this region is astonishing small. Thus, we are in alucky position. In regions as the North Atlantic, where due to high interannual variability thedetermination of monthly means is very difficult, many observations are available. In theSouth Atlantic the data base is much smaller. But both intra-monthly and extra-monthlyvariability are low. The first reduces the individual errors of monthly means, the secondguarantees a limitation of the absolute Kriging error, even if the normalised Kriging errorreaches maximum values.Figure 8.7 Example of a Kriging result.Values are normalised by the total standarddeviation at each grid point.Figure 8.8. As figure 8.7, but for theKriging error8.6 Sampling problems for satellite-derived quantities8.6.1 IntroductionTemporal sampling limitations of a sensor can be the most important source of bothrandom and systematic error in longer term (i.e. monthly and longer) applications. This isespecially bad for quantities like precipitation that exhibit a highly intermittent behaviour inspace and time and can also have a significant diurnal cycle. The only way out of this dilemmais to combine measurements of the same kind of sensor at different orbits, or measurementsfrom different instruments like infrared sensors on geostationary satellites and microwavesensors as done in the GPCP. Since the sampling situation is worst for precipitation the nextsection deals exclusively with that variable. For all other satellite-derived quantities, thesampling errors found for precipitation can be regarded as a maximum estimate.8.6.2 Sampling errors for precipitationA paper by Salby and Callaghan (1997) concentrates on the under-sampled diurnalvariability using polar-orbiting measurements. They compared the time-mean behaviour ofglobal cloud distributions to the true time-mean behaviour determined from the high-resolution8 - Random and Sampling Errors 119 8.6 Satellite sampling errorsReport of the Working Group on Air Sea Fluxes June, 2000Global Cloud Imagery (GCI) according to different orbital and viewing characteristics of oneand multiple platforms. The main conclusion is that the pattern of the time-mean error closelyresembles the pattern of error in the mean diurnal cycle. This means that precessinginstruments like TRMM can describe the right time-mean behaviour only as accurately as theycan determine the mean diurnal cycle. If measurements are averaged over many months tomake the determination of the diurnal cycle more consistent, the time-mean behaviour iscontaminated by seasonal variations, which is also not desirable.Another study of satellite orbits has been performed by Shin and North (1988). Theyused a stochastic model and data from the GATE I experiment to represent rainfall statistics.They stated sampling errors for a low inclination satellite at a height of 300km like TRMM of8-12% for a monthly mean rainrate over a grid box of 5° x 5° in the tropical ocean. This isroughly the same as what is found for instruments in a sun-synchronous orbit like the SSM/Iwith heights of around 750km. It should be noticed that for a polar orbiter the maximumsampling error is located near the equator where the sampling interval is sometimes only 24h.For a precessing instrument like TRMM the sampling error increases with increasing latitudebecause of the uneven sampling if so called flush visits (the whole grid box is seen at one visit)are considered. This effect is counteracted by the increasing sampling area with latitude whenpartial visits are considered (Shin and North, 1988). Because there can be some doubt aboutthe validity of GATE rainfall statistics for the whole tropical ocean, Oki and Sumi (1994) usedradar-derived rainfall maps around Japan and found for a 5° x 5° grid box a sampling error of16% which increases at a box of 2.5° x 2.5° to 24% for the TRMM TMI swath. The study ofSoman et al. (1995) using data from the Darwin radar in Australia also stated high samplingerrors up to 65% for sun-synchronous orbiters with a sampling interval of 24h and much lowererrors (5%-10%) for non-sun-synchronous orbiters. However, the last two studies also includedata over land surfaces where the diurnal cycle of precipitation is much more distinctive, and inthe case of the Japanese data, not only tropical convective rainfall but also midlatitude cycloneswere included in the analysis.All the above mentioned studies found that any combination of sun-synchronous withsun-synchronous or with non-sun-synchronous orbits reduces the sampling error to less than10% if an optimal time sampling interval is chosen. With four SSM/Is in space, and twoAMSR's to come within the next year, it should be possible to estimate the time-meanbehaviour of precipitation and all other parameters detectable at microwave frequencies withreasonable quality. But only if all available data are used which is not the case for mostexisting climatologies. Combination of microwave and infrared instruments on low orbit (300-750 km) and geostationary satellites remains attractive if an optimal coupling of methods isapplied.8.6.3 Scatterometer Sampling ErrorsThe irregular sampling from polar orbiting spacecraft leads to significant errors andloss of equivalent resolution in gridded wind fields created from remotely sensed data. Thus,although measurements are acquired densely within swaths, the limited swath widths andspacecraft revisit times result in errors in gridded fields constructed from satellitemeasurements, owing to variability in the true surface field that was not measured by thesatellite instrument. The magnitudes and types of these "sampling" errors are complicatedfunctions of the instrumental coverage (i.e., swath width and orbit) and the natural spatial andtemporal variability of the true surface wind field.Sampling errors and degraded equivalent resolution have important negative impacts onthe scientific utility of remotely sensed data. Ocean and climate models respond sensitively towind forcing on a broad range of scales. Modelling studies by Large et al. (1991) and Chin etal. (1997) demonstrate clearly that large-scale, low-frequency upper ocean circulation issensitive to high-frequency, small-scale wind forcing - the large-scale circulation patternschange if the small-scale wind forcing changes. Small-scale, high-frequency wind forcinginformation is thus a fundamental requirement for climate modelling and prediction,8 - Random and Sampling Errors 120 8.6 Satellite sampling errorsReport of the Working Group on Air Sea Fluxes June, 2000notwithstanding the fact that the climate system variables of interest involve inherently largetemporal and spatial scales. Large et al. (1991) suggest that 2-3 day temporal resolution at 2 x2 degree spatial resolution is required to properly reproduce the intra-annual streamfunctionvariance and barotropic Rossby wave response of the ocean in the mid and high latituderegions. The required temporal resolution should be finer going equatorward, because theRossby wave becomes faster.As an attempt of determining a desirable resolution for scatterometer products, analysismetrics have been developed by Chelton and co-workers (e.g. Greenslade et al., 1997) toquantify the effective resolution of remote sensing systems and have been applied to presentand planned scatterometer systems by Chelton et al. (1997). The latter analysis showed that asingle polar-orbiting, dual-swath scatterometer with NSCAT sampling (600 km wide swathsseparated by a 329 km nadir gap) yielded an equivalent resolution of 2 x 2 degrees and nearly20 days at 30 degree latitude. (Resolution metrics are based on a threshold of 0.72 ms-1sampling-induced random component error. Increased equivalent temporal resolution can beobtained at the expense of lower spatial resolution. These values depend on the selectedthreshold value.) In the satellite scatterometer science community, there are many efforts ofconstructing gridded products using different construction procedures and different temporaland spatial resolutions (e.g. Zeng and Levy, 1995; Kutsuwada, 1998; Kubota and Yokota,1998; Kelly et al., 1999). Continuous swath instruments (such as the one to fly on QuikSCATand ADEOS-2) can achieve smaller spatial and temporal resolutions given the QuikSCAT orbit.Thus, although wind forcing information from spaceborne scatterometers has greatscientific potential and utility, no single polar-orbiting vector wind instrument can provide thedata required to produce wind forcing fields with complete and proper temporal and spatialresolution for climate modelling (independent of the measurement accuracy and resolutionwithin the satellite swath). However, tandem missions, such as QSCAT and ADEOS-2, orpossibly a SeaWinds and an ASCAT instrument, would provide data with sufficient equivalentresolution to allow accurate climate modelling and prediction.9 – Evaluation Methods 121 9.1 Introduction9.2 Sources of reference dataReport of the Working Group on Air Sea Fluxes June, 20009 METHODS OF EVALUATING FLUXES AND FLUX RELATED PRODUCTS9.1 IntroductionThis Chapter will consider methods which can be used for verifying flux fields.Accurate flux data which can be used as a reference for verification purposes are difficult toobtain. In general there is a trade-off between accuracy and temporal and spatial coverage.Thus the most accurate flux estimates are obtained from dedicated air-sea interactionexperiments which are mounted in a restricted area for a limited period, perhaps one or twomonths. The accuracy of these research quality flux measurements will be discussed using theTOGA-COARE experiment as a "State of the Art" example (Section 9.2.1). "Flux" buoys havebeen developed to obtain measurements of high enough quality such that the derived fluxes maybe used for verification purposes. Such buoys have been deployed at a limited number oflocations for typical periods of up to a year, occasionally longer. The data obtained is lesscomprehensive than that from air-sea interaction experiments but covers longer periods andmore locations. The data quality (Section 9.2.2) is significantly better than that obtained fromoperational meteorological buoys. Research ships have been used in "opportunistic" mode toobtain time series of automated meteorological data during cruises designed for other purposes.As an example of these data, the data set obtained from the WOCE Hydrographic Programmeships will be considered (Section 9.2.3). This data set is important in that a special effort wasmade to assemble the data into a single data resource to which consistent quality controlprocedures had been applied. Such ship data can sample a wider range of geographicallocations compared to the high quality buoy data; however the ships are a poorer instrumentplatform, and a long time series from any given area is generally not available. Finally, withregard to sources of flux reference data, the only routinely measured direct flux data is that forradiation, albeit from a limited number of locations. This will be briefly discussed in Section9.2.4 (for more detailed discussion of direct radiation measurements see Section 5.2).For many flux field products, suitable flux reference data are not available. In this casemuch can be learnt by intercomparing one product with another and this method will be usedextensively in Chapters 10 and 11. In this Chapter we will consider two specific cases of suchintercomparisons; the comparison with specialised products (Section 9.3.1) and comparison ofthe variability patterns in flux fields (Section 9.3.2). While intercomparisons allow for relativeassessment of the flux field products there still remains the problem of deciding which productmight more nearly represent reality. There is also the need to identify and correct for residualsystematic errors in the data. A number of integral constraints are available and have variouslybeen used for that purpose. These include the global heat and water balance (Section 9.4.1), themeridional heat and freshwater transports (Section 9.4.2), the heat and water balance ofenclosed (or semi-enclosed) seas (Section 9.4.3), and ocean mixed layer budgets (Section 9.4.4).9.2 Sources of reference data9.2.1 The accuracy of research quality measurements: experience from TOGA-COAREThe most accurate source of basic observations for calculation of fluxes or validation ofmodels is expected to be that obtained from ships and moorings during the small (butincreasing) number of intensive air-sea measurement campaigns mounted each year in supportof some particular research project (e.g. BOMEX, TOGA-COARE, SOFIA, JASMINE, etc).The instruments and methodology are usually under the supervision of scientists with particularexpertise in field measurement and instrumentation. However, the harsh marine environmentand limitations imposed by the platform itself, can easily degrade instrument performancerelative to its calibration in the laboratory or exposure on dry land, and in unexpected ways.The state of the art for individual variables will be discussed at more length in Section 10; herewe summarise the accuracy of research quality observations, as revealed by the TOGA-COAREexperiment November 1992 - March 1993.9 – Evaluation Methods 122 9.2 Sources of reference dataReport of the Working Group on Air Sea Fluxes June, 2000The TOGA-COARE expedition involved 15 ships, 7 aircraft and several moorings, oneof which (the IMET mooring from WHOI; Hosom et al., 1995) was developed specifically toobtain high quality meteorological measurements for flux calculation. Six survey ships werespecially equipped for the measurement of fluxes. The cruise plans of these ships were co-ordinated to allow 2 days from the experiment total of 120 days to be dedicated to in situcomparison of individual instruments and, subsequently, the calculated fluxes. This was doneby cruising upwind in close formation in the vicinity of the IMET buoy. Aircraft which wereoperational on those particular days performed repeated low-level fly-overs.The quality of surface flux measurement achieved during TOGA-COARE is due inlarge measure to these field intercomparisons, carefully analysed during a series of workshopsinvolving PI's responsible for measurements on the various platforms (Bradley and Weller,1995a,b; 1997; Bradley et al, 1997). Instrumental faults and calibration errors were discoveredwhich would otherwise have remained undetected. The usual procedure was to obtain “best”estimates for each variable during the intercomparison days. In some cases a particularinstrument was identified as being the most reliable, on the basis of proven performance,stability of calibration, or mode of operation. Otherwise, an ensemble average was taken as the“correct” observation. At the end, bias and calibration corrections were obtained for allinstruments; those with obvious calibration or other problems were recalibrated where possible.The above effort to refine accuracy of the surface measurements was driven by theTOGA-COARE goal of no more than 10 Wm-2 uncertainty in the total surface energy budget ofthe ocean (WCRP, 1990). As Fairall et al. (1996a) point out, this implies accuracy requirementsfor the individual bulk measurements, assumed independent, of about 0.2 ms-1 for wind speed,0.2°C for both air and sea surface temperature, and 0.2 g kg-1 for humidity. Subsequently, Burnset al. (1999, 2000) have compared aircraft measurements with the surface platforms, using datafrom the two days and other occasions of coincidence during the experiment. Table 9.1 givesestimates by some PI's of the accuracy of their measurement following the TOGA-COAREintercomparison process, as provided to Burns et al. (1999, 2000). Some of these estimatesappear rather optimistic; others seem conservative. Without details of how they were arrivedat, and what sources of uncertainty are included, they can be regarded as a guide only, butillustrate some useful points (see also section 10).Table 9.1 Subjective estimates (by the observers) of the accuracy of measurement on someplatforms following TOGA-COARE intercomparison corrections. The last row is an estimateof what might be presently achieved under similar, tropical or subtropical, weather conditions.Accuracies in higher latitudes are likely to be worse.Platform Windsms-1AirTemp.°CHumidityg kg-1SeaTemp.°CShortwaveWm-2LongwaveWm-2R/V Moana Wave ±0.2 ±0.2 ±0.4 ±0.2 ±4 ±2.5R/V Wecoma ±0.2 ±0.2 ±3%R/V Franklin ±0.2 ±0.05 ±0.15 ±0.1 ±3 ±5IMET ±2.5% ±0.1 ±0.1 ±0.1 ±3 ±10ATLAS ±0.2 ±0.17 ±4% ±0.03Present practice ±0.2 ±0.1 ±0.2 ±0.25(SSST)±3 ±2.5❍ Wind speed: all PI's considered that their wind speed measurements met the COARE goalof 0.2 ms-1 accuracy❍ Air temperature and humidity: on R/V Franklin Ta (and Qa ) were measured with aspiratedpsychrometers, mounted on a boom ahead of the ship. Over land, accuracy of one or twohundredths of a degree in both wet and dry bulb temperatures can be achieved with such9 – Evaluation Methods 123 9.2 Sources of reference dataReport of the Working Group on Air Sea Fluxes June, 2000instruments, but shipboard this degrades for several reasons. Other temperature/humiditymeasurements in the table were made with commercial sensors, comprising a Platinumresistance thermometer and moisture-sensitive polymer, basic specifications for whichbarely meet the COARE criterion. However, the most serious source of error (>1˚C)arose from diurnal heating when the sensors were mounted in simple unaspirated screens.For this, a correction algorithm was devised (Anderson and Baumgartner, 1998). Then allair temperature measurements met the COARE goal, but not all humidities.❍ Sea temperature: higher accuracy is possible for water temperature, using precisionthermistors, because solar heating is generally negligible, the water temperature variesslowly, and because the heat capacity of water is much higher than air. During theCOARE intercomparisons, night time sea temperatures agreed within 0.1˚C across severalplatforms when the surface layer was well mixed, implying good calibration technique.However, the quoted accuracies are for sensors at various depths, and do not reflect theaccuracy of skin temperature measurement (SSST); this requires extrapolation to thesurface via models of the cool skin and diurnal heating (Fairall et al., 1996a). The physicsof the former phenomenon is well enough understood that it may be calculated to order0.1˚C. Thus the accuracy of SSST derived from the Moana Wave sensor at ~0.05m depthis probably ±0.25˚C, whereas the other platforms must add the uncertainty of a mixedlayer model to the quoted accuracy. With the best current instruments, direct radiometricmeasurement of SSST has an accuracy of about 0.2˚C (Barton et al., 1995).The COARE intercomparison initiative alerted observationalists to several potential sources oferror in air-sea measurement. This, together with some instrument development since COARE,might be expected to improve the accuracy of measurement of basic flux-related variables insubsequent air-sea interaction studies. The bottom line in Table 9.1 is an attempt to estimatemeasurement accuracy currently achievable, using equipment readily available to theinvestigator. A final word of caution is necessary however. TOGA COARE took place in aregion of relatively benign weather. Achieving these accuracies in stormy conditions at mid tohigh latitudes may be significantly more difficult.9.2.2 High quality buoy dataThe establishment of "Flux calibration sites" for verifying the fluxes from climatologies(based on in situ and satellite data), and from models, was recommended by WCRP (1989).Perhaps the nearest realisation of such sites has been the series of mooring deploymentsundertaken by the Woods Hole group; for example, the Subduction experiment in the NorthAtlantic (Moyer et al., 1996), the Improved Meteorological System (IMET) mooring in thewest Pacific during the TOGA COARE experiment (see previous section - Weller andAnderson, 1996), and in the Arabian Sea (Weller et al., 1998). Although often referred to as"flux buoys" these buoys only make direct measurements of the downward radiative fluxes.The turbulent fluxes are determined using accurate measurements of the basic variables and thebulk aerodynamic formulae.The IMET instrumentation resulted from extensive sensor testing and developmentduring the World Ocean Circulation Experiment (WOCE). In addition to the surface moorings,IMET has been installed on a number of the U.S. Research Vessels and is now being placed onU. S. VOS. The IMET instruments were chosen, following on laboratory and field studies, foraccuracy, reliability, low power consumption, and their ability to stay in calibration duringunattended operation. The actual sensors were combined with front end and digital electronicsto make a module that is digitally addressable (RS- 232 or RS-485), and, importantly, retains itscalibration information. The present set of IMET modules includes wind speed/direction, airtemperature, sea surface temperature, relative humidity, precipitation, incoming shortwaveradiation, incoming longwave radiation, and barometric pressure.Laboratory calibrations and in situ calibration studies and intercomparisons have beenused to assess the accuracy of the IMET sensors as installed on buoys. Figure 9.1 summarisesthe progress that has been achieved in reducing measurement error over a series of air-sea9 – Evaluation Methods 124 9.2 Sources of reference dataReport of the Working Group on Air Sea Fluxes June, 2000interaction experiments. However it must be remembered that the accuracy of the turbulentfluxes will also depend on the accuracy of the bulk formulae used to calculate them (Section 6).050100150200250BunkerLOTUS82LOTUS84FASINEX1FASINEX2IMETSubductionCOARE lowCOARE highArabianCLIVARError (W m-2)Total heatShortwaveLongwaveLatentSensibleFigure 9.1. The estimatedreduction of measurement errorin the components of the heatflux and net heat flux associatedwith surface mooringdeployments during variousexperiments since the early1980s. The errors estimated inclimatologies of the early 1980s,such as Bunker’s, are given as astarting point (from Weller andTaylor, 1999)Future developments of flux buoys could include the addition of instrumentation tomeasure the direct and diffuse solar radiation (e.g. Reynolds, 1998) and to provide turbulentflux estimates using eddy correlation and/or inertial dissipation.A particular cause for concern with buoy data is the accuracy of the measurements ofthe wind speed. In strong winds these are believed to be biased low (Large et al., 1995; Wellerand Taylor, 1999; Zeng and Brown, 1998). During the Storm Wind Study 2 experiment, SWS-2(Dobson et al., 1999; Taylor et al., 1999b) 10m neutral equivalent winds were estimated usingsonic anemometers on a buoy (at 4.5m) and a nearby research ship (at 17.5m). A preliminarycomparison of the measured wind speed values is shown in Figure 9.2. On average the buoyappears to under estimate the wind by about 5%, however the data are very scattered.30 20 10 0-505101520wind speed (m/s)difference (ship-buoy) % or Error (%)Large et al. (1995)2m4.5m17.5m1.5m3.0mZeng & Brown(1998)Figure 9.2. The difference (closedcircles: (ship - buoy) %) betweenvalues of the 10m neutral windfrom buoy and ship data during theSWS-2 experiment for cases wherethe separation was less than 10 km(anemometer heights 4.5m and17.5m respectively). Also shownare the calculated effect of verticalmovement through a logarithmicwind profiles for instruments atheights between 1.5m and 17.5m(light dashed lines); the mean errorcurve reported by Large et al.(1995) and the polynomial of Zengand Brown (1998). (from Taylor etal. 1999a)Two possible mechanisms for the under estimate have been suggested. First, assumingthat the mean wind profile is logarithmic, an instrument being moved up and down vertically bythe waves will measure an average wind which is less than the wind at the mean measurementheight. Using the observed wave height to wind relationship for SWS-2, this effect has beencrudely estimated for different anemometer heights (light dashed lines on Figure 9.2). Zeng andBrown, (1998) noted that there were a lack of high wind speed data in buoy observations used9 – Evaluation Methods 125 9.2 Sources of reference dataReport of the Working Group on Air Sea Fluxes June, 2000for scatterometer calibration. They used surface air pressure data to infer a low bias for buoywinds at higher wind speeds. Their polynomial relationship (also shown on Figure 9.2) appearsvery similar to what might be expected due to the logarithmic averaging for a 3m anemometerheight - not an unreasonable mean anemometer height for the mix of buoy data which they used.The second mechanism is that the instrument may enter regions where the vertical windprofile is distorted due to the sheltering effect of the waves. Large et al. (1995) have suggestedthat this effect significantly biases buoy wind data for wind speeds above some threshold. Theirpredicted error for a 5m anemometer height is also shown on Figure 9.2, it is much greater thanthat predicted by Zeng and Brown (1998). The preliminary SWS-2 results shown on Figure 9.2.appear to be of similar order to the Large et al. (1995) prediction, however the measured frictionvelocity values suggested the wind error in the 20 to 25 ms-1 region was 3% to 5% (similar toZeng and Brown) rather than 15% or more. More work is needed before the bias in buoy windspeed measurements can be adequately quantified.9.2.3 High-Quality Automated Data from shipsAs mentioned previously, the IMET systems have been installed aboard numerous (sixto eight) US research vessels. Additionally, similar systems are also found on other (i.e. non-US) research vessels. The WOCE Data Assembly Center (DAC) for surface meteorology,located at COAPS/Florida State University, has assembled, reviewed, and made available asizeable record of in-situ surface meteorological data from research vessels participating inWOCE scientific cruises (approximately 1993 to present). Many of these data originate fromIMET (and similar) automated systems that record and report data continuously. These data areconsidered to be of higher quality because of the quality of the instrumentation, complete andcontinuous data collection, and documentation efforts. Because these WOCE data are highlyqualified, extensive quality-control reviews were undertaken and suspect data were flagged (seeSmith et. al., 1996a). For the WOCE period, many of these data are from relatively poorly-sampled regions of the southern hemisphere oceans. Finally, many of these observations do notappear in the COADS nor are they reported via the GTS (implying that they are NOT includedin the reanalyses or NWP products). Some of the ships do report surface meteorological valuesat synoptic hours, but again the data from the automated systems are not typically reported.Work is in progress to quantify the differences between simultaneous COADS and theautomated system observational data.Because of their higher-quality, uniqueness, and data coverage characteristics, theseresearch vessel data are valuable for evaluating various flux products. As is the case for otherhigh-quality reference data, the research vessel data allow an evaluation of flux products onmuch smaller (e.g. synoptic) time scales, rather than climatological or other longer-averagedquantities. The disadvantage of this approach is the limited spatial and temporal coverage (e.g.Figure 9.3). While the ships do sometimes make observations in normally under sampledregions, the research vessel database includes typically few cruises to a region, thus limiting theinterpretation of the results.The approach of implementing the automated research vessel data is straightforward,but there are many details (see Smith et. al., 2000). Automated meteorological data werereviewed and those of the best quality selected for the comparison. The observations are firstheight adjusted, and can be compared to gridded analysis values (nominally every 6 hours).Turbulent flux values (Smith, 1988) can then be calculated and averaged over 6-hour (or longer)periods (to match the integration period of the analysis). These values are also matched togridded analyses (e.g. NCEP Reanalyses) for further analysis.Note that data from other research ships and platforms may be utilised for fluxevaluations. These data may not be of the same high quality as those described above, but otheradvantages such as longer time records and unique combinations of observations may qualifythem for addressing other aspects of the flux fields under review. For example, Ocean Weather9 – Evaluation Methods 126 9.2 Sources of reference dataReport of the Working Group on Air Sea Fluxes June, 2000Ship stations (see section 4.2.2) provide time series of meteorological measurements, mostlyobtained by professional meteorologists, spanning about two decades in 11 sites in the mid-latitudinal North Atlantic and North Pacific with a time resolution of at least 3 hours. Thesedata are considered to be of higher accuracy than the regular VOS observations. In addition,during the period 1981 to 1991, a considerable amount of meteorological measurements werecollected in the Northwest Atlantic by six Russian sister ships (which also operated at OWS C).The data set contains about 50000 meteorological reports with temporal resolution from 1 to 3hours (Gulev 1999). All measurements are taken by trained meteorologists using knowninstruments and at known observational heights. A considerable portion of the routineobservations (about 30%) are accompanied by direct observations of SW and LW radiation thatincreases the value of this regional data set.Figure 9.3 Map of positions ofmeteorological data obtained fromresearch ships during the WOCEexperiment for the period 1988 to1998 as held at COAPS, FSU. Thecolours of the ship tracks indicatethe data sampling rate:9.2.4 Data for verification of satellite productsA. COMPARISON OF SATELLITE PRODUCTS WITH IN SITU DATAA common way to determine the quality of satellite-derived bulk parameters, fluxes,and precipitation is by comparison to different in situ data sources. There is always a problemof finding in situ data of good quality which are globally distributed. For precipitation someauthors, e.g. Smith et al. (1998) support the opinion that the quality of in situ measurements isnot good enough for validation or calibration of satellite methods. For turbulent fluxes it isclear that only a handful of datasets of direct flux estimates exist and are available to thecommunity of satellite algorithm developers. The consequence is that most of theintercomparisons between satellite derived quantities rely on locally restricted datasets fromscientific experiments or on the more widely distributed observations from merchant shipstransmitted via the GTS. Where comparisons are made with ship data, either through individualobservations or mean values, the errors inherent in the ship data must be taken into account.Since the ship data can be of doubtful quality, global in situ products like the da Silva et al.(1994) or SOC climatology (Josey et al. 1999) are sometimes used to avoid the problemsinherent in the individual observations. However, this introduces a further problem that thecomparison is very much dependent on the data analysis procedure used for the production ofthe in situ climatology.A recurrent problem in any comparison of in situ and satellite products on aninstantaneous time scale is the mismatch in time and space between the various measurements;point measurements are compared to field averages. The effect these two mismatches have onthe result of a given comparison depends on the spatial and temporal scale of the variations inthe variable considered.9 – Evaluation Methods 127 9.2 Sources of reference data9.3 Intercomparison of field productsReport of the Working Group on Air Sea Fluxes June, 2000B. SOURCES OF RADIATION PRODUCTSThe direct measurement of radiation was discussed at some length in Section 5.Networks of surface measurements have been implemented for verification. Unfortunately, todate, coverage of the sea is minimal. Available data sets include those from the Baseline SurfaceRadiation Network (Gilgen et al., 1995), the Global Energy Balance Archive (GEBA) databaseof surface flux measurements (Gilgen and Ohmura, 1999), the NOAA/Climate Monitoring andDiagnostics Laboratory (CMDL) data set (Stone et al. 1996). Most GEBA sites are located inthe midlatitudes; a few of them are located on islands.The CERES/ARM/GEWEX Experiment (CAGEX; Charlock and Alberta, 1996) data isavailable on-line at: http://snowdog.larc.nasa.gov/cagex . CAGEX provides a space-time set ofinput data, calculated fluxes, and validating measurements. With the launch of CERES,CAGEX has been extended to the ARM Tropical West Pacific (TWP) site. ARM TWP data isexpected to become a great resource for the study of radiation at the air-sea boundary. TheARM web page is: http://www.arm.gov . Also for CERES, work on the long-term CERESOcean Measurement Platform for Aerosol and Radiation Evaluation (COMPARE) has begun atthe Chesapeake Light, 25 km off the Virginia coast.Otherwise satellite radiation data has been verified by comparison with other, higherquality fields. For example, in addition to GEBA data, the GEWEX SRB files provide forverification the monthly TOA net flux by independent, higher quality measurements from theEarth Radiation Budget Experiment (ERBE; Barkstrom et al., 1989) over most of the globe.Unlike the narrow-band radiometers on the ISCCP satellites, ERBE provides broadbandmeasurements with on-board calibration for SW and LW.9.3 Intercomparison of field products9.3.1 Comparison to Specialised ProductsOperational centres have historically had difficulty producing satisfactory wind fieldsover the tropical oceans (McPhaden, et al., 1988). Specialised products were developed to meetthe need for near-real time tropical wind fields. These specialised products include the muchutilised Florida State University (FSU) tropical Pacific pseudo-stress product (Stricherz, et al.,1997) as well as a similar product for the Indian ocean (Legler, et al., 1997), and for the Atlanticthe ORSTOM/IFREMER product (Servain, et al., 1996). These monthly mean fields of pseudo-stress are based on in-situ data, that is COADS (for real-time production, GTS reports areutilised; sometime later COADS-type data are utilised in a reanalysis to produce a researchquality product) and extend for many years. The Pacific and Atlantic analyses are available for1964 to present. The Indian Ocean analysis from 1970 to present. The analysis methodologyfor these fields is largely subjective for the Pacific product, objective/subjective for the Atlantic,and objective for the Indian ocean. Continually produced in the same way for more than adecade, these analyses have been very popular, but resolvable spatial resolution (primarily in thezonal direction) is rather coarse, and adjustments to account for observation system changeshave not been considered. Nevertheless these analyses are the longest set of consistentlyanalysed (and highly referenced/utilised) tropical wind products available to the community.There is value in evaluating new products such as the NCEP/NCAR (NCEPR) andECMWF (ERA) reanalysis surface wind products in the light of these proven products. Thecomparison can be based on differences of monthly pseudo-stress fields for the commonanalysis period, 1979-1993. Comparison of climatological characteristics as well as anomalousfeatures, particularly those associated with the ENSO phenomena, would be directly useful forpotential users of the reanalysis winds.9.3.2 Comparison of Variability patterns in different flux productsThe task of intercomparison of variability patterns derived from different flux productsis quite different from the intercomparison of climatologies of the mean fluxes. Indeed, even ifthere is an agreement on mean values, variability patterns may not necessarily be in agreement9 – Evaluation Methods 128 9.3 Intercomparison of field productsReport of the Working Group on Air Sea Fluxes June, 2000and vice versa. When the climatological means are removed, the comparability of fluxanomalies can be considered in terms of the general level of interannual variability,characterised by standard deviations, secular tendencies, and space-time patterns of interannualvariability.Comparison of variability in different flux products with long-term time series of highquality measurements is highly desirable. The longest time series of high quality measurements(of up to four decades in length) are those from the Ocean Weather Ships (OWS’s). Forexample, careful analysis of OWS wind records (Isemer 1995) does not support the conclusion,based on COADS data (Wu and Li 1995), that winds and evaporation exhibit significantpositive trends in recent decades. The NBDC buoys provide a somewhat shorter time series ofabout one to two decades which nevertheless can be used for the validation of the variabilityreported by VOS and NWP climatologies. For some variables (e.g. winds, precipitation)comparisons against coastal and island stations may be useful. Coastal and island stationsprovide longer time series than those available over the sea. Bigg (1993) used coastal stationsin the Tropical Atlantic to verify COADS winds and found differences in trend estimatesderived from different sources. However, one has to be very careful in the extensive use ofcoastal and island data for comparison with VOS observations. First, these data may beinfluenced by different local effects, which may affect the data over the land and also over thecoastal waters. Thus the comparison may be unrepresentative of the open ocean. Secondly,historical changes in observational practice may have occurred for both coastal and island data,and the effects may be of the same order of significance as inhomogeneities in the time series ofocean measurements.When different flux products are compared to each other, they may indicate differentvariability patterns, and these differences may have importance for climate studies. In thiscontext it is important to quantify the common patterns which are present in different analysedproducts. Recently Barnett (1999) suggested the use of so-called “common EOF's” for theintercomparison of different space-time fields. The common EOF shows the pattern ofvariability commonly shared by all the fields analysed and illustrates the features which can bediscussed with confidence given the present state of our knowledge. Figure 9.4 shows, as anexample, the first two common EOF's of the latent heat flux from the four flux products whichwere shown analysed separately in Figure 3.1. The first EOF accounts for 28% of the totalvariance and the 2nd EOF explains 12% of variance. The common normalised principalcomponents have similar temporal behaviour and indicate quite pronounced correlation witheach other. For this case, the common EOF's (Figure 9.4) show that the large-scale subpolar-subtropical dipole is present in all the flux products used and therefore can be discussed withconfidence. However, although the Labrador Sea pattern is the outstanding feature ofNCEP/NCAR fluxes, it does not appear in the other flux products.Fig. 9.4 1st two common EOF's for the latent heat flux for the analyses shown in Figure 3.1.(These were the NCEP/NCAR reanalysis; COADS ship reports; the reanalysis sampled atCOADS report positions; and COADS MSTG data.)9 – Evaluation Methods 129 9.4 Integral ConstraintsReport of the Working Group on Air Sea Fluxes June, 20009.4 Integral constraints9.4.1 The global heat and water balanceAn additional heat flux of 10 Wm-2 over one year would, if stored in the top 500m ofthe ocean, heat that entire layer by about 0.15°C. Temperature changes on a decadal timescaleare at most a few tenths of a degree (e.g. Parilla et al., 1994) so the global mean heat budgetmust balance to better than a few Wm-2. At present few global flux products achieve thisbalance, so this is a useful integral test of flux data sets. Using the global water balance as aconstraint is more difficult because of the need to know river run off (section 10.9) and changesin ice amounts.9.4.2 Meridional heat and water transportA. INTRODUCTIONThere is a long standing tradition to judge the reliability of global flux fields on thebasis of the implied meridional heat transport (MHT) and meridional fresh water transport(MFWT). These quantities are the integrals of the zonally averaged surface heat (H) and water(W) balances in the meridional direction:MHT d Hd HMFWT d Wd W=+=+∫∫∫∫ϕλϕλϕϕϕϕ0000 (9.1)Here H0 and W0 are the boundary conditions at the starting point of integration, whichhave to be taken from alternative estimates. For example, at approximately 70°N in the NorthAtlantic, the MHT estimate is traditionally taken from Aagaard and Greismann (1975), who givethe value 0.107 PW, although Gulev (1995) and Bacon (1997) give a somewhat higher value,close to 0.3 PW. For MFWT the boundary condition at the north results from the fresh waterflux through the Bering Strait which is estimated to be 0.78 Sv (Coachman and Aagaard 1988).The MHT and MFWT estimates, obtained from the heat and fresh water balances at theocean surface, may be compared to oceanographic estimates obtained from hydrographic cross-sections. These oceanographic estimates are normally considered to be of higher accuracy thancan be achieved by integration of the surface fluxes. For example, typical uncertainties of MHTthrough oceanic cross-sections in the Atlantic are 0.3-0.4 PW (Hall and Bryden 1982, Rago andRossby 1987, Molinari et al. 1990, Koltermann et al. 1999). In contrast, the uncertainty of thesurface flux estimates propagates quickly into large errors in the meridionally integrated surfaceheat and fresh water budgets. For the Atlantic Ocean, an error in zonally averaged net heat fluxof 15 Wm-2 results in an uncertainty of MHT estimate of ±0.5 PW at 30°N, and up to ±1 PW atthe Equator. And yet even this level of accuracy for the zonal net heat flux is a challengingtarget for any climatology (Gleckler and Weare 1997); the situation is even worse with regard tothe fresh water flux accuracy. The same situation holds for VOS-based, remotely sensed, orreanalysis based products. Thus although MHT is a very important physical parameter, it haslimitations with regard to determining the reliability of surface flux fields.A better proposition is to use the oceanographic estimates of heat and freshwater fluxesthrough chosen hydrographic sections to determine the implied net surface flux between thosesections. This value can then be compared with the mean flux derived from the climatologies.Since the latter do not need to be integrated the resulting comparison is more likely to beinformative.B. ACCURACY OF MHT AND MFWT FROM SURFACE FLUX INTEGRATIONExisting estimates of MHT derived from surface fluxes vary in a very wide range evenin such well sampled area as the North Atlantic. Figure 9.5 summarises the sources of the9 – Evaluation Methods 130 9.4 Integral ConstraintsReport of the Working Group on Air Sea Fluxes June, 2000uncertainties inherent in the existing MHT estimates (Gulev et al. 2000). In order to quantifythe possible range of variability of MHT estimates, individual LMR from the COADS Releases1 and 1a were used for the period 1958-1997, which overlaps the NCEP/NCAR Reanalysisperiod. The most frequently used parameterisations for flux components and variablecorrections were applied to the COADS reports. Their cross-application gave the possibility toproduce an ensemble of the net flux and MHT estimates for the North Atlantic Ocean and tosimulate the wide range of approaches used for flux computation by different authors. Ofcourse not all of these parameterisation schemes are equally valid, so this simulation willinclude the worst possible combination, as might be constructed by someone with completeignorance of the merits of the different algorithms!- 2 0 - 1 00 1 02 03 04 05 06 07 0L A T I T U D E-15-10-50510152025MHT, 10**14 WSTD (variables corrections)STD (different schemes)zonal error of 10 W/m*mNCEP/NCAR MHTJosey et al. (1998)Fig 9.5. Standard deviations of MHTcomputed from the VOS data for the period1957-1998, corresponding to the variabilitydue to the choice of different schemes andvariable corrections. MHT estimatesderived from the NCEP/NCAR Reanalysisfluxes and from the VOS using Josey et al.(1998) method are also shown. The latterauthors chose not to adjust their fluxestimates to give global heat balance; hencethe apparent bias in the MHT estimate.The application of the different parameterisations and corrections to the variables mayresult in a ±4 PW range of MHT estimates at 20˚S. At 25˚N, where we have more or lessconsistent MHT estimates from oceanographic cross-sections, the range is 3 PW. The standarddeviation of MHT estimates with different variable corrections is approximately adequate to 20Wm-2 zonal error in the net flux, and the standard deviation which results from the applicationof different schemes is somewhat greater, equivalent to about 24 to 26 Wm-2. This is perhapsnot surprising since the worst possible combination of parameterisation schemes has beenincluded in the set. Analysis of different factors shows that the correction of winds, the choiceof the equivalent Beaufort scale, and the choice of the scheme for turbulent fluxes are crucialwith regard to the variations of MHT estimates.With regard to the MHT estimates available from NWP systems, Figure 9.5 also showsvalues computed from the NCEP/NCAR Reanalysis fluxes for the 1958-1997 period. In termsof comparability to the oceanographic estimates, it looks like quite reasonable, givingapproximately 1 PW northward transport at 25°N.C. VARIABILITY OF MHT AND MFWTRecent WOCE results based on sections carried out in different years (Koltermann et al.1999) show that both MHT and MFWT can vary over a wide range, for example about 30% to50% for MHT estimates in the mid-latitudinal North Atlantic. Thus Table 9.2 shows estimatesof MHT and MFWT through three North Atlantic cross-sections for the late 1950's, early 1980'sand early 1990's. Large interdecadal variability in MHT and MFWT in the North Atlantic midlatitudes is possibly suggested, with transports in the subtropics being more steady. Modelsimulations of the interdecadal variability (Knochel 1999) also show interannual variability ofMHT and MFWT, although in a smaller range of 0.1 PW.The variability of MHT and MFWT may explain a paradox: transoceanic hydrographicsections have been employed for some time in inverse calculations to determine the exchange of9 – Evaluation Methods 131 9.4 Integral ConstraintsReport of the Working Group on Air Sea Fluxes June, 2000heat between the major ocean basins (Macdonald, 1998). However, according to the methodused, heat transport estimates at one individual section may differ by almost 100%, a range fargreater than the error bars provided with the estimates. The WOCE observations show that inmany parts of the ocean, there is a significant divergence of the meridional heat flux with largeseasonal changes and weaker interannual variability. Hence, considering the "instantaneous"character of a section, a large range in heat flux estimates is to be expected. Recent attention tothis issue by models and observations have not yet narrowed the range, but there is optimismthat further analysis of the diverse WOCE data set will provide better estimates of the spatialand temporal variability of the meridional heat flux, and will narrow the range and produce errorbars of greater significance. As an example, Ganachaud (1999) has used an inverse box modelto provide estimates of the net input/output of heat over large portions of ocean basins, definedbetween WOCE sections with an accuracy ranging from 0.1 to 0.5 PW, which makes theseestimates useful for surface flux evaluation (Figure 9.6).Table 9.2. Meridional heat flux (Pw) and meridional fresh water transport (Sv) in the NorthAtlantic derived from hydrographic data at 24˚N, 36˚N and 43-48˚N (Koltermann et al. 1999)Year LATITUDE24° N3 6 ° N 43-48° NMHT PW MFWT Sv MHT PW MFWT Sv MHT PW MFWT Sv1957-59 1.38 ± 0.29 -1.19 ± 0.31 0.47 ± 0.24 -0.77 ± 0.17 0.27 ± 0.15 -0.87 ± 0.171981-82 1.48 ± 0.20 -1.22 ± 0.22 1.29 ± 0.17 -1.26 ± 0.13 0.62 ± 0.11 -1.03 ± 0.131992-93 1.54 ± 0.19 -1.31 ± 0.21 0.70 ± 0.15 -1.14 ± 0.14 0.53 ± 0.12 -1.02 ± 0.13270˚ 300˚ 330˚ 0˚ 30˚ 60˚ 90˚ 120˚ 150˚ 180˚ 210˚ 240˚ 270˚-60˚-30˚0˚30˚60˚-0.53_ +0.110.5_ +0.280.013_ +0.48-0.33_ +0.50.33_ +0.310.31_ +0.15-0.28_ +0.160.063_ +0.18 0.13_ +0.53 -0.61_ +0.54 -0.28_ +0.21 0.45_ +0.19-0.039_ +0.341.5_ +0.39-0.63_ +0.180.024_ +0.089-0.056_ +0.320.029_ +0.36-0.48_ +0.591 PWUncertaintyINPUT (PW)OUTPUT (PW)HEAT-1.3±0.8-1.6±0.4-1.4±0.20.56±0.11.26±0.10.2±0.30.8±0.60.94±0.40.8±0.20.45±0.150.6±0.11.3±0.150.8±0.20.9±0.6 1.7±0.31.4±0.25-0.01±0.050.56±0.20.5±0.40.6±0.21.36±0.14Figure 9.6. Estimates of heat transport (PW) across hydrographic sections(black labels), and netinput/output of heat (PW) through the surface between sections (ocean cooling in blue, heatingin red) obtained by Ganachaud (1999) with an inverse box model. Error bars are from a verycareful error analysis and are independent of the model. The net heat input between twosections does not match exactly the residual of the net transport because it has been correctedfor mass residuals (noise correction).In general, the quantitative estimate of the magnitude of interannual variations in MHTis still under debate, although most researchers agree on the qualitative conclusion that it isvariable. Therefore, reasonable comparative assessments should be done for certain years (or at9 – Evaluation Methods 132 9.4 Integral ConstraintsReport of the Working Group on Air Sea Fluxes June, 2000least pentads). However this approach leads to more problems with the surface balanceestimates. First, the accuracy of the net flux for the selected years will be reasonably lower incomparison to the accuracy of the long-term climate mean. Second, computing MHT andMFWT for the selected years using surface heat and fresh water balances, one has to account forthe interannual heat storage and fresh water storage changes. Since these changes take place inthe whole ocean column (in contrast to the seasonal changes which occur primarily in the mixedlayer) their accurate estimation is impossible at the present level of hydrographic data coverage.Gulev (1995) made an estimate of the possible uncertainty in MHT due to the heat storagechanges corresponding to the decadal heating (cooling) of 0.2˚C, corresponding to physicallyreasonable zonally integrated interdecadal temperature variations. The uncertainty obtained was0.3 PW at 40°N and 0.5 PW at the Equator in the North Atlantic, and this uncertainty has to becoupled with the random uncertainty of the flux estimates.D. ADJUSTMENT OF FLUX FIELDS USING INTEGRAL CONSTRAINTSFlux fields derived from VOS observations tend to show more heat going into the oceanthan can be realistic. For example, despite carefully choosing the parameterisation schemes andapplying corrections for known observation errors, the recent Josey et al. (1999) fluxclimatology suggested a net heat flux into the ocean of 30 Wm-2. It is therefore not surprisingthat this climatology shows an unrealistic variation of the MHT (see Figure 9.3). Indeed, Figure9.3 shows that even an error of 10 Wm-2, which is often considered to be desirable for climateresearch, integrates to a significant uncertainty in the net heat flux. This problem has generallybeen overcome by adjusting (or tuning) the flux estimates to various oceanographic estimates ofMHT. Based on error estimates for the different variables, reasonable ranges are determined forthe transfer coefficients and other parameters. The inverse problem is then solved to adjustthose parameters, within the allowed range, to give reasonable agreement between the impliedMHT and the known oceanographic estimates. This method was used by Isemer et al. (1989) totune flux estimates for the North Atlantic. Using a similar technique, da Silva et al. (1994)provided both unadjusted flux values, and flux values tuned to various oceanographicconstraints. Earlier, Oberhuber (1988) also tuned his flux estimates in order to arrive at areasonable MHT in the South Atlantic. He decreased the net shortwave and increasedevaporation by imposing an arbitrary increase of the Charnock constant in his computations.Recently, Lindau (2000) also used tuning (to a number of oceanographic sections in theAtlantic) to produce MHT estimates based on the IFM climatology (see Section 11.2.5).Although this approach appears very attractive, and provides the possibility of basinscale closure, there are a number of problems. Tuning does not guarantee that the individualflux components reflect reality (apart perhaps from the tuned net flux). The degree of tuning islimited by the dimensions of the domain, and, for example, tuning of fluxes for the northernNorth Atlantic, based on the cross-sections located north of 36°N, requires physicallyunreasonable changes to the variables and coefficients. The observed decadal variability ofMHT suggests that time-dependent tuning may be required, at least for the mid latitudes. Joseyet al. (1999) did not tune their flux estimates to the MHT arguing that to do so on a global scalewould degrade the agreement between their flux estimates and high quality buoy observations.They argued that regionally varying flux corrections are required and that the number ofoceanographic values was too few to allow adequate regional corrections to be defined.However the quality and number of such estimates is increasing and further progress on thisproblem should soon be possible.In addition to matching the MHT, matching the MFWT would provide a furtherconstraint. However there are only few VOS-based estimates of MFWT (e.g. Wijffels et al.1992), and they have larger uncertainty than that for MHT due to the large errors in theprecipitation.In summary, the problem of the reliable estimation of MHT and especially MFWT fromdifferent sea-air flux fields is still open. Although there are obvious uncertainties inherent to theoceanographic estimates, we have to conclude that the accuracy of these estimates is still9 – Evaluation Methods 133 9.4 Integral ConstraintsReport of the Working Group on Air Sea Fluxes June, 2000significantly better than that obtained by integrating surface flux estimates. Achievingconvergence between the two kinds of estimates is highly desirable. However, as measures ofthe reliability of flux products, the MHT and MFWT result in significant uncertainty. Nor doescompatibility of the estimates of MHT and MFWT guarantee the reliability of the individual fluxcomponents. Thus, the development of flux products should be primarily based on thejustification of parameterisations, variable corrections and interpolation techniques. DirectMHT and MWFT estimates have to serve as important and independent basin scale measures ofreliability, but should not result in physically unreasonable corrections being made to fluxes orflux related parameters.9.4.3 Enclosed Seas Used for Flux ValidationsA. INTRODUCTIONFor the entire world ocean the net energy budget between ocean and atmosphere can beconsidered as balanced, since any long-term temperature changes are small compared to theinaccuracy of ship derived flux fields. However, data coverage, especially in the SouthernOcean, is too small to calculate a reliable global average. Enclosed seas give an attractiveopportunity to verify ocean-atmosphere flux fields. Usually the heat and water budgets ofenclosed and semi-enclosed seas are considered to be known to higher accuracy than basin-scalebalances from oceanic cross-sections. The Mediterranean and the Baltic Sea, with theirrelatively high number of observations, are most suitable for such a purpose. Moreover, theyrepresent rather different climate zones. If it would be possible to close the energy and waterbudget for these two different seas by using the same set of parameterisations, then credibilityfor worldwide application of those parameterisations would increase.On the other hand, the special nature of enclosed seas, in particular the fact they aresurrounded by land, means that there are a number of problems concerning the heat and freshwater budgets:❍ river runoff becomes even more important for the smaller scale seas, and it is poorlyknown.❍ many of the enclosed seas (Baltic, Caspian, Black) are characterised by continuous icecover over a considerable part of the area and period. The number of observations in ice-covered areas is very small, and parameterisations of the flux components are poorlydeveloped in comparison to the case of open water.❍ consideration of budgets for enclosed seas has to account carefully for the analysis of thefluxes in the vicinity of coastlines. Local corrections are required for a number ofvariables in the VOS data (first of all, winds). Reanalysis and operational analysis data,especially those derived from the models with relatively coarse resolution (T62 andcoarser), need to be carefully checked.❍ the uncertainty of the estimates depends crucially on the size of the domain analysed, andincreases for the large basins such as Mediterranean. On the other hand, consideration ofsmaller enclosed domains is difficult due to under sampling. In this sense, the use ofenclosed seas is characterised by the same methodical problem which is typical for theMHT and MFWT estimates.B. MEDITERRANEAN SEA.Bunker et al. (1982) suggested the use of the Mediterranean and Red Seas as a test areafor the bulk formulae. For this purpose, the parameterisations of Bunker (1976), originally usedin the North Atlantic, were examined. Using individual merchant ship reports, they computedthe net sea surface energy flux for the Mediterranean and the Red Sea. For the Mediterranean, aheat loss of about 5 Wm-2 would be compatible with the measured heat import through the Straitof Gibraltar. However they found a strong surplus of energy at the surface After improving theparameterisation for longwave radiation by using climatological values of upper air humidity, inaddition to surface observations, the surplus decreased from 36 to 20 Wm-2. However, this9 – Evaluation Methods 134 9.4 Integral ConstraintsReport of the Working Group on Air Sea Fluxes June, 2000value was still in poor agreement with the oceanic observations. The authors speculated that adrastic increase of the transfer coefficient CE up to values of 2 x 10-3 would be the most plausibleway to achieve a near balanced state. Similar conclusions were obtained for the Red Sea.Gilman and Garrett (1994) also considered the energy balance of the Mediterranean Seato detect possible inconsistencies in the bulk flux formulae. As a starting point they used theCOADS derived heat fluxes presented by Garrett et al. (1993) which show an imbalance of 29Wm-2. Changing both short and longwave radiation, Gilman and Garrett (1994) were able toclose the Mediterranean energy budget. On the one hand, the mean longwave radiation wasincreased by 10 Wm-2 compared to Bunker's results, when the formulae suggested by Bignami(see Bignami et al. 1995) was applied. On the other hand, they reduced the values for theshortwave radiation by a total amount of 19 Wm-2. This was justified as follows: in order totake into account the effect of both anthropogenic and mineral aerosols which increase theatmospheric absorption over the Mediterranean, the transmissivity of the atmosphere wasreduced by a factor of 5% for one half of the year; this weakened the solar radiation by 6 Wm-2 .However, the major part of the shortwave reduction (13 Wm-2 ) was achieved by correcting theprevious misapplication of Reed's (1977) formula (5.4). This was particularly effective becauseof the very low cloud amounts over the Mediterranean compared to other ocean areas.For low cloud fractions the actual radiation Qsw from the Reed formula would exceedthe clear sky radiation value. Following Malevskii et al. (1992), a small increase over clear skyradiation may be possible due to clouds. However, Reed recommended to neglect the cloudreduction for cloud covers less than 0.2, that means setting (Qsw /Qswo) to be not greater than 1.A non-linear relationship between radiation and cloud cover results. After removing thispossible overestimation of shortwave radiation, Gilman and Garrett obtained a balanced energybudget for the Mediterranean.However, Lindau et al. (2000) balanced the Mediterranean heat budget by using theparameterisations of Malevskii et al. (1992) and Bignami et al. (1995) for the short andlongwave radiation respectively, and the corrected Beaufort scale of Lindau (1995) togetherwith the exchange coefficients of Isemer & Hasse (1987) for the turbulent fluxes. They found anet oceanic energy gain of only 2 Wm-2, which is in reasonable agreement to the measurementsof heat transport through the Strait of Gibraltar (MacDonald et al., 1994), which imply a meanannual heat loss of 5 Wm-2 from the sea surface.Angelucci et al. (1998) analysed individual variables from the three different NWPproducts - the ECMWF operational analyses, NCEP operational analyses, and ECMWFReanalysis, merged with the COADS MSTG and da Silva et al. (1994) cloudiness data, tocompute and intercompare the heat budget of the Mediterranean Sea. The Reed (1977), Kondo(1975) and May (1986) parameterisations for SW radiation, turbulent fluxes, and LW radiationrespectively, were applied to different combinations of the data. Angelucci et al. (1998) foundquite a wide range of heat balance estimates, varying from -50 to 50 Wm-2 depending on the dataset used. This range can be considered as the measure of the impact of different data on thebalances examined. Note here, that the use of VOS data would probably make this range evenlarger. The closest to the Bethoux (1979) estimate of about -6 Wm-2 was obtained usingECMWF operational analyses. However that may have been just a function of the selection ofparameterisations chosen.Although Garrett and co-workers had checked that their balance for the heat budget wascompatible with the freshwater balance, there have otherwise been few studies of the couplederror in the Mediterranean heat and fresh water balance estimates. There are attempts to deriveclosed budgets from some sub-areas of Mediterranean, for instance Adriatic Sea (Maggiore etal. 1998), although these estimates have a more uncertain imbalance (heat outflow to the openMediterranean) to be tuned to.C. BALTIC SEABumke et al. (1998) made a comprehensive study of the evaporation over the Baltic Seaon the basis of VOS data. This included an investigation of the importance of corrections to9 – Evaluation Methods 135 9.4 Integral ConstraintsReport of the Working Group on Air Sea Fluxes June, 2000variables for coastal effects, and an intercomparison of several frequently used schemes for thetransfer coefficients. The range of the estimates obtained was from 460 to 620 mm per year forthe annual evaporation; indicating 20 to 30% uncertainty in the latent flux. This emphasised theneed to carefully choose the parameterisation scheme; otherwise the uncertainty for the net fluxmay be of the same order as the mean flux values.A particular problem for the Baltic Sea is the effect of sea ice. The sea ice changes thealbedo of the surface and consequently the net shortwave radiation. Moreover, sea icediminishes the effective surface temperature so that outgoing longwave radiation and theturbulent fluxes are reduced. In order to take these effects into account, the equilibriumtemperature of the ice surface has to be iterated under the constraint that the four atmosphericfluxes and the conductive heat flux through the ice are in balance (Maykut & Untersteiner,1971). If that is not possible at temperatures below 1˚C, the surplus of surface energy is usedfor melting, which again would affect the albedo.Using monthly mean ice charts from GISST data and applying the above scheme, theice corrected atmospheric fluxes can be estimated. As an illustration, the mean state for the icecovered part of the Baltic Sea in the month of January is given in Figure 9.7. Averaging theindividually iterated temperatures for the ice surface, a value of -6.3˚C is obtained, whereas themean reported air temperature for that month is -6.6˚C. A latent heat flux of 15 Wm-2 results.Although the sum of atmospheric fluxes cools the ice surface, melting occurs due to strongconvective fluxes through the relative thin ice (of on average 24 cm). The oceanic flux at thebottom of the ice is considered to be constant at 5 Wm-2 , so that the freezing at the bottomprevails over the surface melting. For the month of January a mean ice growth of 36 cm permonth results. Using this ice correction scheme together with the same flux parameterisationsas in the Mediterranean, a long time mean heat loss for the Baltic Sea of 1 Wm-2 is obtained,very close to the observed quasi-balanced state.60 50 40 30 20 10 0ce coolingce heatingFlux (W/m2)SWLW LH SHOcn3534 15 2net heat loss = 43 Heat intoiceHeat out of iceFigure 9.7 Modelled mean energy fluxes over sea ice for the Baltic Sea in January (Wm-2 ). Theair temperature was –6.6˚C and the ice surface temperature –6.3˚C. At the top ice surface theheat into the ice from shortwave radiation (SW) was 3 Wm-2 whereas the surface cooling fromlongwave radiation (LW), latent heat flux (LH) and sensible heat flux (SH) totalled 51 Wm-2.Thus the net heat loss to the atmosphere was 48 Wm-2 . This was offset by convective heattransfer upward through the ice of 53 Wm-2 . Thus melting occurred at the top surface.However since only 5 Wm-2 heat entered the bottom of the ice from the ocean, the net heatbalance for the ice was an overall cooling of 43 Wm-2 and the ice grew downward. The meanice thickness was 0.24m and the thickness growth rate was 0.364 m/month.Thus the combination of parameterisations used by Lindau et al. (2000) providereasonable results for both the Mediterranean and the Baltic Sea. Moreover, the meridional heattransport in the Atlantic Ocean is consistent with independent oceanographic data, givingfurther confidence in this choice of parameterisations. Nevertheless there are questions such asto whether the Bignami et al. (1995), which was developed for the Mediterranean, should beapplied to open ocean areas. This will be discussed further below (Section 11.2.6).9 – Evaluation Methods 136 9.4 Integral ConstraintsReport of the Working Group on Air Sea Fluxes June, 2000D. OTHER ENCLOSED SEASAnalysis of the energy balance of the Black Sea has a long history. The first study wasdone by Neumann and Roseman (1954), who used a limited data set based primarily onobservations from coastal stations. They arrived at a 36 cm per year annual imbalance inprecipitation minus evaporation that constituted approximately 30 percent of the mean annualvalues. Although during later decades better data were collected and more advancedparameterisations applied, progress was not too obvious. Efimov et al. (1985) used probablythe most complete collection of data, but found considerable underestimation of both heat andfresh water balances (e.g. about 20 Wm-2 for the heat) in comparison to those derived fromhydrographic data. Note that Efimov et al. (1985) used the Malevskii et al. (1992) scheme (in aprevious version, which does not deviate strongly from the final version), and that this scheme,largely used in budget studies, gives relatively high values of the transfer coefficients incomparison to Large and Pond (1982) and Smith (1988).Investigation of the balances for the Caspian sea is even less developed than for otherenclosed basins. For this, the largest lake on the globe, additional uncertainty results from theestimates of sea level changes, which are influenced by strong interannual variations and stillpoorly documented. Moreover, nearly 1/3 of the Caspian Sea (its northern part) has a depth ofabout 1m to 5m and it is unclear to what extend the open ocean bulk parameterisations can beapplied there. In particular, the formulation for the radiative fluxes must definitely bereconsidered. The most advanced study, of Larin and Panin (1985), showed quite largeimbalances and it was pointed out that a reasonable closure requires ad-hoc adjustments of thetransfer coefficients up to values which are greater than all known estimates of the coefficients.There is a useful integral constraint on the heat and mass balance of the Arctic Ocean.Nakamura and Oort (1988) have presented polar heat balances. For the Arctic they relate theadvection of heat northward across 70°N to the surface heat balance and the top of atmosphereheat balance. They show a net annual surface heat balance of 2 Wm-2 . This agrees quite wellwith the net ice production of 0.2 m yr-1 or an export of about 10% of the Arctic ice cover's masseach year through Fram Strait. This freshwater export from the Arctic Ocean is crucial indetermining the stability and convective potential of the Greenland Sea; a similar transportthrough the archipelago affects convection in the Labrador Sea. There is no similar tidyconstraint on the Antarctic ice cover, although it is true that the ice is generally formed closer toAntarctica and flows northward carrying fresh water (as ice) to melt further from Antarctica.9.4.4 Ocean Mixed Layer BudgetsThe overall accuracy of air-sea flux measurement can, in principle, be validated throughclosure of the heat (and freshwater) budgets of the ocean mixed layer. This requires accuratemeasurement of the various oceanic heat (and salinity) transport processes; and in practice hasnever been very successful (e.g. Pollard and Thomas, 1989). However, because of the particularcare with measurement accuracy in COARE, two publications have so far appeared describingupper ocean budget closure over the 130 km square survey area (Smyth et al., 1996: Feng et al.,1998). These showed that horizontal and vertical advection were major components of both theheat and salt balances, and emphasised the need to determine these components very well forthe technique to be useful. Subsequently, Godfrey et al. (1999) conducted a budget closureexperiment in the equatorial Indian ocean specifically designed to test our ability to determinethe oceanic advective components with sufficient accuracy to validate the COARE bulk fluxalgorithm, and the accuracy of the measured input variables. By following a volume of water“tagged” with a drogued buoy over a 13-day period, they succeeded in closing the heat budgetto within 5 Wm-2 and freshwater to 1 mm day-1, again finding that the advective terms were offirst order. The SEMAPHORE experiment was also designed with the aim of closing the oceanmixed layer budget (Eymard et al. 1996; Eymard 1998).Final report of theJoint WCRP/SCOR Working Group on Air-Sea Fluxes(SCOR Working Group 110)INTERCOMPARISON AND VALIDATION OFOCEAN-ATMOSPHERE ENERGY FLUXFIELDSbyMembers of the WGASFEdited by P.K.TaylorSection B – Chapters 10 to 12,References and AppendicesJune 200010 - Basic Variables Evaluation 137 10.1 Introduction10.2 Sea Surface TemperatureReport of the Working Group on Air Sea Fluxes June, 200010 BASIC VARIABLES - STATE OF THE ART10.1 IntroductionIn previous Chapters, factors determining the requirements for surface flux fields havebeen reviewed (Chapters 2 and 3) and the available data sources have been considered (Chapter4). It has been shown that it is impracticable to determine the flux fields by directmeasurement (Chapter 5) and that parameterisation formulae must be used (Chapters 6 and 7).This Chapter will consider the present state of the art for determining the basic variables. Thevariables considered are those used in the parameterisation formulae and also other variableswhich are otherwise relevant to the surface flux problem. The evaluation of these variables hasbeen delayed until now because the discussion of the random and sampling errors (Chapter 8),and the consideration of methods of evaluation (Chapter 9), are relevant to fields of the basicvariables as well as to the flux fields (which will be evaluated in Chapter 11).The main property of the sea surface which is relevant to the surface fluxes is the seasurface temperature, this will be considered first (Section 10.2). Although variations of seasurface salinity (SSS) are not significant with regard to air-sea flux calculation, SSS isimportant for flux verification and for the freshwater forcing of ocean general circulationmodels; it will be considered in Section 10.3. The difference between the SST, and theimplied sea surface humidity, compared to the air temperature (Section 10.4) and airhumidity (Section 10.5) determines the potential for transfer of heat and water vapour. Themagnitude of the transfers is determined by the wind (Section 10.6) which, of course, is alsoimportant for the momentum flux, the wind stress. The efficiency of the transfers of heat,water and momentum depends on the atmospheric stability, also a function of the air-seatemperature and humidity contrasts and the wind. It is also likely that these transfers areaffected by the sea state and waves (Section 10.7), which are important climatic variables intheir own right.Cloud amount (Section 10.8) is used in some parameterisations of the radiative fluxesand is also an important variable for the verification of NWP and climate models. It is obtainedfrom ship reports and satellites. Precipitation (Section 10.9) also may be estimated from shipreports; although satellite based rainfall estimates offer better future potential. Other variablesaffecting the ocean freshwater budget are river inflow (Section 10.10) and sea ice. Sea ice(Section 10.11) is an important climate variable and the surface fluxes are significantlymodified in sea ice areas.Finally in this Chapter we will discuss a particular dataset of basic variable values, theCOADS monthly summary trimmed groups, MSTG (Section 10.12).10.2 Sea Surface Temperature (SST)10.2.1 Introduction - definition of sea surface temperatureSea surface temperature is the basic ocean variable which influences the magnitude ofthe surface turbulent fluxes and also the upward flux of longwave radiation. The state of ourpresent knowledge of SST fields was examined at a recent GCOS workshop (Arkin, 1999); thefollowing sections have made considerable use of the conclusions of that workshop.First we must consider the definition of sea surface temperature. The sea surface skintemperature (SSST) is the temperature that physically determines the surface heat fluxes. Itmay be measured radiometrically from ships and other in situ platforms, and by satellite-borneradiometers provided the atmospheric effects are properly corrected. The cooling due tosensible and latent heat fluxes and the longwave emission occurs at the skin, whereas theshortwave heating is distributed over a greater depth. Thus, most of the time, the SSST iscolder than the water just beneath the skin, typically by a few tenths ˚C. This differenceincreases with increased surface cooling and decreases with increasing wind speed. However10 - Basic Variables Evaluation 138 10.2 Sea Surface TemperatureReport of the Working Group on Air Sea Fluxes June, 2000occasions can exist (for example with advection of warm, moist air over a colder sea producinglow cloud or fog) when the SSST may be warmer than the water directly below.If there is a significant net heat flux from the ocean to the atmosphere then the waterbelow the skin will be well mixed and in situ measurements will record a temperature,normally reported as the SST, which will be warmer than the skin temperature. For traditionalbulk formulae (Section 7.2) the transfer coefficients have been determined with respect to thisbulk SST, so for application of these formulae, this would be the more appropriate temperatureto use. The bulk temperature also has the advantage of varying relatively slowly with time;thus bulk temperature observations may be composited over several days. Indeed, mosttraditional SST products are, in effect, fields of estimated bulk SST.However there are problems with defining SST as being the bulk value. Newer formsof the bulk formulae (Section 7.3.2) use transfer coefficients derived from surface renewaltheory. For these the skin temperature is the appropriate value. Also, under daytime lightwind conditions (less than, say, 3 ms-1) a diurnal warm layer may form below the surface skin.Sea temperatures in this warm layer may be a few degrees C higher than in the bulk of theocean mixed layer. Since the layer may not be well mixed and in any case is shallow (say oneor two metres), the numerical value of a bulk temperature measurement beneath the surfaceskin will vary with the measurement depth. The COARE bulk algorithm (Fairall et al., 1996a –see Section 7.4.1) incorporates models of both the cool skin and warm layer, and so canestimate SSST from a bulk measurement at known depth. For determination of precise fieldsof SST or SSST the use of such models would appear to be the way forward particularly if fluxestimates are required with high time resolution in tropical regions.Fortunately, the offset of the surface skin temperature should not be considered tooserious a problem for mean flux determination. Averaged over 70 days during COARE, takingaccount of the cool skin increased heat input to the ocean by about 11 Wm-2, while the warmlayer decreased it by about 4 Wm-2 (although the effect can be up to 50 Wm-2 at midday – seeFairall et al., 1996b). Over much of the mid-latitude and high latitude ocean, assuming asurface skin offset of about 0.3˚C would probably be adequate. Since this is also the order ofaccuracy of radiometric measurements (see below), a large programme of operational in situradiometric SST measurements is not justified; although research campaigns to improve ourknowledge of the skin effect may still be encouraged.10.2.2 in situ SST measurementsA. HIGH QUALITY IN-SITU MEASUREMENTSThe accurate measurement of sea surface skin temperature (SSST) is a matter of wideimportance in climate-related research and simulation, particularly for tropical regions. TheTOGA program specified an accuracy of ±0.3°C for SSST over a 2 x 2 degree region (WCRP1985) as a target for validation of space-borne radiometers. An error of 0.3°C changes sensibleand latent heat fluxes calculated with a bulk algorithm by 2 Wm-2 and 10 Wm-2 respectively, fortypical climatic conditions in the tropics (winds 3-4 ms-1, air and sea temperatures around 30°C,75% humidity). However, it would cause an error of only 1.8 Wm-2 in the calculation oflongwave exitance, which is still more accurate than direct pyrgeometer measurement at sea(see Section 5.2.3 ).In response to this specification, the past few years have seen development of severalhigh-resolution infrared radiometers (Smith et al., 1996c). Barton et al. (1995) describevalidation of the Along Track Scanning Radiometer (ATSR) on ERS-1 during several cruises ofR/V Franklin. High-resolution, self-calibrating infrared radiometers were mounted on the shipto measure SSST, using narrow spectral bands similar to the 11 and 12 µm channels of theATSR. Barton et al. (1995) quote the absolute accuracy of their instrument as 0.1°C, and theSSST measurement as accurate to 0.2°C, after correction for reflected sky radiation and surfaceemissivity (see below). They conclude from their validation results that ATSR can provideSSST within its design accuracy of 0.3°C, and foresee an improvement to 0.2°C.10 - Basic Variables Evaluation 139 10.2 Sea Surface TemperatureReport of the Working Group on Air Sea Fluxes June, 2000Measurements of SST from a high-quality commercial CTD, well-calibrated andmounted on a mooring, SeaSoar or hydrographic rosette are extremely accurate, in the order ofa few thousands °C (see for example Table 1 of Hosom et al., 1995). This degree of precision,achieved by using a highly stable platinum resistance thermometer (PRT), is usually far greaterthan necessary when calculating heat and moisture fluxes from a bulk algorithm. The mainvalue of such accurate (and usually costly) measurement of sea temperature is in thedetermination of small vertical gradients, conversion of conductivity to salinity, and cross-calibrating alternative sensors which may be more convenient for continuous deployment.These would include lower quality PRT systems (accuracy 0.01°C) or thermistors which arenowadays available with accuracies of 0.05°C to 0.2°C (depending on cost). These figuresrepresent the basic accuracy of an SST measurement, when the sensor is deployed clear of theship or on a mooring. When used in a research vessel’s thermosalinograph, two otheruncertainties arise; possible changes in the water temperature through contact with the ship’shull or (depending on the sensor arrangement) en route from intake to sensor, and uncertaintyabout the original depth of the water entering the intake.The latter is of importance when using a model of the mixed layer to obtain SSST asnoted above. There are few estimates of the accuracy of SSST obtained in this way;comparison with a high-quality infrared radiometer (of the type discussed above) is required.During COARE, measurements from the R/V Franklin’s thermosalinograph, taking watereffectively from 1m depth, were extrapolated to the surface using the COARE bulk algorithm.Comparison against an early version of the Barton et al. (1995) IR radiometer showed anaverage bias of –0.11°C with rms deviation of 0.47°C, for a 10-day period during which 3 daysexperienced diurnal surface warming greater than 3°C. It is impossible to apportion sources oferror between the model and the radiometer, but we note that the correction for reflected skyradiation was fixed at the average value of 0.4°C, not measured continuously. Depending onsky conditions and atmospheric water vapour content, this correction can vary from close tozero to at least 1°C; i.e. coincident observations (sky temperature or atmospheric soundings)needed to make this correction are essential if the final estimates of SSST are to be within thespecified accuracy.It is possible that improvements in infrared radiometers for industrial purposes maymake them more readily available and affordable for use on ships, particularly enablingsimultaneous seaward and skyward observation. Donlon et al. (1998) describe tests at sea ofone such radiometer (spectral response 8-12 µm, beam half-angle 4.3°, cost ~US$600) by fourseparate research groups, with excellent results. Comparison against an 0.1°C accurateradiometer, of the type described above, showed a bias of only 0.1°C with rms 0.08°C. Withappropriate protection from the marine environment, such an instrument could be used widelyin an SST validation program.B. VOSShip SST data are obtained mostly from Engine Room Intake (ERI) thermometers or(about 1/3 of the modern data) from SST buckets. A small but increasing number of ships usehull contact sensors which, if carefully calibrated, appear to give the most consistent SST data(Kent et al., 1993a; Emery et al., 1997). Although the VOS are asked to report temperatures intenths °C, many reports are given to half or whole degrees, probably reflecting the confidencethe ship's officers have in the accuracy of the reading. At the OOPC/AOPC Workshop (Arkin,1999), Taylor et al. (1999c) presented results that suggested that, compared to hull contactsensors, ERI SST data were warmer under most conditions, on average by 0.35C although therewas significant scatter about this typical value. Individual ships using ERI readings had meanbiases between -0.5°C (too cold) and +2.3°C (too warm). Bucket measurements were foundonly to be biased compared to hull values during sunny daytime conditions when they gave onaverage SST values about 0.3°C warmer. This is more likely due to the buckets heating ondeck prior to use rather than to near surface ocean heating. However, at the same meeting,Parker presented data that showed different characteristic biases between the observing10 - Basic Variables Evaluation 140 10.2 Sea Surface TemperatureReport of the Working Group on Air Sea Fluxes June, 2000methods. A plot of the zonal averages of "bucket" minus "non-bucket" SST values for theperiod 1975 to 1981 indicated the non-bucket values to be 0.1 to 0.2°C warmer except in thehigh latitude northern hemisphere (Folland et al., 1993). A more detailed study of the regionalcomparisons on which the zonal averages were based suggested that the buckets were biasedcold in some high ocean to atmosphere heat flux areas.Kent et al. (1999) found that the rms errors for a single VOS SST observation weretypically between 1°C to 2°C. The median rms error was 1.4°C and the mean rms error 1.5°C.The highest values, up to 3°C occurred in high latitude coastal regions, and higher valuesoccurred in the Gulf Stream and Kuroshio regions than in other areas at the same latitude.However it is possible that in these cases the error was being overestimated due to the effect ofvarying SST values on very short spatial scales.A historic reconstruction of the global SST time series (Folland and Parker, 1995)assumed that SST data were, on average, biased cold in 1860 by about 0.1°C with this biasincreasing to 0.4°C in 1940 due to the increased use of canvas (rather than wood) buckets. Itwas then assumed that wartime conditions resulted in a wholesale switch to ERI SST data; thosebuckets used after that time were assumed to be of insulated construction. Thus, no correctionis applied for the period 1942 to the present. Comparisons of the trend in the corrected SSTwith night time marine air temperatures and temperatures over land implied that theseadjustments were successful. However the differences between present day bucket and ERImeasurements suggests that further evaluation is necessary.C. BUOY DATADrifting buoys provide data away from shipping lanes and are used for adjusting thesatellite calibration. However, the SST Workshop (Arkin, 1999) suggested that we do nothave sufficient knowledge of the characteristics of the various different types of drifting buoyswith respect to SST measurement. The type of each drifting buoy should be known and thecalibration details fully documented. This is not presently the case. Thus the accuracy of buoydata varies but it is usually better than 0.5°C, significantly better than the ship data (Reynolds,1999).The most useful moored buoys are those in the open ocean, for example the JMAbuoys and the TAO and PIRATA arrays. Unfortunately many moored buoys are deployed nearcoasts where high SST gradients are likely. They are of limited value for satellite dataverification and are not routinely used for satellite calibration adjustments.Research buoys designed to allow flux determination (such as the IMET system) areimportant for verification of SST analyses and climatological SST data sets (Taylor et al.,1999c,d). Carefully calibrated sensors may be deployed at various depths in radiation shields(e.g. Weller et al., 1998). Since these buoys are limited in number and deployment duration,perhaps their best use is as a "withheld" data source which has not been used in, or had asignificant impact on, the various SST analyses.10.2.3 Remote sensing of SSTInfrared radiometers carried by satellites provide the potential for SSST measurementover the global ocean on a regular repeat basis. Unfortunately, at any time significant areas ofthe ocean are cloud covered and data from different over-passes must be composited.Atmospheric water vapour, aerosols, and clouds have all the potential to significantly bias thedata unless adequate correction procedures are implemented. Reliable cloud clearance remainsa problem, as does the effect of sub-pixel cloudiness. The measurements rely upon a smallnumber of sensors with the possibility of changes in sensor characteristics between satellites.Accurate operational SST retrievals from the AVHRR carried on the NOAA series ofpolar orbiting satellites have been available since late 1981 (Reynolds, 1999). The error budgetfor the AVHRR is dominated by the instrument calibration accuracy and atmospherictransmission effects. Thus the instrument must be calibrated against drifting buoys and the10 - Basic Variables Evaluation 141 10.2 Sea Surface Temperature10.3 Sea Surface SalinityReport of the Working Group on Air Sea Fluxes June, 2000calibration accuracy continually verified. Walton et al. (1998) reviewed the algorithms usedwhich are different depending on whether it is day or night. They found that the daytime rmsaccuracy (compared to drifting buoy data) had improved from 0.8°C in 1989 to 0.5°C in 1998,whereas the night time rms had remained constant at about 0.5°C. The bias was normallybetween -0.2°C to +0.4°C. However large errors can occur due to changes in atmosphericaerosol loading. For example the eruption of Mount Pinatubo resulted in a regional cold bias oforder 2°C over much of a two year period. Explicit water vapour corrections using SSM/Iderived vertically integrated water vapour content (or "precipitable water") have been tested andshown to be successful (Emery et al., 1994); however they have not been implementedoperationally.Compared to the AVHRR, the Along-Track Scanning Radiometer (ATSR) hasimproved sensor stability and built-in calibration systems. However there have been problemsin ensuring that clouds do not contaminate the data. For cloud-free pixels, Simpson et al.(1998) compared ATSR and drifting buoy data and found a mean difference (ATSR - buoy) of-0.24°C ±0.51°C (standard deviation); similar to what might be expected given the typical skin-bulk difference. Merchant and Harris (1999) used the TOGA TAO array buoys for verificationand found the mean and standard deviation of the differences to be -0.16°C and ±0.37°Crespectively. Using the TAO data to calculate a skin temperature they obtained an even closermatch: +0.07°C ±0.27°C. They concluded that about 5% of the ATSR retrievals had errorswhich they attributed to residual cloud contamination. For the uncontaminated data theysuggested a standard deviation of ±0.19°C. Examining retrievals for the period when theatmospheric aerosol loading due to Mount Pinatubo was decreasing, they found no significanttrend in the error (0.00 ±0.16°C/year), suggesting that the retrieval scheme was robust toaerosol effects. With regard to the cloud contamination problem, Jones et al. (1996a,b) alsosuggested that 4.65% of the data was cloud contaminated and suggested a scheme for removingthe residual cloud pixels.Thus the ATSR appears capable of achieving accuracies which are significantly betterthan can be achieved with ship data, and apparently better than the drifting buoy data.However the sampling is limited by the 500km wide swath. If the ATSR retrieval algorithmsare confirmed to be "aerosol proof" then this instrument may be best used both as a source ofSST data and as a source of calibration data for the wider swath AVHRR instrument.10.2.4 SST analysesReynolds (1999) has summarised intercomparisons of different available climate scaleSST analyses, both by Hurrell and Trenberth (2000) and as reported at the Ocean ObservingPanel for Climate SST Workshop (Arkin 1999). These analyses (Table 10.1) typically havetemporal resolutions from weekly to monthly and spatial resolutions from 1° to 5°.. It wasfound that differences among the analyses were smaller within the tropics compared to otherareas. Monthly rms differences between analyses were in the range 0.2°C to 0.5°C betweenabout 40°S and 60°N except in coastal areas. Analyses which did not use satellite data haddifferences greater than 1°C south of 40°S. The largest differences occurred north of 60°N dueto uncertainties near and within the Arctic sea ice. This was because different schemes are usedto determine SST from ice cover or concentration, and different ice cover analyses exist. Itwas stressed that the use of satellite data (AVHRR) without bias correction has the potential tointroduce large errors into the analysis.10.3 Sea Surface Salinity (SSS)10.3.1 IntroductionThe sea surface salinity (SSS) is an ocean variable which enters the calculation of thefluxes only by reducing the saturation water vapour pressure (SVP) over sea water compared tofresh water. This is normally accounted for by assuming a relative humidity of 98% for the airin direct contact with the sea surface. Since the variability of SSS is not sufficient to causesignificant variations in SVP (except in regions such as river estuaries) the SSS does not10 - Basic Variables Evaluation 142 10.3 Sea Surface SalinityReport of the Working Group on Air Sea Fluxes June, 2000explicitly enter the air-sea flux formulae. However it is well correlated to the freshwater flux,and is essential for an accurate verification of freshwater flux estimates. Furthermore, it hasbeen until now the most important component of the freshwater forcing of ocean generalcirculation models.Table 10.1 Summary of different climate scale SST analyses (adapted from Reynolds, 1999).The columns show the period available, the spatial resolution, whether satellite data is used("Yes","No") or used with additional bias correction ("Corr.") and whether ice data is used todefine SST's in high latitudes. N.B. some parameters may vary between different releases ofsome analyses.Analysis Period Res'n Sat. Ice ReferenceBMRC Jul-93 on 1° Corr. Yes (quoted by Reynolds, 1999)UKMO GISST 1871 on 1° Corr. Yes Rayner et al. (1996)JMA 1982 on 2° No No (quoted by Reynolds, 1999)LDEO 1856 on 5° No No Kaplan et al. (1998)UK MOHSST 1856 on 5° No No Parker et al. (1994)NCEP EOF 1950-1998 2° No No Smith et al. (1996b)NCEP OI Nov-81 on 1° Corr. Yes Reynolds & Smith (1994)NRL 1979 on 0.25˚ Yes Yes (quoted by Reynolds, 1999)NOAA/NASAAVHRR Pathfinder1985 on 0.18˚ Yes Yes Brown et al. (1993)10.3.2 In situ measurement of SSSIn-situ measurements of sea surface salinity are far less numerous than in-situmeasurements of sea surface temperature. One major reason is the technology which is farheavier, more expensive and less reliable for conductivity than for temperature sensors.Conductivity sensors were added to disposable instruments (XCTD) only 15 years ago, and stillmost VOS lines are, mainly for cost reasons, limited to the routine acquisition of SST. Inconsequence, our knowledge of the surface salinity has been largely established from acompilation of the available hydrographic sections (Levitus et al., 1994). At present, if anaccurate climatological estimate for the annual mean sea surface salinity is required for theglobal ocean, the present data set presents severe deficiencies in large regions of the ocean(mainly in the southern hemisphere in winter conditions). As a result, our knowledge of theclimatological seasonal cycle at a global scale is still largely inaccurate, and investigation of theinter-annual variability has to be restricted to a very few well sampled oceanographic regions oflimited area. The situation is expected to improve in a near future, driven by the emergingneeds of operational oceanography and the rapid development of a global ocean observingsystem. With respect to salinity, the ARGO programme with its large number of profilingfloats is expected to be a major contribution.10.3.3 Remote sensing of SSSDespite the crucial role sea surface salinity plays in determining the ocean circulationand in understanding the water cycle up to the present it has not been monitored from space.Levitus et al. (1994) showed that the number of in situ sea surface salinity observations reacheda peak in the late 1960's and has significantly decreased in the following decades. This, and therelatively poor geographical distribution of in situ salinity measurements over the world oceans,could be significantly improved by measurements from space. In early papers, Klein and Swift(1977) and Swift and McIntosh (1983) gave a detailed description of the physical basis of how,and at which frequencies, it would be possible to obtain sea surface salinity from space. Arecent paper on remote sensing of sea surface salinity by Lagerloef et al. (1995) recycled thisidea with the aim of proposing a salinity mission to NASA.10 - Basic Variables Evaluation 143 10.3 Sea Surface SalinityReport of the Working Group on Air Sea Fluxes June, 2000Basically, the ocean radiometric brightness temperature TB is related to the sea surfacetemperature TS and the emissivity ε:TB = ε(TS,S,ν,θi,pol)TS (10.1)The emissivity is a function of sea surface temperature, salinity, frequency, incidence angle, andpolarisation. The dependence on salinity is expressed through the dielectric constant which isgoverned by electrical conductivity and frequency. The conductivity itself is a function ofsalinity and temperature. Other effects on salinity, not covered by (10.1) but which must beallowed for in a retrieval of sea surface salinity, are wind, foam, and rain, and their impact onthe surface within the field of view of the radiometer. Additionally, for an accuratecomputation of a signal measured at satellite altitude (10.1) has to be extended by the upwardand downward components of atmospheric emission, and the transmitted and reflected part ofthe galactic space emission.Lagerloef et al. (1995) stated that frequencies of 1.4 and 2.65 GHz seem to be the bestchoice to retrieve salinity since the variation of brightness temperature due to salinity is at itsmaximum at these frequencies. Another argument is that the relation between TB and TS andsalinity differs at these two frequencies which should allow a simultaneous retrieval of salinityand TS. However, the brightness temperature variation at 1.4 GHz is not greater than ~5 Kunder open ocean conditions (see Fig.2 in Lagerloef et al., 1995) which is small compared tothe dynamic ranges of ~100K caused by variations in water vapour or rain for the SSM/Ichannels. Another problem to consider is that the wind speed, or better the surface stress,must be known at the same time and location where the salinity will be retrieved. However,Lagerloef et al. (1995) showed that it is possible to measure salinity with the airborne 1.4 GHzElectrically Scanning Thinned Array Radiometer (ESTAR). They monitored the sea surfacesalinity in a transect from seaward of the Gulf Stream to Cape Hatteras. The large salinitychanges from coastal to off-shore waters are clearly seen and are in agreement with frontalfeatures noted in sea surface temperature maps.Sea surface salinity is used in the air-sea flux problem for the relaxation of oceangeneral circulation models forced by the changing fresh water flux on the surface (Barnier et al.1995), and for the correction of humidity variables. The latter does not require the salinitymore accurately than 1 psu. Ocean model simulations require at least 0.1 psu accuracy formonthly averaged salinity and it can be expected that the increase of model resolution willrequire even higher accuracy for the reliable simulation of ocean circulation. Although thisaccuracy is problematic for the potential satellite techniques, it is also hardly attainable fromthe traditional alternative sources of the salinity data (in-situ STD, XBT and MBTmeasurements), except for well sampled ocean regions such as the North Atlantic andnorthwest Pacific. Thus, progress in the future is likely to be achieved by betterinstrumentation and by combining in situ and remote sensing information.A new study on critical requirements for ocean salinity retrieval by Drange et al. (1999)addresses this issue in the light of the optimised and threshold requirements for GODAE. Thusto describe and quantify many important ocean processes, the accuracy target for sea surfacesalinity should be 0.1 psu over a distance of 100 or 200 km for a time period of 10 days. Severalretrieval simulations revealed the following results:❍ Measurements of horizontally and vertically polarised brightness temperatures at 1.4GHz are needed to obtain reasonable retrievals.❍ Accurate retrieval of salinity requires small or no bias in the measurement of thebrightness temperature. This has implications on the engineering design andcalibration of a passive microwave salinity sensor.❍ If wind speed fields with accuracy better than 0.5 ms-1 and sea surface temperaturefields with accuracy better that 0.5K can be obtained, then the error in the retrieved10 - Basic Variables Evaluation 144 10.3 Sea Surface Salinity10.4 Air TemperatureReport of the Working Group on Air Sea Fluxes June, 2000salinity will be lower than 0.4 psu. This is not unrealistic because the accuracyconcerns averages over (200 km)2 and 10 days.❍ The time scale of mesoscale changes in the ocean is such that a simultaneousmeasurement of SST should not be necessary.❍ Polar and subpolar waters are extremely sensitive to changes in sea surface salinity.Those regions will require retrieval accuracies around 0.01 psu to be fully useful.Since the accuracy of remotely sensed salinity decreases with decreasing sea surfacetemperature, it will be very difficult to obtain reliable salinity measurements at highlatitudes from space.The study concluded that the simulations have shown that the GODAE requirements can beclosely met; however some aspects of the problem were not tackled in this study e.g.geometrical effects inherent to a certain radiometer or the effects of rainfall in modifying thesurface roughness. The Drange et al. (1999) study is related to an activity initialised by a groupof European and American scientists. They proposed to the European Space Agency (ESA) asatellite mission called the Soil Moisture and Ocean Salinity (SMOS) Satellite ( http://www-sv.cict.fr/cesbio/smos/ ) which consists of an L-band (1.4 GHz) 2D interferometric radiometerthat should be able to detect soil moisture over land and sea surface salinity over oceans. Themission has been selected for further study during the next two years. The satellite launch isplanned for the year 2004.10.4 Air Temperature10.4.1 In situ air temperature measurementA. HIGH QUALITY IN-SITU MEASUREMENTSInstruments used for measurement of the basic variables at sea are often different fromthose used routinely over land. This is because of the increased demands on ruggedness,waterproofing, and, in the case of those intended for use on buoys, power consumption andlong-term unattended operation. For atmospheric temperature and humidity, the most accurateinstrument in regular use is the psychrometer (wet and dry bulb thermometer) whosemeasurements are based on well-established thermodynamic theory. The most criticalrequirements to attain its potential accuracy are adequate ventilation of air past the sensingelements (3 - 4 ms-1 flow rate), to ensure the full wet bulb depression, and adequate shieldingfrom solar radiation. This usually means a double shield with the space between also ventilated.Basic accuracy depends on the type of sensing element used; for the familiar sling and Assmanpsychrometers this is the precision of the particular mercury-in-glass thermometer, 0.1°C atbest.For automatic recording, 0.01°C is not difficult to achieve in principle using platinumresistance thermometers, although in practice environmental sources of error appear,particularly when the instrument is used at sea (salt spray, signal interference, etc). In TOGA-COARE the use of aspirated psychrometers aboard R/V Franklin revealed the inadequacy oftemperature sensors on other platforms which were enclosed in radiation screens without forcedaspiration (Bradley and Weller, 1995a, b). Temperature errors during daytime could be wellover 1˚C due to solar heating (Anderson and Baumgartner, 1998). Aboard many researchvessels and moorings, air temperature is measured with PRT's or precision thermistors, oftenintegral with a commercial temperature/RH instrument (see Section 10.5.1); quoted accuraciesare between 0.1˚C and 0.25˚C (Hosom et al., 1995; Freitag et al., 1994; Burns et al., 2000).B. VOS DATA - THE VSOP-NA PROJECTIt has been known for a long time that temperature measurements on board merchantships have considerable systematic errors. Their consequences on heat flux calculations hadbeen discussed by Dietrich (1950). Kent et al. (1999) found that, while a typical RMS airtemperature error for a single temperature observation from VOS was about 1.4°C, there were a10 - Basic Variables Evaluation 145 10.4 Air TemperatureReport of the Working Group on Air Sea Fluxes June, 2000significant number of higher error values representing high latitude coastal regions with meanair temperatures of 10°C or lower. This may indicate a failure to remove spatial variabilityfrom the error estimate (for example in the Gulf Stream and Kuroshio areas) or it may be thaterrors in air temperature observations are greater in cold conditions. For example,psychrometers may not be exposed outside the wheelhouse for a long enough period.The exposure of thermometer screens on the VOS selected for the VSOP-NA project(Kent et al. 1993a) varied from good (e.g. screens hung on stanchions on the outboard rails ofeither bridge wing) to very bad (e.g. "the screen is made of brown varnished wood and fitted tothe side of the wheelhouse in the 'porch' of the bridge wing on the port side"). The effect on thetemperature readings is illustrated in Figure 10.1. This shows the mean difference between theVOS air temperature observations and an atmospheric forecast model for sensor exposureclassed as "good", "medium", or "poor". The left part of this figure shows night data plottedagainst cloud cover and the right part shows day time observations plotted against the solarradiation. The latter was calculated from the ships' position and the reported cloud cover. Atnight the better exposed sensor recorded lower temperatures than the model, suggesting apossible bias in the model. The poorly exposed sensors were about 0.5°C warm. During theday all the sensors showed increasingly warm readings with increasing solar radiation. For thebetter exposed sensors this bias was up to 2°C; for the poorly exposed sensors the mean biasreached over 4°C.10008006004002000-200-400-600-800-1000-1000 -1.0-0.50.00.51.01.52.02.53.03.54.0GoodMediumPoorTotal Cloud Cover (oktas) / Short Wave Radiation (W/m2)VSOP - Model Air Temperature (°C)Sensor Exposure 7 5 3 1 02004006008001000nightdayFigure 10.1. The effect ofdifferent instrument exposureon air temperatureobservations. Night time dataare plotted against total cloudcover, day time data areplotted against the calculatedincoming solar radiation. Seetext for discussion. (from Kentet al. 1991)Data obtained from hand held psychrometers showed solar heating effects similar to thebetter exposed screens suggesting that the bias represents a heat island effect caused by the ship.The bias in data from better exposed sensors (which constituted the majority of theobservations) was found to depend both on the solar heating and the relative wind speed and acorrection scheme was devised (Kent et al., 1993b).C. VOS - COMPARISON WITH WEATHER SHIPS The important values for sensible and latent heat fluxes are the small differencesbetween air temperature Ta and wet bulb temperature Tw, respectively, compared to the seasurface temperature Ts. To quantify the systematic errors in these quantities, Lindau (2000) hascompared temperature measurements of Ocean Weather Ships, on the assumption that they arecorrect, with reports of merchant ships in the vicinity of the OWS. Data were available from15 Stations, 11 in the North Atlantic and 4 in the North Pacific, operating during differentperiods between 1945 and 1989 (see Section 4.2.2). Simultaneous merchant ship observationswithin a radius of 300 km were extracted from COADS. In this way, 700,000 pairs ofobservations were obtained. To avoid errors due to an unsymmetrical distribution of merchantships around the OWS, data were interpolated on a two-dimensional linear field for each monthand each station.10 - Basic Variables Evaluation 146 10.4 Air TemperatureReport of the Working Group on Air Sea Fluxes June, 2000The air temperature measured on merchant ships was higher than OWS reports for allstations, except OWS M (Figure 10.2). The mean difference (Ta(VOS) – Ta(OWS)) variedconsiderably from - 0.00oC at OWS M to 0.69oC at OWS P. Because no relation to climatezones was obvious, the difference was averaged over all stations giving an overestimation of0.259oC. The comparison of wet bulb temperatures yielded similar results. Tw was generallyoverestimated by merchant ships, the mean bias was 0.235oC (Figure 10.3). For the calculatedspecific humidity (or dew point ) of the air to be unaffected by the temperature error, Tw needsto change by an amount which depends on the air temperature (Hoeber, 1995; Kent and Taylor,1996) but which is about half the air temperature error. Thus compared to the weather shipdata, the ship wet bulb values were too warm, but only by about 0.1oC. For SST, averaging theresults from all stations, Lindau (2000) found a negligible bias (0.005oC) between merchantships and OWS measured Ts (Figure 10.4).Figure 10.2 The mean difference of airtemperature measurements (Merchant shipsminus Ocean Weather Ships) at AtlanticOWS's A, B, C, D, E, I, J,K, L, M, R, andPacific OWS's P, N, T, V. The dashed lineshows the mean of all stations.Figure 10.3. As Figure 10.2 but for wetbulb temperature.Figure 10.4. As Fig 10.2 but for sea surfacetemperature.However, the important quantities for determining heat fluxes are mainly the errors forthe air-sea temperature differences. Thus, merchant ships were reporting a too warmatmosphere, and one which contained too much water vapour. As the values at sea surfacewere fairly well determined, the vertical temperature and humidity gradients were too small, sothat the sensible and latent heat fluxes would be underestimated. Thus the estimatedtemperature biases in the merchant ship observations found by Lindau (2000) were:∆Ta = 0.259˚C ∆Tw = 0.235˚C ∆Ts = 0.005˚CThis is in contrast to Kent et al. (1993a), who found higher over-estimations for all threetemperatures reported by VOS. If not only air and wet bulb temperature should beoverestimated by a comparable bias, but also the SST, much of the overall error effect on10 - Basic Variables Evaluation 147 10.4 Air TemperatureReport of the Working Group on Air Sea Fluxes June, 2000fluxes would be compensated. Thus the mean correction suggested above is not beyondquestion.10.4.2 Surface air temperature from the satellite SSM/I sensorThe near-surface air temperature Ta enters the bulk formula for sensible and latent heatfluxes in different ways. It is needed directly to determine the air-sea temperature difference inthe computation of sensible heat flux. But it is also involved in the computation of exchangecoefficients, air density, and latent heat of evaporation for both turbulent heat fluxes. Becausethere is no means to directly obtain the air temperature from the satellite measurementscurrently used for heat flux estimation, some indirect methods of varying accuracy have beendeveloped.A very simple method is to assume slightly unstable conditions at any location at anytime and set Ta = TS - 1, where TS is the sea surface temperature. The results of Wells andKing-Hele (1990) show that most of the observed air-sea temperature differences in the tropicaloceans are of the order of 1˚C. However if, instead of the assumed unstable conditions, stableconditions occur, the exchange coefficient for latent heat flux will be underestimated at lowwind speeds by ~50% (Schulz et al. 1997). Another simple method is to compute Ta from theretrieved air specific humidity (see Section 10.5.2) assuming a constant relative humidity, e.g.80% (Liu, 1988), or using a climatological relative humidity. This might be accurate enoughto compute the exchange coefficients but seems to be too rough an estimate to determine thesensible heat flux.A more sophisticated approach using a relationship between TS and the Bowen ratio atlong time scales (compared to the synoptic time scale) has been undertaken by Konda et al.(1996). This work is based on the previous work of Hicks and Hess (1977) who establishedthis relationship from experimental data. Their aim was the reconstruction of sea surface skintemperature (rather that the bulk temperature measured by ships) in order to compute long termaverages of the turbulent fluxes more correctly. Adapted from Konda et al. (1996) theturbulent heat fluxes can be written as:Hc KTzEL KqzpHaE =−∂∂   =−∂∂   ρρ ; (10.2)where ρ is the air density, L is the latent heat of evaporation, cP is the specific heat at constantpressure, KH and KE are the diffusivity coefficients of heat and humidity, Ta is air temperature,and q specific humidity. Assuming that KH = KE we obtain for the Bowen ratio:β ==∂∂HEcLTqp a (10.3)If one uses the bulk formulae for the fluxes (for example, (7.8)) the Bowen ratio reads:β =−−cC T TLC q qpH zEz()()00(10.4)where CH and CE are the bulk transfer coefficients and the indices 0 and z denote values at thesea surface and at the reference level over the surface, respectively. (10.3) and (10.4) togethergive:LcqTLC q qcC T T pa TTEzpH zz∂∂==− ()−00 ()(10.5)If q is written as αqS(T) with α relative humidity and qS(T) denoting the saturation humidityfunction (10.5) can be written as:10 - Basic Variables Evaluation 148 10.4 Air Temperature10.5 HumidityReport of the Working Group on Air Sea Fluxes June, 2000Cq qCT TqqTqTTqTTEzHzzSzSzTTSzTT zz()() ( )()()00−−=∂∂+∂∂ ==α(10.6)The second term on the right hand side of (10.6) has been neglected by Konda et al. (1996)because it is much smaller than the first term and it is not directly determinable from satellite orbuoy measurements.Using the empirical relationship between total precipitable water, obtained from SSM/Imeasurements and near-surface humidity established by Liu (1986), and AVHRR multichannelsea surface temperatures adjusted to skin temperatures, Ta can be computed from (10.6). Theerror of a monthly mean satellite-derived air temperature is then approximately -0.3˚ ± 3.1˚C asfound by comparison to TOGA TAO buoys and buoys of the Japan Meteorological Agency.This accuracy is not high enough to study sensible heat fluxes on a monthly scale, but can beuseful for analysing interannual variations. Improvements to this technique by using furtherdevelopments of the retrieval schemes for TS and q have not been explored. However,comparison with the approach based on the assumption of a constant relative humidity showthat the Konda et al. (1996) method is better in bias and rms error.A recent paper by Jones et al. (1999) tried to invert monthly means of Ta from SSM/Imeasurements of total precipitable water, W, and sea surface temperature, TS, analysis fromNCEP using neural network techniques. The network was trained with data extracted from theSurface Marine Data provided by da Silva et al. (1994). The neural network was used in a sortof double loop where in the first round the network was trained only with W and TS and in thesecond round the bias between the algorithm’s outcome and the truth data set was used as anthird input parameter for the network. From this procedure it was not surprising that the globalmean bias between the Ta produced by the network and the da Silva data set was negligible.The global rms was stated to be 0.72 ± 0.38˚C which is much lower than the results obtainedwith the Konda et al. (1996) method or the method of Jourdan and Gautier (1995) who obtaineda global rms error of 2.6 ± 1.4˚C from a polynomial fit between W and Ta. However, acriticism is that the da Silva et al. (1994) data set was regarded as ground truth. Given the largedifferences between in situ climatologies found by Josey et al. (1999), compared toindependent high quality buoy measurements, this may not be appropriate. Since the satellite-derived data sets are expected to improve on the in situ climatologies, a real challenge forverification would be to compare with the independent long term buoy data sets like that of theSubduction experiment (Moyer and Weller, 1997).10.5 Humidity10.5.1 In situ humidity measurementA. HIGH QUALITY IN-SITU MEASUREMENTSThe wet and dry bulb psychrometer is the traditional meteorological instrument for theroutine measurement of temperature and humidity. In general, however, psychrometers are notsuitable for continuous routine measurement of atmospheric humidity at sea in stand-alone orautomatic mode because of their need for attention (e.g. washing salt from the wick,replenishing the water reservoir). Dewpoint hygrometers are also based on soundthermodynamic theory, measuring the temperature at which a film of dew forms on a cooledmirror, but are generally too complex to serve as operational instruments. Their main use inair-sea studies is as a reference standard; accuracy of 0.2˚C in dewpoint is readily achievable(corresponding to 0.2gkg-1 at about 22˚C).Nowadays, thin-film polymers which absorb or desorb water as the relative humiditychanges are the most common humidity sensors, and recent developments have greatlyimproved their accuracy and stability of calibration. The polymer usually forms the dielectricof a capacitance in a circuit which provides an electrical output proportional to relativehumidity. Conversion to mixing ratio, specific or absolute humidity requires the temperature of10 - Basic Variables Evaluation 149 10.5 HumidityReport of the Working Group on Air Sea Fluxes June, 2000the air surrounding the dielectric. Provided this temperature is obtained from a co-located PRTor thermistor, errors in humidity measurement are less severe than those of temperature, watervapour being a conservative quantity. However, for accurate measurement thesetemperature/RH sensors should be shielded from solar radiation in a double screen with forcedventilation in the same way as a psychrometer. At very high humidity, or if the polymerbecomes physically wet, the humidity measurement can become unrealistic (greater than 100%relative humidity). Earlier versions of these sensors often failed to recover from this, butrecent models seem to have largely overcome the problem and recover, although sometimesonly after several hours.More sophisticated instruments which measure humidity fields (e.g. Raman lidar) aredescribed by Post et al. (1998). Most of these are still at prototype stage, and are unlikely toimpact the routine measurement of humidity for flux calculation for many years.B. VOS HUMIDITY DATAIn assessing the accuracy of VOS observations, Kent et al. (1999) examined the randomerrors in specific humidity because that is the humidity variable that is used in calculating thelatent heat flux. However they found that the errors in specific humidity reflected the variationof saturation vapour pressure with temperature. Higher errors occurred in tropical regionswhere the air is warm. The humidity variable reported by the ships is dew point temperature.Errors for dew point tended to be about 2°C in colder regions and somewhat less, between 1°Cand 2°C in warmer regions (Figure 10.5)30 25 20 15 10 5 00.00.51.01.52.02.53.0Mean Air TemperatureSpecific Humidity Random Error Figure 10.5. Randomobservational error in specifichumidity (gkg-1) as a functionof mean air temperature (°C).The upper line is the error inspecific humidity arising froma 2°C error in dew pointtemperature and the lower thatfrom a 1°C error in dew point.A relative humidity of 80% hasbeen assumed in thecalculation.(from Kent et al. 1999)The VSOP-NA results (Kent et al. 1993a) showed that psychrometers produced lower(and therefore presumably more accurate) dew point readings compared to screens. Since theship may often be a source of heat but is rarely a significant source of water vapour, shipboardhumidity readings may be of better quality than the temperature data (see for example Kent &Taylor, 1996). The humidity data is vital for determining the latent heat flux which, over muchof the ocean, represents the major loss of heat to the atmosphere and which tends to dominatethe interannual variability of the fluxes. However humidity is at present not reported by a largenumber of the VOS. Equipping those ships with wet and dry bulb psychrometers andencouraging them to report humidity observations would represent a major improvement to theVOS data set.10.5.2 Remote sensing of near surface humidityA. INTRODUCTIONBecause the interaction of the radiation field with water vapour at a distinct level is notmeasurable, all methods for determining the near-surface humidity make use of a verticallyintegrated water vapour content (obtained from a passive microwave instrument such as theSSM/I) as a predictor for the near-surface specific humidity, q , required for the bulk approach.10 - Basic Variables Evaluation 150 10.5 HumidityReport of the Working Group on Air Sea Fluxes Draft: January, 2000The correlation between both quantities depends heavily on the time scale considered. At anygiven time, Schlüssel (1989) showed that the total precipitable water, W , is only weaklycorrelated to q. He computed the correlation coefficients between adjacent atmospheric layersand atmospheric layers separated by a distance of 50 hPa or more. Vertical profiles of thecorrelation coefficient were larger than 0.9 for adjacent layers and values larger than 0.8 forlayers with a separation distance of 50 hPa. However, the correlation profile always exhibiteda significant minimum near the mean height of the atmospheric boundary layer whenever thedistance between the layers was larger than 50 hPa. This indicates a decoupling of the moisturein the boundary layer from that in the free troposphere. Liu (1990) found similar results whenhe examined vertical variance profiles of semi-daily and daily radiosonde ascents for twotropical stations and one station in mid-latitudes. All three variance profiles showed amaximum at approximately 800 hPa caused by varying atmospheric boundary layer height anda varying contrast with the water vapour content above the boundary layer. The variancemaximum as well as the correlation minimum are much less distinct if monthly averages areconsidered. This is the reason why Liu and Niiler (1984) had success in establishing thepolynomial relationship between monthly averages of q and W.B. THE LIU AND NIILER (1984) ALGORITHMThe method of Liu and Niiler (1984) determines the monthly marine surface-layerhumidity with a simple polynomial regression of q versus W. The relation was found byexamining radiosonde data from 11 mid-ocean island stations and weather ships scattered overthe Atlantic and the Pacific ocean. The accuracy stated for the global regression formula was0.8 gkg-1 (Liu, 1986) and 1 gkg-1 (Liu et al., 1991) depending on the validation data source.This simple formula can be used with any retrieval algorithm which determines W, for examplethat of Schlüssel and Emery (1990):Wkk T k T T vv v =+ − + − − 0122222 37 280 280 ln( ) (ln( ) ) (10.7)which has as accuracy of 1.5 kgm-2 for instantaneous SSM/I measurements. Upper indices ontemperature denote the SSM/I channel and the lower index stands for the polarisation. Somescientists have tried to also use this method for the production of daily values of q but, as maybe expected from the previous discussion, this reduces the accuracy in q so much that the resultbecomes more or less useless.C. THE SCHULZ ET AL. (1997) ALGORITHMBecause of the inability of Liu’s method to determine q for individual situations,Schulz et al. (1993) developed a new method that first derives the integrated water vapourcontent of the atmospheric boundary layer wB (using a somewhat artificial height of 500 m) andthen deduces q with a simple linear regression from wB. Schulz et al. (1993) showed that wBcan be independently retrieved from W and that the correlation between q and wB is muchhigher than that with W. Schlüssel et al. (1995) improved this technique slightly by obtainingthe specific humidity directly from the brightness temperatures, thus avoiding the errorpropagation that occurs in the two-step method. The standard error for this globally validretrieval was stated to be 1.1 g kg- 1 for an instantaneous SSM/I measurement. Comprehensivecomparisons have been made by Schlüssel (1995) and Schulz et al. (1997), using data frommerchant ships, OWS and scientific experiments. They confirmed the global validity of thismethod and found rms errors not much higher than the stated retrieval error. A large portion ofthe error was due to the mismatch of the measurements in time and space as shown by Wentz(1997). However, a problem was found over tropical oceans where a systematicoverestimation of q occurred if the mid-tropospheric humidity was high. Schulz et al. (1997)reported that the correlation between the surface q and the water vapour content in the lowest500 m of the atmosphere was much lower than that for extratropical atmospheres. A reasonmight be that a 500m layer does not represent the boundary layer depth in a case of convectiveactivity and high mid-tropospheric humidity very well.D. THE CHOU ET AL. (1995) EOF METHODRecently, an alternative approach has been reported by Chou et al. (1995b; 1997).They estimated q from the total water vapour content W and wB, retrieved with the algorithms10 - Basic Variables Evaluation 151 10.5 Humidity10.6 WindReport of the Working Group on Air Sea Fluxes Draft: January, 2000of Wentz (1989) for W and Schulz et al. (1993) for wB, using an EOF (empirical orthogonalfunction) method with different EOF's for six classes of W. The accuracy attained for q wasnot much different from the Schulz et al. (1993) method since the weights for wB in the EOFanalysis were two orders of magnitude larger than those for W. Some corrections wereintroduced for dry and wet biases occurring respectively at the low and high end of the humidityspectrum. At the low end, Chou et al. (1997) only used W within the EOF analysis to correctfor an underestimation of q during wintertime over extratropical oceans. The other correctionconcerned a positive humidity bias during summer in regions where warm air moves over acolder ocean surface. In that case they constrained the surface air humidity to the saturationhumidity at sea surface temperature. A side effect of this was that, in each case with anoverestimated wB , a positive bias will be automatically corrected.E. THE JONES ET AL. (1999) NEURAL NETWORK APPROACHAs for the near-surface air temperature (see section 10.4.2) Jones et al. (1999) used aneural network to obtain monthly averages of q. Input to the neural network were W and sea-surface temperature extracted from the Surface Marine Data provided by da Silva et al. (1994),and the monthly averages of SSM/I brightness temperatures on a 1˚ x 1˚ grid. Since the biasbetween satellite derived q and in situ data is incorporated in a second loop of the neuralnetwork, again it is not amazing that this method showed no bias when compared to the da Silvadataset. The global rms error stated was 0.77 g kg-1 with smaller errors in the North Atlanticand in the North Pacific (0.6 g kg- 1) and larger errors in the southern Indian, Pacific, andAtlantic Oceans (1.2 g kg-1) reflecting the small observation density in the da Silva dataset inthose regions. This method was only compared to that of Liu (1986) and showed the expectedimprovement. Comparisons to Chou’s and Schulz’s methods and to independent in situmeasurements are needed to evaluate this method more carefully.10.6 Wind10.6.1 IntroductionAccurate wind data are important because wind stress increases roughly as (windspeed)2.7 and mixed layer deepening with (wind speed)4 while accurate high resolution windfields are crucial for accurate wave modelling. Furthermore, the scalar fluxes calculated usinga bulk flux algorithm are directly proportional to the wind speed and the wind stress is animportant factor in determining the atmospheric stability. Thus any error in wind speed willresult in errors in the latent and sensible heat fluxes.Concerning the wind stress, a correction has to applied because observation errors, evenif random, would cause a systematic bias. There are two opposite effects: the first is anoverestimation of the wind stress due to errors in the wind speed by the factor1+∆W∆WWW (10.8)where the second term denotes the relative error variance (Figure 10.6). The second effect is anunderestimation of the true wind stress due to errors in wind direction by the factor cos∆dwhere ∆d denotes errors in wind direction (Figure 10.7). The errors decrease the steadiness ofreported wind fields and lead consequently to a lower mean wind stress. As discussed inSection 8.2 the errors can be determined by comparing pairs of simultaneous wind reports as afunction of the distance between the ships (Lindau, 1995a). Considering both the speed andsteadiness error, the latter dominates in most wind climates, so that the stress may beunderestimated by 5% to 10%.Wind data are available both from in situ observations and by remote sensing. Withregard to in situ data (Section 10.6.2) it will be argued that even the available "high quality"data is likely to contain biases. This is important since these data have been used to verify theroutine wind data and also to develop and validate remote sensing algorithms. The routine wind10 - Basic Variables Evaluation 152 10.6 WindReport of the Working Group on Air Sea Fluxes Draft: January, 2000observations available from the VOS are obtained either from anemometer readings or by visualestimate. Each technique will be shown to have advantages and disadvantages. Remotelysensed wind data is available from active radar instruments (scatterometer and altimeter) andpassive microwave radiometers. There have been four scatterometer missions during the 1990sthat provided wind vector information. The passive microwave SSM/I instrument can alsoprovide wind speed information. Compared to these instruments the altimeter derived data islimited to wind speed values along the sub-satellite track. However it should be noted that therehas been a sequence of altimeter missions over a number of years, allowing these data to beused as a useful climatological data set for both significant wave height (e.g. Cotton and Carter1994b) and wind speed.Figure 10.6 Relative overestimation of thewind stress as a function of squared windspeed. Individual crosses depict monthly 5o by10o averages in the North Atlantic. A linear fitwas applied, to be used for the correction.Figure 10.7 Relative error of the winddirection as a function of squared wind speedWW. Individual crosses depict monthly 5° x10° averages.10.6.2 In situ wind dataA. HIGH QUALITY IN-SITU MEASUREMENTSThe accuracy of ship and buoy winds has recently been discussed by Taylor et al.(1999a), who stress that accurate measurement of wind over the ocean is difficult. It might beexpected that the best data sources would be anemometer measurements from ocean weatherships (OWS), research ships, or meteorological buoys. However there are biases in each ofthese data types. For example, examining data from the period 1992 to 1994, Taylor et al.(1995) showed that the wind observations from OWS Cumulus were not properly corrected forship motion when the ship was drifting or hove to. This resulted in errors of up to 1.5 ms-1.Isemer (1995) has attempted to evaluate the ocean weather ship wind data more generally. Asfor any other ship, the wind data from OWS and research ships are in error due to the air flowdistortion caused by the ship (Yelland et al., 1998); errors of order 10% are quite possible. Asdiscussed in Section 9.2.2, wind speeds from meteorological buoys are believed to be biasedlow in strong winds (Large et al., 1995; Weller and Taylor, 1999; Zeng and Brown, 1998).Two possible causes are the vertical movement of the buoy through a non-linear near surfacewind profile, and the distortion of that profile due to the effect of high waves; again errors oforder 10% may be possible.10 - Basic Variables Evaluation 153 10.6 WindReport of the Working Group on Air Sea Fluxes Draft: January, 2000It is often overlooked that the appropriate wind speed for use in bulk flux algorithms isrelative to the ocean surface (see Equation (7.8)). In many cases currents are a small fraction ofthe wind speed, and the contribution to error in (uz - uo) also small, but this is not always so.Also, for light winds with a stable ocean surface layer, the wind-driven current can be asignificant correction (Kudryavtsev and Soloviev, 1990). This introduces another source ofuncertainty, because the surface velocity is very seldom measured. There are two ways inwhich conversion from relative to true wind can take some account of the surface velocity; bycombining the ship motion in earth coordinates (e.g. from GPS) with currents from the ship’sADCP; or by using the Döppler-log/gyro which provides the ship’s motion through the water.Both methods incur additional sources of instrumental error, and furthermore the measuredcurrents are at considerable depth (of order 10m) where the locally induced wind drift will bevery small. Thus we can assume that, for the bulk formulae, the wind induced surface drifthas been accounted for in the determination of the transfer coefficient. However any oceancurrent, other than that locally induced by the wind, should be taken into account. B. VOS - ANEMOMETER WINDSFor the VOS data, Kent et al. (1999) found that a typical root mean square (RMS)error for a single wind speed observation is about 2.2 ms-1. However this was after instrumentalobservations had been corrected for the height of the anemometer above the sea surface (usingthe WMO47 data) and visual observations corrected using the Lindau (1995a) version of theBeaufort scale. For the observations as reported, the errors were about 15% greater - about 2.5ms-1. This demonstrates that, despite the varying effects of air flow distortion around the ship,correcting the data for anemometer height does reduce the errors. The RMS wind speed errorsappeared to be lower than average in tropical regions, however no significant dependence onwind speed was found.The VOS in the VSOP-NA project (Kent et al. 1993a) reported the anemometerestimated, relative wind speed in addition to the calculated true wind speed. Kent et al., (1991)showed that a major cause of error was the calculation of the true wind speed. Only 50% of thereported winds were within 1 ms-1 of the correct value, 30% of the reports were more than 2.5ms-1 incorrect. For wind direction, only 70% were within ±10° of the correct direction and13% were outside ±50°. These are large, needless errors which significantly degrade thequality of anemometer winds. A similar conclusion was reached by Gulev (1999).Preliminary results from a questionnaire distributed to 300 ships' officers showed that only 27%of them used the correct method to compute true wind. The problem is not confined to VOSobservations. A majority of the wind data sets obtained from research ships during the WorldOcean Circulation Experiment showed errors in obtaining true wind values (Smith et al., 1999).Taylor et al. (1995) reanalyzed the VSOP-NA results for wind speed. They found that,having corrected OWS Cumulus data for ship motion and corrected the VOS data foranemometer height, there appeared to be agreement between the OWS and VOS data for windsbelow 10 ms-1. For higher wind speeds the VOS winds were biased high - by about 1.5ms-1 to 2ms-1 at 20 ms-1 wind speed. If this bias is real, the reasons might include mis-reading of theanemometer dial (gust values rather than mean winds being reported) and air flow distortion.The use of computational fluid dynamics (CFD) to analyse the air flow over typical VOS shapesshows that there is a plume of accelerated air above the wheelhouse top (Taylor et al. 1999a).The shape of this plume depends on the geometry of the ship's accommodation block. Ananemometer mounted above the wheelhouse may be below, in, or above the plume maximumdepending on how high and how far aft it is mounted. Below the plume the wind will besignificantly underestimated, in or above the plume an overestimate will occur.C. VOS - VISUAL WINDSKent and Taylor (1997) reviewed the various Beaufort Equivalent Scales and found thatthat of Lindau (1995a) was most effective at giving similar wind speed distributions for bothanemometer estimated and visual monthly mean wind data. They also confirmed Lindau'ssuggestion that the characteristic biases of the earlier Beaufort scales could be explained by the10 - Basic Variables Evaluation 154 10.6 WindReport of the Working Group on Air Sea Fluxes Draft: January, 2000statistical method by which they were derived. It should be noted that the Lindau scale is moresimilar to the WMO1100 scale used for the observations (and also to the "UWM" scale used byda Silva et al. (1994)) compared to the so called "scientific scale" recommended by CMMIV(Table 10.2, see also WMO, 1970). Derivation of an accurate scale is not simple so the methodwill be briefly described.The Lindau (1995a) scale was derived by comparing wind speed measurements fromAtlantic OWS with visual estimates from passing merchant ships (VOS). To ensure a truecomparison two conditions had to be fulfilled: the error variance and the natural variability mustto be equal in both data sets. Lindau averaged over four individual OWS observations but oversix VOS observations to compensate for their different accuracy. The OWS data wereaveraged over 24 hours and calculated for the VOS data for each location and each season theradius that contained the same variability as for one day. The new scale was derived fromthese averages by the method of cumulative frequencies, which treats the variances of both datasets in a symmetric manner. As a result, the new scale (Table 10.2) was able to reproduce theOWS wind speed without systematic errors in either the average or in the variance.Table 10.2. New 10m-equivalent values compared to the WMO Code 1100. N gives thenumber of data pairs, which consists of daily means for OWS measurements and spatial meansfor Voluntary Observing Ships (VOS).Bft 0 1 2 3 4 5 6 7 8 9 10 11 12WMO 0.0 1.7 4.7 8.4 13.0 18.3 23.9 30.2 36.8 44.0 51.4 59.4 67.7New 0.0 2.3 5.2 8.9 13.9 18.9 23.5 28.3 33.5 39.2 45.5 52.7 61.1N 6 378 2287 8441 17197 11598 8870 4655 2068 597 122 15 1The definition of the Beaufort scale has changed during time (as have the characteristicsof the ships from which the estimate is made). A time dependent equivalent scale may thereforebe necessary. Lindau (1995b) checked North Atlantic wind observations of the last hundredyears against simultaneous individual pressure differences between the reporting ships.Assuming a constant relationship between wind speed and pressure gradient throughout theyears a time dependent scale was obtained (Figure 10.8) . Applying this scale to COADS theapparent positive trend in wind speed since 1945 was removed.D. RELIABILITY OF COADS MONTHLY WINDSThe reliability of the COADS Release 1a 2-degree monthly winds has been tested bycomparison with instrumental measurements in the North-West Atlantic for the period 1981-1991 (Gulev, 1999). The instrumental data set contained anemometer measurements of highhomogeneity and quality, taken by six research sister-ships with known anemometer heights inthe North West Atlantic. Care was taken to process the instrumental data to providecompatibility with the COADS winds. In particular, the error variances for monthly averagesof wind speed were tested to allow a two-way regression comparison. The results showed anoverestimation of the COADS winds in the low ranges and underestimation of the strong andmoderate winds. Such a relationship is different from those obtained in the other validationstudies (e.g. Lindau, 1995b; Kent and Taylor, 1997); indeed, application of alternativeBeaufort scales did not remove the bias, but made it even more pronounced. Thus, theconclusion of Gulev (1999) was that the disagreement resulted mainly from errors in theanemometer measurements in COADS, primarily from the incorrect evaluation of the true wind(Section 10.6.2B). Taylor et al. (1999a) suggested that airflow effects, for the class of researchship used, may also have contributed to the differences. In either case a possible conclusion isthat Beaufort estimates may well be less biased in the COADS than anemometer measurements.Similar arguments to these are used to justify the continuing preference of a number ofmeteorological agencies for visually estimated winds rather than anemometer readings.10 - Basic Variables Evaluation 155 10.6 WindReport of the Working Group on Air Sea Fluxes Draft: January, 2000Figure 10.8 Time dependentequivalent scale illustrated bythe temporal evolution ofBeaufort forces 2 to 7.10.6.3 Remote sensing of surface windA. SCATTEROMETRIC SURFACE WIND VECTORSWind vector estimates from scatterometers are based on empirical relationships ("modelfunctions") relating backscattered energy to wind speed at 10m under neutral conditions.Geophysical validation over a wide range of wind speeds has been difficult due to a) thedifficulty in acquiring suitable in-situ observations and b) the inherent incompatibility betweenscatterometer measurements and buoy observations (Freilich and Dunbar, 1999). However themodel functions have improved over time with the introduction of more sophisticatedtechniques and also as more scatterometer and high-quality in situ data become available to testthe full parameter space of the backscatter-wind relationship. Scatterometer data are availablefrom numerous sources (each using a different model function). Thus it is important to note theparticular version and source of scatterometer data.The fundamental scatterometer design results in multiple possible wind directions (so-called ambiguities) from which the most likely solution is determined. This ambiguity selectionprocess is fairly accurate; for regions with rms wind speed greater than 4 ms-1, the NSCATambiguity removal skill is conservatively estimated to be 95% (Gonzales and Long, 1999). Thesampling characteristics (number and width of data swaths, data coverage) of the scatterometerdata govern the ability to estimate synoptic fields of winds. This issue was addressed morefully in Section 8.6.3.The accuracy of the scatterometers is relatively excellent compared to errors for windsfrom VOS. Validation of the CERSAT ERS-1/2 scatterometer wind retrievals found ansystematic underestimation of nearly 0.75 m s-1 and an RMS error of ~1.3 m s-1 (Graber et. al.,1996). Directional biases and RMS errors were ~7° and ~22° respectively. Other comparisonsfurther explore such dependencies as wind direction on incidence angle (e.g. Ebuchi andGraber, 1998) and validation in coastal seas (Kent et. al., 1998).Evaluation of the 25-km NSCAT instrument winds has been much morecomprehensive. Compared to high-quality ship winds (Bourassa et. al., 1997), the NSCATwinds had RMS differences for speed and direction of 1.8 m s-1 and 14˚. When compared tobuoys, the bias was -0.3 m s-1 and RMS error ~1.3 m s-1 (Freilich and Dunbar, 1999). Othervalidations are available and include a wide range of approaches, including gridded productevaluation through direct comparisons to other products (e.g. Atlas et. al., 1999); comparison of10 - Basic Variables Evaluation 156 10.6 WindReport of the Working Group on Air Sea Fluxes June, 2000ocean models forced by various products (e.g. Verschell et. al, 1999); and comparisons for usein air-sea carbon exchange (e.g. Boutin et. al., 1999).Some numerical weather prediction (NWP) centres have been incorporatingscatterometer data into their operational surface wind analyses. For example, since January1996, ERS scatterometer winds have been assimilated in the operational ECMWF analysis.This will likely continue as the QuikSCAT data systems include the operational distribution ofQuikSCAT data by NOAA.B. PASSIVE MICROWAVE SURFACE WIND SPEEDS FROM SSM/IIntroduction: Thermal radiation emitted by the sea surface at millimetre frequencies isstrongly modified by wind induced sea surface roughness and partial foam coverage. Theclassical description of the emissivity of a foam covered rough ocean surface as a function offrequency ν, incident angle θι, and polarization P is:ενθ ενθ ε νθ (, ,;, ) ( )(, ,;) (, , ) i ff i ff i PuC C Pu C P =− + 1 (10.9)where Cf is the fractional foam coverage, εrw is the emissivity of a wind roughened surface, andεf is the emissivity of foam. In the algorithm development history different theoretical andempirical models have been used to relate Cf, εrw, and εf to the physical state variables of the seasurface and to the wind field above. In the material that follows four types of wind speedalgorithms for the SSM/I are described. These are the modified D-Matrix approach byGoodberlet and Swift (1989), the radiation transport model and regression based algorithm ofSchlüssel and Luthardt (1991), neural network algorithms by Stogryn et al. (1994) andKrasnopolsky et al. (1995), and the wind speed part of the SSM/I all weather algorithm byWentz (1997) and Wentz and Spencer (1998). The selection of these four algorithms does notcover all the available wind speed algorithms but it gives a description of differentmethodologies and a brief survey of the history of algorithm development.The original D-Matrix algorithm as given in Lo (1983) computes the wind speed at areference level of 19.5m from a linear combination of SSM/I brightness temperatures. It uses11 sets of coefficients representing particular seasons and latitude bands. Goodberlet and Swift(1989) found that the original D-Matrix algorithm did not meet the accuracy criteria of 2 ms-1when results obtained from SSM/I measurements were compared to NOAA buoys.Additionally, they found a low bias at high wind speeds and zonal discontinuities due to thecoefficient scheme used. They developed an alternative new D-Matrix algorithm from SSM/Ibrightness temperatures and buoy wind speeds using linear regression analysis that met the 2ms-1 criterion for all seasons and latitude bands. Since this algorithm works only for rain freecases, the D-Matrix algorithm uses a rain flag system to discard affected measurements. Suchan algorithm is quite robust but doesn't point the way to future improvements since it is purelystatistical; no information on the influence on the measurements of the surface emissivitiesgiven in (10.8), or of the atmospheric part of the signal, can be quantified. Goodberlet andSwift (1992) improved their algorithm with a nonlinear version that should account for water-laden atmospheres. As stated by Krasnopolsky et al. (1995) this retrieval has a singularity at∆37 = 30.7 K which may fall within the useful range of brightness temperatures.The Schlüssel and Luthardt (1991) algorithm is based on studying theoretically theradiative transport within the SSM/I channels for a large set of oceanic/atmospheric situationswith respect to wind speed. The retrieval formula is derived by multivariate regression analysisfrom the simulated synthetic measurements. It derives the wind speed mainly from thebrightness temperature difference between horizontally and vertically polarised components atthe same frequency so it is, like the Goodberlet and Swift (1989) algorithm, a linearcombination of SSM/I channels. The theoretical accuracy for the globally valid passive windspeed retrieval is stated to be 1.4 ms-1 under conditions where the satellite measurements are notaffected by heavy rain.10 - Basic Variables Evaluation 157 10.6 WindReport of the Working Group on Air Sea Fluxes June, 2000This retrieval was evaluated by Schlüssel and Luthardt (1991) by comparing satellitederived wind speeds with objectively analysed in situ wind speeds over the North Sea (Luthardt,1985) during the period July 1987 to June 1988. They obtained a standard error of 1.9 ms-1with a small bias of 0.2 ms-1. Schulz (1993a) compared 3403 globally distributed buoy andship measurements with retrieved wind speeds during the period July 1987 to September 1987and found a standard error of 2.1 ms-1 with the same bias as for the North Sea. The systematicerror was only significant at wind speeds above 15 ms-1 and could have been caused by aninsufficient parameterisation of the emissivity of the ocean surface at high wind speeds. This isdifficult to prove since there are only a few measurements during high wind speeds andmoreover still fewer measurements of the emissivity in the microwave region under thoseconditions. However, this comparison revealed also that this algorithm produces a high bias of0.81 ms-1 at latitudes between 20°N and the equator, showing that the retrieval scheme issensitive to liquid water in the atmosphere.Consequently, Schlüssel (1995) modified the algorithm to allow more liquid water inthe atmosphere during the radiative transport simulations and also determined new coefficientsfor the algorithm (using the same channels) if light rain was present. The choice of retrievalwas decided using a rain flag system similar to Goodberlet and Swift (1989). The accuracy forthe light rain retrieval was determined to be 1.6 ms-1.Neural network approaches are alternative empirical methods to derive wind speedfrom passive microwave brightness temperatures which have become popular during the lastfive years. Neural networks can be advantageous if:❍ nonlinearities occur in the transfer function, from brightness temperature to the soughtgeophysical parameter, that vary over the range of measurement space.❍ there is no a priori knowledge with regard to an analytical representation of the transferfunction.Stogryn et al. (1994) and Krasnopolsky et al. (1995) have developed neural network retrievalschemes that outperform all of the previously described algorithms. The biases and rms errorsas stated by Krasnopolsky et al. (1995) were 0.05 ms-1 and around 1.6 ms-1, respectively, whencompared to buoy measurements in their test data set. Krasnopolsky et al. (1995) introduced anew rain flag system that was based on liquid water content and which recovered 40% ofmeasurements rejected by the Goodberlet rain flag system.However, these algorithms can have limitations because:❍ the training data sets must include enough low and high wind speed cases. Otherwise ahigh bias at low wind speed and a low bias at high wind speed occurs and the varianceof the wind speed distribution is too low.❍ neural networks are more sensitive to sensor-dependent systematic errors (likecalibration). For example, Krasnopolsky et al. (1995) trained their network for theDMSP F8 satellite and when they apply their coefficients to data of the F11 satellitethey obtain a bias of -0.9 ms-1 and a rms error of 1.85 ms-1 although the brightnesstemperature differences between F8 and F11 were only ~1K for all channels. Thiseffect can be diminished by computing coefficients for each satellite but it should bekept in mind if neural networks are blindly applied.With respect to equation (10.8), the same arguments hold as for the empirical regressionalgorithms if we ask for physical explanations of the change of surface emissivity with windspeed. As for any empirical algorithm, they may give good estimates of wind speed but littlecan be learnt about the physics. This could be changed if the neural network were not trainedon buoy-satellite match ups but rather used in conjunction with radiative transfer models thusgiving more control over the situations considered.The Wentz (1997) all weather algorithm represented a more physical approach inretrieving the wind speed from SSM/I measurements in rain free situations. Wind speed was10 - Basic Variables Evaluation 158 10.6 Wind10.7 WavesReport of the Working Group on Air Sea Fluxes June, 2000retrieved, along with columnar water vapour and columnar cloud liquid water, using anonlinear optimisation method. The wind-induced emissivity of the sea surface wasparameterised by a two scale theory (Wu and Fung, 1972; Wentz, 1975) which was based on aknowledge of the root mean square slope of the large gravity waves, the standard deviation ofthe small irregularities, and the correlation length of the small surface structures. In practice,Wentz (1997) expressed the wind-induced emissivity as a monotonic function of wind speedwhich consisted of two linear segments connected by a quadratic spline such that the functionand the first derivatives in wind speed were continuous. The coefficients for this model werederived from collocated buoy and SSM/I observations.Wentz (1997) also investigated the role of wind direction on the retrieval accuracy andfound that errors of approximately 3 ms-1 could occur, especially if the radiometer looked in theupwind direction. He developed a correction of this effect which brought the error down to 0.5ms-1. Additionally, he claimed that the information on wind direction inherent to the SSM/Imeasurements can be retrieved if the signal to noise ratio can be enhanced by building averagesover large scales and long times.Importantly, Wentz (1997) also gave an error estimation that resolved the error budgetin terms of model errors, wind direction errors, radiometer noise, sampling mismatch betweensatellite and buoys, and other errors that could not be resolved. This enabled him to subtractthe sampling mismatch error from the total observed error. For his own retrieval he ended upwith a small systematic error of 0.3 ms-1 and an rms accuracy of 0.9 ms-1 ; significantly betterthat all previous algorithms.In a new all weather algorithm (Wentz and Spencer, 1998) the retrieval methods areextended to rain cases. With respect to wind speed, this simply consists of discarding theretrieval and filling in values from neighbouring pixels, or using a monthly climatologicalvalue derived from SSM/I. This algorithm has been used to compute a 10 year time series ofwind speed from the chain of SSM/I sensors which is, together with other atmosphericvariables, available via the internet under http://www.ssmi.com.10.7 WavesWave information is currently available from measurements on buoys and other in-situplatforms, VOS observations, satellite measurements (altimeter and SAR), and from modelexperiments.Wave recorders at NDBC and JMA buoys, light vessels, and OWS's providemeasurements of significant wave height with continuity of records from several months tonearly two decades. These are relatively high quality data which can be used for validation ofother products, but their spatial coverage is poor, since most of the buoys located in offshoreregions are in the subtropical northwest Atlantic, mid-latitudinal northeast Pacific and equatorialPacific. Long-term observations from shipborne wave recorders (SBWR's) are available in anumber of locations in the northeast midlatitudinal Atlantic, particularly at OWS L and SevenStones Light Vessel. These two records (with a continuity of 12 to 20 years) were used toidentify secular changes in significant wave height (SWH) by Bacon and Carter (1991, 1993).However, it is unclear to what degree the records from NDBC buoys and SBWR's areconsistent. There are some specific biases in both buoy and ship records and these have not yetbeen quantified.Reports containing visual wave observations appear in COADS in the early 1960's, andfor later years the contribution of reports with wave observations exceeds 40%. These visualVOS observations were used to produce “Ocean Wave Statistics” (Hogben and Lumb 1967) and“Global Wave Statistics” (Hogben et al. 1986), which contain statistical distributions based onlimited visual wave data from 50 and 104 areas of the World Ocean respectively. These havebeen widely used by sailors and naval engineers. Gulev and Hasse (1998) brought the visual10 - Basic Variables Evaluation 159 10.7 WavesReport of the Working Group on Air Sea Fluxes June, 2000wave observations from COADS reports up to date, producing a North Atlantic waveclimatology on a 5˚ grid for the period 1964-1993. They derived heights, periods and directionsfor wind sea and swell. SWH was derived using a combined approach of Barratt (1991) whereSWH is assumed to be the square root of the sum of squares of sea and swell heights for thecases when sea and swell directions are within a certain directional sector (found to be equal to30° (Gulev and Hasse 1998)); for other cases it is taken as higher of the two components.Relatively good agreement was obtained for the resulting SWH in comparison to in situobservations from NDBC buoys and OWS measurements. Some known biases in the COADSobservations include the overestimation of very small seas and swell (which results from thecoding of all waves smaller than 0.5 m with the unique code figure “1”), generalunderestimation of both sea and swell periods by approximately 0.1 to 0.4 seconds, and largeoverestimation of swell periods for 1968-1969 due to a change in swell period codes in 1968which was not implemented simultaneously by all observers.The only observation-based wave height climatologies available at present are derivedfrom the altimeters flying on board GEOSAT, ERS-1/2 and TOPEX/POSEIDON. Productsderived from the ERS-1 and ERS-2 scatterometers are still under development. Firstassessments showed that the algorithms used to retrieve Hs from the scatterometer severelyunderestimated wind sea, but gave very reasonable estimates of swell (Bauer and Heimbach1999). Due to the limited sampling, altimeter derived wave climatologies are only available asmonthly means. Cotton and Carter (1994a) calibrated these monthly means against co-locatedbuoy measurements. They found the accuracy of the calibrated data to be better than 18 cm.This made it possible to combine time series from different missions and construct a globalclimatology which has continuity over about 12 years (starting in 1985) with some gaps in early1990's. Bauer and Staabs (1998) compared the statistical properties of altimeter derived Hswith those of measurements from OWS and in general found good agreement. They also,however, observed that the algorithm used before 1994 to retrieve Hs from the ERS-1 altimeterproduced an unrealistic high frequency of occurrence of Hs = 2m, a deficiency that had alreadybeen noted by Cotton and Carter (1994a).By now, wave models driven by winds from NWP systems can produce long-termclimatologies of waves (hindcasts) covering global ocean or individual ocean basins for periodsof several decades. These wave products are not fully independent, because they are largelyaffected by the wind forcing used. Sterl et al. (1998) produced a 15 year climatology of wavesby driving the WAM wave model with the ERA winds (see also Section 2.4 of this report).While the patterns of Hs thus obtained compared favourably with those derived from altimetermeasurements, the absolute wave heights were generally found to be too low. As the mostprobable reason the authors identified the limited resolution of ERA, stressing once more theimportance of the wind input for the modelling of waves. OceanWeather Inc. performed anumber of 40-yr (1958-1997) wave hindcasts using the ODGP-2 1G fully discrete spectral wavemodel and ODGP 3G wave model with a grid resolution of 2.5˚ longitude and 1.25˚ latitude(1G) and 1.25˚ x 0.625˚ (3G) (Cox and Swail 2000). These hindcasts were driven by theNCEP/NCAR 10m winds. Time resolution of this product is 6 hours, and all major waveparameters, such as significant wave height, sea and swell heights are available. Details of theOceanWeather Inc. wave hindcasts were described in Cox and Swail (2000).Recently Young and Holland (1996) and Young (1999) produced a combined waveproduct based on satellite remote sensing and NWP wave outputs. This global scale waveclimatology covers a 10-year period from 1986 to 1995 and has 2˚ spatial resolution formonthly means of significant wave height and period. Data from ERS, GEOSAT andTOPEX/POSEIDON, as well as from the ECMWF operational system (WAM), were used toproduce the climatology. Analysis of the global fields of Young (1999) shows that this productstill requires comprehensive validation efforts.10 - Basic Variables Evaluation 160 10.8 CloudinessReport of the Working Group on Air Sea Fluxes June, 200010.8 Cloudiness10.8.1 IntroductionComprehensive, ocean-basin scale observations of clouds are based on either (1)synoptic reports or (2) satellite data. Some ship campaigns have also used intensive passiveand active remote sensing systems (e.g. ceilometers and cloud radars) for a more precisecharacterisation of cloudiness in a restricted region.10.8.2 In situ cloud dataConsidering the VOS observations, the main problem is the quality of the cloud reportsreturned by the relatively untrained observers. There have been hardly any studies of thequality of the reports. With regard to these data, Warren et al. (1988) remains the standardreference climatology. Ship synoptic reports were obtained from the “SPOT” archive of theFleet Numerical Oceanography Center (FNOC) and Release 1 of the COADS for 1930-1979.The painstaking work on this atlas yielded gridded monthly and seasonal cloud cover, cloudtype amounts, frequencies of occurrence, cloud base heights, harmonic analyses of annual anddiurnal cycles, and interannual variability and trends. Warren (personal communication) notesthat since 1988, the analysis methods have been improved; moonlight data is now included.Work on a new atlas will cover the 44 years 1952-1995. A digital forms of the atlas isavailable. The main problem is the degree of reliability that can be ascribed to visual cloudobservations made by observers with little relevant training and whose view of the sky is inmany cases obscured.Information on cloud base height and cloud overlap from synoptic atlases aresometimes used as a cross check on satellite-based retrievals of parameters, such as surfacedownwelling LW (e.g. Gupta et al., 1999).10.8.3 Remote sensing of cloud dataISCCP has combined geostationary and polar orbiting satellite data to produce ageographically comprehensive cloud record. The ISCCP second generation “D2” data isavailable for July 1983 – December 1998. ISCCP is an officially reviewed WCRP product, andCD ROM’s of ISCCP clouds are issued by the NASA Langley DAAC. ISCCP cloud retrievalsare based primarily on two imaging channels, the reflected SW and LW in the emissionwindow. For polar latitudes only, a third channel at 3.5 micron is also used. Pixel resolutionis nominally 4 km. The pixels are sampled every 30 km and 3 hours (yielding the advancedresearch product “DX”, which is internal to ISCCP). The main ISCCP D product is gridded in280 by 280 km equivalent areas. Sunsynchronous NOAA satellites equipped with the AVHRRare used at high latitudes. Low latitude retrievals are based on geostationary data. Therequirement to produce comprehensive coverage has resulted in the application of fairlyrudimentary, but ingenious, algorithms in ISCCP. Calibration of ISSCP has resulted in aimproved record from the radiometers on operational satellites (Brest et al., 1997), but goodcalibration remains a challenge for ISCCP (for example, Klein and Hartmann 1993). ISCCPcalibration is sufficient to identify the geographical patterns of interannual variability, such asthat due to ENSO. However, no presently operational cloud imager is considered to have thestability to confidently identify a small secular change in global mean cover. That includes theVIRS imager on TRMM, which is used by CERES to process coarser resolution broadbanddata. The CERES broadband radiometers are, however, sufficiently stable to identify changesin global mean radiation (in Wm-2 rather than per cent cloud cover) to within 0.2%.10.8.4 Combined productsAnother source of cloud observations is the United States Air Force Real-TimeNephanalysis (RTNEPH, Hamill et al. (1992)). It is operationally produced 8 times daily onNorthern and Southern Hemisphere polar stereographic grids (horizontal resolution 47.625 kmat 60o latitude). Data sources are primarily the infrared and visible channels on the DMSPsatellites, with some information from NOAA polar orbiters. Surface observations are used to a10 - Basic Variables Evaluation 161 10.8 Cloudiness10.9 PrecipitationReport of the Working Group on Air Sea Fluxes June, 2000significant extent when available, and a manual bogus procedure is used during each analysiscycle. The RTNEPH data set consists of total clouds and up to four distinct cloud layers. Eachgrid point contains cloud coverage, geopotential height of the cloud layer bases and tops, timeof observation, and diagnostic information.The analysis system makes assumptions to estimate the magnitude of lower cloudobscured by higher clouds. There are rarely more than two distinct cloud layers. Hamill et al.(1992) note that the intent is to maximise the probability of cloud detection; therefore both clearand overcast conditions tend to be overestimated. The IR retrieval is a one-channel algorithm;therefore, if there is no visible channel or surface data available, the analysis tends to miss warmlow stratus and high thin cirrus or to place the cirrus too low in the atmosphere. There are plansto improve the algorithm by using multi-spectral satellite techniques. Masutani et al. (1999)have compared several estimates of clouds, including ISCCP and RTNEPH.10.9 Precipitation10.9.1 Surface-based precipitation dataA. HIGH QUALITY IN SITU PRECIPITATION MEASUREMENTSThe advantages and disadvantages of different methods of high quality precipitationmeasurements has been discussed at some length in Section 5.4 which dealt with the directmeasurement of precipitation as a flux. It must be recognised that the number of in-situprecipitation measurements over the whole world ocean is still probably no more than severalthousand samples. Also, there is no possibility of covering much more than a few percent of theocean with radar measurements. Therefore these measurements (be they from mechanical oroptical gauges, disdrometers, or radar) are better considered as a means of verifying satellitemeasurements and NWP products rather than the basis for the creation of a precipitation atlas.However, the development of accurate and reliable raingauges, and their introduction intooperational use would provide a sharp increase in the number of open ocean samples within afew years.Despite the progress which has been achieved in precipitation measurement, there aresome problems which are still unresolved. In particular, wintertime solid precipitation in mid-and high latitudinal regions is still poorly determined, and it is unclear at the moment how thiscan be best overcome. Should the existing gauges be improved or completely new instrumentsdeveloped specifically to measure solid precipitation?B. RADAR MEASUREMENT OF PRECIPITATIONRadar meteorology has a long history of application to precipitation measurement, andto the study of atmospheric dynamics from fields of backscatter intensity and Doppler velocity.Its strength is the ability to display spatial patterns of rainfall within some hundreds ofkilometres surrounding the radar position. Unfortunately, radar-derived rainfall measurementcannot be regarded as direct; to make the observed returns quantitative a number of error-pronesteps are involved. The fundamental problem is determination of the relationship between radarreflectivity, Z (mm6m-3), and rainfall rate, R (mm hr-1), which has to be established empiricallyfor each situation. The traditional form is:Za Rb = (10.10)where the constants a and b fall within rather broad bounds, a being typically a few hundredwhile b ranges between about 1.2 and 1.8 (Battan, 1973)1. Z - R relationships have rather anextensive literature; see for example the discussions by Austin (1987) and Sauvageot (1994).The accuracy of measurement therefore depends on a number of factors; the radar calibration,signal attenuation due to the rain along the radar path and water on the radome, the vertical 1 Radar meteorologists usually quote the reflected signal in dbZ whereZ(dbZ) = 10.log10 Z (mm6m-3)10 - Basic Variables Evaluation 162 10.9 PrecipitationReport of the Working Group on Air Sea Fluxes June, 2000structure of the precipitating system, and knowledge of the scan volume geometry. The latterpresents a particular challenge when the radar is mounted on a ship, because of the motion.Finally, it is usual practice to adjust the radar Z - R relationship to whatever in-situ gauge arraymay be under the radar beam (Steiner and Houze, 1997). Clearly, this presents difficulties overthe open ocean, and often leads to dependence on the nearest island-mounted gauge where therainfall may not be representative.A recent use of radars for measuring precipitation at sea occurred during COARE whenC-band Doppler radars were deployed simultaneously on two ships for a total period of 90 days.Each radar antenna was stabilised, using the combination of an Inertial Navigation Unit (INU)and the Global Positioning System (GPS) to calculate all instantaneous components of shipmotion and their time derivatives so that the antenna appeared to be fixed to a horizontal plane.The pointing accuracy was estimated to be 0.1 degrees. The COARE radar rainfall data haveundergone close scrutiny in the light of the initial disagreements with shipborne in situ raingauges (see Section 10.9.4E), and are well documented (Rutledge et al., 1993; Short et al.,1997; Petersen et al., 1999). Several sources of error were discovered, and improvements madeto the signal processing and data analysis. These included careful recalibration of the system,corrections for range attenuation and gain, and separate Z - R relationships for convective andstratiform rainfall. Tokay and Short (1996) showed that, for the same reflectivity, stratiformrain rates are about half those of convective precipitation because stratiform produces morelarge raindrops than convective rainfall due to aggregation, and fewer small to medium drops.Tropical stratiform falls from a layer of melting aggregates, which cause the well-known radar"bright band", a region of enhanced reflectivity just below the zero degree isotherm. Short etal. (1997) give reflectivity Z = 323R1.43 for stratiform and Z = 120R1.43 for convective rainfall.For monthly rainfall, however, Steiner and Houze (1997) question the value of using separaterelationships for convective and stratiform rainfall.COARE demonstrated that the combination of high-quality Doppler radar with precisepointing and stabilisation has application in both oceanographic and meteorological studiesassociated with precipitating systems over the ocean. Research vessels (e.g. the US R/V RonaldH. Brown and the Japanese R/V Mirai) are beginning to install such systems as part of the ship’sinventory of equipment, but it is clear from the above that it is far from being a system forgeneral use. At the same time, Lebedev and Tomczak (1999) have described a method ofrecording and analysing the raw reflectivities from a ship’s navigational radar with a view to itspossible use to measure open ocean precipitation routinely from research vessels and ships ofopportunity.A novel use of Doppler wind profilers for precipitation profile measurements was alsointroduced during COARE. The NOAA Aeronomy Laboratory has developed UHF windprofiler technology for tropical dynamics and climate research (Carter et al. 1995). Theprofilers, originally intended for the measurement of lower tropospheric winds in the tropics,have now been applied to precipitation research (Gage et al. 1994, 1996; Ecklund et al. 1995;Williams et al. 1995, 1999). The UHF profilers are sensitive to hydrometeors and provide ahighly-resolved time-height cross section of precipitating cloud systems. A unique precipitationdata set was collected using UHF profilers at Integrated Sounder System (ISS) sites duringCOARE. Preliminary studies show reasonable agreement between profiler and scanning radarsin the statistical partitioning of tropical precipitation. The profiler uses the vertical structure ofhydrometeor echoes, their Doppler velocity, and spectral width, as well as their temporalcontinuity in determining precipitation type (Williams et al. 1995). Since COARE, a networkof wind profilers has been maintained operationally, particularly in the western Pacific region,to observe precipitating cloud systems. Profiler data at Darwin have also been analysed forrainfall (Cifelli et al. 1996; Ecklund et al. 1997).C. VOS PRECIPITATION ESTIMATESPrecipitation is not measured on ships but is determined from the "present weather"descriptions reported in the ships' weather observations (Tucker, 1961). Weather codes which10 - Basic Variables Evaluation 163 10.9 PrecipitationReport of the Working Group on Air Sea Fluxes June, 2000indicate that precipitation is occurring are modelled by formulae which describe theprecipitation rate based on the contribution from three continuous rain categories: light,moderate, or heavy. The relationship between these categories, and the rain rate which theyrepresent, was found empirically. On a global basis accuracy of any “present-weather” basedprecipitation estimate is not likely to be high. Dorman and Bourke (1978) adapted Tucker'sscheme for tropical regions by taking temperature into account. Lindau (2000) proposed asimple rain algorithm, which needs as input the reported weather code and specific humidity.The algorithm was derived by using wind-corrected rain measurements of German lightships.With weather code and specific humidity they were able to explain more than 70% of theintermonthly variance. It has been used to determine evaporation minus precipitation on aglobal basis.10.9.2 Precipitation by ocean freshwater budgetsAn integral method of rainfall determination involves calculating ocean budgets fromsurveys of temperature and salinity in space and time over the ocean mixed layer. Typically,T/S profiles are obtained over an area of ocean from an array of moorings, or from ships makingCTD casts at frequent stations along the cruise track or with a towed CTD (e.g. mounted on aSeaSoar). It is necessary to analyse heat and freshwater fluxes in three dimensions, becauseadvective components have been found to be of first order. The closure of freshwater budgetsduring COARE by Feng et al. (1998), Smyth et al. (1996) and in the Indian ocean by Godfrey etal. (1999) are examples of the successful integration over space and time of the patchy rainfallpattern, indicating that with proper care the ocean can be its own rain gauge.10.9.3 Satellite remote sensing of precipitationA. INTRODUCTIONThis section describes the existing global estimates of precipitation obtained fromsatellites that might be used for model evaluation or creation of freshwater flux data sets. Theorder in which the different products are described should not be considered as a ranking of datasets. The major problem facing satellite estimates of precipitation is the lack of alternativefields for validation. Early attempts at validation have been restricted to comparisons withclimatologies obtained from ship present weather reports which also suffer from largeuncertainties (Jaeger, 1976; Legates and Willmott, 1990). However in recent years threeAlgorithm Intercomparison Projects (AIP) have been sponsored by the GPCP. In addition, theNASA WetNet project has also sponsored three projects called Precipitation IntercomparisonProjects (PIP). The aims and results of both types of project are discussed below (Section10.9.4).B. GPCPGlobal climatological fields of monthly mean precipitation over both ocean and land,derived from a combination of satellite and terrestrial observations made over the period July1987 to March 1998, have recently been produced within the Global Precipitation ClimatologyProject, GPCP (Huffman et al., 1997). The data are provided on 2.5˚ x 2.5˚ latitude-longitudegrids. The values were obtained using a combination of passive microwave and infraredtechniques. The infrared techniques rely on a correlation between cloud top brightnesstemperatures and rain rate and are more appropriate for use with deep convective systems.Used within 40˚ of the equator, they exploit the frequent sampling afforded by geostationarysatellites. The microwave technique detects the radiative effects of hydrometeors and has astronger correlation with surface rainfall. It relies on sensors on polar orbiting satellites (toobtain adequate spatial resolution) and hence has a relatively low sampling rate, as long as onlyone satellite is in use, but can be employed to higher latitudes in the GPCP analysis. A futureversion of GPCP will deliver a daily precipitation map on a 1˚ x 1˚ grid.C. CMAPThe CPC Merged Analysis of Precipitation, CMAP (Xie and Arkin, 1997), consist of a17 year period, from 1979 to 1995, of gridded global (ocean and land) monthly precipitation10 - Basic Variables Evaluation 164 10.9 PrecipitationReport of the Working Group on Air Sea Fluxes June, 2000fields with a resolution of 2.5˚ x 2.5˚ in latitude and longitude. Merged into this dataset are avariety of satellite measurements, gauge observations, and NCEP-NCAR reanalysis. Thus itsvalue for evaluating models is somewhat reduced. However, it might be a very useful datasetfor climate analysis and hydrological research. Many of the data sources used are the same asfor the GPCP dataset. In particular, the Geostationary Precipitation Index, GPI (derived frominfrared data of geostationary satellites) and the SSM/I-derived components - like the Ferraro etal. (1994, 1996) dataset described below. Thus differences between CMAP and GPCPremained small over tropical and subtropical oceans. Over extratropical oceans significantdifferences have been found. These were caused by the additional use of MSU data (Spencer,1993) and the Outgoing Longwave Radiation (OLR) based Precipitation Index (OPI) (Xie andArkin, 1998).D. PURE MICROWAVE-DERIVED PRODUCTSIn addition to the merged datasets described above, some of the microwave algorithmsestablished for the SSM/I have also been used to derive global precipitation climatologies.Two of these are the Ferraro et al. (1994) and the Bauer and Schlüssel (1993) algorithms.Those datasets have a higher spatial and temporal resolution, typically 1˚ x 1˚ in latitude andlongitude and one to five days. An obvious deficiency of these datasets is that they wereconstructed from data from only one SSM/I at a time; this enhanced the sampling errorsignificantly. The sampling error could be strongly reduced if all available SSM/Imeasurements were to be used (at least two sensors have been in space simultaneously since1992).For the construction of freshwater flux fields (like HOAPS) from SSM/I data it mightbe essential to use SSM/I only algorithms since it is important that the precipitation is notsampled in a different manner than the evaporation.10.9.4 Intercomparison projectsA. INTRODUCTIONThe following conclusions from the recent intercomparison projects on rainfallmeasurements from space are mostly adapted from two important papers by Ebert and Manton(1998) and Smith et al. (1998) that gave a comprehensive overview of the results of the AIP-3and PIP-2 projects. The AIP and PIP projects had the common objective of improving satellitealgorithms. The goal of the AIP projects was to validate different satellite algorithms againstground based gauges and radar estimates, which were treated as truth in terms of bias and rmserror. The underlying idea was to calibrate satellite algorithms with ground basedmeasurements and merge them together as was done in the GPCP dataset described above.Thus the AIP's were focussed on regional areas with different atmospheric conditions. AIP-1was conducted over Japan and surrounding waters using data from June to August 1989, AIP-2was located over the British Isles and surrounding waters using data during February to April,1991. AIP-3 attracted the largest number of algorithms (57) for a comparison over the radarcovered area of the TOGA COARE experiment domain during November 1992 - February1993. The philosophy of the PIP's was quite different from the AIP's since they rejected thenotion that ground data are representative enough to serve as a final calibration standard (Smithet al., 1998). The PIP's were directed at establishing a passive microwave algorithm thatincorporated the best features of all existing algorithms. While PIP-1 and PIP-3 were focussedon monthly rain accumulations over global grids, PIP-2 was dedicated to instantaneous rainrate estimates. The aim was to understand the systematic differences between the algorithms,when applied to globally distributed test cases, rather than to use the differences from groundbased measurements as an indication of the uncertainty.Results from all these intercomparison studies are now in the literature with theexception of PIP-3. AIP-1 results are described in Arkin and Xie (1994), and in more detailedin Lee et al. (1991). AIP-2 results are published in Allam et al. (1993) and Liberti (1995). Theresults of PIP-1 are documented in a special issue of Remote Sensing Reviews. Since AIP-310 - Basic Variables Evaluation 165 10.9 PrecipitationReport of the Working Group on Air Sea Fluxes June, 2000(Ebert, 1996; Ebert and Manton, 1998) and PIP-2 (Smith et al.1998) contain the newest findingstheir conclusions are summarised below.B. AIP-3 CONCLUSIONSComparisons of satellite algorithms using GMS VIS/IR, AVHRR, and SSM/I data (oninstantaneous and monthly time scales) to Doppler radar data (sampled every 10 minutes andmapped to a 0.5˚ grid) revealed that:❍ Over the whole TOGA COARE experiment the median value of the ratio of satelliteestimated to radar-derived rain rates was 1.3, with considerable variations for singlephases of the experiment. No categorical differences in bias between algorithms werefound.❍ Infrared, visible/infrared, and mixed infrared/microwave algorithms exhibited slightlyhigher correlations at monthly time scales than did estimates based on AVHRR orSSM/I alone. But the difference in correlation was too small to be statisticallysignificant due to the small number of quasi-monthly samples.❍ On an instantaneous time scale, the SSM/I algorithms outperformed all otheralgorithms having a statistically significant, higher correlation to the radar data. Thesuccess of the SSM/I algorithms was related to their ability to distinguish raining fromnon-raining areas.❍ The coupled infrared/SSM/I algorithms did not perform better than infrared alonealgorithms because for these the best SSM/I algorithms were not used.❍ Results from NWP models showed similar biases and rms errors as the satellitealgorithms but exhibited much lower correlations because they can not resolve theraining and non raining areas.C. PIP-2 CONCLUSIONSPIP-2 examined a number of globally distributed case studies and intercompared thealgorithm results in various ways: to one another, to algorithm category composites(algorithms were divided in categories by their solution method, channel input, and screeningapproach), to the overall algorithm composite, and to ground-based data. From this the mainconclusions drawn were:❍ Passive microwave rainfall algorithms applied to SSM/I data performed credibly. Thebiases and rms errors found corresponded reasonably to the AIP-3 results.Nevertheless, it was stated that the differences between algorithms were not larger thatthe uncertainty in the validation data. PIP-2 gave evidence that ground-based gaugedata from reputable sources were not reliable as a calibration standard for instantaneousrain rates.❍ The results of the intercomparison were strongly dependent on the algorithms' decisionas to where it was raining and where it was not. This was most important at low rainrates. Thus algorithm developers were requested to seek a common rain/no rainthreshold and a common rain screening method for determining the area of rainfall. Inthe future, rain screening should deserve more attention than the conversion ofbrightness temperatures into rain rates.❍ Despite the first conclusion it was concluded that, before a calibration model isdeveloped, intercomparison projects like AIP and PIP are useful to improve algorithms.❍ The final, somewhat contentious, conclusion stated that the point has been reachedwhere ground validation data are no longer effective in determining the precision ofsatellite algorithms. As an alternative validation strategy, a comprehensive calibrationmodel should be developed consisting of the most complete radiative transfer modeltogether with a detailed microphysical model.10 - Basic Variables Evaluation 166 10.9 Precipitation10.10 River InflowReport of the Working Group on Air Sea Fluxes June, 2000D. BEST ESTIMATEIn the light of the findings of all the AIP's and PIP's it is difficult to say whichclimatological dataset might be the best estimate. All strategies to build long term datasets canbe defended on scientific grounds. However, the user of these datasets must be cautious withregard to the purpose for which the dataset will be used. For example, it might not bemeaningful to use a dataset which contained reanalyses data to verify the reanalyses. It wouldbe helpful if each dataset were accompanied by error maps giving the size of the systematic andthe random errors. For instance, products derived from one satellite only, like the purepassive microwave datasets, would have an error map that gave an estimation of the samplingerror as the most important systematic component.Another conclusion that can be drawn from the intercomparison projects is that not onlyshould the quality of satellite estimates be improved but also the status of routinely gainedsurface based measurements. This is considered important, despite the PIP-2 conclusions,because the development of a calibration model as suggested by PIP-2 is not easy and will be along-term project. A first step in this direction is the attempt to archive high quality groundradar data for comparison with measurements of the TRMM sensors. With this sensor packagea new era of remote sensing of rain has recently begun with infrared, passive and activemicrowave instruments specially dedicated to measure rain on the same platform. Thus, for thefirst time, synergistic algorithms can be developed.E. COARE MEASUREMENT RECONCILIATIONAs mentioned above (Section 10.9.1B), serious disagreements between variousestimates of rainfall during COARE led to intensive study into the reasons, and reappraisal ofinstruments and methods. When all instrumental corrections had been made to the variousrainfall methods, the IOP-average disagreement was reduced but still too large. Ciesielski andJohnson (1999; see also Bradley and Weller 1997, p.17; Bradley et al. 1997) resolved thesituation through analysis of atmospheric heat and moisture budgets. This technique, describedby Yanai et al. (1973), makes use of atmospheric soundings of temperature and humidityobtained over space and time, to determine the “apparent heat source” (known as Q1) and the“apparent moisture sink” (Q2) of a volume of the atmosphere. Being an integral method, itovercomes small-scale variability. An extensive array of sounding sites was distributed over theCOARE domain, within which the budgets for specific regions were determined using theinterpolation technique of Nuss and Titley (1994).Ciesielski and Johnson (1999) determined the IFA-averaged rainfall rate for the IOP tobe 8.2 mm/day compared to a radar estimate of 4.8 mm/day (because of compensating effects,correcting the soundings for the humidity bias described in section 6.3.3 increased the budgetestimate by only 2.5% to 8.4 mm/day). However, when their budgets are computed just overthe radar area (which covers about one-third of the IFA) and for the same 101 days that theradar was operational, their rainfall rate is 5.2 mm/day, which is much closer to that of theradar. For these same 101 days but over the entire IFA, their budget estimate is 7.3 mm/day.These results indicate that (a) the radars were situated in a relatively dry region of the IFA, and(b) the 101-day radar exposure was from a relatively dry period of the IOP. They serve as areminder that, for meaningful comparisons of quantities as variable as rainfall, sample regionsand periods must be strictly coincident.10.10 River InflowRiver runoff perceptibly lowers the salinity of the ocean. It might be thought that theconsequences on the buoyancy would be very local in comparison to the size of ocean basins.However salinity anomalies of fluvial origin may be advected rather far by ocean currents. Forexample, the impact of major rivers like the Amazon, the Congo or the Saint-Laurent on thesurface salinity of the Atlantic ocean are perceptible several hundred of kilometres away fromthe mouth of the rivers (Figure 10.9). The inflow of freshwater from the rivers is of extremeimportance in the global freshwater balance of the ocean. This balance is the result of the loss10 - Basic Variables Evaluation 167 10.10 River InflowReport of the Working Group on Air Sea Fluxes June, 2000of water by evaporation, the gain of freshwater by precipitation, and coastal river runoff. Thecontribution of rivers to this balance was estimated at 1.26 Sv (1 Sv=106 m3/s) by Baumgartnerand Reichel (1975), nearly 10% of the contribution of precipitation. Neglecting thiscontribution would increase the average salinity of the upper Atlantic ocean (first 50 m) by 1.5psu after 10 years.Global river runoff data are available from the International Hydrography Program(IHP) which published a Global River Discharge Catalogue (Vorosmarty et al., 1996), and fromthe Global Runoff Data Centre (GRDC). IHP data available in reports published by UNESCO,are a selection of monthly discharges at 949 stations over six continents (Africa, Asia, NorthAmerica, South America, Europe and Australia/Oceania). Several stations may exist for thesame river, and only 219 stations are listed as corresponding to a direct discharge into an oceanbasin. These reports do not include runoff from the Arctic and Antarctic regions. The lengthof the records from which climatic monthly means can be estimated varies from 1 to 100 years,the average length being 19.3 years. A great disparity exits between continents, Europe andNorth America presenting the longest records. These data do not include the inflow offreshwater into oceans due to underground water.Figure 10.9. Mean sea surfacesalinity (PSU) in the NorthAtlantic from the climatic atlasof Reynaud et al. (1998).Regions of low salinity are inlight grey and correspond toregions near the major rivers(Amazon, Congo, Saint-Laurent) and regions ofimportant sea-ice melt(Labrador Sea).Using the IHP data set, Boukthir et al. (2000) have computed a monthly meanclimatology of the direct contribution of rivers to the freshwater flow into the ocean. In theiranalysis, these authors excluded the contribution of rivers for which flow does not exceed 800m3/s for at least one single month in the year, and they also neglected rivers for which nocontinuous records at least 24 month long were found. Thus their calculation used 145 stationsfor 109 river mouths. For the major rivers, the discharge computed by Boukthir et al. (2000)agreed very well with the analysis of Hagemann and Dümenil (1996) who used data fromGRDC for the purpose of model validation. The cumulative runoff for all the rivers consideredby Boukthir et al. (2000) sums to 0.57 Sv. A previous comparable estimate based on formerUNESCO reports published in 1969 is that of Baumgartner and Reichel (1975) who found 0.73Sv, a value significantly larger, but which includes all rivers for which records were available(even with very small discharge), and also includes the contribution of regions beyond the polarcircles which they estimated from other sources. Note that in the analysis of Baumgartner andReichel (1975), the direct contribution of river discharge accounts for only 60% of the total10 - Basic Variables Evaluation 168 10.10 River Inflow10.11 Sea IceReport of the Working Group on Air Sea Fluxes June, 2000freshwater runoff which is 1.26 Sv. They obtained this latter value from a global hydrologicalbalance calculation and part of the runoff was estimated as a residual.GRDC data are in the form of daily and monthly discharge data for several thousandstations for more than 2,800 rivers. However, many stations are far from the mouth of therivers, and many, being quite recent, do not yet have records long enough for climatologicalestimates. If one is interested in long time series, GRDC and IHP likely provide, at present,very similar information.Therefore, the data set gathering carried out by the IHP and more recently by theGRDC are initiatives that deserve great support. However a considerable amount of work isstill necessary to reach an accurate estimate of the amount of water which reaches the ocean. Inparticular, the contribution of underground waters and small streams cannot be measured andhas still to be estimated, often as a residual of the global budget. Furthermore, it is likely thathuman activities, with increase damming and irrigation, have significantly reduced the riverdischarge reaching the ocean in the last twenty years (by 50% for the Nile River for example).It is therefore important to encourage the type of studies which permit synthesis of theinformation contained in these huge data sets in terms of freshwater flux to the ocean, andwhich quantify recent changes.10.11 Sea Ice10.11.1 Sea ice amountSea ice concentration and extent data are extracted from satellites routinely. The firstmultichannel instrument, SMMR, the Scanning Multichannel Microwave Radiometer, waslaunched in 1978 on SeaSat and Nimbus 7. Shortly before the demise of the latter in 1987, anew series was launched: the Special Sensor Microwave/Imager, SMM/I; this series continuesflying today (see Section 4.3.2). A single-channel instrument the Electronically ScanningMicrowave Radiometer, ESMR, flew on Nimbus 5 from 1973 to 1976; its best application is indefining ice extent.The multichannel sensor resolution varies between 12 and 150 km, smaller for higherfrequencies. Data products are produced at 12 and 25 km grid spacings, which are ideal for alarge-scale geophysical view. Coverage is excellent: global except poleward of 84° latitude forSMMR and 87° for SSM/I. Because the atmosphere is fairly transparent at their frequencies,microwave sensors are "all-weather" providing data in darkness and through clouds.Over sea ice the microwave signals are usually interpreted as concentrations of all iceand of only multiyear ice. The 15% concentration contour is usually taken as the ice edge.Estimates of the uncertainties in these quantities vary: ice extent, ±12 km; ice concentration,±6%, with possible biases of similar magnitude; and multiyear concentration, ±20%, again withbiases of similar size.Data sets are available through the National Snow and Ice Data Center (http://www-nsidc.colorado.edu) for October 1978 to the present, for total, first-year and multiyear iceconcentrations. They are on a 25 x 25 km grid, daily and monthly, for both hemispheres. Thepassive microwave record is presented in three atlases: Gloersen et al. (1992), Parkinson et al.(1987), and Zwally et al. (1983).10.11.2 Flux-related variables over iceAir surface temperature in the Arctic is available from buoys and land stations. Therms error is 2.5˚C (Rigor et al., 2000). In the Antarctic, with far fewer observations, the bestsource is reanalyses, and the rms error of these is likely 3 to 5˚C.In polar regions, the default source of surface winds is NCEP and ECMWF reanalysisdata sets. The extreme stability of the planetary boundary layer in winter is an importantcomponent for an accurate estimation of surface winds, but stable PBL’s are not handled wellin these numerical weather prediction models. Some ice models estimate surface winds from10 - Basic Variables Evaluation 169 10.11 Sea Ice10.12 COADS MSTGReport of the Working Group on Air Sea Fluxes June, 2000surface pressure fields and do not treat the varying stability in the planetary boundary layer,producing errors up to 50% in speed and 30 degrees in direction. In the Arctic surface windsfrom reanalysis (or operational model) surface pressure and TOVS-Polar Pathfinder dragcoefficients and turning angles have errors of ±4 ms-1 in speed and ±5 degrees in direction. Inthe Antarctic there are no TOVS-Polar Pathfinder products and there are many fewer pressuredata than in the Arctic.Arctic cloud fraction data sets are available individually from TOVS and AVHRR,from the ISCCP project, and both the NCEP and ECMWF reanalyses. The intercomparison ofradiation data by Rothrock and Zhang (1997) shows quite large discrepancies, both in theannual cycle and in five-year means, which vary from a low of 44% for NCEP to a high of 70%for ECMWF. A comparison of TOVS-Polar Pathfinder and AVHRR-derived cloud fractionsfrom the ISCCP project (Schweiger et al., 1999) shows reasonable correlations between TOVS-derived cloud fractions and surface observations from Russian drifting stations at time scales ofgreater than 3 days. Mean monthly cloud fractions from TOVS-Polar Pathfinder for the areanorth of 80˚ agree within 15% with a climatology based on meteorological surface observations(Hahn et al., 1995). Arctic cloud fractions from the ISCCP project (D-series) are do not appearto capture properly the annual cycle and actually show lower cloud fractions during summerthan during winter in disagreement with climatologies.Comparisons of cloud fractions from TOVS-Polar Pathfinder, AVHRR-CASPR, andsurface observations (which include standard meteorological reports) as well as a sophisticatedcloud lidar/radar system during SHEBA paint a more complicated picture. Meteorologicalreports (such as those from the north polar drifting stations) appear to under report clouds by asmuch as 30% during winter compared with lidar/radar. Lidar/radar observations also show abetter agreement with TOVS- and AVHRR-derived cloud fractions during winter. Based onSHEBA data the annual cycle of cloudiness in the Arctic appears much less pronounced thanpreviously thought.Over the Arctic Ocean, precipitation has been estimated from 1979 to present fromTOVS. The procedure uses TOVS moisture profiles and NCEP reanalysis wind profiles andcomputes the moisture flux convergence, which equals P-E (precipitation - evaporation) at thesurface. Evaporation, E, is computed independently from TOVS-derived surface winds andvertical moisture gradients, satellite ice concentration data, and different transfer coefficients forthe ice-covered portion and the open water portion. Adding E to P-E gives the precipitation P(Groves, 1999). Monthly precipitation fields are computed as the sum of net precipitation andevaporation. These fields are completely independent of any parameterised precipitationprocesses and can serve as good tests of such parameterisations in models. Daily fields areavailable; monthly data are believed to have an accuracy of about ±1 mm/month. 10.12 COADS Monthly Summary Trimmed Groups (MSTG) Variables.The most extensive collection of the basic individual variables presently available is theComprehensive Ocean-Atmosphere Data Set (COADS), which beside the collection ofindividual observations (known as long marine reports - LMR), also provides monthlysummaries (MSTG) which contain basic monthly statistics, computed by the COADScommunity (Woodruff et al. 1998). These MSTG data have been widely used by climateresearchers and so will be considered here as a special example of the basic variable data sets.The MSTG statistics are available at ftp://ftp.cdc.noaa.gov/Datasets/coads1a.enh (coads1b.enh)for the 1a and 1b releases respectively (see below).At present, the available MSTG statistics are based on the very latest update ofCOADS which was specially reprocessed in support of the NCEP/NCAR Reanalysis project.The time period covered by this new update is from 1950 to 1995 (with extension to 1997expected in early 2000). The data set for the period 1980-1995 is known as COADS Release1a, and MSTG statistics for 1950-1979 belong to COADS Release 1b. Monthly statistics for 2˚and 1˚ boxes were computed for SST, air temperature, specific humidity, relative humidity,10 - Basic Variables Evaluation 170 10.12 COADS MSTGReport of the Working Group on Air Sea Fluxes June, 2000scalar wind speed and its north and east components, sea level pressure and total cloud cover.Additionally, MSTG includes pseudo flux variables computed on the basis of individualobservations, for example products of temperature and humidity differences with scalar wind.These can be used, together with the fluxes derived from the COADS based climatologies (e.g.da Silva et al. 1994, Josey et al. 1999), for pilot studies and comparative assessments.COADS MSTG normally provides, as the basic statistics for each variable, monthly means,monthly standard deviations, and the number of observations. Additionally information isavailable about the mean day of the month, mean coordinates, and the fraction of observationsin daylight. Pre-processing of the individual reports used for computation of the COADSMSTG included different quality control procedures and duplicate checks (Woodruff et al.1998). Trimming in Releases 1a and 1b was performed using both 4.5σ and 3.5σ level limits,which correspond to the so-called “enhanced” and “standard” MSTG products respectively.Use of the 4.5σ limits is recommended because of the possible impact of trimming procedurebased on 3.5σ limits on some very pronounced anomalies, in particular those associated with ElNiño events (Wolter 1997).In general, the MSTG products are of use for different multidisciplinary studies dealingwith sea-air flux fields. Of course, the MSTG products still contain the so-called "known"biases in COADS. In particular, visual wind speed estimates in MSTG are based on theWMO1100 scale, no corrections for measurement height were applied, and there were nocorrections of biases in SST and humidity. Use of MSTG pseudo-flux products can only partlyavoid the averaging effects caused by the “sampling-to-classical” biases in the fluxcomputations (see Section 8.3.4). Thus, one has to be careful when using MSTG products forclimate research and especially for budget studies. For example, artifacts in the MSTG windscan lead to very pronounced biases, both climatological and time dependent (Ward 1992, Wardand Hoskins 1996, Isemer 1995, Gulev 1999). However, the MSTG data also have someimportant advantages for the climate studies. First, they are easily available to the flux usersfrom the CDC FTP server. Second, with respect to the advanced climatologies (da Silva et al.1994, Josey et al. 1999) they have better sampling in some very poorly sampled areas, whichare nevertheless crucially important from the viewpoint of flux variability. Indeed, in someregions of the Labrador Sea, GIN Sea, and Southern Ocean, the sampling density in MSTG isseveral times greater than the sampling density provided by the flux climatologies. That isbecause the latter only use reports which contain values for all the basic variables. However itis not immediately obvious whether biases connected with undersampling (which is the cost ofthe better variable processing and flux computations) are smaller than those inherent in MSTG.Taking into account that in the future COADS Releases, for instance Release 2, expected in2001, many of variable corrections can be included in the new updates of MSTG, this productwill continue to have an important future for climate research.11 - Evaluation of Flux Products 171 11.1 Introduction11.2 In situ ProductsReport of the Working Group on Air Sea Fluxes June, 200011. EVALUATION OF FLUX PRODUCTS11.1 IntroductionIn this Chapter we will attempt to evaluate a number of the flux products. These willinclude climatologies based on in situ data (Section 11.2), in particular the UWM (da Silva et al.1994), SOC (Josey et al. 1999) and IfM (Lindau, 2000) climatologies. Satellite basedproducts (Section 11.3) will include an evaluation of the HOAPS (Schulz et al. 1997) surfacefluxes and a number of radiative flux products of US and Japanese origin. The Residualmethod (Trenberth and Solomon, 1994), which used top of the atmosphere satellitemeasurements together with model derived flux divergences, will also be considered in thissection. The fluxes from the reanalysis projects will be discussed in Section 11.4 for ERA15(Gibson et al. 1997), GEOS1 (Schubert et al. 1995), NCEP1 (Kalnay et al. 1996; Kistler et al.,2000), and NCEP2 (Kanamitsu et al. 2000). We have not attempted to evaluate operationalmodels, however the situation for these will be summarised in Section 11.5. The state of ourknowledge for fluxes over sea ice will be discussed in Section 11.6. Sea State and Waves willbe considered in section 11.7. Finally 11.8 will provide an illustration of the difficulty ofproviding a comparative evaluation of the flux products.Comparison of the various flux products is not easy. Wherever possible, thecomparisons must be done for the same time period, however in some cases the products do notoverlap. In particular high quality in situ data is only available for limited periods and areas.Variables such as temperature, humidity and winds should be compared at the same height.However only time-mean values are normally available, and it is not possible to accuratelyconvert a 2m mean value to 10m or vice versa. Nevertheless it will be shown that there aredifferences between the various flux products which are significantly greater than would beintroduced by the differences in specification for the variables compared. 11.2 Flux Products Based on in situ Data11.2.1 IntroductionThe differences between the various flux climatologies are due to several factors:whether "sampling" or "classical" flux calculation was used, what wind scale was used tointerpret the visually observed winds, what other corrections (if any) were applied to theobservations, the formula used for each of the radiative flux components, the formula used foreach of the turbulent flux components, and other factors such as the smoothing algorithm, gridscale, and time period of the data set used. Climatologies of the heat fluxes produced prior to1990 include the North Atlantic flux maps of Bunker (1976) and Isemer and Hasse (1987 andIsemer et al. 1989), and the global climatologies of Esbensen and Kushnir (1981), Hsiung(1986), and Oberhuber (1988). More recent climatologies are those of da Silva et al. (1994),Josey et al. (1999), and Lindau (2000). The characteristics of these various studies is shown inTable 11.2.1. The characteristics of the selected pre-1990 studies will first be summarised (based onthe comparison of sensible and latent heat fluxes by Kent and Taylor, 1995). The post 1990studies will then be described in more detail before a comparative assessment is presented.11.2.2 Pre-1990 flux studiesA. BUNKER (1976)Bunker calculated the wind stress and sensible and latent heat fluxes from individualship reports using transfer coefficients tabulated as a function of air-sea temperature differenceand wind speed. These were based on his review of experimental studies between 1957 and1974. Based on the results from BOMEX (Holland et al., 1972), Bunker increased the transfercoefficients by 10% compared to the experimental values to allow for the difference betweenmerchant ship data and research platforms. This he assumed accounted for the effects of theship on the local environment as well as the anemometer being generally higher than 10m11 - Evaluation of Flux Products 172 11.2 In situ Products(11.2.2 pre-1990 flux studies)Report of the Working Group on Air Sea Fluxes June, 2000height. He assumed that the transfer coefficient for sensible heat was equal to that for latentheat. Fluxes were not calculated if all the necessary parameters were not present for a givenship report. The fluxes were averaged by 10° square, month and year. Annual and monthlyaverages for subdivisions of the 10° squares, called gerrymanders, were presented. Thenumber and shape of the gerrymanders depended on the number of observations in the region,with an attempt being made to enclose homogeneous water masses.Table 11.2.1 - Characteristics of climatological studies of the air-sea heat fluxes. The tableshows the area and time period covered, the spatial resolution, the origin of the sensible andlatent heat bulk formula coefficients, implied Beaufort wind scale, and whether the fluxes werecalculated from the individual samples (“yes”) or the means of the observations (“no”). IH87calculated their flux values using mean values but then applied a correction.Study AreaPeriodGrid Scale ShortWaveLong Wave Sens/Lat Ht Wind scale Sam-pleBunker 1976 N Atl.41-721°x 1°(coast)2°x 5°(ocean)Budyko1963Budyko 1963 Bunker WMO 1100 yesEsbensen &Kushnir 1981Globe46-794° x 5 ° Budyko1963Berliand &Berliand1952Liu et al. 1979 WMO 1100 noHsiung 1986 Globe46-795 °x 5 ° Reed type Budyko 1963 Bunker WMO 1100 noIsemer & Hasse1987 (IH87)N Atl.41-721°x 1° Reed 1977 Budyko 1974 Bunker(modified)Kaufeld1981no +adj.Oberhuber 1988 Globe50-792° x 2 ° Zillman’72 / ReedBerliand &Berliand 1952Large & Pond’82 (modified)WMO 1100 noda Silva et al.(1994)Globe45-891° x 1 ° Reed type Rosati &Miyakoda 88Large & Pond1982da Silva etal. 1995yesJosey et al.(1999)Globe80-931° x 1 ° Reed 1977 Clark 1974 Smith 1988 Lindau1995ayesLindau (2000) N.Atl.40-791° x 1 ° Malevskiiet al. 1992Bignami et al.1995Isemer &Hasse 1987Lindau1995ayesB. ESBENSEN AND KUSHNIR (1981)Esbensen and Kushnir used 5° x 5° monthly mean climatological parameters. Thesevalues were transformed onto a 4° latitude by 5° longitude grid using a Gaussian spaceaveraging technique designed to uniformly suppress features with wavelengths shorter thanabout 1600 km. The data are therefore both presented on a large scale grid and also have verylarge spatial filtering applied. Additional smoothing was applied in low data density areas.The fluxes were calculated with the skin temperature version of the Liu et al. (1979) transfercoefficients (Section 7.3.2) although the observed bulk temperature of the ocean surface wasused. This increased the calculated fluxes slightly. For the North Atlantic, Kent and Taylor(1995) estimated an increase of 3Wm-2 and 7Wm-2 for sensible and latent flux respectively.C. HSIUNG (1986)Hsiung produced a 5° x 5° global flux product using the Bunker transfer coefficientsapplied to monthly means of the basic meteorological variables. Bunker had stated that hiscoefficients were determined from data averaged over short time periods and should not be usedwith monthly means of the bulk variables. However Hsiung justified her choice by summarisingstudies which compared the "classical" and "sampling" methods (see Section 8.3.4).D. ISEMER AND HASSE (1985, 1987)The atlas of Isemer and Hasse (1987) contains two distinct sets of flux maps, thosecalculated using Bunkers original parameterisations (see above), and also fluxes calculated11 - Evaluation of Flux Products 173 11.2 In situ Products(11.2.2 pre-1990 flux studies)Report of the Working Group on Air Sea Fluxes June, 2000using revised parameterisations which had been adjusted using an inverse technique based on aheat budget study. Isemer and Hasse used the monthly mean averages of the basic variables(Isemer & Hasse, 1985) for each of the gerrymanders as calculated by Bunker (1976). Theyattempted to update the parameterisations used by Bunker in several ways. To account for morerecent open ocean determinations of the transfer coefficients, they reduced that for sensible heatby 17% and latent heat by 13%. They used an approximate conversion to the Kaufeld (1981)wind scale. Although applied to the mean values, the correction was weighted according to thefrequency distribution of wind speeds as calculated by Bunker. Thus they assumed that, forthe period up to 1972 used by Bunker, all wind speeds were visual estimates. Stability effectswere calculated using virtual temperature, rather than temperature, resulting in a more unstabledensity stratification and larger fluxes particularly in moist areas such as the tropics.The Isemer and Hasse study therefore has fluxes calculated using the ‘classical’ methodbut with corrections applied in order to approximate fluxes calculated using the 'sampling'method. To attempt to correct their flux values for any errors so introduced, Isemer and Hasseadjusted their calculated fluxes for each gerrymander by the ratio of [(Bunkers original results) /(fluxes calculated using the monthly mean values with Bunkers original parameterisations)].The second set of flux maps presented by Isemer and Hasse (1987) were adjusted usinginverse analysis with the oceanographic constraint of 1 PW of heat transport across 25°N (Halland Bryden, 1982). The parameters included in the inverse analysis were the atmospherictransmission factor and cloud cover coefficient for short-wave radiation, cloud cover exponentin the infrared formula, the transfer coefficients, and the air-sea differences in temperature anddewpoint. The uncertainties in the variables were estimated and the additional constraintapplied that the sum of the changes in the variables normalised by the estimated uncertainty ofeach variable was an rms minimum. The adjustments added just over 16 Wm-2 to the net heatflux. The main changes were due to a increase of 5.7% in CE (CH by 4.4%) and an addition of-0.07°C to the air-sea and dewpoint-sea temperature differences. Isemer et al. (1989)confirmed these changes using a selection of different constraints.E. OBERHUBER (1988)Oberhuber used the COADS data set of filtered monthly means of hourly valuesaveraged onto a 2° x 2° grid as prepared by Wright (1988). Although he stated that the transfercoefficients used were from Large and Pond (1982), it would appear that this only applied tothe corrections to neutral stability. The neutral transfer coefficients were calculated using theCharnock (1955) relationship (see Section 7.3.2) with the Charnock parameter taken initially as0.0064. However he found that he needed approximately another 20Wm-2 heat loss from theocean to agree with estimates of the meridional heat transport. He therefore increased theCharnock parameter to 0.032. He suggested that this was necessary to allow for the use ofmean, rather than instantaneous values, in the bulk formulae, in other words, to correct for hisuse of the "classical method". However, this seems unlikely to be the case (Section 8.3.4).The transfer coefficients for sensible and latent heat were calculated assuming constantroughness lengths for temperature and humidity. Since the Charnock formula predicts that zoincreases with wind speed, this implies that all the transfer coefficients increase with windspeed. Oberhuber (1988) also used a constant albedo of 0.06 rather than the varying albedoused in other studies.F. OTHER CLIMATOLOGIESHellerman and Rosenstein (1983)This wind stress climatology is still in widespread use for forcing models or comparingwind stress products. However the drag coefficient used was essentially that of Bunker (1976)which resulted in wind stress values some 20 to 30% higher than those computed by, forexample, Harrison (1989) using the more realistic Large and Pond (1982) coefficients (seeSection 11.2.6F). Josey et al. (2000) found a similar difference in comparison to the SOCclimatology (Josey et al. 1999) except at high latitudes where differences were ascribed to thedifferent periods of the climatologies. In contrast, Böning et al. (1991) found that differences11 - Evaluation of Flux Products 174 11.2 In situ Products(11.2.3 UWM/COADS)Report of the Working Group on Air Sea Fluxes June, 2000from the Isemer & Hasse (1987) climatology were dominated by the visual wind correction usedby the latter authors.Hastenrath and Lamb (1977, 1978, 1979)Hastenrath and Lamb (1977) produced a climatic atlas of the Tropical Atlantic andEastern Pacific Oceans which was the basis of the heat budget study of Hastenrath and Lamb(1978). Similarly a climatic atlas of the Indian Ocean was used for the heat budget study ofHastenrath and Lamb, (1980). Hastenrath (1980) considered the heat budget of the tropicaloceans more generally. These studies have not been discussed in detail here since they werelimited to the tropical ocean, a region for which significantly more data has become available inrecent years.11.2.3 UWM/COADSA. INTRODUCTIONThe UWM/COADS climatology (da Silva et al. 1994 - http://www.cdc.noaa.gov/coads/uwm.shtml.) was the result of a collaboration between researchers at the Department ofGeosciences of the University of Wisconsin--Milwaukee (UWM) and the Ocean ClimateLaboratory (OCL) at the National Oceanographic Data Center (NODC). The aim was to extendthe COADS data set to include "true" flux values in addition to the "pseudo" flux valuescontained in the COADS monthly means. Objective, global analyses of observed and derivedsurface marine parameters were based on individual observations found in the ComprehensiveOcean-Atmosphere Data Set (COADS) Release 1 complemented by an interim release for the1980's. Results of the analyses are presented in Atlas of Surface Marine Data 1994, a fivevolume series depicting 45-year seasonal climatologies (1945 - 89), anomalies, and standarddeviations of various quantities which characterise the surface marine climate (da Silva 1994).In-depth documentation of the data set is found in Volume 1. Besides describing allparameterisations, calculations, and analysis methods used in creating the data set, this volumediscusses possible biases the COADS data may contain and explains the bias correctionsemployed. Also discussed are the constraining of heat and fresh water fluxes by oceanographictransport estimates (see below), sampling and fair weather biases, surface layer formulation,equations for astronomical calculations (e.g., altitude of the sun), and NetCDF data accesssoftware. Volumes 2 through 5 represent a graphical representation of the data set. Theseconsist mainly of seasonal contour plots of climatology, standard deviation, and anomalies ofthe analysed quantities organised according to parameter type. Volume 2 contains directlyobserved quantities, Volume 3 contains heat and momentum fluxes. Fresh water fluxes can befound in Volume 4, and Volume 5 has an assortment of miscellaneous derived quantities. Themore recent Volume 6 contains the Heat Flux Sensivity to Sea Surface Temperature.B. DERIVATIONThe analyses presented in the Atlas of Surface Marine Data 1994 were derived from theindividual observations found in Compressed Marine Reports--Product 5 (CMR-5) of COADSRelease 1 (Slutz et al. 1985, Woodruff et al. 1987) for years 1945 through 1989. The transfercoefficients used were from Large and Pond (1981,1982) except that CD10n was modified forwind speeds below 3 ms-1 following Trenberth et al. (1989). The visual winds were adjusted asdiscussed by da Silva et al. (1995). Following Hahn et al. (1995), cloudiness observationswere rejected if they were made under dark sky conditions (calculated from the phase andaltitude of the moon and the altitude of the sun). Present weather observations were adjustedfor an error in the version of COADS used by da Silva et al. (1994) so that clear weatherobservations were not assumed to be missing. No other corrections were made to the data forobservational errors. Precipitation estimates are based on Present Weather reports using thealgorithm of Tucker (1961) with a variant of Dorman and Bourke's (1978) corrections.Mean climatological fluxes were calculated by averaging the 45 years of COADS datainto 1˚ x 1˚ grid cells and then applying the successive correction technique described byLevitus (1982) (Section 8.5.2) with radii of influence equal to 1541 km, 1211 km, 881 km, and771 km on successive passes. Below 40˚S only the first two passes were used. By calculating11 - Evaluation of Flux Products 175 11.2 In situ Products(11.2.4 SOC)Report of the Working Group on Air Sea Fluxes June, 2000a complete array of atmospheric forcing parameters, and by using the same grid and analysisscheme as in Conkright et al. (1994) and Levitus et al. (1994), the UWM/COADS data set wasintended to be used as a complement to the surface and subsurface marine analyses found inConkright et al. (1994) and Levitus et al. (1994a,b,c).C. FLUX TUNINGda Silva et al. (1994) found that their fluxes indicated a global imbalance of about 30Wm-2 . For the period 1981 to 1992, White (2000) found the smaller imbalance of 23 Wm-2;the difference may have been partly due to different methods of taking ice covered regions intoaccount. Whereas the individual flux components, evaporation and precipitation provided inUWM/COADS were not tuned or constrained, the net heat flux, buoyancy flux, and (E-P) fieldswere presented, with the requirement that there should be no net, global heat transport through65˚S and that the equivalent freshwater transport should be 0.06 Sv. The main factors wereabout a 10% decrease of SW radiation and a 14% increase in latent heat flux. To balance thefreshwater budget, precipitation was increased by about 12%. However da Silva et al. (1994)stressed that these values were only an example and that it was to be expected that other userswould need to apply different constraints. To that end sensitivity fields associated with each ofthe tuning parameters were presented. Thus users may specify their own values for the errorestimates for the observed quantities and hence calculate new fields of constrained fluxes.D. VERIFICATIONThe UWM/COADS flux fields have been compared to other climatologies (see forexample 11.2.4E) and have been extensively compared with results from models, bothreanalysis (see Section 11.4) and operational.11.2.4 SOCA. CONCEPTThe Southampton Oceanography Centre (SOC) flux climatology (Josey et al. 1999 -http://www.soc.soton.ac.uk/JRD/MET/fluxclimatology.html ) was based on the methods usedby Kent and Taylor (1995) for the "best estimated fluxes" which they used to compare previousNorth Atlantic flux fields. The idea was that, if the observations could be corrected forsystematic error, then the correct flux values would be calculated using the best estimates of thetransfer coefficients derived from air-sea interaction experiments without any adjustment ofthose coefficients. The error correction was based on the results of the VSOP-NA experimentwhich identified relative biases due to different instrumentation types (see Section 8.3.3). Inorder to apply the VSOP-NA corrections, additional information was needed. This wasobtained from the International List of Selected, Supplementary and Auxiliary Ships ("WMO47", e.g. WMO, 1990) which is published regularly by the World Meteorological Organisation(WMO). The details of the instrumentation carried on most of the 7000 plus VoluntaryObserving Ships include the method of SST, air temperature and humidity measurement, andwhether the ship carried an anemometer and, if so, the anemometer height. These meta-datawere merged onto the COADS release 1A data set using the ship's call sign on a report by reportbasis. The success rate in matching the COADS and WMO47 data increased with time as theinformation contained in WMO47 became more comprehensive (Figure 11.2.1). Because manyship reports from years earlier than 1980 have not been archived with valid call signs, thismethod limited the range of years for which the SOC climatology could be calculated.The observations were corrected for biases in SST data from engine room intakethermometers, in daytime air temperatures errors due to solar heating, and in dew pointtemperatures from fixed thermometer screens. In addition measured winds were corrected forthe anemometer height taking into account the atmospheric stability. Visual winds wereadjusted to the Lindau (1995a) scale. The impact of the various data corrections on thecalculated net ocean to atmosphere heat flux varied from area to area and season to season beinggenerally being within the range ±15 Wm-2. Fluxes were decreased in the northern oceans inwinter (mainly due to the wind and sea temperature corrections) and increased in the tropicaloceans (mainly due to the temperature and humidity corrections (Josey et al., 1999). These11 - Evaluation of Flux Products 176 11.2 In situ Products(11.2.4 SOC)Report of the Working Group on Air Sea Fluxes June, 2000corrections for ship observation error were smaller than might have been expected, partlybecause some flux errors cancel.19931992199119901989198819871986198519841983198219811980 50000100000150000200000250000total matched reportstotal ship reportsYearFigure 11.2.1 A time series ofthe number of reports used inthe SOC climatology for whichmatching WMO47 data wasavailable. The top (grey) lineshows the monthly number ofreports from ships. The lowerline shows the number of theseship reports matched (adaptedfrom Taylor et al. 1999f).B. VERIFICATION OF HEAT FLUXESDespite these corrections, the SOC climatology was similar to other studies in that itshowed a net heat flux into the ocean of about 30 Wm-2 . On a year to year basis this imbalancewas remarkably consistent with a standard deviation of ±7Wm-2 (implying a standard error onthe 14 year mean imbalance of < 1Wm-2 ). However, based on comparisons with high qualitybuoy data, Josey et al. (1999) did not attempt to correct their fluxes to produce a balancedproduct. Figure 11.2.2 shows the difference between the SOC and buoy values for theSubduction experiment (Moyer and Weller, 1997), FASINEX (Weller et al. 1995), TOGACOARE (Weller and Anderson 1996) and the Arabian Sea (Weller et al. 1998). For theradiative fluxes (Figure 11.2.2a) the comparison varied from site to site, particularly for the SWflux. At a number of sites the SW and LW differences were of opposite sign as might beexpected if the cloud amount was in error. Site to site variation also occurred for the turbulentfluxes (Figure 11.2.2b). The largest difference was for FASINEX where the climatologyunderestimated the total turbulent cooling by about 50 Wm-2 due to the sea being colder, andthe atmosphere moister, in the SOC climatology compared to the buoys. For the net heat flux(Figure 11.2.2c), adjusting the fluxes to achieve a global heat balance (following da Silva et al.1994) did not generally improve the agreement with the buoy values. Furthermore the two siteswhere significant improvement occurred were characterised by significantly different biases.For FASINEX the improvement was due to increasing the SOC latent heat flux (by 13%)whereas for TOGA this worsened the turbulent flux comparison. At that site the improvementcame through reducing the SOC SW heating values (by 8%). Thus Josey et al. (1999) arguedthat regionally varying corrections were required, a conclusion which was supported bycomparisons with flux values derived from ocean heat transport data.C. TROPICAL HEAT FLUXESThe comparisons with the TOGA COARE buoy were considered in more detail byTaylor et al. (1999e). The SOC climatological fluxes predicted about 23 Wm-2 greater net heatflux into the ocean compared to the buoy. For the specific months of the buoy deployment, theSOC values over estimated the shortwave heating by about 24 Wm-2 and under estimated thelongwave cooling by about 8 Wm-2, thus causing the over estimate of the ocean warming.The shortwave difference could be accounted for if the cloudiness had been under-estimated byabout 1 octa (true value 6.2 octas). In contrast, the SOC turbulent flux values slightly overestimated the magnitude of the turbulent fluxes (by about 8 Wm-2 ). This was surprising; itwas expected that the sensible and latent heat fluxes would be under estimated in the SOCclimatology under the low wind conditions of the TOGA-COARE area, since constant transfer11 - Evaluation of Flux Products 177 11.2 In situ Products(11.2.4 SOC)Report of the Working Group on Air Sea Fluxes June, 2000coefficients for sensible and latent heat were used, the Smith 1980 drag coefficient (CD10n) was(unjustifiably) extrapolated to zero ms-1, the merchant ship data were unlikely to have detectedany diurnal warm layer, and no gustiness factor was implemented.(a)-50-40-30-20-1001020304050NE Buoy SE Buoy SW Buoy NW Buoy C Buoy FASINEX TOGA Arabian Sea(c)-70-60-50-40-30-20-100102030405060NE Buoy SE Buoy SW Buoy NW Buoy C Buoy FASINEX TOGA Arabian Sea(b)-40-30-20-100102030405060NE Buoy SE Buoy SW Buoy NW Buoy C Buoy FASINEX TOGA Arabian SeaFigure 11.2.2 Comparison of the SOCclimatology and data from IMET buoydeployments: (SOC-Buoy) difference (Wm-2 ),heat into the ocean is considered positive.(a) Radiative fluxes: (striped) LW; (shaded)SW. (b) Turbulent fluxes: (striped) sensible(shaded) latent. (c) Qnet (striped)unadjusted; (shaded) adjusted. The net fluxcomparisons for FASINEX assume the LWflux values (which were not measured) wouldhave agreed with the climatology. (FromJosey et al. 1999).Taylor et al. (1999e) showed that the over estimate of the turbulent fluxes was, in fact,due to the extrapolation of the Smith (1980) formulae for CD10n. Using a CD10n formula withincreased drag at low wind speeds (Yelland and Taylor,1996) reduced the calculated fluxsignificantly. The reason was that the original CD10n formula had resulted in small values forthe friction velocity at low wind speeds, very unstable values for the stability parameter (z/L),and therefore large values for CE . The use of a larger CD10n gave less unstable conditions andtherefore smaller transfer coefficients for sensible and latent heat. These effects were onlysignificant below about 5ms-1 and therefore would not be not important for much of the world'socean. Bearing in mind that the behaviour of CD10n at low wind speeds is not well known(Section 7.3.2), this example serves to emphasise just how difficult it is to obtain reliable fluxestimates in low wind speed tropical regions using ship data.No correction for this bias in the TOGA COARE area has been applied in the SOCclimatology. While it might be considered at least a partial source of the apparent global heatimbalance, it is not clear whether the problem is confined to the highly convective regions of thewest Pacific warm pool and the relatively narrow ITCZ, of whether it is applicable to thetropical ocean in general.D. VERIFICATION OF WIND STRESSJosey et al. (2000) have verified the SOC wind stress climatology against the IMETbuoy deployments. A comparison of the mean wind stress components for each buoydeployment is shown in Figure 11.2.3. The wind stresses were generally in good agreement.The only consistent difference was for the Arabian Sea were the SOC stress values were greaterthan those from the buoy during the latter part of the deployment. Noting that prior to thereplacement of the anemometer there had beeen good agreement, Josey et al. (2000) suggestedthat a poor anemometer calibration might have been the cause. Although higher mean stress11 - Evaluation of Flux Products 178 11.2 In situ Products(11.2.4 SOC)Report of the Working Group on Air Sea Fluxes June, 2000components were observed at the Subduction site compared to the Arabian sea, this was due tothe greater consistency of the wind velocity. Good agreement was found at that site.0.10 0.05 0.00 -0.05 -0.10-0.10-0.050.000.050.10IMET buoy stress componentSOC stress componentFigure 11.2.3 Comparison ofthe mean wind stress valuesbetween the SOCclimatology and the IMETbuoy deployments. Open(closed) symbols representthe eastward (northward)component. Circles:Subduction buoys (Moyerand Weller, 1997); Squares:TOGA COARE (Weller andAnderson 1996); Triangles:Arabian Sea (Weller et al.1998); Squares with cross:FASINEX experiment(Weller et al. 1995). The lineof agreement is shown (datafrom Josey et al. 2000)E. COMPARISON WITH UWM/COADSJosey et al. (1999) discussed differences between the SOC and unconstrainedUWM/COADS climatologies. These may have resulted from the effects of those observationalbias corrections included in the SOC analysis but not in UWM/COADS, the use of differentparameterisation formulae, the difference in the time periods sampled, and the different scalesused in the objective analysis. The main variations were found to occur in the sensible andlatent heat flux fields. The radiative flux fields were typically in agreement to within 10 Wm-2.The major differences in the latent heat flux occurred over the Gulf Stream and the sub-tropicalNorth Pacific. In UWM/COADS, the Gulf Stream region of strong latent heat loss was broaderand appeared to be displaced further south than in the SOC analysis. This resulted in adjacentbands of positive and negative heat flux difference. The narrower region of strong heat lossfound in the SOC analysis may have been due to the choice of a smaller objective analysis scalerelative to that used for UWM/COADS. Similar behaviour was seen over the Kuroshioalthough the differences were smaller in magnitude. Over a broad area in the sub-tropicalcentral North Pacific, the UWM/COADS heat loss was stronger by up to 30 Wm-2. It isunlikely that this feature was a result of the ship corrections as these only resulted in differencesof order 5-10 Wm-2 in this region. It may have been due to the different periods used togenerate the two climatologies; either because of changes in the instrumentation used by shipsin this area , or to an actual climatic change.Differences between the two climatologies were also seen in the sensible heat flux fieldfor which the UWM/COADS analysis had losses of order 10-20 Wm-2 greater in the annualmean over the Gulf Stream and Kuroshio and localised areas of the high latitude North Atlanticand North Pacific. In contrast, the effect of the ship corrections used by Josey et al. (1999) wastypically less than 5 Wm-2. The difference in objective analysis scales may again have beenpartly responsible. The net heat flux difference field was dominated by the effects of thedifferences in the latent and sensible heat fluxes. Typically the difference was within ±15 Wm-2,although regions of stronger heat loss in the UWM/COADS fields over the Gulf Stream andsub-tropical central North Pacific were up to 50 Wm-2. However despite these regionaldifferences the global heat imbalance for the SOC and unconstrained UWM/COADSclimatologies were remarkably similar; around 20 to 30 Wm-2 excess heat into the ocean.11 - Evaluation of Flux Products 179 11.2 In situ Products(11.2.5 IfM)Report of the Working Group on Air Sea Fluxes June, 200011.2.5 IFMA. DERIVATIONThe Institut für Meereskunde (IfM) surface fluxes and flux related parameters (Lindau,2000 - http://www.ifm.uni-kiel.de/me/research/Projekte/WGASF ) were derived from theComprehensive Ocean Atmosphere Data Set (COADS). This raw data set contains severalmillion individual ship observations from the world ocean since about 1850. However thecomputations were restricted spatially to the Atlantic Ocean and temporally to the period 1940to 1979, where the best data coverage was available. This was necessary, since the intentionwas not so much to describe the longtime climate of the Atlantic but rather to provide data forstudies on climate variability. Consequently, the IfM data set contains not only the meanannual cycle but also the year to year variations of several climate relevant parameters on anominal spatial grid of 1˚ by 1˚. To attain such resolution the appropriate statistical treatment ofthe raw data plays an important role.Since the observations were concentrated along narrow ship routes a kriging techniquewas used to obtain completely covered fields. For this aim correlation lengths depending onthe geographical direction were derived for all considered parameters. Using the informationon intra-monthly variance the accuracy of individual monthly means was derived and taken intoaccount in the further calculations. In contrast to ordinary kriging, Lindau did not prescribe afixed radius of influence, but searched by iteration for that combination of grid points whichgave the lowest errors at the prediction point. This minimum was reached when the potentialincrease of redundancy and error variability was higher than the information gain from anyadditional grid point. In that way Lindau produced twin fields: each monthly mean map isaccompanied by a map giving an estimation of the error variance.These pairs of maps show the anomaly and its error not as absolute values, butnormalised by the observed standard deviation of the longtime monthly time series at eachlocation. For the renormalisation, two maps are available for each calendar month andparameter; these give the standard deviation and longtime mean. Lindau decided to leave thefields in that form (as they were originally computed), because although the kriging techniqueused is also applicable for the absolute values, it is optimal only for normalised anomalies.In deriving the IfM fluxes, much effort was spent in correcting the known biases inwind speed, which is an essential parameter not only for the wind stress but also for theturbulent heat fluxes. Since the majority of marine wind observations in the Atlantic duringthis period were Beaufort estimates, a correct equivalent scale was of fundamental importance.Wind measurements from Ocean Weather Stations (OWS's) carried out in the 1960's give agood opportunity for calibrating an equivalent scale. However, there are good reasons forassuming a possible drift in the relationship between the reported Beaufort force and true windspeed since definition of the Beaufort scale and observation practice gradually changed duringthe times. Consequently, Lindau used a time-dependent equivalent scale, derived firstly for the1960's when measurements from OWS's were available, and expanded to other decades byusing the spatial pressure differences between the reporting ships (see Section 10.6.2C)An additional correction was applied to the wind stress calculations, because due to thequadratic dependence of the stress on wind speed, observation errors, even if random, cause asystematic bias (Section 10.6.1). In addition, COADS temperature measurements of air, seasurface and dew point were compared to the OWS's in the Atlantic and Pacific, in order toremove systematic errors in the turbulent heat fluxes.B. MERIDIONAL HEAT TRANSPORTConsidering longterm averages and neglecting decadal changes of the mean oceantemperature, any net heat loss at the sea surface has to be compensated by an oceanic heattransport into this region. These horizontal transports are sufficiently well determined fromoceanographic sections to serve as an independent constraint for the computed net air-sea heat11 - Evaluation of Flux Products 180 11.2 In situ Products(11.2.6 Assessment)Report of the Working Group on Air Sea Fluxes June, 2000flux. As the Atlantic is laterally enclosed only the meridional heat transport (MHT) contributeto a heat exchange with other oceans. The heat transport across 65˚N (where the northernboundary of the Atlantic may be defined) is small. According to Gulev & Tichonov (1989) thenet heat loss into the Arctic Ocean is equal to 0.275 PW, whereas Aargaard & Greisman (1975)estimated a smaller value of 0.098 PW. The integration of the net air-sea heat flux from 65˚Nsouthward, yields, according to (9.1), the MHT as function of latitude.Beginning with 0.098 PW at 65˚N, Lindau(2000) found a maximum northward heattransport of 1.09 PW at 23˚N (fig.11.2.4) while an energy rate of 0.78 PW crossed the equator.Southward of 10˚S the MHT remained at a nearly constant northward flow of about 0.5 PW,because the heat budget in this region is approximately balanced. The vertical bars in figure11.2.4 give the oceanographic estimate of Bryden & Hall (1980) for 25˚N, that of Wunsch(1984) for the equator, that of Speer et al. (1996) for 11˚S and that of Holfort (1994) for 30˚S.Since Lindau's results were in good agreement with these independent estimates no additionaladjustment of parameterisations was considered necessary.Fig.11.2.4: Atlantic meridionalheat transport (PW) comparedto the results of I & H: Isemer& Hasse (1987) and H & L:Hastenrath & Lamb (1978).These authors used differentdata bases. For comparability,their parameterisations havebeen applied to COADS.Results of oceanographicsections are indicated at therelevant latitude together withtheir error bars: 30˚ S: Holfort(1994), 11˚S: Speer & al.(1996), 0˚: Wunsch (1984),25˚N: Bryden & Hall (1980).C. GLOBAL PRECIPITATION PRODUCTFor the derivation of the Lindau (2000) precipitation algorithm the weather andhumidity observations of 24 years from four light vessels in the German Bight were calibratedagainst the wind corrected daily rain measurements carried out aboard these ships. In a secondstep the quality of the resulting rain algorithm was checked by its application on independentCOADS data. For monthly means a correlation of 0.85 is obtained, for the climatic annualcycle even one of 0.96.A comparison with SSM/I derived rain (Ferraro & Marks,1995) showed an acceptableagreement in the global distribution and mean oceanic precipitation for January 1988.Applying this algorithm for the entire period 1980 to 1993, 81% of the world ocean is coveredwith data and a mean annual precipitation of about 1300 mm results (Figure 11.2.5).Comparison with evaporation shows an imbalance of -298 mm, which can be explained by thewater export on the continents and low data coverage of the Southern Ocean where rainprevails.11.2.6 Comparative assessmentA. INTRODUCTIONIn this section we will compare the characteristics of the various studies describedabove in order to determine the strengths and weaknesses of each. As we have already noted,the differences between the various flux climatologies are due to a number of factors: whether"sampling" or "classical" flux calculation was used, what wind scale was used to interpret thevisually observed winds, what other corrections (if any) were applied to the observations, the11 - Evaluation of Flux Products 181 11.2 In situ Products(11.2.6 Assessment)Report of the Working Group on Air Sea Fluxes June, 2000formula used for each of the radiative flux components, the formula used for each of theturbulent flux components, and other factors such as the smoothing algorithm, grid scale, andtime period of the data set used.Figure 11.2.5Precipitation minusevaporation derived byLindau (2000) for theperiod 1980 to 1993 fromCOADS.B. "SAMPLING" OR "CLASSICAL"Apart from the major effort of Bunker (1976) and co-workers, the restricted computingresources available resulted in all the pre-1990 climatologies being calculated from mean values- the "classical" method. Because correlations of the basic variables are neglected, use of theclassical formula is likely to have introduced a bias of order ±5% to ±15% which varies fromone climatological region to another and even with the flux parameterisation used (Section8.3.4). Isemer and Hasse (1987) attempted to correct for this on the basis of the Bunker (1976)calculations. Each of the more recent climatologies has been calculated using the "sampling"method. This is to be preferred, at least where observations are plentiful; thus we favour thepost-1990 studies. However it should be noted that the sampling method has the disadvantageof requiring all the basic variables needed for flux calculation to be available with eachobservation. This reduces the number of available data significantly and may increase the noisein many areas. It also makes it more difficult to combine data sets; for example to calculate aflux product based on remotely sensed and in situ data. Thus there are cases whereclimatologies based on the "classical" method still may be preferred.C. VISUAL WIND SCALEThe wind scale defined by WMO for visual wind observations is known as"WMO1100" (WMO, 1970). The different variations on this "Beaufort" scale were discussed inSection (10.6.2.C). We emphasise that, not only did Lindau (1995a) devise a scale whichproduced good agreement between visual and anemometer winds, he also demonstrated that thedifferences in the older scales were due to the method used in their derivation. Kent and Taylor(1997) confirmed Lindau's results with respect to the VOS. Independently da Silva et al.(1974) produced their own version of the Beaufort scale (da Silva et al. 1995). Fortunately thisscale and that of Lindau (1995a) are not very dissimilar to WMO 1100. Thus the onlyclimatology listed in Table 11.2.1 which used a significantly different Beaufort scale was that ofIsemer and Hasse (1987). That study has effectively been replaced by that of Lindau (2000).D. OTHER OBSERVATION CORRECTIONSWith the exception of Esbensen and Kushnir (1981), most of the pre-1990 studies usedincreased transfer coefficients to allow for some degree of observational error assumed tocharacterise the data sets. This was either done initially (e.g. Bunker, 1974) or after some formof inverse analysis (e.g. Isemer and Hasse, 1987, Oberhuber, 1988). The results of recentinvestigations into VOS observational errors were too late to be incorporated in the UWM (daSilva et al. 1994) study (although they are mentioned in the documentation). The more recent11 - Evaluation of Flux Products 182 11.2 In situ Products(11.2.6 Assessment)Report of the Working Group on Air Sea Fluxes June, 2000studies at SOC (Josey et al. 1999) and IfM (Lindau, 2000) have applied explicit corrections tothe data (see sections 11.2.4, 11.2.5.). Taking the SOC corrections as an example, the effect onthe heat fluxes varied region by region. In general the heat loss was increased but generally byless than 10 Wm-2 . On a global mean basis the SOC values agreed closely with the unadjustedUWM values to within a few Wm-2 in the flux components and the net flux, a somewhatsurprising result. Compared to the SOC fluxes, the temperature corrections suggested byLindau would have increased the ocean cooling, probably by around 10 Wm-2 or less. Themain difference between the SOC and IfM is likely to be due to the different transfer coefficientschemes (see below).E. RADIATIVE FLUX FORMULAEFor shortwave flux, Bunker (1976) and Esbensen and Kushnir (1981) used the Budyko(1963) shortwave parameterisation. Dobson and Smith (1988) considered this formula to bequestionable. Most other studies have used some variation on the Reed (1977) algorithm whichwas recommended by the comparative studies of Frouin et al., (1988) and Dobson and Smith(1988). The latter found a site dependant long term bias of -1 to +12 Wm-2 and a monthlymean rms error of about 8 Wm-2 . We should note that, using comparisons with data fromseveral air-sea interaction experiments, Katsaros (1990) found this formula to be biased high byabout 20 Wm-2. The comparison between SOC and buoy values (Figure 11.2.2) does notsupport this, however it may be that the instrumentation is at the limit of its accuracy (Section5.5.2). The IfM study (Lindau, 2000) used the Malevskii et al. (1992) algorithm which hasbeen found to be less biased in comparison to the Reed formula, at least in mid latitudes(Niekamp, 1992).In a comparative study of algorithms for estimating longwave radiation Katsaros (1990)found little difference between the models compared. In general the biases were less than 10Wm-2 and the rms scatter was significantly less than for the shortwave parameterisations. Ofthose tested, Josey et al. (1997) evaluated two of the better performing algorithms, that ofClark (1974) which was based on the earlier Berliand and Berliand (1952) formula and that ofBunker (1976) which was based on Efimova (1961). In comparison against pyrgeometermeasurements from several cruises they found that the Clark algorithm performed better andhence chose it for calculating the SOC climatology. In contrast they found that the Bignami etal. (1995) formula underestimated the downward longwave flux by around 25 Wm-2 . Thisfitted with the Schiano et al. (1993) observations in the western Mediterranean that almost allthe formulae they tested over estimated the downward LW flux by 25 to 40 Wm-2. It was basedon those results that Bignami et al. (1995) had developed their algorithm. Garrett et al. (1993)suggested that this greater radiational cooling was needed to balance the heat budget of theMediterranean and Gilman and Garrett (1994) suggested that the difference was due to thedifferent atmospheric properties over this land locked sea. Thus the use of the Bignamiformula for the IfM LW flux over the Atlantic may not be an appropriate choice. It will givesignificantly greater cooling compared to the formulae used for the UWM or SOC studies. Incomparison to the buoy data (Figure 11.2.2a) the SOC values showed too little cooling but onlyby about 6 Wm-2 (standard error ± 3Wm-2 )F. TURBULENT FLUX FORMULAEThe studies evaluated here all used the "traditional" bulk formulae (Section 7.2) anddifferences caused by variations in the algorithms are likely to be small compared to those dueto different choices of transfer coefficient. Since latent heat is usually the dominant flux wewill discuss here the choice of CE10n and note that the discussion for CH10n would be very similar.The transfer coefficient for latent heat as used in the different studies is shown in Figure11.2.6. There is clearly a significant difference between the relationship assumed by Bunker(1976) and that recommended in the review of Smith (1989) (1.2 ± 0.1) or the observations ofDeCosmo et al. (1996) (1.12 ± 0.24). Ignoring any error in the ship data, at first sight it wouldappear that the SOC climatology which uses the Smith (1989) value should be more accuratethan the IfM climatology which uses the original Isemer and Hasse version of CE10n.11 - Evaluation of Flux Products 183 11.2 In situ Products(11.2.6 Assessment)Report of the Working Group on Air Sea Fluxes June, 2000However it is interesting to note that most of the coefficients that have been adjusted tosatisfy some form of oceanographic constraint (global heat balance or meridional heat transport)have similar values at typical midlatitude wind speeds of about 10 ms-1. Thus the Oberhuber(1988), adjusted Isemer and Hasse (1997), and constrained da Silva (1994), all have 1000 xCE10n between 1.3 and 1.4 in that wind speed range. So too does Lindau (2000) whose CE10nvalues were based on the unadjusted Isemer and Hasse (1997). While such values are highcompared to Smith (1989) or DeCosmo et al. (1996) they are not outside the range ofpossibility. At wind speeds higher than about 12 ms-1 there is very little data on the behaviourof CE10n with wind speed. DeCosmo et al. could not rule out a tendency for CE10n to increasewith wind speed and recent determinations of CE10n by Dupuis et al. (1999) actually indicated anincrease of CE10n with wind speed similar to that assumed by Lindau (2000).The lower CE10n values used in the SOC climatology were justified by Josey et al.(1999) mainly on the grounds that the fluxes calculated were in reasonable agreement with theIMET buoy values. However the IMET fluxes were calculated using the version 2.5 (or earlier)of the TOGA COARE algorithm (Section 7.4.1). That algorithm uses a constant Charnockparameter of 0.011 with the result that the values of CE10n and CH10n tend to decrease withincreasing wind speed. Using a Charnock parameter which increases with wind speed asobserved (Yelland et al. 1998, Hare et al. 1999) results in more constant values of CE10n andCH10n (version 2.6 of the TOGA COARE algorithm).30 25 20 15 10 5 01.01.21.41.61.82.0Bunker 74Oberhuber 88Smith, 1989 DeCosmo 96IH, 97 adj.DaSilva adj.Lindau ??U10n (m/s)CE10n (x1000)Figure 11.2.6. The transfercoefficient for latent heat asused in different studies plottedas a function of the 10m neutralwind speed.The conclusion is that, despite all the various air-sea interaction experiments, ourknowledge of the magnitude of the transfer coefficients is still by no means adequate. New fastresponse instruments are beginning to become available for use at sea and their use to determineCE10n and CH10n , particularly at wind speeds above 10 ms-1, is to be strongly encouraged.With regard to the drag coefficient, CD10n, the present state of our knowledge issomewhat better. One might allow that there is a dispute with regard to the magnitude of CD10nat wind speed above, say 15 ms-1 (e.g. Janssen, 1999, 2000; Taylor & Yelland, 2000b), andevidence that swell can alter CD10n at lower wind speeds (say below 5 ms-1) (Donelan et al, 1997;Rieder and Smith, 1998). However there is a growing body of evidence for the wind speedrange 5 to 15 ms-1 that supports the lower CD10n values suggested by the observations of Smith(1980), Large and Pond (1982), and Yelland et al. (1998). Thus the higher CD10n values (Figure11.2.7) used by Hellerman and Rosenstein (1983) or Oberhuber (1988) are not supported by themajority of the observations. It follows that the wind stress values contained in the latterclimatologies will be significantly too high for most wind speeds. However, in poorly sampledregions like the Southern Ocean the stresses contained in almost all in situ climatologies arebelieved to be severely underestimated, particularly in winter.11 - Evaluation of Flux Products 184 11.2 In situ Products(11.2.6 Assessment)Report of the Working Group on Air Sea Fluxes June, 200030 25 20 15 10 5 00123OberhuberdaSilvaH & RSmith 1988Smith 1980U10n (m/s)CD10n (x1000)Figure 11.2.7 The dragcoefficient CD10n as used indifferent studies plotted as afunction of the 10m neutralwind speed.G. SMOOTHING ALGORITHMThe disadvantages of the successive correction technique as used for UWM/COADSand the SOC climatology has been discussed in Section 8.5.2, and the advantages of thekringing method (used for the IFM fluxes) in Section 8.5.3. The disadvantage of kriging isthat it is computationally expensive and, at this time, global flux fields have not beencomputed using this technique. However, with increasing computer power, there is no doubtthat future global flux climatologies will be based on improved analysis methods such askriging, reduced space optimal analysis (Kaplan et al. 1997, 1998), or other techniques.H. CONCLUSIONMuch effort has been invested to ensure that each new release of COADS has a morenearly complete collection of the historical data. For that reason alone, the newerclimatologies are to be preferred to those calculated in the past. With regard to theparameterisations used for the heat fluxes one might have hoped to categorically state that thenew climatologies are superior to previous studies. However, at present we can only state thatwe believe that to be the case. For wind stress the case is more definite. There is strongevidence that climatologies such as Hellerman and Rosenstein (1983) or Oberhuber (1988)have over estimated the wind stress in most ocean regions. Thus, for wind stress the newerclimatologies are to be preferred. However the poor in situ sampling in the southernhemisphere, and particularly in the Southern Ocean, suggests that the lengthening data set ofsatellite derived wind data is a promising alternative.Of the recent global in situ climatologies it is hard to choose between theUWM/COADS and SOC products. The SOC product has been found to be in marginallybetter agreement with buoy measurements than the unconstrained UWM/COADS product(e.g. Weller et al. 1998). Thus the SOC fluxes may be preferred for regional studies.However if global net heat flux must be in balance (e.g. for forcing models) then theconstrained UWM/COADS product must be preferred even though comparisons with buoydata are significantly worse for that product (Weller et al. 1998, Josey et al. 1999).Compared to the SOC climatology the UWM/COADS covers a significantly longer period.Extension of the SOC fields back in time is hindered by the lack of metadata for applying theship corrections. However, perhaps the greatest strength of the UWM/COADS climatologyis the provision of sensitivity fields so that users can apply their own error estimates andoceanographic constraints. It is recommended that more use be made of that facility.For the Atlantic Ocean, the IFM product has the advantage of a good mappingtechnique, good error estimates, and providing a realistic meridional heat transport. Whilethe latter has been attained through the use of radiation parameterisations and transfercoefficients which have allowed balanced budgets to be obtained for the Baltic andMediterranean Seas, these are not, at present, the generally accepted choices for open ocean11 - Evaluation of Flux Products 185 11.3 Satellite Products(11.3.2 HOAPS)Report of the Working Group on Air Sea Fluxes June, 2000regions. This is the newest product and further verification is desirable and doubtless will beforthcoming.Finally we emphasise again that there is still a need for many more measurements ofthe transfer coefficients, particularly in open ocean moderate to high wind conditions.Whenever possible such determinations should be made using both the eddy correlation andthe inertial dissipation methods. Both methods are likely to have systematic errors whenused on ships. However the nature of those errors is different for the two techniques. A largedata set of consistent transfer coefficient estimates obtained using the two methods by severalresearch groups working from various ships would give confidence that the systematic errorshad properly been accounted for.11.3 Flux Products Based on Satellite Data11.3.1 IntroductionThe next section will evaluate a recent satellite based flux product, HOAPS, whichrepresents one of the first attempts to use satellite data to produce fields of all the surfacefluxes except SW radiation. Satellite radiation products will be discussed separately inSection 11.3.3 amd 11.3.4 for US and Japanese products respectively. Finally Section 11.3.5will briefly consider fluxes produced using the Residual Method, a combination of satellitemeasurements and model calculations.For development of flux climatologies from satellite data the consistency of data overtime is of paramount importance for the flux field producer. As an example we will considerthe SSM/I sensor. This has been flown on several DMSP platforms (see Section 4.3.3) andhas been widely used to construct long time series of flux fields and flux related variables.Differences between the same sensor on different platforms in different orbits can causedifferences in zonal monthly averages. For example, for wind speed differences ofapproximately 1 ms-1 can occur (Jost et al., 1999). Colton and Poe (1999) conducted a studyof sensor-to-sensor differences for six DMSP platforms starting in 1987 and ending in 1997.Their paper summarises the prelaunch and postlaunch performances found throughcomprehensive calibration/validation activities. Besides several sensor-specific componentstwo major results of this study were:❍ The remarkable stability of the SSM/I sensor provided the opportunity to quantify theincremental brightness temperature differences to which the SSM/I's can beintercalibrated. The so called "noise floor" has been determined using distributionfunctions constructed from the brightness temperatures. The noise floor is estimatedto be 0.25-0.35K for the five low-frequency channels and 0.45K for the 85 GHzchannels.❍ The interpretation of sensor to sensor differences based solely on monthly averagedbrightness temperatures is not recommended for multi-year time series but can besufficient for limited periods because regressions between different SSM/I arerelatively stable.From these results it is suggested that flux producers should focus not only on high accuracyof the retrievals but also on constructing consistent time series of satellite data.11.3.2 Evaluation of HOAPSA. INTRODUCTIONThe Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data(HOAPS) dataset (Schulz and Jost, 2000) is solely based on the use of infrared andmicrowave satellite data within retrievals developed with the aid of radiative transfer theory.A time period of 10.5 years (July 1987 - December 1996) is freely available for use by otherscientific groups1, and this will be extended in the future. It contains the following variables: 1 from http://sop.dkrz.de/HOAPS11 - Evaluation of Flux Products 186 11.3 Satellite Products(11.3.2 HOAPS)Report of the Working Group on Air Sea Fluxes June, 2000evaporation [mm h-1], rain rate [mm h-1], E-P [mm h-1], near surface air humidity [g kg-1], 0.98∗ specific saturation humidity at sea surface temperature [g kg-1], wind speed [m s-1], latentheat flux [W m-2], sensible heat flux [W m-2], and longwave net radiation [W m-2]. Thefollowing subsections describe the methodology used to derive the fields and give a summaryof attempts to verify the product through a comprehensive intercomparison with a range ofobservation types, from merchant ship observations to data from high quality IMET buoys.B. METHODOLOGYThe evaporation at the sea surface is parameterised using a bulk approach asdescribed in Chapter 6. The wind speed u and the atmospheric specific humidity ql aredetermined from SSM/I measurements using the retrieval of Schlüssel and Luthardt (1991) asdescribed in Section 10.6.2 and the method of Schlüssel et al. (1995) for ql as described inSection 10.5.2.The saturation specific humidity at the sea surface is essentially a function of the seasurface temperature. Since continuous handling of large data sets like the AVHRR raw datarequires large amounts of computer and man power, it was decided to use the NOAA/NASAOceans Pathfinder Sea Surface Temperature product (see Table 10.1). These products areavailable as daily fields with a spatial resolution of (9 km)2 at best.Because all the needed variables can not be inverted from the available satellitemeasurements, some empirical assumptions have to be incorporated into the computations.The most important one is an assumption about the atmospheric stability, needed to computethe transfer coefficient for latent and sensible heat. For use in the scheme of Smith (1988), itis assumed that the atmosphere has a relative humidity of 80% at any time and any place.With that assumption the missing air temperature can be computed from the measured ql. Theerrors in the transfer coefficient that occur if this assumption is wrong are between 2% at highwind speeds and strong unstable conditions, and 50% at low wind speeds and strongly stableconditions (Schulz et al., 1997). Other assumptions concerning the surface pressure or airdensity are not of great importance.Measurements of rainfall at the sea surface with radiometers in space have been thetopic of many studies in recent years. In an early stage of the preparation of the climatology itwas decided to use the rain algorithm of Bauer and Schlüssel (1993) which delivers aninstantaneous rain rate. This algorithm has been successfully tested within the PrecipitationIntercomparison Project (PIP) and the Algorithm Intercomparison Projects (AIP) described inSection 10.9.4.In addition to the variables needed for computing E-P the radiative longwave net fluxand the sensible heat flux have been included in the climatology. The longwave net flux iscomputed by a scheme described in Schlüssel et al. (1995) (see Section 6.4.2) and thesensible heat flux is determined using the air temperature derived under the assumption of80% relative humidity.The rain retrieval is applied first because all other retrievals can only be applied inrain free or light rain cases. If the derived rain rate is below a certain level the other retrievalsare applied to instantaneous SSM/I measurements to obtain the basic state variables u and ql.Within the bulk approach, u and ql are combined for each instant whereas the instantaneous uhas been combined with the mapped weekly averaged qS computed from the Pathfinder SST.The latter is bilinearly interpolated to the location of the SSM/I pixel. Weekly averages mustbe used for qS because of the cloud cover problem. After applying the retrieval algorithms,all the fluxes are computed on this quasi-instantaneous basis using the bulk approach of Smith(1988).The derived, quasi-instantaneous fluxes are mapped to grids with three differentresolutions: 0.5°, 1.0°, and 2.5° . The time averaging is then performed on the maps and thefields are stored as daily, pentad, monthly, yearly, and climatological fields.11 - Evaluation of Flux Products 187 11.3 Satellite Products(11.3.2 HOAPS)Report of the Working Group on Air Sea Fluxes June, 2000C. LIMITATIONSObviously, the present satellite-derived climatology has limitations just as anyclimatology has. Since large parts of the climatology are based on measurements from onlyone SSM/I at a time, a serious sampling error can be expected, particularly for precipitation.A case study with data obtained during the TOGA COARE campaign showed that thesampling error for grid resolutions of 2° by 3° and a sampling time of 3 weeks can be as highas 30% if only one satellite overpass per day were available. If data from two SSM/I's wereused this error is diminished to less than 4%. So a recomputation of the time series using allthe available satellite data is a major task for the future.Another systematic error that has not yet been fully explored is caused by the use ofradiometers on different platforms (AVHRR on NOAA and SSM/I on DMSP). It is expectedthat this error is rather small because the sea surface temperature obtained from the AVHRRdata is a relatively inert quantity compared to the atmospheric variables obtained from SSM/Imeasurements. But this argument holds only if the measurement of the sea surfacetemperature is not hampered for weeks by persistent cloudiness or by long periods ofdarkness over polar regions. For the latter case the sea surface temperature could be biaseddue to the more difficult cloud detection using infrared data.Additionally, the climatology is limited by the fact that the river runoff into the oceanis not implemented. The observed monthly averages of runoff for several rivers are available(Dümenil et al., 1993) and there would be in principle no obstacle to use them to check thelong term mean of the E-P fields.D. VERIFICATION OF FLUXESIntroductionThe HOAPS data set has been evaluated using a two way validation strategy. As afirst step, each retrieval algorithm used has been validated with in situ data from differentsources such as scientific experiments or data from the GTS. These intercomparisonsfocussed mostly on the quality of the instantaneous retrievals, with the exception of using asample of GTS data to compare on a monthly time scale but with very coarse spatialresolution. The second step focused more on comparisons of the gridded HOAPS dataset tolong term buoy measurements like the Subduction Experiment (Moyer and Weller, 1997) andon comparisons to global climatologies derived from in situ data, namely that of theSouthampton Oceanography Centre (SOC - Josey et al. 1999) and of the University ofWisconsin-Milwaukee (UWM - da Silva 1994). Some of these intercomparisons usingdifferent types of in situ data are briefly described below.Globally distributed comparisons to GTS dataSchulz et al. (1997) described intercomparisons of satellite-derived wind speed, near-surface air humidity, and latent heat flux with ship data taken from the GTS during a periodfrom July to September 1987. Table 11.3.1 gives bias and standard deviation between bothdatasets.Table 11.3.1 Bias and standard deviation for an intercomparison of satellite-derived windspeed, near-surface air humidity, and latent heat flux with GTS data at two different timescales. N denotes the number of match ups for the instantaneous time scale and the numberof 2˚ x 2˚ grid boxes at the monthly time scale, respectively.Temporal scale N u (ms-1) ql (gkg-1) Qlat (Wm-2)bias σ bias σ bias σinstantaneous 11108 0.2 2.1 0.06 1.6 3 50monthly average 136 -0.05 1.3 0.08 1.1 0.5 2011 - Evaluation of Flux Products 188 11.3 Satellite Products(11.3.2 HOAPS)Report of the Working Group on Air Sea Fluxes June, 2000The bias of 3 Wm-2 in latent heat flux was mostly caused by systematic higher latentheat flux estimates from the satellite south of 40˚S, where only very few in situ observationscontribute to the comparison. All standard deviations must be interpreted with respect toerrors in both the satellite retrieval and the ship measurements. As shown by Wentz (1997)for wind speed the mismatch of the ship point measurement and an instantaneous SSM/I pixelcan be the largest component in the error budget. If we conservatively assume that at least10% of the obtained standard deviation is caused by the mismatch and distribute theremaining error equally then the accuracy for an instantaneous estimate of latent heat flux isapproximately 30 Wm-2. This was a great improvement compared to older techniques likethat of Crewell et al. (1991) applied to SMMR measurements over the North Atlantic.Although they only derived the near-surface humidity from the satellite, and took the windspeed and sea-surface temperature from in situ measurements, a much larger standarddeviation (73 Wm-2 compared to 50 Wm-2) was obtained. So from this intercomparison it canbe concluded that the retrieval of low-level water vapour content and the use of AVHRRderived sea surface temperature considerably improved the satellite derived fluxes.Table 11.3.1 also shows values for the bias and standard deviation for monthlyaverages of u, ql, and latent heat flux . The numbers in the table were gained from acomparison on a grid with a 2˚ x 2˚ resolution. Such a coarse resolution was necessarybecause the ship measurements were widely scattered over the ocean. Another limitingcondition was that to obtain a representative monthly mean each grid box must be coveredwith in situ measurements on at least 25 observation days. As a result of this the number ofboxes was decreased to only 136, which indicates that all the numbers should be interpretedcarefully. To estimate the error of the satellite-derived latent heat flux Schulz et al. (1997)computed the noise of the ship data for the 136 bins by randomly dividing the in situ data ineach bin in two subsets and taking the standard deviation between the subsets as noise.Applying this procedure the error of the satellite-derived flux has been estimated to be ~15Wm-2. This error estimate fits very well in the upper and lower bound error estimation ofChou et al. (1995b) and is slightly higher than that stated by Schlüssel et al. (1995). Thelarger error could be caused by the fact that many of the grid boxes are located in coastalareas where ql (which contributes most to the error in ) is much sensitive to the land-ocean contrast (Liu et al., 1992). However compared to earlier attempts to derive from satellite data this was an significant improvement because phenomena such asphysically unrealistic downward fluxes in the mid- and high-latitude North Pacific and NorthAtlantic Oceans during summer were eliminated. Such problems were found by Esbensen etal. (1993) who compared COADS data with satellite data obtained using Liu's (1986) method.Comparisons to weather ship dataSchulz et al. (1997) also compared HOAPS to data from the weathership M for a oneyear (August 1987 - August 1988) time series using the "instantaneous" values. The aim wasto check that the retrieval schemes for wind speed, air humidity, and sea surface temperatureworked well over the seasonal cycle. The results of this comparison showed two majorthings. Firstly, most differences between the in situ and satellite-derived quantities weremuch smaller than in the comparison with unfiltered GTS data. For both wind speed and airhumidity, no bias and standard deviations of 2.0 ms-1 and 0.97 gkg-1 respectively wereobtained for the whole time series. Whereas the deviation for speed is similar to thecomparison above the value for humidity is strongly decreased. This indicates that only highquality in situ data are really useful for the assessment of retrieval errors. The second findingconcerns the chosen sea surface temperature dataset which was in this case the MCSST(Multi Channel Sea Surface Temperature) described in McClain (1981). The surfacesaturation humidity showed a systematic under-estimation during July, August, and October1987 and also during the first three months in 1988. This resulted in a mean under-estimation of the sea surface saturation humidity of 0.5 gkg-1. This bias was caused byextensive periods of cloud cover and/or darkness that led to a shift toward a nighttime bias inthe day/night observation ratio. Cloud detection is more difficult during nighttime thanduring daytime since a combination of tests using the three infrared channels of the AVHRR11 - Evaluation of Flux Products 189 11.3 Satellite Products(11.3.2 HOAPS)Report of the Working Group on Air Sea Fluxes June, 2000is required, rather than simple reflectance tests. So less measurements, and a deterioratedcloud detection, can lead to a significant cold bias in the sea surface temperature. However,this problem has been recognised and producers of newer datasets (e.g. the Oceans PathfinderSea Surface Temperature dataset) are aware of such problems.Comparisons to scientific experiment dataComparisons to data sets gained during scientific experiments have mostly the aim ofshowing that the satellite algorithms are able to describe the local temporal variability ofenergy fluxes and flux related variables. HOAPS retrievals have been compared to datagained during TOGA COARE (Bradley and Weller, 1997) and CEPEX as well as duringARKTIS'93 (Brümmer et al., 1993). Both intercomparisons represent regions with ratherextreme climates. The ARKTIS'93 experiment was conducted in February and March 1993in the northern part of the Norwegian Sea. The in situ measurements were taken by standardinstruments aboard the German research vessel Valdivia. The major result was that in mostcases the measurements of the bulk parameters and the parameterised fluxes were in goodagreement. The exception was some cases relating to situations where the ship was too closeto the coastline. The errors found were comparable to the weathership M exercise. Anotherresult was that the wind speed algorithm was not able to reproduce high wind speeds (around20 ms-1). The same result was found in tests of other algorithms (Goodberlet et al., 1989 andStogryn et al., 1994). This problem, inherent with most of the existing wind speed retrievals,can be caused by insufficient accuracy of the emissivity parameterisation during high windspeed events. However, a slightly surprising result was that the retrievals of wind speed andhumidity gave good results even in the case when the ships instrumentation recorded lightrain of up to 2 mmh-1.The TOGA COARE and CEPEX field phases took place in purely tropicalenvironment. For both experiments, data from the US research vessel Vickers have beenused to make intercomparisons to the retrieval schemes. It was found that precipitable water(both total and of the 500 m bottom layer) and the surface wind speed were retrievedexcellently by the SSM/I during both experiment phases. Errors were not larger than in otherregions of the world oceans. However, the retrieval for near-surface air-humidity showed asystematic overestimation of 1-2 gkg-1 during a 12 day period in February 1993. The failurewas in some way connected to the vertical structure of the water vapour distribution, sinceduring that period the radiosonde data exhibited very high water vapour loadings in the uppertroposphere with relative humidities higher than 90%. When the tropospheric humidity waslower the retrieval worked quite well. The reason for this malfunction is not yet fullyclarified. On one hand it can be argued that the high water vapour loading leads to a signalsaturation effect so that any variation below this thick water vapour layer can not be detectedwell. Against this is that the 500m integrated water vapour was retrieved quite well duringthis phase. On the other hand there was a considerable decrease of correlation between theintegrated water vapour amounts and the near-surface air humidity (which is almost constant);thus the assumed linear relationship might not be valid in the Western Pacific warm pool.Whether this behaviour of the humidity retrieval is typical for tropical oceans can't beanswered definitely because the CEPEX data (which were recorded along 2˚ south and northof the equator) showed that the retrieval worked well shortly after the ship left the warm poolarea. Additional comparisons to data from the TAO buoy array in the equatorial Pacific anddata from the Pilot Research Moored Array in the Tropical Atlantic (PIRATA) are necessary.However these datasets lack the information on vertical atmospheric humidity structure whichmight be essential for resolving possible retrieval errors.Comparisons in enclosed seasBecause of the rather coarse spatial resolution of microwave radiometers, only twointercomparison studies over the North Sea region have been conducted. Objectivelyanalysed and gridded fields with a spatial resolution of (42 km)2 and a time resolution of 3hours have been used as in situ data (Luthardt, 1985). The first study (Schlüssel, andLuthardt, 1991) was mostly aimed at validating the wind speed retrieval. The second study,11 - Evaluation of Flux Products 190 11.3 Satellite Products(11.3.2 HOAPS)Report of the Working Group on Air Sea Fluxes June, 2000by Schulz (1993b), examined one case study over the North Sea and compared air-humidityand sea surface temperature, wind speed, and latent heat flux. Concerning wind speed bothstudies found that the wind speed is recovered by the SSM/I with an accuracy of 2ms-1 in theinner areas of the North Sea but that the accuracy decreases approaching coastal waters due tothe field of view of the antenna including signals from land surfaces. For sea surfacetemperature it was found that the in situ measurements were not able to reproduce the finescale structure obtained from the AVHRR data and that in situ measurements were positivelybiased by approximately 1K. This can be explained by the possible existence of a cool skinat the surface, and the well known overestimation of the temperature at cooling water intakeson ships. With respect to air-humidity the same results as in the global comparison havemostly been found. The parameterised latent heat flux in this case study shown in Figure11.3.1 exhibited generally a good agreement but some hot spots of high differences betweensatellite and in situ data occur in regions where the coverage of in situ measurements was low.It should be noticed that the quality of the in situ temperature and humidity fields is not asgood as the wind fields because the fields couldn't be adjusted by a geostrophic constraint.Hence, the observation density plays a greater role in the comparison of the thermodynamiccomponents.Figure 11.3.1: Distribution of latent heat flux on 3. August 1987 in Wm-2 (left), and differencesbetween in situ latent heat flux at 6:00 a.m. and those derived from satellite data in Wm-2 (right).The contour interval is 10 Wm-2, negative isolines are dashed.Comparisons to global in situ climatologiesTo establish differences between HOAPS and climatologies derived from in situmeasurements comparisons have been made with the SOC climatology described by Josey et al.(1999) and the UWM climatology described by da Silva (1994). Since the findings are verysimilar for both in situ climatologies the following discussion is restricted to the SOCclimatology. Because HOAPS and SOC do not cover the same time period, only a five and ahalf year climatology from July 1987 to December 1993 was considered to ensure thatphenomena like El Niño were, or were not, included in both. Figures 11.3.2 to 11.3.4 show theglobal climatological mean distributions of evaporation E, precipitation P, and E-P from (a)HOAPS, (b) SOC, and (c) the difference (HOAPS - SOC).11 - Evaluation of Flux Products 191 11.3 Satellite Products(11.3.2 HOAPS)Report of the Working Group on Air Sea Fluxes June, 2000In the evaporation distribution (Figure 11.3.2), it can clearly be seen that thedifferences are smallest in the North Atlantic where the in situ data density is at its maximum.A positive outcome of the comparison is that the Gulf Stream is located at the same position inboth datasets, which is by no means a matter of course. Also, only small differences areobserved in the strength of the evaporation over the Gulf Stream which are most significantduring winter.Large zonal homogeneously distributed biases occur over the tropical oceans and thewhole southern hemisphere. The differences in the tropics are manifested by a negative bias ofabout -1.5 ms-1 in wind speed and a positive bias of about 1.5 gkg-1 in air-humidity. Apossible explanation for the bias in air humidity may be the malfunctioning of the satellitealgorithm, as found in the comparisons with the COARE data. However that would assumethat the atmospheric conditions in the warm pool could be considered to be typical for tropicaloceans in general. This is contradicted by the results from the CEPEX comparison whichcover a much larger tropical area. The wind speed bias is not yet explained; all comparisons ofwind speed retrievals to tropical experiment data show no bias of the magnitude although thenumber of comparisons is rather small. It should be kept in mind that the observation densityof the in situ measurements is quite low in the tropics (see Figure 2 in Josey et al., 1999) so thatwind speed and air-humidity values may be influenced by subtropical conditions resulting inhigher wind speed and lower humidity values compared to HOAPS. This would also partlyexplain the zonally homogeneous distribution of these biases.In the southern oceans, HOAPS shows a higher evaporation than SOC which isexpressed by a zonal homogeneous positive bias in wind speed of about 2 ms-1 whereas thebias in air-humidity is negative in the range of -0.5 to -4 gkg-1 with maximum differences foundto the south of South Africa. These southern ocean biases are surely caused by undersamplingin the in situ climatology with the result that the analysed values are strongly influenced bymeasurements from better sampled regions further north.Considering precipitation, it is obvious that in situ precipitation estimates can only beuseful where the spatial observation density is high because precipitation has a very intermittentspatial structure. Figure 11.3.3 shows that the precipitation pattern, in the southern hemisphereat least, appears to be unrealistic for SOC but reasonable for HOAPS. Due to the smallobservation density the analysis of in situ data tends to create clusters of high precipitation inthe South Pacific and Indian Ocean with rainfall that is higher than that in HOAPS by a factor of2 to 3. Whereas HOAPS shows large areas to the west of the continents with minimum rainfallof less than 0.5 mmd-1, those areas are much smaller in the SOC dataset. But even in the wellsampled North Atlantic, substantial differences occur over the Gulf Stream region and to alesser extent over the Kuroshio in the North Pacific during winter time (Figure 11.3.3).Rainfall amounts in HOAPS are as high as in the ITCZ which seems to be not unrealistic if theindependent estimates from the TRMM Precipitation Radar (Kidd, 1999, pers. comm.) over thesouthern part of the Gulf Stream are considered. So the reason for the difference may lay inthe method of Tucker (1961) for estimating rainfall from actual weather observations whichhas an upper limit of 8.13 mm/(3 hours). For tropical precipitation was found that this schemeunderestimated rainfall seriously and an empirical temperature dependent correction wasdeveloped by Dorman and Bourke (1978). Because this correction does not take effect over theGulf Stream, in situ climatologies may underestimate rainfall there. An examination of SSM/Irainfall estimations for single orbits during the winter months of 1992, 1993 and 1997 showsthat SSM/I derived rain rates are on average twice as large as the maximum possible in situ rainrate of 2.7 mmh-1. In addition, the work of Tucker (1961) showed that the observationfrequency of weather codes related to this maximum is rather low.The differences in the precipitation fields have large implications for the freshwaterbudget, E-P , which is shown in Figure 11.3.4. Whereas the satellite-derived field ischaracterised by a quite thin ITCZ and a somewhat broader SPCZ where the ocean is providedwith an precipitation excess of about 6 mmd-1, on average the ITCZ covers a much broaderarea in the in situ climatology. The patterns in the northern hemisphere are characterised by11 - Evaluation of Flux Products 192 11.3 Satellite Products(11.3.2 HOAPS)Report of the Working Group on Air Sea Fluxes June, 2000Figure 11.3.2 Comparison of evaporation (mm/day)from (top) HOAPS, (middle) SOC,(bottom) HOAPS-SOC difference.11 - Evaluation of Flux Products 193 11.3 Satellite Products(11.3.2 HOAPS)Report of the Working Group on Air Sea Fluxes June, 2000Figure 11.3.3 Comparison of precipitation (mm/day) from (top) HOAPS, (middle) SOC,(bottom) HOAPS-SOC difference.11 - Evaluation of Flux Products 194 11.3 Satellite Products(11.3.2 HOAPS)Report of the Working Group on Air Sea Fluxes June, 2000Figure 11.3.4 Comparison of (E-P) (mm/day) from (top) HOAPS, (middle) SOC, (bottom)HOAPS-SOC difference.11 - Evaluation of Flux Products 195 11.3 Satellite Products(11.3.2 HOAPS)Report of the Working Group on Air Sea Fluxes June, 2000evaporation excess in the trade wind belts and precipitation excess along the North Atlantic andNorth Pacific storm tracks in both climatologies. Because of the differences in the precipitationfields, the largest differences (even in sign of E-P ) exist over southern parts of the warmcurrents. Over the southern hemisphere both climatologies show very different patterns withthe SOC pattern very intermittent due to the structure of the precipitation field. The satellite-derived E-P values show a more realistic pattern with a slightly larger evaporation excess in thetrade wind belts compared to the northern hemisphere, and slight freshening near the Antarcticice margin. Here the SOC climatology shows values up to 8 mmd-1 compared to a maximumof 2 mmd-1 in the HOAPS climatology.Comparison to the Subduction Experiment buoysBecause climatologies can not serve as a validation dataset for each other, independentground truth must be used for deeper analysis of the errors. Josey et al. (1999) have used thedata from the Subduction Experiment described by Moyer and Weller (1997) for verification.The same task has been repeated for the HOAPS climatology. Unfortunately, buoy arrays withvery high quality measurements are not available at many locations in the oceans so that thiscomparison can only serve as a tool to identify possible regional biases in either climatology.A time series of monthly mean values from the Subduction buoys was compared with 1˚x 1˚ field data from the HOAPS dataset. The time series at all buoys agreed fairly well withcorrelation coefficients greater than 0.95 for all basic state variables and a slightly lessercorrelation for the derived fluxes. The variability of the bulk parameters within one month wasalmost of equal size for both datasets for all months during the two year period. That impliesthat, even with only two satellite overpasses per day, it appears to be feasible to represent theintra-monthly variability of basic state variables - at least in the Subduction area. However,positive biases were found for wind speed at both northern buoys over the whole buoydeployment period. The reason for this bias can only be found through a comparison of thewind speed spectra derived from simultaneously measured buoy and SSM/I data. A possiblereason could be an underestimation of the wind speed at the buoy, because the sensors wereinstalled at a height of 2 to 3m so that the instruments may be in the wave trough region duringhigh wind speed events (Section 9.2.2). Negative biases were found for sea surfacetemperature at both southern moorings during the first year of the deployment period. Thesuggestion that this bias was caused by aerosols from the Mt Pinatubo eruption, withsubsequent deterioration of the cloud screening in the AVHRR dataset, seems to be unrealisticbecause this feature should be found at all buoy sites. Estimates of specific air humidity arealmost bias free at all moorings during the deployment period.The averages and mean differences between HOAPS and all five Subduction buoys aresummarised in Table 11.3.2. The positive bias in wind speed resulted in an overestimation ofsensible and latent heat flux at the North East and the Centre buoy whereas the negative biasesin sea surface temperature cause a negative bias in latent heat flux at both southern buoys.Although the bias for sensible heat flux equalled zero at three of the five buoys, this shouldonly be taken as an indication that crude assumptions, like a constant relative humidity of say80%, may be sufficient to determine a climatological mean value (rather than the annual cyclewhich appears somewhat exaggerated in the HOAPS dataset). Estimates of longwave net fluxare not generally biased low or high. Because of the dependence of the downwelling radiationon actual cloud cover, differences in satellite and buoy estimates can be caused by satelliteresolution effects. The satellite instrument can't resolve the cloud spatial and time scalesbecause of its coarse spatial resolution - only 30 - 70 km and only two measurements per dayper satellite. All the more surprising is that the mean differences in net longwave fluxesbetween HOAPS and the buoys are not much larger than differences between buoy data and theSOC climatology.Comparison of the mean differences between the HOAPS dataset and the Subductionbuoy measurements, for all fluxes and basic state variables, with the findings summarised in thestudy done by Josey et al. (1999) reveals that the satellite dataset appears to be competitive withthe in situ product even in this not too badly sampled region.11 - Evaluation of Flux Products 196 11.3 Satellite Products(11.3.3 US Radiation Products)Report of the Working Group on Air Sea Fluxes June, 2000Table 11.3.1 Mean heat fluxes and differences for the Subduction experiment buoy array.Location Source QsensWm-2QlatWm-2ums-1qSgkg-1qLgkg-1QLWWm-2NE Buoy, N=23 HOAPS 14 ± 9 102±24 6.7±1.2 15.2±2.4 11.2±2.0 74±12(33° N, 22° W) Buoy 9±4 97±20 5.5±0.6 15.2±2.4 11.1±2.0 66±8HOAPS-Buoy 5±9 5±21 1.2±0.8 0±0.3 0.1±0.7 8±10SE Buoy, N=14 HOAPS 7±21 95±53 7.3±0.7 15.9±1.4 12.5±2.1 84±26(18° N, 22° W) Buoy 7±3 103±25 7.3±0.8 16.6±1.5 13.0±1.6 47±16HOAPS-Buoy 0±20 −8±33 0±0.6 −0.8±0.6 −0.5±1.0 37±13SW Buoy, N=14 HOAPS 5±15 101±43 6.9±0.9 18.1±1.3 14.1±1.9 60±16(18° N, 34° W) Buoy 5±3 129±25 6.6±0.5 18.6±1.4 13.7±1.8 60±11HOAPS-Buoy 0±13 −28±28 0.3±0.7 −0.6±0.6 0.4±0.8 0±11NW Buoy, N=13 HOAPS 7±8 86±23 6.5±1.5 15.9±2.7 12.3±2.5 66±13(33° N, 34° W) Buoy 7±3 84±20 5.1±1.1 16.0±2.9 12.0±2.2 76±9HOAPS-Buoy 0±7 2±22. 1.4±0.6 −0.1±0.3 0.3±0.5 −10±9C Buoy, N=18 HOAPS 16±10 116±27 6.5±0.8 16.6±1.7 12.1±1.8 76±12(25.5° N, 29° W) Buoy 7±3 107±18 5.7±0.8 16.8±1.8 12.2±1.8 66±13HOAPS-Buoy 9±8 9±18 0.7±0.5 −0.2±0.3 −0.1±0.6 11±1311.3.3 US satellite radiation productsA. INTRODUCTIONThe evaluation of US produced satellite-based global products for surface radiativefluxes is described here. Japanese radiation products will be described in the next section.Networks of surface measurements have been used for validation, but coverage of the sea isminimal. Surface measurements and satellite-based retrievals are improving; they are indeeduseful for many ocean modeling studies. However, intensive, local scale studies haveuncovered significant problems in both the measurement and theoretical computation of surfaceSW flux. The surface SW insolation retrieved by the satellite-based programs and computed byradiative transfer theory generally exceeds measured values; the discrepancies betweenmeasurement and theory are significant and unresolved. It should also be noted that none of theLW algorithms described below yet accounts for corrections to SST due to the cool skin (~0.3K) discussed in Section 10.2.1.The CERES/ARM/GEWEX Experiment (CAGEX; Charlock and Alberta, 1996)illustrates SRB issues for both SW and LW CAGEX provides a space-time set of input data,calculated fluxes, and validating measurements. Following the launch of CERES, CAGEX isbeing extended to the ARM Tropical West Pacific (TWP) site. ARM TWP data is expected tobecome an important resource for the study of radiation at the air-sea boundary. The ARM webpage is: http://www.arm.gov/B. GEWEX SRBVersion 1The first version of global products by the GEWEX Surface Radiation Budget (SRB)Project (Whitlock et al., 1995) have been in use for a few years. Access is through the NASA11 - Evaluation of Flux Products 197 11.3 Satellite Products(11.3.3 US Radiation Products)Report of the Working Group on Air Sea Fluxes June, 2000Langley Distributed Active Archive Center (DAAC) with the web address:http://eosweb.larc.nasa.gov/The GEWEX SRB is an official project of the WCRP, and it is formally reviewed bythe WCRP WG on Radiative Fluxes. The GEWEX SRB uses ISCCP (operational satellite;Rossow et al., 1991) data and covers 1985-1988, a period for which the adjusted ISCCPcalibration was fairly stable. GEWEX SRB presently issues SW products only. Daily andmonthly-averaged insolation is available in the 280 by 280 km equivalent area grid system fromthe primary algorithm by Pinker and Laszlo (1992); and monthly retrievals from a backupalgorithm due to Staylor (see Darnell et al., 1992); both algorithms are based on radiativetransfer physics. In addition to the surface SW insolation (downwelling), the Pinker algorithmproduces the surface upwelling flux, the surface net flux, the surface albedo, and the top-of-theatmosphere (TOA) net flux.For validation, the GEWEX SRB files provide:(1) monthly TOA net flux by independent, higher quality measurements from the EarthRadiation Budget Experiment (ERBE; Barkstrom et al., 1989) over most of the globe. Unlikethe narrowband radiometers on the ISCCP satellites, ERBE provides broadband measurementswith on-board calibration for SW and LW.(2) measured monthly-averaged surface insolation for a small fraction of the land massas compiled by the Global Energy Balance Archive (GEBA; Gilgen and Ohmura, 1999).Most GEBA sites are located in the midlatitudes; a few of the GEBA surface sites are located onislands (note earlier remarks on pyranometer accuracy and GEBA in Section 5.2.2). October1985 was a fortuitous month in that the monthly mean bias of the Pinker algorithm relative tomidlatitude GEBA obaervations was only 2.7 Wm-2.The estimated global and monthly averaged bias for GEWEX SRB insolation is about15 Wm-2; that is, the retrieved global insolation for a 24-hour day is probably too large by 15Wm-2; most GCM’s produce even larger values for surface insolation. The estimated rms errorfor monthly mean GEWEX SRB insolation in a 280 by 280 km grid box is 20-25 Wm-2; a figureindicated smaller values for European GEBA sites in October 1985. Errors are smaller over themidlatitudes and larger over the tropics. The largest errors are found in regions with extensivebiomass burning. We do not know if the errors in surface insolation are larger (or smaller) oversea. At the TOA, there is not a significant land versus sea bias in the GEWEX SRB whencompared to ERBE TOA measurements. The ERBE global mean uncertainty for SW is 5-7Wm-2; this estimate for ERBE uncertainty is based on the imbalance of ERBE TOA SW andLW observations. The bias in GEWEX SRB surface insolation appears to be due to anatmosphere that absorbs more SW than is predicted by radiative transfer theory. The estimatederror for the GEBA surface insolation measurements is 3%; this error may have a significantsystematic component.Version 2A new Version 2 of the GEWEX SRB will be issued in early 2001(p.w.stackhouse@larc.nasa.gov). An improved Pinker algorithm will be used for SW. A 2/4stream version of the Fu and Liou (1993) algorithm will be used in the LW. The new Version 2will provide a higher spatial resolution of 1 by 1 degree; time spacing will be 3-hourly, dailyaverage, monthly-hourly (i.e., the monthly mean flux from 0200-0300 UTC, etc.), and monthlyaverage; covering July 1983 to at least June 1995.GEWEX SRB Version 2 will be based on ISCCP DX (pixel scale) data, rather than theISCCP C1 (280 by 280 km equivalent area gridboxes; 2.5 by 2.5 degree at the equator; numberof gridboxes changing with latitude) used now; this will provide a true enhancement of spatialresolution, as opposed to a simple interpolation of C1 to 1 by 1 degree (a few suchinterpolations are circulating at present). The pixels in both ISCCP DX and C1 are sampled atthe same ~30 km spacing, however; unlike ERBE and CERES (whose pixels are almost11 - Evaluation of Flux Products 198 11.3 Satellite Products(11.3.3 US Radiation Products)Report of the Working Group on Air Sea Fluxes June, 2000contiguous), the pixels in ISCCP samples are quite separated; the sampling produces spatialnoise in a gridbox product.Both ERBE and ISCCP are liable to large instantaneous errors of up to 50 Wm-2because of uncertainty in angular distribution models (ADMs) used to invert from directionalradiance (W/sr - energy per unit solid angle measured by a scanning satellite radiometer) todirectionally-integrated flux (Wm-2 irradiance - energy per unit area). ADM errors are reducedenormously by averaging, but they may be the source of the present 5-7 Wm-2 imbalancebetween SW and LW in the ERBE record.C. LANGLEY SRBDarnell et al. (1996) and Gupta et al (1999) have released an 8-year CD-ROM"Langley SRB Dataset" for monthly-mean downwelling and upwelling, shortwave andlongwave fluxes. Monthly, 280 by 280 km retrievals are available from :http://eosweb.larc.nasa.gov/PRODOCS/srb/table_srb.html .The "Staylor" SW (Darnell et al., 1992) and the Gupta et al. (1992) LW algorithms areused together with the ISCCP C1 cloud products (Rossow and Schiffer, 1991) that provideinformation on clouds, the atmosphere and the surface, and the ERBE top-of-atmosphereshortwave and longwave fluxes (Barkstrom et al., 1989). Accuracy with the Staylor SWalgorithm is comparable to that with the Pinker SW. The Pinker algorithm was selected (overthe Staylor algorithm) for official GEWEX SRB processing because, with more comprehensivephysics, Pinker produces a TOA flux which may be compared with ERBE. The Guptadownwards LW flux was larger than BSRN measurements at the mean of 7 sites for 3 months in1992 by 5 Wm-2; the rms error was 20 Wm-2. In comparison with the GEBA database ofsurface flux measurements (Gilgen and Ohmura, 1999), the NOAA/Climate Monitoring andDiagnostics Laboratory (CMDL) dataset, and the CSIRO, Australia, dataset, the overalluncertainty in monthly mean downwelling fluxes was estimated to be about ± 15 Wm-2 for bothshortwave and longwave.For October 1985, the surface net SW flux (absorption by the surface; global mean160.6 Wm-2) is larger than the surface net LW flux (negative value denotes cooling; global mean-46.6 Wm-2) and dominates the geographical pattern of surface net total (SW+LW) flux (globalmean 114 Wm-2) over the ocean. The interannual variability for 8 Octobers showed that the netSW (global mean standard deviation 10.2 Wm-2) dominates the net LW (global mean standarddeviation 6.22 Wm-2) in setting the pattern of the surface net total interannual variability (globalmean standard deviation 11 Wm-2). Patterns of interannual variability are strongest over thetropical oceans. For an individual gridbox, the interannual variability is smaller than the error inthe satellite retrieved value for a given month. Satellite retrievals do, however, demonstrateskill in identifying the geographical patterns in interannual variability. A regional analysis ofinterannual variability in the SW SRB demonstrated that the interannual variability retrieved bysatellites represents the pattern and maginitude of interannual variability in the GEBA groundmeasurements quite well (Alberta et al., 1994).D. CERES RADIATION DATAThe ADM problem (see section 11.3.3B) is approached by sophisticated RotatingAzimuth Plane Scanning (RAPS) in CERES (Wielicki and Barkstrom, 1991). The firstlaunches of CERES were in November 1997 on TRMM (tropical and midlatitude coverage) andin December, 1999 on Terra (formerly EOS-AM1 - global coverage). SRB retrievals will bevalidated and released 2-3 years after launch. Gridded products (1x1 degree) will eventually beavailable 3-hourly, daily, and monthly. Instantaneous SRB at the individual "footprints" of thebroadband CERES scanner will typically have diameters of 10-50 km. The goal of the CERESSRB is to reduce the error in the GEWEX SRB by a factor of two. CERES has extensivedocumentation of its algorithms and validation activities at http://asd-www.larc.nasa.gov/ceres/ASDceres.html .11 - Evaluation of Flux Products 199 11.3 Satellite Products(11.3.3 US Radiation Products)Report of the Working Group on Air Sea Fluxes June, 2000A verification facility (CERES Ocean Validation Experiment COVE) has beenestablished at the Chesapeake Light, 25 km off the Virginia coast. Measurements of shortwavespectral radiances reflected by the sea surface are obtained using a southward facingSpectrophotometer. The measurements are intended to improve classic formulations of oceanbi-directional reflectance and albedo. The radiance scans are complemented by collocated,continuous, long-term observations of surface radiation and aerosols and meteorology, windwaves, and sea swell. COVE measures broadband fluxes, both upwelling and downwelling, andparticipates in the BSRN.E. BISHOP SHORTWAVE RADIATION PRODUCTBishop et al. (1997) have produced a global dataset of daily-mean downwellingshortwave fluxes at the surface covering an eight year period (July 1983 -- June 1991) at 2.5˚latitude-longitude resolution. This dataset is available by request from Dr. James K.B. Bishop(e-mail: jkbishop@lnl.gov) or from the archives at the National Center of AtmosphericResearch in Boulder, Colorado (dataset reference = DS741.0 SURFACE SOLARIRRADIANCE BY BISHOP).These fluxes are calculated by applying an approximate radiative transfer method(Bishop and Rossow, 1991) to the ISCCP C1 cloud products (Rossow and Schiffer, 1991) thatprovide information on clouds, the atmosphere and the surface. The Bishop et al. (1997)method uses the cloud properties retrieved from the narrowband satellite radiances (also basedon a radiative transfer model) as input into a calculation of the fluxes that explicitly accounts formultiple scattering in and between clouds and the surface: it was tested by comparison to a fullradiative transfer model using the same input data. The results were further verified bycomparison with the GEBA database of surface flux measurements over land (Gilgen andOhmura, 1999) and an extensive number of buoy-based measurements from a series of oceanexperiments. Overall uncertainty is estimated to be about ± 25 Wm-2 rms for daily-mean values,due mostly to cloud sampling errors, and ± 15 Wm-2 for monthly mean values, due mostly tocalibration and aerosol optical thickness errors.A new global product of daily mean surface solar irradiance is now being produced witha spatial resolution of 0.5˚ latitude-longitude based on the revised ISCCP cloud products(Rossow and Schiffer, 1999). This dataset is complete for a couple of years; more informationabout this dataset can be found at ( http://www.giss.nasa.gov/data/seawifs/ ).F. BREON SURFACE NET SHORTWAVE FLUX AND ALBEDOBreon et al. (1994) have produced a global dataset of monthly-mean surface netshortwave and albedo covering a two-year period (1985-1986) at 2.5˚ latitude-longituderesolution. These surface quantities are determined from ERBE (Barkstrom et al., 1989)monthly-mean full-sky and clear-sky albedos, together with the top-of-atmosphere solarirradiance, by removing the atmospheric and surface scattering and absorption effects usingresults pre-calculated with a radiative transfer model developed by Tanre et al. (1990). Thismethod is conceptually similar to that proposed by Li et al. (1993). The results were evaluatedby comparison with surface shortwave fluxes determined from narrowband satellite radiancesby Gautier (1988). The estimated uncertainty of the monthly-mean values is +15 Wm-2 biasand ±14 Wm-2 rms. This dataset may be obtained by contacting the authors.G. NASA GODDARD INSTITUTE FOR SPACE STUDIESRossow et al. (1995) have produced a global dataset of downwelling and upwelling,shortwave and longwave fluxes at the top-of-atmosphere, in-atmosphere, and at the surfacecovering an four year period (April 1986 -- January 1989, every third month) with a 3-hr timeresolution and a 2.5˚ latitude-longitude spatial resolution. More information about this datasetand sample statistics can be found at ( http://isccp.giss.nasa.gov/flux.html ) and the wholedataset can be obtained by sending a request to Dr. Yuanchong Zhang, NASA Goddard Institutefor Space Studies, 2880 Broadway, New York, NY 10025, USA.11 - Evaluation of Flux Products 200 11.3 Satellite Products(11.3.4 Japan Radiation Products)Report of the Working Group on Air Sea Fluxes June, 2000The analysis approach follows that proposed by Rossow et al. (1989) and Rossow andLacis (1990). The fluxes are calculated by applying a full radiative transfer model from theGISS climate GCM (Zhang, et al., 1995) to the ISCCP C1 cloud products (Rossow and Schiffer,1991). This model treats the full spectral dependence of absorption and scattering byatmospheric gases, including all radiatively important trace species, six different types ofaerosol, clouds and the surface. The results were verified by comparison with the ERBE top-of-atmosphere fluxes (Barkstrom et al., 1989), the GEBA database of surface fluxmeasurements (Gilgen and Ohmura, 1999), several other specific surface site measurementsand/or field experiment datasets, and by comparison with other satellite analyses. Overalluncertainty is estimated to be ±10-15 Wm-2 for monthly-mean top-of-atmosphere fluxes and±15-25 Wm-2 for monthly-mean surface fluxes.A new global radiation flux product covering the period from July 1983 through topresent is planned, based on application of an improved radiative transfer model applied to thenew ISCCP D-series cloud products (Rossow and Schiffer, 1999). The model improvementsinclude a refined treatment of cloud scattering dependence on cloud optical thicknesses andparticle sizes, including for non-spherical ice cloud particles, extension of the aerosol propertiesto include two size ranges for each of six types, refinement of the spectral representation oflongwave absorption and the water vapor continuum, and improved specification of surfacealbedos and emissivities. Input quantities have been improved by including both ice and liquidwater clouds, a revised aerosol optical thickness climatology to include more absorbing species,a revised atmospheric temperature and humidity dataset, and treating cloud type dependenceand layer structure more completely. The new product will report, every 3 hr at 2.5˚ latitude-longitude resolution, the cloudy and clear, upwelling and downwelling, shortwave andlongwave fluxes at five levels: surface, 680 mb, 440 mb, 100 mb and top-of-atmosphere.Cloud-type dependence will also be reported. Production of this new product is expected tobegin early in 2000 and to be available from Dr. Yuanchong Zhang (see above).H. SUMMARY - US SATELLITE RADIATION PRODUCTSFigure 11.3.5 shows the climatological zonal mean net surface shortwave radiation fromseven different products discussed in this Section. In the low latitudes the satellite productssuggest that substantially more radiation reaches the surface compared to the VOS estimates andreanalyses. Throughout the low latitudes, the flux in both reanalysis is substantially smaller, aspointed out in Section 11.4 a likely result of cloud cover problems in the models. Discountingthe reanalysis, the spread among the estimates is still on the order of 25 Wm-2 in the zonal mean.11.3.4 Japan surface shortwave and longwave productsA. KIZU (1999) SW FLUXESKizu(1999) has produced surface shortwave radiation fluxes covering from October1992 to Sepember 1993 with monthly and 1.0° x 1.0° latitude-longitude resolution. The spatialcoverage is from 80°E to160°W and from 60°N to 60°S. He used the 3-hourly Visible andInfrared Histogram Data from GMS/VISSR with 0.25° latitude-longitude spatial resolution as aprimary data source, and additionally monthly precipitable water from SSM/I (Wentz,1994),monthly total ozone amount from TOMS/Nimbus7, and seasonal climatology of troposphericaerosol from NOAA/AVHRR (Husar et al, 1997). Long-term degradation of the VISSRinstruments was corrected by Kizu(2000). A simple parameterization model by Iqbal (1983)was used to formulate fundamental radiative transfer processes in the atmosphere, and the five-daily GMS imagery data was used to estimate the cloud albedo. The methodology wasdescribed in detail in Kizu(1995). Based on comparison with routine ground measurements ofinsolation, mainly in Japan and Australia, the statistical errors of daily and monthly mean11 - Evaluation of Flux Products 201 11.3 Satellite Products(11.3.4 Japan Radiation Products)Report of the Working Group on Air Sea Fluxes June, 2000Figure 11.3.5 The climatological zonal mean net surface shortwave radiation from three satelliteproducts, two VOS based products, and two reanalysis products.values were estimated to be less than 20% and 10%, respectively. The validation procedure iscontinuing by collecting other available in-situ measurements on both land and sea, and theproducts will soon be extended over 11 years from March 1987 to September 1999.B. IWASAKA(1999) LW FLUXESIwasaka(1999), using the narrow band model proposed by Goody(1964), has estimatedmonthly products of surface longwave radiation flux in each 1.0° x 1.0° latitude-longitude gridover the western Pacific and eastern Indian Oceans (80°E-160°W, 60°S-60°N). The primarydata source was Infrared Radiation (IR) Histogram Data derive from GeostationaryMeteorological Satellite (GMS-4), and additionally Multi-channel Sea Surface Temperature(MCSST) data derived from NOAA/AVHRR data and climatological SST data derived fromCOADS were used. The narrow band radiation model proposed by Goody (1964) was used tocalculate atmospheric absorption and emission between the cloud bottom and sea surface. Thismodel covers the H2O continuum, the CO2 15 micrometer and the H2O 6.3 micrometer bands.The parameters in the model were given by Rogers and Walshaw (1966), Goldman and Kyle(1968) and Roberts et al .(1976). The spectral range of 0-2200 cm-1 was divided into 23 narrowbands to calculate the radiation flux.11 - Evaluation of Flux Products 202 11.3 Satellite Products(11.3.4 Japan Radiation Products)Report of the Working Group on Air Sea Fluxes June, 2000Construction procedures were as follows. First, in order to detect cloud coverage ineach pixel, a pseudo sea surface temperature was computed from IR Histogram data andcompared with the MCSST data. A pixel where the difference between the pseudo SST andMCSST exceeded 3.0K was assumed to be cloud-covered. Next, the height of cloud base wasdetermined under the assumption of constant cloud thickness and the cloud base temperaturewas calculated from NCEP reanalyses data. The downward longwave radiation value wascalculated from black body radiation theory using the assumption of the emissivity = 0.9 at thecloud base. Atmospheric absorption and emission between the cloud base and sea surface wereassessed by a narrow band radiation model. On the other hand, in a cloud free area downwardradiation was calculated in a layer below 300 hPa level determined by the NCEP data. Upwardlongwave radiation was calculated from the black body theory using the sea surface emissivityof 0.984 based on Konda et al.(1994). Downward longwave radiation values observed atKwajalein Island were used to compare with the estimated values in this data set. The resultsrevealed that the estimates had a tendency to be over-estimated in cloudy conditions and under-estimated in a cloud-free conditions. The bias and random error were both 10-20 W m-2 formonthly mean product. These data sets will be available on the Internet in the near future athttp://dtsv.scc.u-tokai.ac.jp/.11.3.5 Residual methodDuring the last 20 years various estimates of oceanic meridional heat transport havebeen made by combining satellite and atmospheric data. These 'residual method' calculationshave provided meridional transport profiles which have been compared to estimates derivedfrom hydrographic transects as well as those implied by net heat flux climatologies derivedfrom COADS. One disadvantage of this method is that only the net surface heating iscomputed. On the other hand, the only other means to date of estimating the net surface heatingon a large scale has been via COADS type climatologies or reanalysis, and there are clearlylarge uncertainties in both. Thus the residual method has the potential to be a valuable means ofestimating the total surface energy balance of the global ocean.Subtracting atmospheric flux divergence from the net radiation at the top of theatmosphere, Trenberth and Solomon (1994) were the first to rigorously apply a residual methodto estimate surface net heating locally. Making use of ERBE radiation data and ECWMFanalysis for a single year (1988), Trenberth and Solomon derived the spatial distribution of theannual mean net surface heat flux for the global oceans. Keith (1995) applied similar methodswith four years of ERBE data (1995-1988) and combination of ECMWF and NMC analysis.One significant problem encountered in these studies was large imbalances over land(up to + 100 W/m-2) in the annual mean net heat flux. Both studies suggest that theseimbalances were due to problems associated with orographic effects in the atmospheric models,and Trenberth and Solomon estimated errors to be on the order of 30Wm-2, on the order of theglobal imbalances in the SOC and unadjusted da Silva climatologies. Trenberth and Solomoncorrected for these land imbalances by applying an atmospheric mass correction and also solveda Poisson equation with the constraint of no heat flux normal to continental boundaries. Sinceboth studies, examination of the ECMWF model has revealed that land surface imbalances werelargely attributed to a non adiabatic condition at lowest level of the land surface model, whichhas since been rectified.A map of the annual mean Trenberth and Solomon estimate of annual mean net heatflux is shown in Figure (11.8.2) along with estimates from the SOC and da Silva climatologies,and the ERA and NCEP reanalysis climatologies. There are many encouraging similarities withother estimates. The noise is likely attributable to the fact that there is no geophysicalconsistency between the satellite and analysis.11 - Evaluation of Flux Products 203 11.4 Reanalysis products(11.4.2 Compare with in situ)Report of the Working Group on Air Sea Fluxes June, 200011.4 Evaluation of the Reanalysis Products11.4.1 IntroductionReanalysis has provided a long, consistent record of the interchange of momentum,energy and moisture between the earth's surface and the atmosphere. This record presents thebest opportunity to date to evaluate the accuracy of air-sea fluxes from atmospheric dataassimilation systems. There is no "ground truth" for most of these fluxes, since they are notdirectly measured over most of the globe and must be estimated indirectly from observationsand sometimes significantly tuned to ensure net energy balance. In this section we comparesurface energy fluxes, winds, wind stress and precipitation from the four reanalyses to eachother and to independent estimates and review other work evaluating the reanalyses.11.4.2 Comparisons to in-situ observationsZeng et al. (1998) used TOGA COARE ship data and TAO moored buoy data tocompare six different bulk aerodynamic algorithms, including the ones used in the ERA15,GEOS1 and NCEP reanalyses. They found that the ERA15 algorithm gave slightly better fits toroughness lengths than the GEOS1 and NCEP1 algorithm and that the GEOS1 algorithm gaveslightly better fits to momentum and latent heat fluxes using TOGA COARE data. Theequation for roughness length for heat in the NCEP1 and NCEP2 algorithm appeared to beinappropriate under strong wind conditions and to overestimate latent heat flux. None of thereanalysis algorithms considered the vapour pressure reduction of 2% over salt water versusfresh water, which has a significant impact on latent heat flux under strong winds.Smull and McPhaden (1998) found that annual mean NCEP1 surface winds wereweaker and less divergent (more zonal) than those observed by the TOGA-TAO moorings in thetropical Pacific. Directional differences of 20-30o were common, although locally these were asmuch as 90o near the ITCZ. Magnitudes differed by about 1-2 ms-1 in the annual mean, but byas much as 5-6 ms-1 locally in monthly means. In the western Pacific NCEP1 tended to be tooeasterly. ERA-15 displayed broadly similar biases, but had 10-20% smaller differences with theTAO observations, and performed well at intra-seasonal time scales. Bony et al. (1997)examined the NCEP1 and GEOS1 reanalyses in the tropics and concluded that NCEP1overestimated evaporation in regions of subsidence.Shinoda et al. (1999) compared NCEP1 surface fluxes in the tropical warm pool of thewestern Pacific and Indian Ocean to surface fluxes estimated from mooring data during TOGACOARE and to independent gridded estimates. The independent estimates were based onoperational wind and surface pressure analyses and satellite observations of rainfall, short waveradiation, and outgoing long wave radiation. Stress and latent heat flux that depend primarilyon surface wind variations agreed better than surface short wave radiation and precipitation thatdepend largely on fluctuations in convection. The intra-seasonal (periods of 25-100 days)variance of short wave radiation and precipitation in NCEP1 was about half that estimated fromin-situ observations and satellite observations. NCEP1 stress and latent heat variationsassociated with the Madden-Julian oscillation (MJO) agreed well in magnitude and phasingwith the independent estimates; NCEP1's composite short wave radiation and precipitation forthe MJO agreed in phase but had less than half the amplitude of the satellite-based estimates.Klinker (1997) compared latent and sensible heat flux from ERA15 to the TOGACOARE IMET buoy at 156E and 1o45'S and found that ERA15's latent heat flux had a 17 Wm-2(16.5%) positive bias. ERA15 represented major events with strong latent heat fluxes well,reflecting good analyses of wind speed, but produced too much latent heat flux during low windspeed periods. ERA15 had a drier boundary layer than later versions of the ECMWF model.ERA15's sensible heat flux failed to increase during convective events, as the IMET buoy's11 - Evaluation of Flux Products 204 11.4 Reanalysis products(11.4.2 Compare with in situ)Report of the Working Group on Air Sea Fluxes June, 2000sensible heat flux did, and had a negative bias compared to the buoy. ERA15's precipitation didnot agree well with the precipitation observed at the IMET buoy. Planton et al. (2000) foundthat ERA15, compared to in situ observations and independent climatologies, underestimatednet short wave and overestimated latent heat flux in the tropics and overestimated net long wavein mid-latitudes.Smith et al. (1999) compared NCEP1 near-surface meteorological and air-sea fluxfields to high-quality observations collected by numerous international research vessels duringthe World Ocean Circulation Experiment (WOCE). These observations were all recordedcontinuously by automated systems, thus enabling the data to undergo a higher level of qualitycontrol. The data covers a wide range of conditions over the world's oceans, but do nottypically repeatedly sample the same region. The data are first height adjusted to referencelevels of the reanalysis using Smith (1988). Air-sea fluxes are then calculated, again usingSmith (1988). All meteorological and air-sea fluxes are then compared to co-located reanalysisvalues. The RMS differences varied with latitude. They found that the reanalysis atmosphericpressure values are too weak, most notably in the tropical regions. Additionally, pressureextremes were underestimated. The winds are underestimated at all latitudes. Air temperaturesand humidity agree better and indicate no clear pattern of differences over varying parts of theglobe. For the fluxes, the reanalysis wind stresses matched the wind results, i.e. stresses wereunderestimated at all latitudes. NCEP1's sensible and latent heat fluxes were surprisingly toolarge. The RMS differences increased with latitude, but the biases in sensible heat flux werefairly uniform (5 W M-2). Reanalysis latent heat flux was too large (~10-30 W M-2). The bias islargest in the Northern Hemisphere, especially in the 20-40°N band. Use of the Smith (1988)algorithm on NCEP1 near-surface fields led to considerably better agreement for latent andsensible heat fluxes with ship-based fluxes.Renfrew et al. (2000) compared meteorological surface observations taken aboard theR/V Knorr during the Labrador Sea Deep Convection Experiment from Feb. 6 to Mar. 13, 1997to operational analyses by ECMWF and reanalyses by NCEP1. (The ECMWF operationalsystem used in 1997 was very similar to the system used in ERA15.) They found that theanalyses reproduced the observed surface layer well, with ECMWF performing somewhatbetter, perhaps due to its higher resolution. They did find discrepancies in sea surfacetemperature, due to the scarcity of satellite determined SSTs and the crude sea ice mask used inthe models, and in relative humidity, where ECMWF was 10% higher than observed andNCEP1 25%. The reanalyses did not include a marginal ice zone.Renfrew et al. (2000) also calculated bulk estimates of surface momentum and heatfluxes from the ship observations, using the bulk flux algorithm of Smith (1988) with neutralexchange coefficients updated to those of DeCosmo et al. (1996). They compared these fluxesto fluxes from ECMWF and NCEP1. The time series of heat fluxes had correlations above 0.9;however, ECMWF had up to 30% more sensible and latent heat flux and NCEP1 up to 100%more sensible and 50% more latent heat flux during high heat flux events. Averaged over theentire cruise, ECMWF’s heat fluxes were 15% stronger than the estimate from the Knorr, withinan estimated uncertainty of 15% in exchange coefficients (Garratt, 1992). NCEP1overestimated the mean sensible and latent heat fluxes by 50% and 30%. Discrepancies inmomentum fluxes between the reanalyses and the Knorr reflected mainly discrepancies in the10-meter wind. Renfrew et al. (2000) found that recalculating fluxes from model surface-layerdata with the Smith/DeCosmo algorithm dramatically reduced the disagreement in fluxes. Theyfound that the roughness length formula used by NCEP1 (and NCEP2 as well) overestimatedheat fluxes in cases of large air-sea temperature differences and high wind speed. Zeng et al.(1998)’s bulk formula, a simplification of the TOGA-COARE algorithm (Fairall et al., 1996a),was introduced in the NCEP operational model in Jan. 1998 and appears to correct theseproblems.11 - Evaluation of Flux Products 205 11.4 Reanalysis products(11.4.2 Compare with in situ)Report of the Working Group on Air Sea Fluxes June, 2000Josey (2000) compared surface fluxes from NCEP1 and ERA15 to those determinedfrom five meteorological research buoys deployed in the north-east Atlantic between 18N-33Nand 34W-22W during the Subduction Experiment between June 1991 and June 1993. Thebuoys used radiometers to measure radiative fluxes, while the COARE flux algorithm (Fairall etal., 1996) determined sensible and latent heat fluxes from one minute mean meteorologicalmeasurements. Josey (2000) found that ERA15 and NCEP1 overestimated the meanevaporation for the buoy by 16 and 11 Wm-2, underestimated net short wave by 14 and 22 Wm-2,and underestimated the net downward heat flux into the ocean by 32 and 35 Wm-2. Fields fromthe SOC flux dataset based on ship reports (Josey et al., 1998) displayed better agreement withthe estimates from the buoys.Josey (2000) also calculated the implied ocean heat transport from ERA15 and NCEP1climatologies for 1980-93 and found reasonable agreement with independent hydrographicestimates. Trenberth (1998) examined the heat budget of the atmosphere and ocean in theNCEP1 reanalysis. He adjusted the NCEP1 surface heat flux south of 30S to achieve balance inthe surface energy budget over the ocean; the resulting ocean heat transports were consistentwith independent estimates.Trenberth et al. (2000a) calculated surface fluxes from the ERA15 and NCEP1reanalyses using the residual method. A comparison of these derived surface fluxes with thesurface fluxes calculated directly in the reanalyses and with the UWM COADS-based estimatesfound that all the estimates showed the same broad scale features. The reanalyses' surfacefluxes agreed very well with the COADS estimates over the northern extratropical oceans, butall three were biased and were not suitable for determining ocean heat transports. The paperfound "…no useful common signal in the tropics". The derived surface fluxes from the residualmethod were found to produce more reasonable ocean heat transports. The results implied that"…clouds are a primary source of problems in the models' fluxes, both at the surface and theTOA".Trenberth et al. (2000b) compared troposphere temperatures measured by a satellitemicrowave sounder (MSU) to analyzed temperatures from the ERA15 and NCEP1 reanalyses.The NCEP and MSU temperatures agree fairly well. The ERA reanalyses has twodiscontinuities in the tropics in late 1986 and early 1989 and also displays "... further spuriousinterannual variability", indicating too much month to month tropical variability in the ERA15reanalysis and reflecting at least partly problems with biases in satellite radiances.Curry et al. (1999) compared satellite-derived surface fluxes to ERA15 over the tropicalocean for TOGA COARE. The satellite-derived stress showed short-lived, high-wind eventsthat ERA15 did not. They also found that ERA15 surface net short wave flux exceeded thesatellite-derived estimate during cloudy periods and was less during clear conditions. Dailyvalues of latent heat flux and wind stress were substantially correlated; daily values of surfaceradiative fluxes had low correlations.Wild (2000) compared radiative fluxes from the NCEP1, ERA15 and GEOS1reanalyses to collocated observations of surface radiation over land (including island stations)from the Global Energy Balance Archive and satellite observations of top of the atmosphere(TOA) radiation from the Earth Radiation Budget Experiment. NCEP1 and GEOS1overestimate the downward solar radiation at the surface due to a lack of absorption in theatmosphere, especially at low latitudes and even in cloud-free regions; ERA15 shows goodagreement with observations. All the reanalyses underestimate aerosol absorption and showlittle difference in vertically integrated water vapour. NCEP1’s excessive downward solarradiation is offset by too high surface albedo. Wild et al. (1998) found that ERA15underestimated the downward long wave flux at the surface by 5-10 Wm-2 compared to surfaceobservations.11 - Evaluation of Flux Products 206 11.4 Reanalysis products(11.4.3 Polar Regions)Report of the Working Group on Air Sea Fluxes June, 200011.4.3 Polar regionsAt the 2nd International Conference on Reanalyses in 1999, a number of papersidentified shortcomings in the reanalyses in the polar regions, including overly simplisticrepresentations of sea-ice, albedo parameterisation, treatment of snow cover and clouds in polarregions. The shortage of data over Antarctica in the earlier years of the reanalyses compromisesthe reliability of trends or changes in the reanalyses. Data assimilation systems often haveproblems assimilating isolated observations, since drifts in the "first guess" forecast in theabsence of data can cause isolated observations to be rejected.In NCEP1 summertime precipitation in the Arctic is unrealistic, there is insufficientcloud cover, solar radiative fluxes are too large and distortions in the moisture fields are evident.In ERA15 there is a marked two-metre temperature bias and wrong topographic heights overGreenland and Antarctica, with particular consequences notable as a result of an error in theelevation of the Vostok station. In ERA40 a full range of ice concentrations will be representedand perhaps variable ice thickness. An improved blended ice/sea surface temperature field willbe used, sea-ice thermal characteristics will be better treated, there will be a refined treatment ofsnow cover and much more accurate topographic data sets will be used together with the innateadvantage of higher resolution in representing topography. NCEP2 corrected the valley snowproblem and other mistakes in NCEP1, has more recent physical parameterisations than NCEP1and has a better treatment of soil moisture that uses observed precipitation (Kanamitsu et al. ,2000).11.4.4 Ocean surface energy balanceDa Silva et al. (1994) (University of Wisconsin at Milwaukee (UWM)) carefullycalculated oceanic heat fluxes from several decades of carefully corrected COADS(Comprehensive Ocean-Atmosphere Data Set) ship observations. The result did not produce areasonable global heat balance, reflecting at least in part the scarcity of COADS data over muchof the ocean outside the mid-latitude Northern Hemisphere. Other COADS-based air-sea fluxestimates, such as the climatology calculated by the Southampton Oceanography Centre (Joseyet al., 1998, 1999), display similar biases. To obtain a reasonable heat balance, da Silva et al.(1994) mathematically tuned the fluxes, effectively increasing the evaporation by 15% anddecreasing the net short wave (NSW) by approximately 7% globally. They changed thesensible heat and net long wave (NLW) much less. The tuning was applied uniformly over theglobe and not predominately where ship reports are least abundant.Table 11.4.1: Global mean ocean surface energy balance in Wm-2 estimated from COADS databy da Silva et al. (1994) (without tuning), from the four reanalyses and from satellite-basedestimates (SRB)UWM ERA15 GEOS1 NCEP1 NCEP2 SRBSensible heat 10.1 9.8 10.5 10.9 5.4Latent heat 88 103.4 79.5 92.7 104Net short-wave 170.4 160.5 197.8 165.9 167.2 173.4Net long-wave 49.2 50.5 68.0 56.4 50.6 41.9Net heat flux 23.3 -3.7 40.0 5.8 7.2Table 11.4.1 shows global mean components of the ocean surface energy balance forthe four reanalyses for 1981-92. It also displays da Silva et al. (1994)'s untuned air-sea fluxes11 - Evaluation of Flux Products 207 11.4 Reanalysis products(11.4.4 Ocean Energy Balance)Report of the Working Group on Air Sea Fluxes June, 2000averaged over 1981-92 (UWM) and satellite-based estimates of surface NSW by Darnell et al.(1992) and net long-wave radiation (NLW) by Gupta et al. (1992) averaged over July 1983-June1991. These satellite-based estimates from the Langley Surface Radiation Budget (SRB)Project (see section 11.3.3C) are derived from satellite observations of TOA radiation,International Satellite Cloud Climatology Project (ISCCP) cloud estimates and radiative transfercodes.The ERA15 fluxes shown are from twice-daily 12-24 hr forecasts; GEOS1, NCEP1 andNCEP2 fluxes are from the four times daily 0-6 hr forecasts used as the first guess orbackground field in the analysis cycle. The ERA15 hydrological cycle intensifies ("spins up")between the 0-6 hr and 12-24 hr forecasts; the global hydrological cycle and surface energybudget are in better balance in the 12-24 hr ERA15 forecasts than in the 0-6 hr forecasts. Thesystem used in the NCEP1 reanalysis appears to have less global "spin-up" than ERA15.ECMWF recommends the use of 12-24 hr fluxes from ERA15.ERA15's and NCEP2’s global evaporation disagree the most with the untuned UWMestimate, but agree the best with the tuned estimate; NCEP2 has the least sensible heat flux. ForNSW, the UWM untuned estimate displays the best agreement with the satellite estimate;GEOS1 displays considerable higher radiative fluxes than the other estimates. NCEP1, NCEP2and ERA15 are nearly in balance over the global ocean; GEOS1 and untuned UWM produce asubstantial net heat flux (NHF) into the ocean.Kiehl and Trenberth (1997) found that climatological estimates of global mean surfaceNSW and NLW varied by 20 Wm-2. Estimates of global mean surface sensible and latent heatvaried by 11 Wm-2. Section 11.3.3 implies that the SRB surface NSW may have a positive bias.Satellite-based estimates of surface NLW display more qualitative disagreement with each otherthan do satellite-based estimates of surface NSW (White, 1996a). Gupta et al. (1992)’s globalmean estimate for NLW is lower than most of the estimates. The global mean net surfaceradiation implied by Darnell et al. (1992)’s and Gupta (1992)’s estimates is larger than mostestimates examined by Kiehl and Trenberth (1997) and would require substantially highersensible and/or latent heat fluxes for balance than those found in UWM and the reanalyses.11.4.5 EvaporationFigure 11.4.1 displays the zonal mean latent heat flux over the ocean averaged over1981-92. Both the original UWM and the tuned UWM estimates are shown. The GEOS1reanalysis has the lowest evaporation of all, while ERA15 shows good agreement with the tunedUWM estimate and NCEP1 lies between the original and tuned UWM estimate. NCEP2 has thehighest evaporation in the subtropics. The large differences between the original and tunedUWM estimates indicate a significant uncertainty in the UWM estimate (and in COADS-basedestimates in general). Significant differences between UWM and the reanalyses can be seennear the North Pole. The reanalyses show more evaporation in late spring and early summerand less the rest of the year. In the polar regions ship reports are more abundant during summerthan winter, perhaps biasing the UWM estimates. The reanalysis estimates over the oceans usedhere include fluxes over sea ice as well as over the open ocean.Figure 11.4.2(a) shows the geographical distribution of the original UWM estimate oflatent heat flux. ERA15 (Figure 11.4.2(b)) shows more evaporation nearly everywhere, withmaximum differences in the northern subtropical oceans and slightly south of the equator,particularly in the southern winter, in the mid-Pacific and western Atlantic. All the reanalysesevaporate less than UWM in the Far East equatorial Pacific. GEOS1 (Figure 11.4.2(c)) has lessevaporation over the Gulf Stream and Kuroshio, especially in the fall and early winter, and inthe eastern tropical Pacific. All four reanalyses display more evaporation than UWM near 60S.NCEP1 (Figure 11.4.2(d)) has more evaporation in the subtropics but generally less along theequator. Like ERA15, NCEP2 (Figure 11.4.2(e)) shows more evaporation than UWM nearly11 - Evaluation of Flux Products 208 11.4 Reanalysis products(11.4.5 Evaporation)Report of the Working Group on Air Sea Fluxes June, 2000everywhere. ERA15, NCEP1 and NCEP2 appear to have a more tightly defined Gulf Streamthan UWM, perhaps reflecting differences in spatial resolution and smoothing.Figure 11.4.3 shows the temporal correlation of UWM's monthly mean evaporationwith evaporation from NCEP1 for the years 1981-92. Correlations are highest where shipreports (Figure 11.4.4) are most abundant (such as over the Northern Hemisphere oceansbetween 20 and 60N, and off the western coast of Africa). Correlations are near zero ornegative near the poles. Low correlations tend to occur in regions of few ship reports, but canalso occur in regions of low variability such as the South Atlantic.Figure 11.4.1 Zonal mean latent heat flux over the oceans for 1981-92 in Wm-2.Figure 11.4.5 compares the zonal averaged correlations with UWM estimates ofevaporation for all four reanalyses for monthly mean values for 1981-92 (Figure 11.4.5(a)), forthe monthly mean annual cycles averaged over 1981-92 (Figure 11.4.5(b)) and for monthlyanomalies from their own climatologies for 1981-92 (Figure 11.4.5(c)). The reanalyses allshow very similar levels and patterns of agreement with UWM. ERA15’s climatology showsthe best correlation with the UWM estimate, while NCEP1 and NCEP2’s anomalies agree morewith UWM than do ERA15 or GEOS1. GEOS1 shows somewhat less agreement with UWMthan the other three reanalyses. In Figure 11.4.5(b) correlations are much higher in the lowerlatitudes of the Southern Hemisphere than in Figure 11.4.5(a) or Figure 11.4.5(c), exceeding 0.6from 65N to 40S. Correlations of anomalies in Figure 11.4.5(c) exceed 0.6 only from 25N to55N. NCEP2’s correlations with UWM are slightly higher than NCEP1 for the climatologicalannual cycle and slightly lower than NCEP1 for anomalies.11 - Evaluation of Flux Products 209 11.4 Reanalysis products(11.4.5 Evaporation)Report of the Working Group on Air Sea Fluxes June, 2000Fig.11.4.2(a).Fig.11.4.2(b).Figure 11.4.2: (a) Latent heat flux from the original COADS estimate (UWM) by da Silva etal. (1994) averaged over 1981-92. Contour interval 20 Wm-2,negativevalues shaded(b) Difference in latent heat flux ERA15 minus UWM averaged over 1981-92.Contour interval 10 Wm-2, zero omitted, negative values shaded.(c) As (b) but for GEOS1 minus UWM.(d) As (b) but for NCEP1 minus UWM.(e) As (b) but for NCEP2 minus UWM.11 - Evaluation of Flux Products 210 11.4 Reanalysis products(11.4.5 Evaporation)Report of the Working Group on Air Sea Fluxes June, 2000Fig.11.4.2(c).Fig.11.4.2(d).Fig.11.4.2(e).11 - Evaluation of Flux Products 211 11.4 Reanalysis products(11.4.5 Evaporation)Report of the Working Group on Air Sea Fluxes June 2000The reanalyses include other sources of data over the ocean than ship reports and caninterpolate and extrapolate other sources of data from land, from other levels in the atmosphereand from previous analyses. Operational forecasts display considerable skill in the SouthernHemisphere, indicating that modern data assimilation systems produce accurate daily analysesof the Southern Hemisphere. Anomalies in evaporation from the four reanalyses correlate wellwith each other over the oceans except near the poles and the equator. The differences incorrelations between COADS data rich and data poor regions evident in Figure 11.4.3 is notnearly so evident in correlations of the reanalyses with each other or with independent estimatesof fluxes and precipitation based on satellite data (see below). This suggests that the asymmetryin correlation is due to the relative lack of ship observations outside 20-60N and suggests thatthe usefulness of COADS in defining inter annual variability may be largely limited to theNorthern Hemisphere mid-latitudes and a few other regions. Figure 11.4.5(b) implies, however,that COADS estimates of evaporation are useful in defining the climatological annual cycle inthe lower latitudes of the Southern Hemisphere.Figure 11.4.3 Correlation of monthly mean evaporation from UWM and from NCEP1 for1981-92. Contours 0, .4, .6, .8, .9, and .95. Values over 0.6 are shaded.Figure 11.4.4 Average number of ship reports available to NCEP1/month/2.5o box for 1981-92. A nine-point smoother was applied to the field. Values over 10 are shaded.11 - Evaluation of Flux Products 212 11.4 Reanalysis products(11.4.5 Evaporation)Report of the Working Group on Air Sea Fluxes June, 2000Figure 11.4.5(a).Zonal mean correlation betweenthe reanalyses and UWM ofmean latent heat flux over theoceans for individual monthsduring the period 1981-92.Figure 11.4.5(b).As in (a), but for climatologicalmonthly means averaged over1981-92.Figure 11.4.5(c).As in (a), but for monthly meananomalies from each estimate'sclimatology for 1981-92.Figure 11.4.6 shows the root-mean-square (RMS) differences between the reanalysesand UWM for monthly mean values (Figure 11.4.6(a)), for the monthly mean annual cycleaveraged over 1981-92 (Figure 11.4.6(b)) and for monthly anomalies from the climatology(Figure 11.4.6(c)). The RMS differences in Figure 11.4.6(a) exceed 20 Wm-2 except between11 - Evaluation of Flux Products 213 11.4 Reanalysis products(11.4.5 Evaporation)Report of the Working Group on Air Sea Fluxes June 200030 and 60N where ship reports are most abundant and near Antarctica where evaporation is low.NCEP2 has the largest RMS differences from UWM, reflecting the largest month-to-monthvariability (nearly 30% more than UWM between 20 and 60N) (see Figure 11.4.7 below).GEOS1’s anomalies are the least different from UWM estimates, reflecting lower month–to-month variability than the other reanalyses, while NCEP1’s annual cycle generally is leastdifferent from UWM. GEOS1 has the weakest annual cycle and the weakest anomalies of thefour reanalyses, and a weaker annual cycle than UWM (10% weaker between 20 and 60N).ERA15, NCEP1 and NCEP2 generally have a stronger annual cycle than UWM and theiranomalies tend to be larger than UWM in the Northern Hemisphere mid-latitudes. Note that thelargest differences in the anomalies occur in the Southern Hemisphere mid-latitudes and aretwice as large as differences in the Northern Hemisphere mid-latitudes.Figure 11.4.6a.Zonal mean root-mean square(RMS) difference in monthlymean latent heat flux over theoceans between the reanalysesand UWM for 1981-92 in Wm-2.Figure 11.4.6b)As in (a), but for climatologicalmonthly means averaged over1981-92.Figure 11.4.6c. As in a), butfor monthly mean anomaliesfrom each estimate's 1981-92climatology.11 - Evaluation of Flux Products 214 11.4 Reanalysis products(11.4.5 Evaporation)Report of the Working Group on Air Sea Fluxes June, 2000Figure 11.4.7(a) compares the standard deviation averaged over 20-60N of monthlyevaporation from UWM and the reanalyses for the period 1981-92. NCEP2 has the largestmonth-to-month variability while GEOS1 has the lowest variability of the reanalyses. TheRMS difference averaged over 20-60N of the reanalyses from UWM is shown in Figure11.4.7(b). By this measure NCEP2 is most different from UWM and GEOS1’s anomalies areclosest to UWM. However, if one normalises the RMS difference of a reanalysis from UWMby dividing it by the standard deviation of evaporation in that reanalysis, as is shown in Figure11.4.7(c), GEOS1 now appears the most different from UWM. NCEP1 shows the leastnormalised difference from UWM.Figure 11.4.7(a)Standard deviation averagedover 20-60N of monthly meanlatent heat release over theocean in Wm-2. Full valuescalculated from monthly meansfor 1981-92, annual fromclimatological monthly meanannual cycles averaged over1981-92, and anomalies fromdepartures of monthly meansfrom the climatological annualcycle.Figure 11.4.7(b)RMS difference averaged over20-60N of monthly mean latentheat in the reanalyses fromUWM in Wm-2.Figure 11.4.7(c)Ratio of the RMS differencefrom UWM to the standarddeviation for each of thereanalyses.11 - Evaluation of Flux Products 215 11.4 Reanalysis products(11.4.5 Evaporation)Report of the Working Group on Air Sea Fluxes June 2000Figures 11.4.8(a) and 11.4.8(b) compare anomalies in evaporation from the fourreanalyses over the Gulf Stream (a data-rich area) and the central equatorial Pacific (a data-poorarea). Figure 11.4.8(a) shows that anomalies in the four reanalyses show very good agreementwith each other over the Gulf Stream, even though GEOS1 (Figure 11.4.2(c)) has less meanevaporation there than the other reanalyses (Figures 11.4.2 (b), (d), (e)). Over the centralequatorial Pacific anomalies from the four reanalyses (Figure 11.4.8(b)) show less agreementthan over the Gulf Stream. ERA15 displays significantly lower evaporation in Figure 11.4.8(b)after 1986 than before, a pattern not evident in the other reanalyses. A similar discontinuity hasbeen observed in other fields in the ERA15 reanalysis (Stendel and Arpe, 1997). This mayreflect an interaction between the ERA15 analysis/forecast system and TOVS radiances inresponse to a sudden, large change in the MSU channels in November 1986 (Fiorino, 2000).ERA15 was the only reanalysis that used satellite radiances rather than temperature soundingsderived from satellite radiances.Figure 11.4.8 (a): Time series of latent heat fluxanomalies (Wm-2) from the fourreanalyses for 1981-92 for aGulf Stream Region: 35-40N,65-60W. Figure 11.4.8 (b):As (a) but for the centralequatorial Pacific: 2.5S-2.5N,177.5E-177.5W.11.4.6 Sensible heat fluxFigure 11.4.9 compares the zonal mean sensible heat flux over the ocean from UWMand the four reanalyses for the years 1981-92. The reanalyses are quite different from UWMover the poles, but agree with UWM fairly well in the best-sampled region between 30 and 60N.Near the North Pole UWM shows a substantial upward sensible heat flux except in summerwhile the reanalyses show downward fluxes most of the year. NCEP1 has the strongest sensible11 - Evaluation of Flux Products 216 11.4 Reanalysis products(11.4.6 Sensible Heat)Report of the Working Group on Air Sea Fluxes June, 2000heat flux. ERA15, GEOS1 and NCEP1 have more sensible heat flux than UWM near 60S,particularly in winter, and less than UWM nearer Antarctica where GEOS-1 and NCEP1 showsensible heat fluxes into the ocean. NCEP2 has the least upward sensible heat flux and has thestrongest downward flux in higher latitudes.Figure 11.4.9. Zonal meansensible heat flux over theoceans for 1981-92 in Wm-2.The 12-year mean regional distribution of sensible heat flux from UWM is shown inFigure 11.4.10a. ERA15 (Figure 11.4.10b) and especially GEOS1 (Figure 11.4.10c) have lessupward sensible heat flux than UWM over the Kuroshio and Gulf Stream (especially in late falland winter), where NCEP1 (Figure11.4.10d) has more than UWM as well as more upward fluxin the subtropical oceans. NCEP1 has somewhat more sensible heat flux into the ocean in thenorthern mid-latitudes in summer than UWM. ERA15, GEOS1 and NCEP1 exceed UWMestimates in the eastern subtropical oceans. Large differences appear near Antarctica and in theArctic Ocean. NCEP2 (Figure 11.4.10e) has more upward sensible heat flux than UWM overthe Gulf Stream and near Japan, but less than UWM elsewhere in the North Pacific and in theSouthern Hemisphere mid-latitudes.Zonally averaged correlations of sensible heat from the reanalyses with UWM estimates(not shown) display similar patterns and values to Figure 11.4.5. The reanalyses display lessagreement with UWM in the tropics than for evaporation, reflecting small values of sensibleheat flux in the tropics. GEOS1 displays less agreement with UWM than the other reanalysesdo. ERA15 and NCEP2 tend to show the best agreement with UWM, slightly better thanNCEP1.Figure 11.4.11 shows the root-mean-square (RMS) differences between the reanalysesand UWM for monthly mean values (Figure 11.4.11a) and for monthly anomalies from theclimatology (Figure 11.4.11b). ERA15 generally shows the smallest differences from UWM.NCEP2 has the largest RMS differences, reflecting the largest month-to-month variability.Differences in the climatologies between the reanalyses and UWM are less than 10 Wm-2between 50N and 50S. The largest differences between UWM and the reanalyses occur near 60oin both hemispheres, where sensible heat fluxes exhibit considerable variability. However,sensible heat fluxes also exhibit more variability between 30 and 45N than elsewhere outsidethe polar regions and display relatively small differences there, indicating, as do thecorrelations, better agreement between UWM and the reanalyses there than elsewhere. GEOS1shows the least variability in sensible heat flux of the five estimates; NCEP1 and NCEP2display more variability than the UWM estimate between 20 and 60N. ERA15’s climatologicalannual cycle displays the least normalised RMS difference from UWM between 20 and 60N,where NCEP1’s anomalies have the smallest normalised RMS difference from UWManomalies. NCEP2 displays slightly larger normalised RMS differences than NCEP1 or ERA;GEOS1 has the largest.11 - Evaluation of Flux Products 217 11.4 Reanalysis products(11.4.6 Sensible Heat)Report of the Working Group on Air Sea Fluxes June 2000Figure11.4.10(a)Figure11.4.10(b)Figure11.4.10(c)11 - Evaluation of Flux Products 218 11.4 Reanalysis products(11.4.6 Sensible Heat)Report of the Working Group on Air Sea Fluxes June, 2000Figure11.4.10(d)Figure11.4.10(e)Figure 11.4.10: (a) Sensible heat flux (Wm-2) from UWM averaged over 1981-92, negativevalues shaded.(b) Difference in sensible heat flux ERA15 minus UWM. Contour interval5Wm-2, zero omitted, negative values shaded.(c) As (b) but for GEOS1 minus UWM.(d) As (b) but for NCEP minus UWM.(e) As (b) but for NCEP2 minus UWM.11.4.7 Net short-waveHere both UWM and reanalysis estimates of surface net short-wave (NSW) radiationare compared to Darnell et al. (1992)’s satellite-based (SRB) estimates of NSW. The ERA15reanalysis used a prognostic cloud parameterisation tuned to ISCCP clouds, the clouds used incalculating the SRB estimate of surface NSW. The NCEP reanalyses used a diagnostic cloud11 - Evaluation of Flux Products 219 11.4 Reanalysis products(11.4.7 Net Short Wave)Report of the Working Group on Air Sea Fluxes June 2000scheme tuned to Air Force nephanalyses, which produce lower cloud amounts than ISCCPestimates.It has been suggested that satellite estimates of surface NSW may be more reliable thanother global estimates of surface NSW, although satellite estimates of surface NSW can differsubstantially from point surface measurements of NSW (White, 1996a). Figure 11.4.12 showsthe zonal mean surface NSW over ocean for July 1983-June 1991 from the four reanalyses, theuntuned UWM estimate by da Silva et al. (1994) and SRB.Figure 11.4.11(a) RMS differences in monthlymean sensible heat flux over theoceans between the reanalysesand UWM for 1981-92 in Wm-2.Figure 11.4.11(b) As in a) butfor monthly mean anomaliesFigure 11.4.12 Zonal meansurface net short wave radiationover the oceans for 1981-92 inWm-2.11 - Evaluation of Flux Products 220 11.4 Reanalysis products(11.4.7 Net Short Wave)Report of the Working Group on Air Sea Fluxes June, 2000 Figure 11.4.13(a) shows the regional distribution of SRB NSW. GEOS1 (Figure11.4.13(d)) has considerable more NSW than SRB poleward of 30o and in the easternsubtropical oceans, but less in the western tropical Pacific. GEOS1 has very strong NSW in theeastern subtropical oceans where other estimates show relative minimums due to the presence oflow-level stratus clouds. NCEP1 (Figure 11.4.13(e)), NCEP2 (Figure 11.4.13(f)) and ERA15(Figure 11.4.13(c)) have less in the tropics and more in higher latitudes than SRB; NCEP2 andERA15 appear to have slightly larger biases than NCEP1 in the tropics and NCEP2 has a largerpositive bias in higher latitudes than NCEP1 or ERA15. UWM (Figure 11.4.13(b)) shows thebest agreement with SRB in the tropics, in spite of the simplicity of the UWM parameterisationof NSW. In higher latitudes UWM and the reanalyses tend to have more NSW in summer thanthe SRB estimate. The time-mean differences of the reanalyses’ TOA NSW from the EarthRadiation Budget Experiment (ERBE) satellite estimate are similar to their differences from theSRB estimate of surface NSW. Since satellites observe TOA NSW directly, this similarity lendscredence to the reliability of the SRB estimate of surface NSW.The time-mean NSW from ERA15 (Figure 11.4.14(c)) and GEOS1 (Figure 11.4.14(d))shows no evidence of the influence of low-level stratus clouds on the surface NSW in regionssuch as the ocean off Baja California in the Northern Hemisphere summer; NCEP1's NSW(Figure 11.4.14(e)) does show the influence of low-level stratus cloud. Over the rest of thePacific NCEP1 and ERA15 clearly underestimate surface NSW; GEOS1 overestimates surfaceNSW. NCEP2 (Figure 11.4.14(f)) displays less evidence of stratus clouds off Baja Californiathan NCEP1, but does display higher NSW than NCEP1 or ERA15 (although still less thanSRB) elsewhere in the Pacific. NCEP2 changed the parameterisation of low-level stratus cloudsto require a higher relative humidity threshold than in NCEP1. The UWM summertime surfaceNSW (Figure 11.4.14(b)) resembles SRB (Figure 11.4.14(a)) much more than the reanalyses do.NCEP1's surface NSW benefited from offsetting mistakes. Its ocean surface albedowas too high, and it had too much downward short wave radiation at the surface (Wild, 2000).Its cloud albedo was too high and it had fewer clouds than other reanalyses. NCEP2 correctedthe ocean surface albedo and had more cloudiness. Figure 11.4.15 displays the global meandownward surface short wave for January and July averaged over 8 years. ERA15 and NCEP2agree with the SRB estimate; NCEP1 is 10% high.The annual cycle in surface NSW (calculated over 1984-90) in the reanalyses is wellcorrelated with the SRB estimate (Figure 11.4.16(a)), exceeding 0.9 outside the tropics whereNSW has a small annual cycle. The anomaly correlations of the reanalyses with SRB (Figure11.4.16(b)) (calculated over July 1983-June 1991) are much lower with only UWM anomaliesshowing a correlation greater than 0.6 in the Northern Hemisphere. GEOS1's correlations arelower than the other reanalyses; NCEP2’s correlations with SRB are higher than GEOS1 butlower than ERA15 or NCEP1. UWM NSW anomalies agree the best with SRB anomalies fromthe North Pole to 20S, but show much less agreement south of 30S. Correlation of TOA NSWfrom the reanalyses with ERBE TOA NSW yields similar levels of agreement to Figure 11.4.16.The UWM estimates show the smallest RMS differences from SRB for the full monthlymeans (Figure 11.4.17(a)) and the climatologies and between 15N and 50N for the anomalies(Figure 11.4.17(b)). UWM is most different from SRB near the North Pole and for anomaliesin much of the Southern Hemisphere. In mid-latitudes GEOS1 shows the largest differencesfrom SRB in the full monthly means and in the climatologies, reflecting a larger bias and astronger annual cycle. NCEP1 tends to be closer to SRB than ERA15 and has less variabilitythan SRB. ERA15 has considerably more variability in monthly anomalies in the tropics thandoes SRB. NCEP2’s anomalies are the most different from SRB, reflecting a greater month-to-month variability than the other estimates; in mid-latitudes the normalised RMS difference ofNCEP2 anomalies from SRB anomalies is the least of the reanalyses. For most latitudes theRMS differences of the reanalyses from SRB are more than 20 Wm-2 for the full monthly meansand more than 10 Wm-2 for the anomalies.11 - Evaluation of Flux Products 221 11.4 Reanalysis products(11.4.7 Net Short Wave)Report of the Working Group on Air Sea Fluxes June 2000Figure11.4.13(a)Figure11.4.13(b)Figure11.4.13(c)11 - Evaluation of Flux Products 222 11.4 Reanalysis products(11.4.7 Net Short Wave)Report of the Working Group on Air Sea Fluxes June, 2000Figure11.4.13(d)Figure11.4.13(e)Figure11.4.13(f)11 - Evaluation of Flux Products 223 11.4 Reanalysis products(11.4.7 Net Short Wave)Report of the Working Group on Air Sea Fluxes June 2000Figure 11.4.13: (a) Surface NSW radiation from the SRB satellite estimate averaged over July1983-June 1991. Contour interval 20 Wm-2. Values over 220Wm-2 shaded.(b) Difference in surface NSW UWM minus SRB. Contour interval 20 Wm-2 ;zero omitted, negative values shaded.(c) As (b) but for ERA15-SRB.(d) As (b), but for GEOS minus SRB.(e) As (b) but for NCEP1 minus SRB.(f) As (b) but for NCEP2 minus SRB.Figure11.4.14(a)SRBFigure11.4.14(b)UWM/COADSFigure11.4.14(c)ERAFigure11.4.14(d)GEOSFigure11.4.14(e)NCEPFigure11.4.14(f)NCEP2Figure 11.4.14. Surface NSW radiation from different sources averaged over July 1983-1990.Contour interval 20 Wm-2. Values over 260 Wm-2 are shaded.11 - Evaluation of Flux Products 224 11.4 Reanalysis products(11.4.7 Net Short Wave)Report of the Working Group on Air Sea Fluxes June, 2000Figure 11.4.15 Global mean downward short wave radiation at the surface from SRB and fromERA, NCEP1 and NCEP2 reanalyses for Jan. 1984-91 and July 1983-90 in Wm-2.Figure 11.4.16 (a) Zonal meancorrelation of monthly meanclimatological surface NSWover the oceans of thereanalyses and UWM with SRBfor the annual cycle averagedover 1984-90.Figure 11.4.16 (b) Zonal meancorrelation of monthly meananomalies in NSW over theoceans of the reanalyses andUWM with SRB for July 1983-June 1991. The anomalies arefrom each estimate'sclimatology for 1984-90.11 - Evaluation of Flux Products 225 11.4 Reanalysis products(11.4.8 Net LongWave)Report of the Working Group on Air Sea Fluxes June 2000Figure 11.4.17 (a) RMSdifferences in monthly meansurface NSW over the oceans ofthe reanalyses and UWM fromSRB for July 1983-June 1991 inWm-2.Figure 11.4.17 (b) As (a) but foranomalies.11.4.8 Net long-waveThe zonal mean surface net long-wave (NLW) radiation over the ocean is shown inFigure 11.4.18. Figure 11.4.19(a) shows the regional distribution of the satellite estimate (SRB)of NLW (Gupta et al., 1992). The SRB estimate is lower than the other estimates. UWM(Figure 11.4.19(b)) agrees the best with SRB, showing, as do the reanalyses, the largestdifferences from SRB in the eastern subtropical oceans. GEOS1 (Figure 11.4.19(d)) shows thehighest NLW and the largest differences from SRB. ERA15 (Figure 11.4.19(c)) shows largerdifferences from SRB in the eastern subtropical oceans than NCEP1 (Figure 11.4.19(e)), but hassmaller differences elsewhere. NCEP2 (Figure 11.4.19(f)) is generally closer to the SRBestimate than NCEP1. The large differences of GEOS1, ERA15 and NCEP2 from the SRBestimate in the eastern subtropical oceans may reflect problems in their treatment of low-levelstratus clouds.Figure 11.4.20(a) displays the zonally averaged correlations of the mean annual cyclesin NLW averaged over 1984-1990 from UWM and the reanalyses with SRB over the oceans.Figure 11.4.20(b) displays the zonal mean correlations of the anomalies for July 1983-June1991 from the climatologies averaged over 1984-90. The correlations of the climatologies withSRB are less in mid-latitudes than for other components of the surface energy balance;NCEP2’s annual cycle shows the least agreement with SRB. The anomaly correlations are lessthan 0.6 at all latitudes for the reanalyses and nearly all latitudes for UWM. NCEP1’s NLWanomalies agree the best with SRB of the four reanalyses; GEOS1 displays the worstagreement.11 - Evaluation of Flux Products 226 11.4 Reanalysis products(11.4.8 Net LongWave)Report of the Working Group on Air Sea Fluxes June, 2000Figure 11.4.18Zonal mean surface NLWradiation over the oceans for1981-92 in Wm-2.RMS differences with the SRB estimate are shown in Figure 11.4.21(a) for the fullmonthly means and in Figure 11.4.21(b) for the monthly anomalies. GEOS1 is clearly the mostdifferent from the SRB estimate and has considerably more variability than the SRB estimateoutside the tropics. ERA15’s and UWM's mean annual cycles are different from SRB by about11 Wm-2, while NCEP2’s annual cycle is closer to SRB than NCEP1. NCEP1’s anomalies areclosest to SRB’s except in the northern mid-latitudes where UWM is less than 6 Wm-2 differentfrom SRB. The SRB estimate displays the most variability in the tropics and the least outsidethe tropics.Figure 11.4.19: (a) Surface NLW from SRB averaged over July 1983-June 1991. Contour(Next page:) interval 10 Wm-2. Values greater than 40 Wm-2 shaded.(b) Surface NLW UWM minus SRB. Contour interval 10 Wm-2; zero omitted,negative values shaded.(c) As (b) but for ERA minus SRB.(d) As (b) but for GEOS minus SRB.(e) As (b) but for NCEP1 minus SRB.(f) As (b) but for NCEP2 minus SRB.11 - Evaluation of Flux Products 227 11.4 Reanalysis products(11.4.8 Net LongWave)Report of the Working Group on Air Sea Fluxes June 2000Figure11.4.19(a)Figure11.4.19(b)Figure11.4.19(c)11 - Evaluation of Flux Products 228 11.4 Reanalysis products(11.4.8 Net LongWave)Report of the Working Group on Air Sea Fluxes June, 2000Figure11.4.19(d)Figure11.4.19(e)Figure11.4.19(f)11 - Evaluation of Flux Products 229 11.4 Reanalysis products(11.4.8 Net LongWave)Report of the Working Group on Air Sea Fluxes June 2000Figure 11.4.20 (a) Zonal meancorrelation of monthly meanclimatological surface NLWover the oceans of thereanalyses and UWM with SRBfor the annual cycle averagedover 1984-90.Figure 11.4.20 (b) As (a) but foranomalies for July 1983-June1991 from each estimate'sclimatology for 1984-90.Figure 11.4.21(a) RMSdifferences in monthly meansurface NLW over the ocean ofthe reanalyses and COADSfrom SRB for July 1983-June1991 in Wm-2.Figure 11.4.21(b) As (a) but foranomalies for July 1983-June1991 from each estimate'sclimatology for 1984-90.11 - Evaluation of Flux Products 230 11.4 Reanalysis products(11.4.9 Net Heat Flux)Report of the Working Group on Air Sea Fluxes June, 200011.4.9 Net heat fluxFigure 11.4.22 shows the zonal mean NHF into the ocean averaged over 1981-92. Theuntuned UWM estimate (da Silva et al., 1994) is predominately into the ocean; GEOS1 has aneven stronger downward net heat flux. The tuned UWM estimate, NCEP1, NCEP2 and ERA15show a more balanced distribution of fluxes into the ocean near the equator and fluxes out of theocean in the subtropics. The UWM estimates show much more NHF into the atmosphere nearthe North Pole. Near 45o in either hemisphere NCEP1 and ERA15 show NHF into the ocean, inbetter agreement with the untuned UWM estimate than the tuned. NCEP2 shows more NHFinto the ocean near 45S than ERA15, NCEP1 or UWM. NCEP1, NCEP2 and ERA15 showgood agreement with the untuned UWM estimate between 40N and 60N, where shipobservations are most abundant, but are close to the tuned COADS estimate in the tropics. Thereanalyses show more NHF into the oceans in the Southern Hemisphere mid-latitudes insummer and more NHF into the atmosphere near 60S in winter than the tuned UWM estimate.Da Silva and White (1995) found that replacing the NCEP1 estimate of surface net short-wavewith an estimate based on satellite data (Pinker et al., 1995) improved the time-mean globaloceanic heat balance.The tuned UWM estimate of the 12-year mean net heat flux, shown in Figure11.4.23(a), shows strong heat flux into the ocean in the eastern equatorial Pacific and intenseupward heat flux over the Kuroshio and Gulf Stream and south of Africa. The original UWMestimate (Figure 11.4.23(b)) shows broad areas where it is more than 40 Wm-2 different from thetuned estimate. All four reanalyses show large positive differences from the tuned UWMestimate south of Africa and in the Far East equatorial Pacific and Atlantic. ERA15 (Figure11.4.23(c)) has less downward/more upward net heat flux than UWM over much of the tropicaloceans; NCEP1 (Figure 11.4.23(e)) has less upward heat flux in the mid-latitudes. GEOS1(Figure 11.4.23(d)) has more downward/less upward net heat flux nearly everywhere. ERA15(Figure 11.4.23(c)) and GEOS1 (Figure 11.4.23(d)) show bigger differences in the easternsubtropical Pacific than NCEP1 (Figure 11.4.23(e)) does, perhaps reflecting differences in thetreatment of stratus clouds. NCEP2’s differences from UWM tuned (Figure 11.4.23(f))resemble NCEP1’s differences, but are larger in the eastern subtropical oceans where low-levelstratus clouds are important and between 30 and 60S.Figure 11.4.22. Zonal mean netheat flux into the oceanaveraged over 1981-92 fromCOADS and the fourreanalyses. Positive valuesdenote a flux from theatmosphere into the ocean.Figure 11.4.23: (a) Surface net heat flux into the ocean from the tuned UWM estimate(next page) averaged over 1981-92. Contour interval 25 Wm-2. Shaded areas indicate(negative) upward heat flux.(b) The effect of tuning on UWM (untuned minus tuned). Contour interval 20Wm-2; zero omitted, negative values shaded.(c) As (b) but for the difference in net heat flux for ERA minus UWM tuned.(d) As (c) but for GEOS minus UWM tuned.(e) As (c) but for NCEP1 minus UWM tuned.(f) As (c) but for NCEP2 minus UWM tuned.11 - Evaluation of Flux Products 231 11.4 Reanalysis products(11.4.9 Net Heat Flux)Report of the Working Group on Air Sea Fluxes June 2000Figure11.4.23(a)Figure11.4.23(b)Figure11.4.23(c)11 - Evaluation of Flux Products 232 11.4 Reanalysis products(11.4.9 Net Heat Flux)Report of the Working Group on Air Sea Fluxes June, 2000Figure11.4.23(d)Figure11.4.23(e)Figure11.4.23(f)11 - Evaluation of Flux Products 233 11.4 Reanalysis products(11.4.9 Net Heat Flux)Report of the Working Group on Air Sea Fluxes June 2000The zonal averaged correlations of the mean annual cycles in NHF of the reanalyseswith the tuned UWM estimate resemble the correlations of the mean annual cycles in NSW(Figure 11.4.16(a)). Very high correlations near 1.0 are seen in mid-latitudes, reflecting thegreat variation with season of the net heat flux outside the tropics; near the equator thecorrelations drop to 0.3(GEOS1) to 0.6 (ERA15). The correlations of anomalies in NHF arevery similar to the correlations for latent heat (Figure 11.4.5(c)), with correlations above .6 onlyin the northern mid-latitudes. Correlations for NHF are virtually the same for NCEP1, NCEP2and ERA15 and a little lower for GEOS1. ERA15's annual cycle in these fields tends to agreeslightly more with UWM than NCEP1's or NCEP2’s; NCEP1's and NCEP2’s anomalies tend toagree slightly more with UWM than ERA15's.Figure 11.4.24 (a): RMSdifferences in monthly meanNHF over the oceans of thereanalyses from tuned UWM for1981-92. (left bottom)Figure 11.4.24 (b) As in 24a,but for the climatological annualcycle averaged over 1981-92.(above)Figure 11.4.24 (c) As in 24a, butfor anomalies from theclimatological annual cycle.The climatological annual cycles in NHF in the reanalyses have RMS differences fromthe tuned UWM estimate of more than 20 Wm-2 at all latitudes (Figure 11.4.24(b)) and the fullmonthly means are more than 30 Wm-2 different (Figure 11.4.24(a)) at all latitudes. Anomaliesin NHF (Figure24(c)) are more than 15 Wm-2 different at all latitudes. GEOS1’s climatology is11 - Evaluation of Flux Products 234 11.4 Reanalysis products(11.4.10 Forcing Ocean Models)Report of the Working Group on Air Sea Fluxes June, 2000much more different from the tuned estimate than the other reanalyses; NCEP2’s climatology ismore different from UWM than NCEP1 or ERA15. Anomalies from all four reanalyses showvery similar RMS differences from UWM; NCEP2’s anomalies are slightly more different fromUWM than the other reanalyses and display more month-to-month variability. GEOS1 has thelargest normalised RMS differences from UWM between 20 and 60N.11.4.10 Evaluating the NCEP1 reanalysis by forcing an ocean modelFluxes from NCEP1 and ERA15 reanalyses are now widely used to force ocean circulationmodels at global and basin scale. At present, the mean biases in model solutions make itdifficult to assess the quality of the climatological mean of these fluxes. However, modelsolutions have shown in several cases a seasonal to interannual variability which comparesqualitatively well with ocean observations. For example, Figure 11.4.25 shows variations ofthe maximum mixed layer depth in March in the Labrador Sea during the period 1958-1997,simulated with a 1° primitive equation ocean model forced with fluxes from NCEP1 (fromKnochel, 1999). This parameter shows a significant decadal variability. This variabilityappears consistent with noticeable oceanic events observed in this region. These include weakconvection periods between 1962 and 1972 (related to the Great Salinity Anomaly, GSA,(Dickson et al., 1988)) and between 1976 to 1982 (related to the lesser GSA (Lazier, 1997)) andparticularly active convection periods in 1972-76 (McCartney et al., 1997), and in the earlynineties (1990- 1995), as described by Dickson et al. (1996).This indicates that the low frequency variability found in the reanalysis fluxes couldmake sense and should be useful to understand climate change over recent times (the last 40-50years). These results, which however remain qualitative, indicate that the inter-annualvariability described in the reanalysis products make sense.Figure 11.4.25 Variations of themaximum mixed layer depth inMarch in the Labrador Seaduring the period 1958-1997,simulated with a 1° primitiveequation ocean model forcedwith fluxes from NCEP1 (fromKnochel, 1999).1955 1960 1965 1970 1975 1980 1985 1990 1995 200050010001500200025003000depth in meteryearsDYNA97 NCEP EP : Labrador Sea Mixed Layer De th maximum in March11.4.11 WindsA. COMPARISON OF THE NCEP WINDS WITH INSTRUMENTAL MEASUREMENTS AT OWSIn order to compare surface winds from the NCEP1 Reanalysis with instrumentalmeasurements for the 1980s and 1990s, Zolina and Gulev (1998) used observations at four mid-latitude ocean weather ships (OWS) in the North Atlantic: C, L, M, and R. These observationsare individual three hourly anemometer observations taken at known anemometer heights byprofessional observers. Additionally, wind data from the SECTIONS Programme (Gulev,1994) were used. These data were collected by six sister-ships that also operated at OWS Cfrom 1975 to 1990. All wind measurements from OWS and SECTIONS were adjusted to 10-m11 - Evaluation of Flux Products 235 11.4 Reanalysis products(11.4.11 Winds)Report of the Working Group on Air Sea Fluxes June 2000height and neutral stability. For the comparison NCEP1 data at the nearest grid point of theGaussian grid to the measurement location were used.In general, instrumental wind speed in the North Atlantic mid-latitudes has been foundto be somewhat higher than NCEP1 winds. Annual mean intercepts for the OWS data varywithin the range of 0.1 to 0.35 ms-1; the largest deviations of about 0.7 ms-1 are obtained for theSECTIONS data. Note that OWS data were assimilated in the NCEP1 Reanalysis; therefore,the intercomparison is not independent. However, only 25% of the SECTIONS measurementswere included in the COADS archives and assimilated in the NCEP1 reanalysis. This explainsthe larger differences obtained for this data sub-set.The largest overestimation was observed for the winter months for the SECTIONS dataand all OWS, except for the OWS L. In summer the instrumental measurements of the windswere less than the reanalysis winds by 0.1 to 0.3 ms-1. Estimation of the regression slopes gavevalues varying from 0.88 to 0.97, demonstrating a general tendency of overestimating lowwinds and underestimating moderate and high winds in NCEP1. Since the assimilation of theCOADS winds in NCEP1 should provide in general an opposite tendency due to the usage ofWMO1100 scale in COADS, this bias can be explained by the impact of the NCEP1 model onthe wind fields. Separate analysis of the wind components shows that zonal components in thereanalysed products and instrumental measurements are in better agreement than meridionalcomponents.B. ERA15 AND NCEP1 REANALYSIS TROPICAL WIND EVALUATIONSMonthly mean surface wind products from the NCEP1 and ERA15 reanalysis projectsover the tropical regions have been compared to specialised products from Florida StateUniversity (FSU) and Centre ORSTOM/IFREMER (see section 9.3.1). Monthly mean fieldsfrom the reanalyses for the common period 1979-1993 were first constructed. For comparisonpurposes, the daily or 4-time per day NCEP1 and ERA15 10-m winds were first adjusted to 20-m height (nominal reference height for FSU products) using the BVW parameterisation scheme,and monthly mean fields of pseudo-stress were produced. NCEP1 and ERA15 products werethen interpolated to the same spatial grids as the specialised products to allow directcomparisons. Comparisons of the respective climatologies of the pseudo-stress fields, thedivergence and curl as well as the variations of these fields resulted in a number of conclusionsand Figures 11.4.26 and 11.4.27.Comparisons of the climatological reanalysis products to the FSU and ORSTOMclimatologies indicate the Indian Ocean is much more comparable than the Pacific or Atlantic.During the Indian Ocean southwest monsoon, the ERA15 winds are strongest in the northernbasins, followed by FSU and then NCEP1. The NCEP1 has the strongest trades in the southernthird of all tropical regions. This strength is in sympathy with the strength of the trades (i.e.even stronger during trade maximum during June–December). Model spin-up problems nearisland stations, particularly in ERA15, results in relatively large gradients in the pseudo-stressvector fields. Not only does this impact the pseudo-stress vectors, but also the kinematic fields– especially the curl where numerous large bipolar patterns are co-located adjacent to manyislands. The NCEP1 Inter-tropical Convergence Zones (ITCZ) in the eastern Pacific andwestern Atlantic are poorly resolved during July-December, i.e. when the southerly trades aremaximum and the ITCZ is also strongest and at its maximal northern location. The NCEP1ITCZ is too broad (north south) and too weak. This seems to be linked to the presence ofanomalous weak cross-equatorial southerlies. Not only are the NCEP1 southerlies weak, but inthe equatorial region (0° to 5°S), particularly during January-June, the easterlies are notablyweaker (up to 5 m s-1) than the other analyses. Comparisons of the pseudo-stress anomalieshighlight the outlier nature of the NCEP1 fields in the tropics. These comparative results varyover time. For example, Pacific east west winds along the equator for all three analysescorrelate much better during ENSO events than at any other time. Additionally, NCEP1 windscorrelate better with FSU during 1988-1991 and the ERA equatorial easterlies are stronger thanFSU only for the 1979-1986 period, but afterwards, the FSU easterlies are stronger.11 - Evaluation of Flux Products 236 11.4 Reanalysis products(11.4.11 Winds)Report of the Working Group on Air Sea Fluxes June 2000Figure 11.4.26 Zonal mean climatological pseudo-stress components for FSU, ORSTOM (ATL)and (height adjusted) ERA15, NCEP1 reanalysis products: (a) east-west component for theIndian Ocean region (30°E-120°E); (b) east-west component for the Pacific Ocean region(120°E-70°W); (c) east-west component for the Atlantic Ocean region (60°W-15°E); (d) north-south component for the Indian Ocean; (e) north-south component for the Pacific Ocean region;and (f) north-south component for the Atlantic Ocean region.Figure 11.4.27 Difference (FSU-NCEP1) of FSU and (height adjusted) NCEP1 climatologicalpseudo-stress vectors for the period 1979-1993 with colours indicating magnitude of thedifference of the wind divergence fields. Areas with no vectors are areas where FSU andreanalyses do not overlap.11 - Evaluation of Flux Products 237 11.4 Reanalysis products(11.4.12 Zonal Surface Stress)Report of the Working Group on Air Sea Fluxes June 2000Other approaches to flux product evaluation include the use of ocean models andindependent validation. For example, recent work by Hackert et al. (2000) compares oceanmodel simulations for the 1997-1998 ENSO event forced with NCEP reanalysis, satellite-basedwinds, and other wind products. They found that the satellite wind products perform bestoverall, but add that each wind product has its own respective strengths and weakness withregards to simulating observed SST, subsurface temperatures, and sea level anomalies.11.4.12 Zonal surface stressZonal surface stress is compared to the UWM COADS-based estimates. Zonallyaveraged values over the ocean (Figure 11.4.28(a)) are similar, except in the SouthernHemisphere mid-latitudes where the reanalyses differ in magnitude from each other, but are allstronger than UWM in a region of few ship reports. UWM's weak values near 50S may reflectinterpolation from lower latitudes in the Southern Hemisphere where the zonal stress is less.ERA15 and NCEP2 tend to have stronger maxima than the others, GEOS1 tends to be theweakest.Figure 11.4.28(b) compares zonal surface stress along the equator from the fourreanalyses and from UWM averaged over 1981-92. ERA15 appears somewhat stronger thanUWM while NCEP1 is too weak in the Pacific. Comparisons to observations of surface windsin the east Pacific also indicate that NCEP1 near-surface winds are too weak (White, 1996b).GEOS1 and NCEP2’s zonal surface stresses are closer in magnitude to UWM in the eastPacific, but shift the maximum value to the east.Figure 11.4.28 (a) Zonallyaveraged zonal surface stressover the ocean averaged over1981-92 from UWM and thereanalyses (right) in Nm-2.Figure 11.4.28 (b) As (a) but forzonal surface stress along theequator.1 - Evaluation of Flux Products 238 11.4 Reanalysis products(11.4.12 Zonal Surface Stress)Report of the Working Group on Air Sea Fluxes June 2000Figure 11.4.29(a) displays the mean zonal surface stress from UWM for 1981-92.ERA15 (Figure 11.4.29(b)) displays stronger westerly stresses in mid-latitudes and strongereasterly stress in the tropics than UWM. GEOS1 (Figure 11.4.29(c)) displays weaker stressesthan UWM except south of 30S. NCEP1 (Figure 11.4.29(d)) displays the weakest easterlystress in the central equatorial Pacific, but stronger stresses than UWM elsewhere. NCEP2(Figure 11.4.29(e)) agrees better with UWM than NCEP1 does in the equatorial Pacific and hasthe strongest stresses in mid-latitudes.Figure11.4.29(a)Figure11.4.29(b)Figure 11.4.29: (a) Zonal surface stress from UWM for 1981-92. Contour interval 0.025 Nm-2,easterly stress shaded.(b) Difference in zonal surface stress ERA12 minus UWM. Contour interval0.01 Nm-2, zero omitted, negative (more easterly) values shaded.(c) As (b) but for GEOS minus UWM.(d) As (b) but for NCEP1 minus UWM(e) As (b) but for NCEP2 minus UWM.11 - Evaluation of Flux Products 239 11.4 Reanalysis products(11.4.12 Zonal Surface Stress)Report of the Working Group on Air Sea Fluxes June 2000Figure11.4.29(c)Figure11.4.29(d)Figure11.4.29(e)11 - Evaluation of Flux Products 240 11.4 Reanalysis products(11.4.12 Zonal Stress)Report of the Working Group on Air Sea Fluxes June 2000Figure 11.4.30(a) displays the zonal mean correlations of the annual cycles in zonalsurface stress in the reanalyses with the annual cycle in the UWM estimate from da Silva et al.(1994). The annual cycles are from monthly mean values averaged over 1981-92. All threereanalyses show correlations above 0.6 from 65N to 40S.The correlations of monthly mean anomalies from the climatologies are shown inFigure 11.4.30(b). The pattern is similar to that seen for latent heat flux (see Figure 11.4.5(c));however, correlations are somewhat higher in Figure 11.4.30(b), particularly in the tropics.Correlations for zonal surface stress are virtually the same for NCEP1 and ERA15 and slightlylower for GEOS1. ERA15's annual cycle agrees slightly more with UWM than NCEP1's in thetropics but not in mid-latitudes; NCEP1's anomalies agree slightly more with UWM thanERA15's except in the Southern Hemisphere mid-latitudes. NCEP2 displays slightly lessagreement with UWM than NCEP1, but more agreement than GEOS1. Anomalies from thereanalyses correlate well with each other over the oceans except near Antarctica; correlationsbetween the reanalyses are lower near the equator than in mid-latitudes.Figure 11.4.30(a) Zonal meancorrelations of monthly meanclimatological zonal surfacestress over the reanalyses andUWM for the monthly meanannual cycle averaged over1981-92.Figure 11.4.30(b) As (a) but foranomalies from each estimate'sclimatology for 1981-92.Zonal mean RMS differences between the four reanalyses’ zonal surface stress andUWM are shown in Figure 11.4.31(a) for the full monthly means and in Figure 11.4.31(b) forthe monthly mean anomalies. NCEP2 displays the largest RMS differences from UWM inhigher latitudes, reflecting at least in part the largest variability in both the annual cycle and inanomalies. GEOS1 shows less RMS difference from UWM than other reanalyses, reflecting atleast in part the lowest variability and the weakest stress of all the estimates. ERA15 andNCEP1 are more variable in the northern mid-latitudes than UWM. Between 20 and 60N,NCEP1 has the smallest normalised RMS differences from UWM and GEOS1 the largest.11 - Evaluation of Flux Products 241 11.4 Reanalysis products(11.4.13 Meridional Surface Stress)Report of the Working Group on Air Sea Fluxes June 2000Figure 11.4.31(a) RMS difference inmonthly mean zonal surface stress over theoceans between the reanalyses and UWMfor 1981-92 in Nm-2.Figure 11.4.31(b) As (a) but for anomaliesfrom each estimate's climatological annualcycle in Nm-2.11.4.13 Meridional surface stressThe zonal mean meridional surface stress over the ocean averaged over 1981-92 isshown in Figure 11.4.32. GEOS1 has weaker northward stress in the Southern Hemispheretropics than UWM, particularly in the Southern Hemisphere winter; all the reanalyses haveweaker southward stress in the Northern Hemisphere tropics, especially in the NorthernHemisphere winter. In mid-latitudes in both hemispheres the reanalyses all have strongerpoleward stresses than UWM.Figure 11.4.32 Zonally averagedmeridional surface stress overthe ocean averaged over1981-92 from UWM and thereanalyses in Nm-2.Figure 11.4.33(a) shows the annual mean meridional surface stress for 1981-92 fromUWM. In the east tropical Pacific, a strong northward stress can be seen at the equator and astrong southward stress near 15N. ERA15 (Figure 11.4.33(b)) shows a similar, but slightlyweaker pattern; GEOS1 (Figure 11.4.33(c)) and NCEP1 (Figure 11.4.33(d)) are significantly11 - Evaluation of Flux Products 242 11.4 Reanalysis products(11.4.12 Meridional Surface Stress)Report of the Working Group on Air Sea Fluxes June 2000Figure11.4.33(a)Figure11.4.33(b)Figure11.4.33(c)11 - Evaluation of Flux Products 243 11.4 Reanalysis products(11.4.12 Meridional Surface Stress)Report of the Working Group on Air Sea Fluxes June 2000Figure11.4.33(d)Figure11.4.33(e)Figure 11.4.33: (a) Meridional surface stress from UWM based on COADS. Contour interval0.02 Nm-2, northerly stress shaded.(b) Difference in meridional surface stress ERA minus COADS. Contourinterval 0.01 Nm-2, zero omitted, negative values shaded.(c) As (b) but for GEOS minus UWM.(d) As (b) but for NCEP1 minus COADS.(e) As (b) but for NCEP2 minus COADS.weaker than UWM in the eastern tropical Pacific and in the tropical Atlantic. NCEP2 (Figure11.4.33(e)) shows smaller mean differences from UWM than NCEP1, especially along theequator. All the reanalyses show significant differences from UWM south of 30S.Zonal mean correlations between UWM and the reanalyses for meridional surface stressare similar to, but somewhat lower than, the correlations for zonal surface stress (Figure11.4.30). The annual cycles in the reanalyses agree quite well (0.9 or higher) with UWMbetween 55N and 15S; the correlation of the reanalyses’ anomalies with UWM anomaliesexceeds 0.8 from 25N to 55N. NCEP1’s annual cycle has the worst agreement with UWMbetween 20 and 60N and ERA15’s agrees the best; GEOS1 has the worst agreement elsewhere.GEOS1’s anomalies agree the worst with UWM; NCEP1’s anomalies tend to agree the best.11 - Evaluation of Flux Products 244 11.4 Reanalysis products(11.4.14 Precipitation)Report of the Working Group on Air Sea Fluxes June 2000NCEP2’s annual cycle is slightly better correlated with UWM than NCEP1; its anomalies areslightly worse.Zonal mean RMS differences between the three reanalyses’ meridional surface stressand UWM are shown in Figure 11.4.34(a) for the full monthly means and in Figure 11.4.34(b)for the monthly mean anomalies. Even though GEOS1 displays the least variability of the fourestimates, ERA15’s annual cycle is the least different from UWM. The anomalies in GEOS1are the least different from UWM in high latitudes and have the lowest month-to-monthvariability. NCEP2 tends to show the largest differences from UWM in high latitudes and hasthe largest month-to-month variability. GEOS1 has the largest normalised RMS differencesfrom UWM between 20 and 60N; ERA15 has the least.Figure 11.4.34(a) RMSdifferences in monthly meanmeridional surface stress overthe oceans between thereanalyses and UWM for 1981-92 in Nm-2.Figure 11.4.34(b) As (a) but foranomalies from each estimate'sclimatological annual cycle inNm-2.11.4.14 PrecipitationStendel and Arpe (1997) evaluated the hydrological cycle in the ERA15, NCEP1 andGEOS1 reanalyses. Trenberth and Guillemot (1998) examined atmospheric moisture and thehydrological cycle in the NCEP1 reanalysis and compared NCEP1 precipitation to CMAPprecipitation. Janowiak et al. (1998) compared GPCP precipitation with precipitation from theNCEP1 reanalyses. Arpe et al. (2000) examined the hydrological cycle in the reanalyses andconcluded, "There is not one reanalysis which is superior in all respects and a new evaluation ofthe reanalyses has to be made before each application."Table 1 compares mean precipitation estimates averaged over different oceanic regionsfrom the reanalyses with two independent estimates by Xie and Arkin (1996, 1997) (CMAP)and by the Global Precipitation Climatology Project (GPCP) (World Climate ResearchProgramme, 1990; Huffman et al., 1997). The two independent estimates are based on satelliteestimates of precipitation over the ocean. In mid-latitudes ERA15 and NCEP2 agree the best11 - Evaluation of Flux Products 245 11.4 Reanalysis products(11.4.14 Precipitation)Report of the Working Group on Air Sea Fluxes June 2000with the independent estimates; NCEP1 and GEOS1 are less. In the tropics and over the wholeregion between 50S and 45N NCEP2 and ERA15 exceed the independent estimates, whileNCEP1 and GEOS1 are close to the independent estimates.Table 1 Mean precipitation in mm/day over different regions of the ocean averaged over 1988-92 for the four reanalyses and two independent estimates derived from satellites over the ocean.50S-45N 20-50S 20S-20N 20-45NCMAP 3.33 2.55 4.13 2.75GPCP 3.1 2.64 3.54 2.84ERA15 3.79 2.47 5.13 2.8GEOS1 3.05 1.88 4.2 2.29NCEP1 3.15 2.13 4.12 2.54NCEP2 3.7 2.64 4.8 2.84Figure 11.4.35 Zonal meanprecipitation over the oceansaveraged over 1988-92 from thereanalyses, CMAP (Xie-Arkin)and GPCP in mm/day.Figure 11.4.35 compares zonal mean precipitation over the ocean from the fourreanalyses with CMAP and GPCP for the years 1988-92. Over the ocean ERA15 and NCEP2clearly have the most rainfall in the tropics, where the two independent estimates disagree witheach other by as much as 1 mm/day. GEOS1 lies between the two independent estimates whileNCEP1 agrees with the lower independent estimate (GPCP). CMAP uses similar data sets toGPCP; however, CMAP uses rainfall measurements from tropical atolls to calibrate satelliterainfall estimates over the tropical oceans and GPCP does not. The mid-latitude maxima inprecipitation are further poleward in the reanalyses than in the satellite-based estimate. NCEP2has more precipitation in mid-latitudes than the other reanalyses.Figure 11.4.36 compares the standard deviation of rainfall from the three reanalyses andfrom CMAP over the ocean for the full monthly means (Figure 11.4.36(a)), the annual cycleaveraged over 1988-92 (Figure 11.4.36(b)) and monthly mean anomalies for 1988-92 from theannual cycle (Figure 11.4.36(c)). The results suggest that the ERA15 and NCEP2 reanalysesoverestimate variability and NCEP1 and GEOS1 underestimate variability over the tropicaloceans. All four reanalyses and GPCP have larger precipitation anomalies at higher latitudesthan CMAP. NCEP2 has the strongest variability in mid-latitudes.11 - Evaluation of Flux Products 246 11.4 Reanalysis products(11.4.14 Precipitation)Report of the Working Group on Air Sea Fluxes June 2000Figure 11.4.36(a) Zonal meanstandard deviation of monthlymean precipitation over theoceans for 1988-92 in mm/day.Figure 11.4.36(b) As (a) but formonthly mean climatologyaveraged over 1988-92.Figure 11.4.36(c) As (a) but formonthly anomalies for 1988-92from the monthly meanclimatology.Figure 11.4.37(a) shows the five-year mean precipitation estimate from CMAP. GPCP(Figure 11.4.37(b)) has less rain in the tropics and more near 45o in both hemispheres. The twoindependent estimates based on satellite estimates differ by more than 1 mm/day over much ofthe tropical ocean. ERA15 (Figure 11.4.37(c)) has more intense tropical convergence zonesthan CMAP, less precipitation near 30S and 45N and more near 60N and 60S. GEOS1 (Figure11.4.37(d)) shows less rain in the east Pacific and Atlantic ITCZ and in the South Pacific andSouth Atlantic convergence zones, but more precipitation than CMAP north of the equator inthe western Pacific and Caribbean. It has the least precipitation over the northern mid-latitudestorm tracks. NCEP1 (Figure 11.4.37(e)) also has weaker convergence zones than CMAP.11 - Evaluation of Flux Products 247 11.4 Reanalysis products(11.4.14 Precipitation)Report of the Working Group on Air Sea Fluxes June 2000Figure11.4.37(a)Figure11.4.37(b)Figure11.4.37(c)11 - Evaluation of Flux Products 248 11.4 Reanalysis products(11.4.14 Precipitation)Report of the Working Group on Air Sea Fluxes June 2000Figure11.4.37(d)Figure11.4.37(e)Figure11.4.37(f)11 - Evaluation of Flux Products 249 11.4 Reanalysis products(11.4.14 Precipitation)Report of the Working Group on Air Sea Fluxes June 2000Figure 11.4.37: (a) Mean precipitation from CMAP (Xie-Arkin) for 1988-92. Contour interval1 mm/day. Values less than 1 mm/day shaded.(b) Difference in precipitation GPCP minus CMAP. Contour interval 0.5mm/day, zero omitted, negative values shaded.(c) Difference in precipitation ERA15 minus CMAP. Contour interval 1mm/month, zero omitted, negative values shaded.(d) As (c) but for GEOS1 minus CMAP.(e) As (c) but for NCEP1 minus CMAP.(f) As (c) but for NCEP2 minus CMAP.NCEP1 (Figure 11.4.37(e)) and NCEP2 (Figure 11.4.37(f)) have more rain than CMAP offnortheast Brazil and in the east Pacific dry zone between the ITCZ and the South Pacificconvergence zone. In all the reanalyses the South Pacific Convergence Zone tends to have aneast-west orientation rather than a northwest-southeast orientation. NCEP2 clearly has morerainfall over the tropical oceans than NCEP1 and the independent estimates. ERA15, GEOS1and NCEP1 have less rain than CMAP in the mid-latitude storm tracks, as can be seen to theeast of Japan and the United States, and more precipitation further poleward.Zonal mean correlations of oceanic precipitation estimated by CMAP with GPCP andthe ERA15, GEOS1 and NCEP1 reanalyses are shown in Figure 11.4.38(a) for the annual cycleaveraged over 1988-92 and in Figure 11.4.38(b) for anomalies from the annual cycle. Theregional patterns of correlations in the reanalyses are similar to each other. Low correlationsappear over the east Pacific and Atlantic ITCZ's and other convective regions. The reanalysesalso show high correlations along the equator in the Pacific and lower correlations to the northand south. Over the oceans the reanalyses all show high correlations near 30o and lowcorrelations at higher latitudes in both hemispheres. ERA15 has the highest correlations, whileGEOS1 is somewhat lower than NCEP1 over the ocean. NCEP2 is better correlated with CMAPestimates than NCEP1 in mid-latitudes, especially for the climatological annual cycle.Figure 11.4.38(a) Zonal meancorrelation of climatologicalprecipitation over the oceans forthe reanalyses and GPCP withCMAP (Xie-Arkin) for themonthly mean annual cycleaveraged over 1988-92.Figure 11.4.38(b) As (a) but formonthly anomalies for 1988-92from the climatological annualcycle.11 - Evaluation of Flux Products 250 11.4 Reanalysis products(11.4.14 Precipitation)Report of the Working Group on Air Sea Fluxes June 2000Precipitation in CMAP and GPCP is derived from raingauges over land and byempirical techniques from satellite observations over the oceans. The regional pattern ofcorrelation of precipitation anomalies in GPCP and in CMAP is similar to the regional patternof correlation between CMAP and reanalysis precipitation anomalies. (However, thecorrelations between GPCP and CMAP exceed 0.80 in most regions and are considerably higherthan the correlations between the reanalysis and CMAP.) This similarity of pattern suggeststhat a secondary part of the spatial variation in correlation between the reanalyses and CMAP,such as lower correlations at higher latitudes, may reflect uncertainty in the CMAP estimate ofprecipitation. RMS differences from the monthly means of CMAP are shown in Figure 11.4.39(a) forthe monthly means for 1988-92, in Figure 11.4.39(b) for the annual cycle averaged over 1988-92, and in Figure 11.4.39(c) for the anomalies from the annual cycle. Of the reanalyses, ERA15has the lowest RMS difference over the extratropical oceans. NCEP2 and ERA15 have thelargest RMS difference over the tropics where they display considerably more month-to-monthvariability than CMAP or the other reanalyses. NCEP2 tends to have the largest RMSdifference from CMAP outside the tropics, reflecting greater month-to-month variability inprecipitation than other reanalyses and CMAP. GEOS1 tends to have the largest normalisedRMS difference from CMAP and ERA15 the least. NCEP2’s normalised RMS difference fromCMAP is significantly less than NCEP1’s.Also shown in Figures 11.4.38 and 11.4.39 are correlations and RMS differencesbetween monthly means for GPCP and for CMAP, as a measure of the uncertainty in CMAPestimates of precipitation. Since GPCP and CMAP use similar methods of estimatingprecipitation, they should be regarded as a lower estimate of the uncertainty in CMAP.Correlations between CMAP and GPCP anomalies are considerably higher than the correlationsbetween CMAP and the reanalyses, but are lower over the extra-tropical oceans than in thetropics. RMS differences between GPCP and CMAP anomalies are as large in higher latitudesas the RMS differences between CMAP and reanalysis anomalies and are not negligible in thetropics. Satellite estimates of precipitation are often developed and "tuned" for the tropicaloceans. In addition certain satellite estimates use the cloud-top temperature as a proxy forrainfall rate; this method does not work well far from the equator. Thus satellite estimates maynot be as applicable to the extra-tropical oceans as they are to the tropical oceans. This suggeststhat significant uncertainties remain in the independent estimates of precipitation, perhaps moreso in the magnitude than in the pattern of precipitation, and that a secondary part of thedifferences between the reanalyses' precipitation and satellite estimates of precipitation reflectsuncertainty in the satellite estimates over the oceans.Figure 11.4.39(a) RMSdifferences in monthly meanprecipitation over the oceansbetween the reanalyses andGPCP and CMAP for 1988-92in mm/month.11 - Evaluation of Flux Products 251 11.4 Reanalysis products(11.4.15 Conclusions)Report of the Working Group on Air Sea Fluxes June 2000Figure 11.4.39(b) As (a) but forclimatological monthly meansaveraged over 1988-92.Figure 11.4.39(c) As (a) but foranomalies for 1988-92 from theclimatology.11.4.15 ConclusionsAn enormous effort has gone into the careful calculation of surface fluxes from COADSdata. The best COADS estimates of surface fluxes, however, are still far from a global meanenergy balance (Josey et al., 1999) and it is not at all clear that the imbalance is solely due to thescarcity of ship observations over large regions of the oceans. The adjustments commonlymade to achieve balance include a large reduction in surface net short wave radiation. Theoriginal UWM NSW actually agrees well with the satellite-based SRB estimate; however, theSRB estimate may have a positive bias. The SRB satellite estimate of NLW also appearsquestionable. The global mean SRB NLW appears to be quite different in magnitude than otherestimates and the net surface radiation resulting from the SRB NSW and NLW implies a needfor considerably larger sensible and latent heat fluxes than the estimates examined here. Thisstudy has shown significant time-mean differences between the reanalyses and independentestimates and has not resolved which is correct.Surface fluxes from the NCEP1, NCEP2 and ERA15 reanalyses generally show verysimilar levels of agreement with UWM and SRB estimates; in many cases fluxes from GEOS1show lower correlations with independent estimates. The annual cycle in ERA15 tends to agreeslightly better with independent estimates than NCEP1's annual cycle; NCEP1's anomaliesagree slightly more in general with independent estimates than ERA15's anomalies. NCEP1appears to underestimate variability over the tropical oceans; NCEP2 and ERA15 appear tooverestimate variability in the tropics. NCEP2 has more upward latent heat flux and lessupward sensible heat flux than NCEP1; this may reflect a change in the boundary layerformulation. NCEP2’s sensible heat flux is less than the other estimates. NCEP2 appears tohave fewer stratus clouds and worse short wave radiation in the eastern subtropical oceans thanNCEP1, but corrects two problems in NCEP1: too much downward short wave radiation at thesurface and too high ocean surface albedo. NCEP2 tends to have the largest month-to-month11 - Evaluation of Flux Products 252 11.4 Reanalysis products(11.4.15 Conclusions)Report of the Working Group on Air Sea Fluxes June 2000variability of the reanalyses; GEOS1 tends to have the least.The climatologies from the reanalyses show good agreement with the UWMclimatology in the mid-latitudes of both hemispheres; flux anomalies from the reanalyses showgood agreement with UWM anomalies in the Northern Hemisphere mid-latitudes. The resultssuggest that UWM is useful for defining month to month variability in fluxes in the northernmid-latitudes and a few other regions but may be useful in defining climatological fields in theSouthern Hemisphere north of 40S.The reanalyses appear to have large biases in surface NSW. GEOS1 in particularappears to have quite different surface radiation than the other estimates examined here. Thissuggests that cloudiness needs to be improved in the reanalyses. Whether these biases reflectprimarily problems in the parameterisation of cloudiness or problems in the parameterisation ofthe hydrological cycle is not clear. A major problem in the reanalyses appears to be thetreatment of low-level stratus clouds, particularly in the ERA15, GEOS1 and NCEP2reanalyses.Comparisons with in-situ measurements have revealed differences in sensible and latentheat flux due to differences in flux algorithms; the NCEP algorithm is deficient at high windspeeds where it yields too much latent heat flux. This problem has been corrected in theoperational NCEP model, but not the NCEP2 reanalysis. While the ERA15 algorithm appearsto perform better over the Labrador Sea, it and NCEP2 gives even more evaporation globallythan NCEP1 and display no more agreement in general with UWM estimates. It should benoted that fluxes in NWP analysis/forecast systems reflect the entire physics package and theanalysed near-surface meteorological fields and not just the flux algorithms. One majorquestion to which the answer is not clear is the magnitude of the hydrological cycle.Surface stresses from the NCEP1 and ERA15 reanalyses generally show very similarlevels of agreement with da Silva et al. (1994) COADS-based surface stresses; GEOS1 stressesshow slightly less agreement with UWM. The annual cycle in ERA15 agrees slightly betterwith UWM than the NCEP1 annual cycle; the NCEP1 anomalies agree slightly more withCOADS than the ERA15 anomalies. Time-mean stresses from NCEP2 show less differencefrom COADS than do NCEP1 stresses. NCEP2's stronger tropical surface stress appears to bemore realistic than NCEP1, perhaps reflecting stronger tropical precipitation than in NCEP1.The stronger tropical precipitation in NCEP2 may not be more realistic in magnitude thanNCEP1.The reanalyses appear to have large biases in precipitation. ERA15 appears to besomewhat better in depicting monthly precipitation patterns than NCEP1 or GEOS1; NCEP1may be somewhat better than GEOS1 over the oceans. NCEP2 appears more correlated thanNCEP1 with independent estimates outside the tropics and has less normalised RMS differencefrom CMAP in the tropics as well as mid-latitudes. ERA15 and NCEP2 exceed the independentestimates over the tropical oceans. NCEP1 underestimates variability over the tropical oceans;ERA15 overestimates variability in the tropics and NCEP2 appears to overestimate it nearlyeverywhere.A major uncertainty is the true magnitude of precipitation over the ocean. Smith et al.(1999) found the NCEP1 reanalysis evaporation exceeded estimates derived from shipobservations using the Smith (1988) algorithm by 20 Wm-2. (Both NCEP2 and ERA15 havemore oceanic evaporation and more precipitation than NCEP1.) This implies that globalprecipitation from the NCEP1 reanalysis should also be too high; comparisons with independentestimates shown above suggest that NCEP1 precipitation is not too high over the oceans but thatNCEP2 and ERA are. Comparisons with CMAP and GPCP over land suggest that all thereanalyses are too high over land by 0.43 to 0.7 mm/day between 50S and 45N and by 0.8 to1.19 mm/day between 20S and 20N. This suggests that too high evaporation over the ocean inthe NCEP1 reanalysis (and other reanalyses as well) may be associated with too muchprecipitation over land. In that case the excessive evaporation could be due to errors in theNWP systems’ parameterisation of convection as well as errors in their parameterisation ofevaporation over the oceans.11 - Evaluation of Flux Products 253 11.5 Operational Models11.6 Sea-Ice areasReport of the Working Group on Air Sea Fluxes June, 200011.5 Flux Products from Operational NWP Model'sBecause operational NWP models are constantly evolving, the Working Group on Air-Sea Fluxes decided that flux products from these models could not be evaluated for this report.Rather they requested the Working Group on Numerical Modelling (WGNE) to consider thisproblem. Here we summarise the present position.The increasing interest in seasonal forecasting has resulted in a much greater need forimproved surface fluxes. Additionally, analysis and reanalysis offer some of the best hope forimproved estimates of large-scale fluxes. The WGNE has identified the need for aninfrastructure to assist NWP centres in the evaluation and intercomparison of analysed surfacefluxes. Previous studies (e.g., White, 1995) have studied ocean surface fluxes from singleversions of several operational models. NWP fluxes have been found to be within the range ofdifferences found with COADS-type climatologies, although cloudiness has been identified asa major source of uncertainty. Significant differences from each other and satellite estimates innet surface short wave and OLR suggest important variations in cloud-radiative effects. Whilevaluable, these "snapshot" studies have not enabled operational centres to monitor how derivedfluxes vary with their evolving systems.In response to the urging of the WGNE, Peter Gleckler (PCMDI) and Jan Polcher(LMD) have agreed to reinvigorate the study of NWP surface fluxes. There are severalcomponents to the planned study, the first being a reworking of White (1995) with recentoperational data. The project extends previous efforts in two ways. First, fluxes over oceanand land are to be evaluated. Although different in many ways (e.g. the types of observationaldata available), the study of ocean and land surface fluxes will be coordinated to optimise theefforts involved. The second difference from earlier efforts, and more ambitious aspect of thisproject, amounts to a pilot study. The objective of this exploratory initiative is to examine thefeasibility of developing an operational system to evaluate and intercompare NWP surfacefluxes.Following the data and metadata standards established for AMIP, centres have begun tocontribute 6 hourly fluxes for 1999 at different spin-up intervals. An update to the White(1995) study is expected within one year, and by the 16th Session of the WGNE (Nov 2000)experience should be sufficient to evaluate the usefulness and practicality of establishing anoperational flux evaluation system.Examples from the AMIP2 comparison for the period 1998 - 1999 are shown in Figures11.5.1 and 11.5.2. For latent heat flux (Figure 11.5.1) the results from the operational modelstended to have a similar magnitude or be greater than the ERA15 reanalysis. In comparison theNCEP1 latent heat fluxes were somewhat less and the unadjusted COADS based fluxes,significantly less. In contrast for the net surface SW flux (Figure 11.5.2) the reanalyses haverelatively low values and the COADS based estimates are similar to or less than the satelliteestimates. The operational models span the whole range of the other estimates. However themajority are similar to or even greater than the satellite based estimates.11.6 Evaluation of Fluxes in Sea-Ice Covered Areas11.6.1 Cloud and surface radiative flux data sets in the ArcticA review of downwelling radiative fluxes from models, satellites, and reanalyses showslarge discrepancies among the various estimates of over 50 Wm-2 in monthly means and over30 Wm-2 for 5-year means in reanalysis flux fields (Rothrock and Zhang, 1997). Largediscrepancies are traceable to different estimates of the cloud fraction, shown in Figure 11.6.1.The ISCCP satellite data set show an annual cycle out of phase with the Huschke cloudclimatology, which shows the accepted higher cloud fractions in summer. The two numericalweather prediction models show little annual cycle; NCEP is uniformly low and ECMWF isuniformly high. Five-year mean cloud amounts vary over a huge range from a low of 44 % for11 - Evaluation of Flux Products 254 11.6 Sea-Ice areasReport of the Working Group on Air Sea Fluxes June, 2000NCEP reanalysis to 70 % for ECMWF forecast data. A similar range is seen in 5-year monthlymeans.90S 60S 45S 30S EQ 30N 45N 60N 90NLatitude (area weighted)050100150200Watts/m**2AMIP ModelsVOS (SOC)VOS (da Silva)RNL (ERA)RNL (NCEP)g( )Figure 11.5.1 Zonal averages of the Global Ocean latent heat flux from reanalyses, observations(COADS) and AMIP2 (1998-99)90S 60S 45S 30S EQ 30N 45N 60N 90NLatitude (area weighted)050100150200250300Watts/m**2AMIP ModelsSAT (Pinker)SAT (Staylor)SAT (Li)VOS (SOC)VOS (da Silva)RNL (ERA)RNL (NCEP)Figure 11.5.2 Zonal averages of the Global Ocean net SW flux from reanalyses, observations(COADS and satellites) and AMIP2 (1998-99)11 - Evaluation of Flux Products 255 11.6 Sea-Ice areasReport of the Working Group on Air Sea Fluxes June, 2000Figure 11.6.1 Cloud fraction in percent from four data sources. Huschke’s is a spatially uniformclimatology; the others have been averaged over the Arctic Ocean and over the five years from1986-1990.The mean annual cycle of downward shortwave and longwave for five data sets isshown in Figure 11.6.2. We tend to look on the observations of Marshunova as the bestestimate; the annual average downward shortwave from Marshunova is 100 Wm-2. The extremeestimates of downward shortwave are from the two numerical weather prediction analyses:NCEP is high in every month (because of its low cloud fraction), with an annual average of 128Wm-2, and ECMWF is low through May, with an annual average of 80 Wm-2. The ISCCPsatellite-derived data set is in good agreement with Marshunova's climatology. Althoughsatellite-derived data sets have low cloud amounts in summer, they have compensating highcloud optical depth and low surface albedo that tend to keep the downward shortwave frombeing overestimated.The annual average downward longwave from Marshunova is 224 W m-2 (Figure11.6.2b). Other means vary from 198 for NCEP to 231 for the PW parametric estimates. Mostestimates are above those of Marshunova by 10 to 20 W m-2 in winter and below by 20 W m-2 insummer and fall. The low summer cloud amounts in the ISCCP data show up here as lowestimates of summer downward longwave.Biases of 10 W m-2 in downwelling fluxes correspond to rather large errors (10 to 30%)in ice thickness. The large discrepancies among radiative flux data sets should be understoodbetter before choosing any of them as the “standard” against which to test or with which to forcesea ice or climate models.11.6.2 Precipitation in the ArcticTwo recent studies have examined the quality of both the precipitation and evaporationfields and the atmospheric flux convergence (P-E) in NCEP and ERA reanalyses (Cullather andBromwich., 2000; Serreze and Hurst, 2000). In the central Arctic (north of 75˚N), the spatialpatterns of precipitation are in rough agreement with observations, although both data sets tendto underestimate precipitation over the Atlantic side. The mean annual values of precipitationare overestimated by NCEP and underestimated by ERA, each by about 20%. NCEPprecipitation has its maximum a month earlier than the observed August peak, whereas ERA hasits maximum a month after the observed peak. NCEP fields have spectral noise that has beenremoved in later versions (see note at proof in Cullather et al., 1999).11 - Evaluation of Flux Products 256 11.6 Sea-Ice areas11.7 WavesReport of the Working Group on Air Sea Fluxes June, 200011.6.3 Need for more assessmentBroader reviews of the polar surface flux literature and further intercomparisons andevaluations of flux and flux-related data sets, many of which are quite new, are sorely needed,especially for the Antarctic./SK/SKFigure 11.6.2a (top) Downwelling shortwave fluxes from five data sources. These are five-yearmonthly means for the Arctic Ocean. (b ) As (a) but for the downwelling longwave flux.11.7 Evaluation of Waves11.7.1 Visual wave observationsEvaluation of different wave products have been performed on the basis ofintercomparison with in situ measurements at buoys and the other in situ platforms, withobservations at OWS's (which are expected to have somewhat better accuracy compared toroutine visual estimates from merchant ships), and by intercomparison of different products oneto each other. Visual VOS observations may be biased due to the poor separation of sea and swell inthe VOS reports. Some young swells can be reported as seas, and it is also possible thatobservations of developed seas propagate into the swell domain. Gulev and Hasse (1999)checked joint probability distributions of visual wave height and wind speed against JONSWAPcurves for known wind durations and found that at least the wind sea was well defined in visual11 - Evaluation of Flux Products 257 11.7 WavesReport of the Working Group on Air Sea Fluxes June, 2000observations. Estimation of the observational accuracy of visual VOS wave observation (Gulevand Hasse 1999) shows that the day minus night difference in visual wave estimates is not aslarge as in wind observations. In the North Atlantic it lies within the range several centimetresto 0.2 m and does not have any pronounced spatial pattern.The main question for evaluation of wave heights obtained from VOS, is a way ofdetermining significant wave height from the separately reported sea and swell components.Wilkerson and Earle (1990) intercompared visual wave observations at merchant ships withmeasurements at NDBC buoys in the subtropical Atlantic, tropical and mid-latitudinal Pacific.They selected visual observations within a radius of 25 to 100 km around the buoys location andfound that least biased estimate of significant wave height (Hs) resulted from taking the higherof the sea and swell heights. They also found a random observational error of several tenscentimetres in significant wave height, and large errors for the period estimates. Gulev andHasse (1998) intercompared visual wave observations with measurements at buoys, OWS L,and Seven Stones Light Vessel in the North Atlantic. They found that an estimate of Hscomputed following Hogben (1988):Hh h Sw s =+ ()/ 22 1 2 (11.7.1)where hw and hs are wind sea and swell heights respectively, over estimated the observed Hs inthe majority of cases at each site by several tens of centimetres (mean bias of 0.27m). However,there has been a tendency for frequent under estimation especially in mid latitudes of using theHs estimate of Wilkerson and Earle (1990) - the higher of the two estimates. The best methodwas found to be a combined estimate, similar to that recommended by Barratt (1991).Equation (11.7.1) is used when sea and swell are within the same 30° directional sector (Barratthad suggested 45˚), in all other cases the higher of the two components is used. This combinedestimate gave mean "buoy minus VOS" differences of -0.03m and -0.07m respectively.Visual estimates of wave periods were found to be systematically underestimated inVOS observations. The peak of the wave period distribution may be 1 or 2 seconds less forVOS observations compared to instruments. However the mean difference for the wave periodsis somewhat less. Wilkerson and Earle (1990) reported about 0.2 s buoy minus VOSdifferences. Gulev and Hasse (1998) found a mean departure of about 0.26 s with 0.1 to 0.6 sstandard deviation. Dacunha et al. (1984) and Hogben (1988) reported larger systematic biasesfor wave periods for the Cobb seamount in the North Pacific. In order to correct these biasesseveral methods were developed. Ochi (1978) and Dacunha et al. (1984) recommended to fitjoint probability distributions of wave heights and periods to the observed ones, giving thepossibility to obtain corrected mean periods. Gulev and Hasse (1998) developed a method forthe correction of individual observations of periods which has an accuracy of 0.12 s.11.7.2 Altimeter wave dataExtensive evaluation of altimeter measurements has been undertaken by Cotton andCarter (1994b) who used NDBC buoy measurements to calibrate altimeter wave heightestimates, which then were used by Sterl et al. (1998) to assess the quality of their WAM wavemodel. They found that in general high waves were under, and low waves overestimated. Forthe periods of overlap of buoy measurements with the altimeter measurements, Cotton andCarter (1994b) applied regression analysis which helped to remove biases between the three Ku-band altimeters of GEOSAT, TOPEX/POSEIDON and ERS-1/2. Thus it was possible to mergethe three altimetric products and to create a data set with continuity over about 12 years. Laingand Reid (1999) performed an evaluation of altimetric significant wave height in the SouthPacific using a limited collection of visual data around New Zealand. They found that Hsderived using (11.7.1) under-predicted altimeter measurements by approximately 0.2 m for allranges. It is unclear whether this bias has a regional nature or results from the considerableundersampling by the VOS observations in the South Pacific.A pilot study by Gulev et al. (1998) performed a triple intercomparison of significantwave height derived from visual observations, the WAM model, and altimeters. Climatological11 - Evaluation of Flux Products 258 11.7 Waves11.8 SummaryReport of the Working Group on Air Sea Fluxes June, 2000spatial patterns of significant wave height in all three products were found to be consistent,although the differences between the VOS wave data, altimeter measurements and the modelhindcast were not negligible; the nature of the biases still has to be precisely studied. Thesmallest "VOS minus WAM" and "VOS minus altimeter" differences were found in midlatitudes, where the differences were of opposite sign: VOS waves overestimated the modelhindcast by 0.1 to 0.3 m and underestimated the altimeter Hs by approximately 0.1-0.2 m. Inthe tropics, where the waves and winds are smaller, VOS wave height was higher than thosetaken from WAM, or the altimeter, by 0.3 to 0.7 m. An alternative interpretation is that theWAM Hs and the altimeter data fit better to each other in low latitudes, and show the largestdisagreement in mid latitudes where the "altimeter minus WAM" biases are from 0.3 to 0.5 m.Of the three sets of comparisons, the VOS and altimeter SWH show least scatter, whilst thelargest scatter is obtained for the "VOS-WAM" comparison.Two long-term 40 year wave hindcasts of OceanWeather Inc. (Cox and Swail 1999)were evaluated using in-situ and altimeter measurements (Cox et al. 1999). In-situ comparisonwas based on the NDBC buoy 44138 measurements and a number of platform measurements.This comparison showed that the highest waves are usually under-predicted in the modelsimulations, and alternatively, smaller waves tended to be over-predicted. Comparison withsatellites was done for four regions: Southern Hemisphere, Northern Hemisphere, Tropics, andGlobal. Charts of differences show pronounced overestimation of hindcasted waves in midlatitudes, and slight underestimation in the tropics. The best agreement has been found in theNorth Atlantic Ocean. 11.8 SummaryHere we briefly review some this Chapter's results for the heat flux fields to illustratethe difficulty of determining the "best" product. Figure 11.8.1 summarises results for the globalheat balance that were also presented in Table 11.4.1. For each flux component, and for the netflux, the difference is shown between the fluxes from the various analyses (ERA15, GEOS1,NCEP1, and NCEP2) and the UWM/COADS climatology. Figure 11.8.1a shows thecomparison with the unconstrained UWM product and Figure 11.8.2b shows the difference fromthe tuned values of da Silva et al. (1994). On the latter plot the net heat flux values displayedassume that, for the period considered, the tuned UWM/COADS product had an exact heatbalance. It is clear the GEOS1 product is far from a global balance and inspection of Figure11.8.1 in general shows this product to be an "outlier" so this product can be assumed to beincorrect with regard to the surface fluxes. The other reanalysis products (and the tuned UWMfluxes, which are not shown) all have a global balance within 10 Wm-2.However, achieving global balance does not guarantee that the regional variation of thefluxes, or the partitioning between components will be similar from one data set to another.Thus Figure 11.8.2 shows the net heat flux for the VOS estimates, reanalysis, and the residualcalculation described in Section 11.3.5. The two reanalyses shown at the top of the figure areclearly more similar one to another than the two COADS based flux fields (middle plots).However, this is mainly due to the fact that the COADS products have not been adjusted for theglobal mean heat imbalance, which for both exceed 20 Wm-2. The lower left hand "tuned"estimate of da Silva is more similar to the reanalysis than either uncorrected VOS estimate.Despite the noisiness of the residual method plot, it also shows similarities to the otherplots particularly in regions of excessive heating or cooling such as the western boundarycurrents, eastern Indian Ocean and the western tropical Pacific. However more carefulinspection shows that there are significant differences between the different data sets shown inFigure 11.8.2. These are particularly marked in the Indian Ocean. For example the Residualmethod suggests ocean heating north of Madagascar of a magnitude more similar to the untunedCOADS products than to the tuned product; this is not shown by the reanalyses. On the otherhand the ocean cooling west of Australia is more similar to the tuned COADS fluxes. Thiscooling is seen in the NCEP reanalysis but is displaced westward in the ERA results. Note that11 - Evaluation of Flux Products 259 11.8 SummaryReport of the Working Group on Air Sea Fluxes June, 2000the time period differs for each of these climatologies, but this unlikely to have been a majorcause of the large systematic differences which are evident.That there should be regional variations in the differences between the various net heatflux estimates is not surprising given the likely biases in the individual flux components. Ifthese biases could be understood and corrected the need to tune the COADS would be removed.However we presently do not have that understanding. For example almost all schemes to"correct" the COADS data require the net surface SW to be decreased. However satelliteestimates of net SW show more ocean heating compared to the untuned COADS values, not less(Figure 11.8.3). It is possible that this is caused by a bias in the satellite derived values.However, tuning the COADS values for net SW results in greater disagreement with bothsatellite and reanalysis estimates (compare Figure 11.8.1 (a) and (b)).sen lat SW LW Qnet-40-30-20-10010203040ERA15GEOS1NCEP1NCEP2SRBFluxmore ocean coolingDifference (W/m2)more ocean heatingsen lat SW LW Qnet-60-50-40-30-20-1001020ERA15GEOS1NCEP1NCEP2SRBFluxmore ocean coolingDifference (W/m2)more ocean heatingFigure 11.8.1 a. Global meandifference between differentflux data sets and theunadjusted UWM/COADSclimatology for each fluxcomponent and for the net flux.The SRB data are satelliteestimates of net SW by Darnellet al. (1992) and net LW byGupta et al. (1992) averagedover July 1983-June 1991.Figure 11.8.1 b. As (a) but forthe global mean differencefrom the tuned UWM/COADSclimatology. For the net fluxthe global balance is shown..Tuning also increases the latent heat (or evaporative) flux (Figure 11.8.4). The untunedVOS estimates (SOC and da Silva) compare qualitatively quite well, as do the climatologiesderived from reanalyses. However, in the sub tropical western ocean basins, there is much moreevaporation in the reanalysis than in the VOS estimates. Thus it might seem that there is scopefor increasing the VOS flux values. However it would be difficult to justify a large enoughadjustment to the latent flux to balance the budget if, on the basis of the satellite data, the netSW flux were not adjusted. Figure 11.8.4 also shows two other flux maps. The ERA 0 to 6hour map illustrates (in comparison to the 12 to 24 hour map) how the spin up process in theearly forecast hours affects the hydrological cycle. The satellite derived estimate (HOAPS) fallsinto yet another category. In the tropics this estimate is significantly lower than either the VOSor reanalyses, but elsewhere they are all comparable in magnitude - although the patterns vary.It would appear that further verification and development of the satellite product is required,particularly in the tropics.11 - Evaluation of Flux Products 260 11.8 SummaryReport of the Working Group on Air Sea Fluxes June, 2000Figure 11.8.2 The annual mean climatology of six estimates of surface net heat flux. Shownare values from the NCEP reanalysis (top left), the ERA15 reanalysis (top right), the untunedUWM/COADS climatology (middle left), the SOC climatology (middle right), the tunedUWM/COADS climatology (bottom left), and the Residual method (Trenberth and Solomon,1994) (bottom right).In summary, while there are encouraging similarities between the various estimatesthere are also large differences. The discrepancies are often most obvious in the low latitudesbut they are not restricted to those regions. Winter/summer maps of either the latent heat orsurface net shortwave clarify that the differences are relative, i.e., wherever the fluxes are large,so are the differences with the other flux estimates. This is indicative of relative systematicerrors. The maps certainly do not tell the whole tale of the challenges associated with oceansurface flux estimates. But they do give a glimpse of the difficulty. Each class of estimates hasits own strengths and weaknesses, and the errors associated with them are complex and11 - Evaluation of Flux Products 261 11.8 SummaryReport of the Working Group on Air Sea Fluxes June, 2000extremely difficult to quantify. Clearly, further analysis is needed in this area before we can geta better grasp of the relative quality of the estimates.Figure 11.8.3 The annual mean climatology of six estimates of surface net SW flux. Shown arevalues from the SOC climatology (top left), the untuned UWM/COADS climatology (top right),the Pinker satellite algorithm (middle left), the Staylor satellite algorithm (middle right), theNCEP reanalysis (bottom left), and the ERA15 reanalysis (bottom right).Finally the limited evaluation presented here allows the relative merits of the COADS,model, and satellite products to be considered. COADS based climatologies have given usmuch information about the marine climate. However comparison with the model data suggeststhat, as might be expected, such products perform best where there is adequate sampling. Thusthe COADS climatologies can define both the mean climate and interannual variability ofmonthly means in regions such as the North Atlantic but, at best, can only describe the mean11 - Evaluation of Flux Products 262 11.8 SummaryReport of the Working Group on Air Sea Fluxes June, 2000climatology for Southern Hemisphere regions. For the Southern Ocean in winter they may bebadly biased. To obtain a global heat balance they must be tuned. Flux fields from theFigure 11.8.4 The annual mean climatology of six estimates of surface latent heat flux. Shownare values from the SOC climatology (top left), the untuned UWM/COADS climatology (topright), the 12 to 24 hour estimate from the ERA15 reanalysis (middle left), the NCEPreanalysis (middle right), the 0 to 6 hour estimate from the ERA15 reanalysis (bottom left),and the HOAPS satellite product (bottom right). reanalyses are expected to be of more unifom quality from region to region and they typicallyexhibit a smaller global heat imbalance. However comparisons with satellite fluxes, or eventhe COADS fluxes, suggests that the magnitude and distribution of the radiative fluxes issignificantly in error; shortwave heating in particular. Similarly, if the comparisons with buoydata can be trusted, the latent heat flux is over estimated in many regions.11 - Evaluation of Flux Products 263 11.8 SummaryReport of the Working Group on Air Sea Fluxes June, 2000Finally, although the satellite surface SW flux estimates appear realistic there are stillsignificant differences between products and there may be an overall bias. In comparisonsatellite longwave surface flux estimates are less reliable. A more direct use of the satellite fluxdata is to define the top of the atmosphere fluxes for use in models or in the Residual fluxmethod. The latter technique offers a method of defining the surface heat flux independent ofthe bulk formulae and could be used as an important constraint on fluxes produced by othermethods. With regard to the turbulent fluxes, we have evaluated one satellite based product,HOAPS, which attempts to determine the surface latent heat flux from satellite data. While thisproduct shows promise there is clearly a need for improvement, particularly in the tropics.In short, there is presently no one flux climatology which does not exhibit significanterrors in one region or another in each of the various flux components.264Report of the Working Group on Air Sea Fluxes June, 200012 - Conclusions 265 12.1 Introduction12.2 SummaryReport of the Working Group on Air Sea Fluxes June, 200012. CONCLUSIONS12.1. IntroductionAir-sea interactions play a pivotal role in regulating the Earth’s climate, and there arenow urgent requirements in climate and oceanographic research for quality estimates of largescale air-sea fluxes of heat, momentum and fresh water. In recent years there has beensubstantial progress in our knowledge of ocean surface fluxes. Research has helped us to betterunderstand and correct the historical record of merchant ship observations, and fieldexperiments have led to refined formulae for estimating fluxes. Surface fluxes derived fromsatellites are becoming increasingly promising. Weather forecasting and data assimilationsystems have been used to create a consistent record of the recent past atmospheric state fromwhich surface fluxes are derived. And yet despite these advancements, the air-sea flux problemis extremely difficult, and in many ways remains unsolved. Whether derived from in-situ data,satellites, or analysis systems, the deficiencies are complex and difficult to quantify. Theoptimal product will depend on the application and often will not be easy to identify. Theoverall thrust of this Working Group, guided by the terms of reference, has been to present acoherent picture of the state-of-the-art of our present understanding.12.2. SummaryThe requirements for "well qualified" air-sea flux information are both wide rangingand compelling. As discussed in Chapter 2, they include climate change and diagnostic studies;sea-ice modelling; atmospheric and oceanic synoptic-scale case studies; shelf and coastal seasstudies; oceanic, climate, and earth observing systems; biogenic cycles, etc. The diverse natureof the requirements makes considering them all impractical. Thus this report focuses on themost common requirements and considers only those products that are non real-time, large-scale, readily-available to the scientific community, and documented.One facet of the flux requirements is particularly interesting. As discussed in precedingchapters, the absolute accuracies of air-sea flux products still do not meet the desired standards.However, as demonstrated in Chapter 3, when one considers the requirements for exploring thespatial and temporal characteristics of the flux variability, requirements are identified that arenew and different from those applicable to the mean, climatological fluxes. This topic of fluxvariability is largely unexplored in this report, but is important for many studies and thus is alikely candidate for attention in the future.The sources of data used to construct flux products were discussed in Chapter 4.Broadly speaking, there are three primary sources: in situ, remotely-sensed, and model products.Each of these sources has unique characteristics, which translate into advantages ordisadvantages for estimating fluxes. In situ data have been widely used; however, biasestraceable to the observing techniques, as well as the restricted spatial coverage, limit theirapplicability. Satellites provide global coverage and hold great promise to deliver quality data,but they have not been able to estimate net fluxes except within very limited domains. Finallythere are model products that synthesise several sources of observations and data using short-range forecasting systems to produce gridded fluxes. These need to be continually verified toidentify regional and systematic biases. Other techniques for flux estimation, based on residualand assimilation approaches, have had relatively little application in the past but are expected tobe utilised more in future.Direct measurements of the air-sea fluxes (Chapter 5) are relatively few and thereforelarge scale fields cannot be constructed based solely on these measurements. However, they areimportant for developing, calibrating, and verifying the parameterisation formulae used toindirectly estimate fluxes from the basic variables. Thus the accuracy of the directmeasurements will ultimately limit the accuracy of the parameterised estimates. The quality ofthe direct measurements is related both to instrument design and to our understanding of the12 - Conclusions 266 12.2 SummaryReport of the Working Group on Air Sea Fluxes June 2000underlying physics. Carefully designed experimental campaigns can increase the accuracyachieved.Without direct measurements, air-sea fluxes are often parameterised using formulaebased on the basic meteorological observations. Radiative fluxes are particularly difficult toparameterise (Chapter 6). The shortwave (SW) flux parameterisation has traditionally beenbased on visual observations of cloud amount. Because this method is fairly imprecise, andmoreover due to the limited set of verifying direct measurements, accurate determination of SWthrough this type of parameterisation will continue to present a challenge. A similar situationexists for longwave (LW) radiation. Other approaches to parameterising radiative fluxes arebased on satellite determined top-of-the-atmosphere radiation, coupled with radiative transfermodels to bring these values to the surface. For SW the situation is promising. However forboth SW and LW, difficulties in correctly modelling the presence of clouds and aerosols limitsthe accuracy. New techniques to estimate LW from combinations of satellite information mayhold promise, but they have not been fully evaluated. The range of surface albedo for the oceanand also sea ice over a wide variety of conditions continues to be investigated.For turbulent fluxes, parameterisations have been developed which are based largely onmeasurements of basic variables such as wind speed, temperatures, and humidity (Chapter 7).These "bulk formulae" are the most commonly used technique to estimate air-sea fluxes. Theformulae are dependent typically on wind speed measured at some height, and the difference invalues of appropriate basic variables between some height and the sea surface. There havebeen a number of bulk algorithms published in the literature. Newer bulk formulae (forexample the TOGA COARE algorithm) are improved to the extent that they incorporatetreatments of specific physical processes, such as the warm surface layer, and wind gustiness.Many questions remain as to the accuracy and/or applicability of these formulae in regimeswhere relatively few direct measurements have been made. This is particularly true ofmoderate to high wind regimes; in effect winds greater than 10 ms-1. Additionally, the role ofocean waves in modulating the turbulent flux estimates remains an open question.In the early chapters, we considered the errors when estimating individual air-sea fluxvalues. In Chapter 8, we discuss limitations on the accuracy of flux fields due to the synthesisof these errors. Both theoretical approaches and practical examples of quantifying random,systematic, and sampling errors, indicate that it is possible to estimate these cumulative errors.Additionally, for many applications, the requirement is for gridded flux fields that are completeboth in space and over some significant time period. The non-uniform sampling of the in-situand remotely-sensed fluxes necessitates an analysis method to fill in the gaps. Such techniquesalter the error budgets of the flux fields. For example, depending on the particular choice ofmethod and averaging period, it is not unusual for sampling errors in remotely-sensed data to bedominant compared to systematic biases. What is most important, but rarely satisfied, is for allflux products to include error estimates. Preferably these error estimates should consider asmany contributing factors as possible. Although good error values are often difficult tocalculate, they are extremely valuable in a) establishing confidence limits; b) assessingimprovements; and c) fulfilling many of the requirements described in chapter 2.There are a wide variety of flux products available to the scientific community. Theseproducts span a very large range of characteristics in terms of, for example, temporal andspatial coverage, grid spacing, input data, and parameterisations. Evaluation of all suchproducts would be a very large and continuous effort. We have attempted to concentrate onlyon those products with basin to global coverage, which are easily accessible to the community,and well documented. The determination of the "best product(s)" for specific applications isvery difficult due to the problem specific characteristics. Instead we focus on objectiveevaluations of the strengths and weaknesses of the selected products. For objective evaluationwe have used the following methods:❍ comparisons to reference data sets (i.e. to direct flux measurements or those from high-quality sources);12 - Conclusions 267 12.2 Summary12.3 Meeting the NeedsReport of the Working Group on Air Sea Fluxes June, 2000❍ intercomparisons with similar products;❍ integral constraints of heat and water balance and transports;❍ enclosed basin budgets;❍ other indirect flux estimates.As discussed in Chapter 9, each evaluation methodology has its own inherent advantages andlimitations.For many of the flux estimates the accuracy is limited by the accuracy of the basicvariables that are used in the parameterisation formulae. The state of the art with regards toobserving or estimating these basic variables was considered in Chapter 10. Each variable andmeasurement method has its unique characteristics that must be carefully considered indetermining the usefulness of the data for flux estimation or for verification.The chosen flux products were evaluated in Chapter 11 including a first detailed globalcomparison of the various reanalyses. The following conclusions are based on the results of thatevaluation. The conclusions are grouped according to the requirements for the several types ofactivities which were described in Chapter 2.12.3. Meeting the Needs12.3.1. Budget studiesBudget studies of the ocean surface energy and water balance of the ocean are oftenused as an evaluation method for the data sets rather than to derive values of the budget. Onlonger time and space scales the energy budget can be regarded as balanced, at least incomparison to the uncertainties in the available estimates of net energy flux at the sea surface.Proper evaluation of budget studies requires error estimates, both for the individual fluxcomponents and for the net flux.All flux products presently available (VOS-based, satellite, re-analyses) are imbalanced,that is the fluxes of heat and moisture do not meet known geophysical constraints. Imbalancesof the net heat flux are within several tens Wm-2. VOS - based climatologies tend tounderestimate net ocean heat loss, and this underestimation varies from 20 to 30 Wm-2. Poorsampling, insufficient information to correct variables in the appropriate way, and uncertaintiesin many parameterisations, are considered to be reasons for these imbalances. In this situation,an assessment of the reliability of the VOS flux fields has to be considered with respect to thespecific application for which the flux product is going to be used (for example forcing oceanmodels, analysis of interannual variations in sea-air fluxes, basin-scale budgets). For somepurposes, flux fields adjusted to give a balanced heat budget may be preferred; however thereis evidence that the available tuned flux fields may have significant regional errors.Most reanalyses are much better balanced than the VOS-based flux products, theyachieve imbalances within ±10 Wm-2. Possible reasons are a more adequate description of thefluxes in poorly sampled areas (e.g. the South Pacific), and more data types being taken intoaccount as assimilation input. However, despite the more realistic global balances derived fromreanalyses products, individual flux components are characterised by strong biases. Thissuggests that some sort of tuning, explicit or implicit in the model formulation, may beresponsible.For both reanalyses and VOS-based flux climatologies, the reliability of the fresh waterbudgets is even worse than for the net heat flux. This reflects a lack of direct measurement ofprecipitation over the oceans. Precipitation in NWP systems is sensitive to changes in physicalparameterisations and to “spin-up”. In this sense satellite observations (e.g. SSM/I andTRMM), which already are providing evaporation minus precipitation estimates over anextending, continuous period, are extremely valuable. To produce a freshwater budget, it is12 - Conclusions 268 12.3 Meeting the NeedsReport of the Working Group on Air Sea Fluxes June 2000necessary to consider not only the direct exchange between ocean and atmosphere but also theriver runoff from land surfaces.Meridional heat and fresh water transport estimates still remain very uncertain whencomputed from the VOS - based fluxes. Re-analyses flux products (which are better balanced)provide more plausible estimates at least for MHT. Comprehensive verification of MHT andMFWT estimates derived from surface balances against oceanographic estimates athydrographic sections should, of necessity, take into account the interannual variability of theocean interior. Time-dependent estimates of MHT and MFWT require knowledge of the long-term storage terms for the ocean, which are nevertheless smaller than the errors in flux products.12.3.2. Climate variability studiesThe variability of ocean-atmosphere fluxes on climate time scales is still poorly known.Up to the present, the basic meteorological variables (e.g. SST, pressure, winds) have morefrequently and effectively been used for describing the coupled modes of the ocean atmospheresystem. On the one hand, the errors and uncertainties inherent in the mean climatological fluxfields, particularly those connected with parameterisation errors, do not seriously affect theaccuracy of the climate anomalies of the fluxes. On the other hand, the time dependent biasespresent in various flux products create considerable problems for the analysis of variability withany confidence. These biases result from historical changes in observational techniques andsampling frequency (for the VOS data), from changes in the model configuration (foroperational models), and changes in the assimilation input (for both operational NWP andreanalysis systems). In this context, satellite data will be extremely valuable for climate changestudies once a climatologically significant length of record has been achieved. Even so, thecontinuity of the satellite data can suffer from changes in satellites and from instrument drift.At present the SSM/I and AVHRR provide a continuity of flux time series reaching nearly 15years, the ERS -1 and ERS-2 missions approach 10 years continuity. For sea ice, long timeseries (up to 22 years) are available from SMMR and SSM/I. All these data can be used forstudying short-period variability, for example the interannual variability of monthly meanvalues.The quickest progress in the application of flux time series can be expected with regardto mid-latitudinal variability on interannual to decadal time scales (associated with the NAO,PNA and NPO patterns). This is due to the large magnitude of the anomalies to be characterised(up to 50 Wm-2), relatively high sampling frequency, and the dominance of the turbulent fluxesin forming the net flux anomalies. Reanalyses flux products better resolve variability in poorlysampled areas, but they have still to be verified. Climate variability in tropical atmosphere-ocean fluxes is largely influenced by the anomalies of the radiative fluxes and precipitation.Both are poorly resolved in the VOS and re-analysis products; precipitation anomalies inparticular. Fortunately the weaker synoptic and meso-scale variability in the tropics reduces thesampling requirements compared to mid latitudes. The Atlantic subtropical dipole, Pacifictropical variability, and monsoon intensity variations are the main phenomena which have to bedescribed in terms of surface flux anomalies. Further improvement of the short wave radiationand precipitation from future reanalyses will aid progress.Reliability of interdecadal to secular changes derived from the ocean-atmosphere fluxfields is considerably worse than that for interannual to decadal scale variability. Beside thecrucial importance of time-dependent biases, the reliable time series of the ocean-atmospherefluxes is still restricted to at most several decades. The possible extension of the flux timeseries, at least for a few locations, is very important.Empirical and model studies of oceanographic decadal variability require estimates ofthe climate variability for the basin scale heat and fresh water budgets, as well as quantitativeestimates of the climate variations in MHT and MFWT. Although the uncertainty is large,estimates for selected latitudinal belts, bounded by the pre-WOCE and WOCE sections in theAtlantic ocean, can be made on the basis of VOS, NWP and satellite products.12 - Conclusions 269 12.3 Meeting the NeedsReport of the Working Group on Air Sea Fluxes June, 200012.3.3. Climate modelsThe accuracy of available surface flux estimates limits their value for evaluation ofclimate models. Despite this, it is useful to compare both atmospheric and coupled ocean-atmosphere models with available climatologies, because these models are known to havesubstantial systematic errors in their representation of surface fluxes. Errors in both coupledand uncoupled climate models manifest themselves differently and independent evaluation ofthese models is complementary.Evaluation of coupled model variability is becoming increasing important, and as notedin this report, in some respects our knowledge of variability is more accurate than estimates ofthe mean climate. Further improvements are anticipated with, for example, improvements insatellite based estimates.Improved accuracy of mean climatologies remains the greatest need for climate modelevaluation. It is no coincidence that this is a daunting task and is central to the 'surface fluxproblem' identified in this report. There are many parallels between observational and modeldeficiencies as they are generally reliant on the same techniques and theory. There has beenencouraging progress with the problem of climate drift in coupled model simulations, butfurther advancements may be limited by our deficient understanding of the mean surface energybalance. There is substantial model-to-model scatter in simulated basin-scale means. In somecases observational estimates help reveal serious model biases, but in others the disparitybetween the climatologies is such that model errors cannot be identified.As efforts to improve climatologies continue, additional measures can be taken toevaluate models with available products. More emphasis could be given to the basicobservables described in this report such as surface air temperature, humidity and wind speed.This requires careful vertical interpolation, but it is nevertheless helpful given the uncertaintiesin flux estimates. Additionally, as noted throughout this report, comparison of large scale fluxestimates with reference sites (e.g., 'flux buoys') is proving increasingly useful. Following thisstrategy with climate simulations could yield an important advancement in model evaluation.12.3.4. Ocean general circulation modelsThe wind field produced by satellite-borne scatterometers is the component which byfar matches best the requirements for forcing ocean general circulation models. Its high spatialand temporal resolution allows an accurate estimate of the stress vector and its curl, which isthe dominant forcing for the ocean gyres. One problem is the short length of the record, but thesituation is continuously improving. The continuity in the long term is now assured with anoperational scatterometer program being set-up jointly by ESA and EUMETSAT. NWPforecasts also benefit from scatterometer winds, and, in turn, operational wind products fromNWP centres are challenging scatterometer winds as an alternative forcing for ocean models.Other satellite products have a large potential for improvement and they may beconsidered the best practicable alternative to NWP products for forcing ocean circulationmodels in the future. The greatest expectations concern the radiation fluxes. With theincreasing use of geo-stationary satellites in combination with orbiting platforms, the space andtime resolution could approach the important scales of oceanic turbulence (a few tens ofkilometres, and a few hours). Progress in the estimation of turbulent fluxes and precipitation isslower, and these scales do not seem attainable in the very near future for these variables,however there are possibilities for improvements.The long records of NCEP/NCAR and ECMWF re-analyses, their fairly high space-time resolution, and the reasonable degree of consistency between the various components ofthe forcing (momentum, heat and freshwater), make them extremely valuable to define acomplete forcing function for ocean GCM's. The recent availability of these products is largelyresponsible for the rapid development of ocean variability studies in relation to climate changein recent times. These products are unique for that purpose, and it is essential that re-analysesbe pursued and continuously improved.12 - Conclusions 270 12.3 Meeting the NeedsReport of the Working Group on Air Sea Fluxes June 2000Although covering a similar period, COADS derived flux climatologies have severedeficiencies in the Southern Hemisphere. In such regions they can not compete with NWP re-analysed products for defining the ocean forcing. However, in particularly well sampledregions, such as the North Atlantic and the North Pacific, they remain valuable products thatshould be used, for example, to force basin scale ocean models in studies investigating modelsensitivity to air-sea fluxes or to changes in flux parameterisations. In those regions theCOADS SW fluxes are generally superior to the present reanalysis products, and the LW andsensible heat fluxes superior to satellite based products.It is likely that the most accurate air-sea flux estimates may come from a combinationof various products in which the contribution of NWP and satellite products will be dominant.It remains the case that for ocean general circulation models, the consistency of fluxes fromvarious space-borne sensors will have to be verified and this will be an additional constraint thatthe combined products will have to satisfy. Acknowledging that future combined flux estimateswill have a higher accuracy, such products would appear to be the best choice for theoperational or pre-operational oceanographic systems under development (GODAE, Mercator,etc.).12.3.5. Wave modellingWave models require forcing at a high spatial and temporal resolution. In particular,the required temporal resolution of ~3 hours can at present only be met by numerical products.While reanalysis products have the advantage of being quasi-homogenous in time, their limitedspatial and temporal resolution may be regarded as a disadvantage. However, the planned ERA-40 reanalysis will include a wave model forced at the atmospheric model’s timestep, so timeresolution will not be a problem in that case.12.3.6. Sea-ice modellingFor sea-ice modelling, our knowledge of net surface heat flux in polar regions hasproven adequate to determine a realistic annual cycle of the global sea-ice cover. However, thismean cycle is undoubtedly maintained by surface heat flux components with offsetting errorseasily as large as 50 Wm-2. A principal focus of ice-ocean modellers now is to understandinterannual variability; this objective seems desirable now that we are acquiring multidecadalsatellite and submarine records of ice extent and ice thickness with which to documentinterannual ice variability. So the new direction is to drive sea-ice models with flux and flux-related data sets that are not simply climatologies, but which incorporate interannual variability.If this new understanding is to be pursued, it will require a higher level of accuracy in our dataon the radiative forcing of the ice surface; the present 50 W m-2 uncertainty and lack of data oninterannual variability of snowfall will not suffice. Are greater polar cloud fractions enhancingdownward longwave radiation at the expense of downward shortwave? Are air temperaturevariations changing the turbulent fluxes? Is enhanced snowfall insulating the ice from the coldwinter air temperatures and reducing ice thickness? These questions cannot be answered withour present uncertainties both in surface heat flux components and in precipitation. The errorsin reanalysis and satellite-derived surface flux fields must be better characterised and thenreduced by more thorough comparisons amongst themselves and with in situ observations.Surface wind fields from NWP models and reanalyses are in general satisfactory, but are subjectto errors in magnitude and direction during winter when the planetary boundary layer is stable;PBL formulations need to be examined for their applicability in polar conditions.12.3.7. NWP and data assimilationThe reanalyses provide consistent, complete global fields of near-surface meteorologyand surface fluxes over many years. Even when reanalysis fields have time-mean biases, theirvariability can still be quite realistic. Many scientists from a wide range of disciplines havestudied the reanalyses, providing valuable feedback to the data assimilation and modellinggroups producing the reanalyses. The reanalyses are the most evaluated of all the flux products.It needs to be remembered, however, that the reanalyses are only interpolations of the input12 - Conclusions 271 12.3 Meeting the NeedsReport of the Working Group on Air Sea Fluxes June, 2000observational data that have many gaps in space and time. Physical parameterisations areespecially important in regions of little or no data; differences between reanalyses can highlightwhat fields in what regions are uncertain. The reanalyses so far have provided fields every sixhours; many surface flux users desire them at least every three hours.The GEOS-1 reanalysis was the first extensive reanalysis produced; its surface radiationfluxes are larger than other estimates. The GEOS data assimilation system has developedsubstantially since the reanalysis was conducted. The NCEP-1 reanalysis covers the longestperiod and is the most widely distributed and studied reanalysis. NCEP-1 made many fieldsavailable on the Internet and CD-ROM's. It has the most realistic oceanic low-level stratusclouds of the various reanalyses to date. At the time of writing, NCEP-2 is shorter than NCEP-1. It corrected mistakes in NCEP-1 and used improved short wave radiation and boundary layerparameterisations. Its precipitation patterns, downward short wave radiation at the surface, andequatorial wind stress appear superior to NCEP-1, but its sensible heat flux over the oceans islower than other estimates. It has not been widely distributed and studied, but is available onthe Internet. ERA-15 is the highest resolution reanalysis now available. It appears to producethe most realistic precipitation patterns of the four reanalyses and has more realistic surfacedownward short wave radiation than NCEP-1 and GEOS. It has not been as widely available asthe NCEP-1 reanalysis. The ERA-40 reanalysis is beginning; first results should be availableduring 2000. The reanalyses differ not only in time-mean fields but in the magnitude of month-to-month variability in surface fluxes. GEOS has the smallest variability in general, NCEP-2tends to have the largest.The reanalyses’ fields of sensible and latent heat and wind stress show generally goodagreement with COADS in the regions where ship reports adequately sample the atmosphere.The magnitude of latent heat flux varies significantly (by 24 Wm-2 in the global mean),reflecting differences in the complete model physics packages as well as (if not more than) inthe flux algorithms. The exact magnitude of oceanic evaporation appears uncertain, reflectinguncertainty in our knowledge of the strength of the hydrological cycle. Comparisons withfluxes from ship observations and buoys suggest that NCEP-1, NCEP-2 and ERA-15evaporation may be too large in many areas.Comparison with the reanalyses suggests that the COADS data base appears adequate togive useful information on the interannual variability of the monthly means in the mid-latitudeNorth Pacific and North Atlantic and in parts of the South Atlantic. Over much of the tropicsand the Southern Hemisphere (north of 40S) it appears adequate to give useful information on along-term monthly mean climatology.The reanalyses all appear to have substantial errors in surface radiation fields, reflectingproblems in clouds and clear sky radiation. NCEP-1 has offsetting errors in its radiation fields.A major shortcoming for ERA-15, GEOS-1 and NCEP-2 appears to be a lack of low-levelstratus clouds over the eastern subtropical oceans. SRB short wave radiation appears to havemore realistic patterns than the reanalyses, but may well have a positive bias. Radiative transfermodels in general appear to give too much downward short-wave radiation at the surfacecompared to actual measurements. Since surface long wave radiation is quite sensitive toatmospheric humidity profiles (that are not well-determined globally) and in particular to cloudbase height, it is more uncertain than surface short wave.There are significant differences between the hydrological cycles of the reanalyses. Themagnitude of precipitation appears uncertain and varies widely in the estimates examined here.Twelve-year mean tropical precipitation over the ocean varies from 3.54 mm/day to 5.13mm/day. The difference between NCEP-1 and NCEP-2 in precipitation indicates that dataassimilation systems’ estimates of the hydrological cycle are quite sensitive to changes in thephysical parameterisations. Satellite estimates of precipitation are generally more uncertain inhigh latitudes than in the tropics. All the reanalyses have problems with the time-mean patternof precipitation. Comparisons to CMAP and GPCP over land implies too much precipitation inall the reanalyses. This may be linked to too much evaporation over the ocean.12 - Conclusions 272 12.3 Meeting the Needs12.4 PerspectivesReport of the Working Group on Air Sea Fluxes June 2000Reanalyses were produced with unchanging data assimilation systems, but the availableobservations change with time. ERA-15 has discontinuities in several fields at the end of 1986associated with the use of satellite radiances. NCEP-1 has discontinuities calling into questionlong-term trends. Before 1979 the observational network needs to be considered in assessingthe reliability of reanalysis. During the period 1948-79 the observation network became moreglobal with time; these changes in coverage affect the NCEP-1 reanalysis. The end of 1978marked the beginning of the modern satellite era. In recent years the number of satelliteobservations have increased while observations from the surface have decreased; theconsequences of this needs to be assessed.12.4. PerspectivesBecause of the length of time needed to do a lengthy reanalysis, fluxes from globaloperational NWP systems will much of the time be based on more developed, more complex,and hopefully better physical parameterisations and therefore will be worth evaluating inaddition to reanalysis results. Such evaluations would benefit future reanalyses as well asweather forecasting. We applaud WGNE’s plans to archive and evaluate the fluxes from anumber of global NWP systems.In situ observations and field experiments provide a very valuable check on near-surface meteorology and flux estimates from both satellites and data assimilation. These needto be performed in a wide variety of locations and climates. Successful field experimentsrequire years of analysing and processing the data once the field experiment is actually carriedout. Funding agencies need to be willing to fund such work for an extended period.Uncertainty estimates need to be provided in the resulting data sets. In data assimilationanalysis/forecast systems, all the physical parameterisations influence each other; adjustingone physical parameterisation to fit one independent estimate or one field experiment tooprecisely may distort other physical fields, or the same physical field in other locations.Study of the TOGA COARE experiment has increased our knowledge of air-sea fluxesand improved our ability to measure them, partly because of a willingness to devote severalyears to analysing and processing the data after the actual field experiment. It is not clear thatthe knowledge gained has been completely absorbed yet by the data assimilation/NWPcommunity. The data assimilation/NWP community has paid much attention recently to theassimilation of new satellite data; increased attention also needs to be paid to the developmentand implementation of improved physical parameterisations. Indeed improved physicalparameterisations are needed to take full advantage of new satellite measurements of moistureand the hydrological cycle. Funding needs to be provided for such work and to provideopportunities for people from the TOGA COARE community to work with operational dataassimilation/modelling groups in the interest of improving future reanalyses as well as weatherforecasting.The NCEP-1 Reanalysis is continuing as a climate data assimilation system analysingcurrent data. In the future, reanalysis and climate data assimilation may diverge from dataassimilation for NWP. Such an independent system would be free from the time constraints onoperational NWP and could have a much longer data cutoff time. It could also have morecomplex physics, enabling it to process all observations and eliminating any need for separateretrievals of satellite data for any field. Thus satellite and data assimilation estimates of surfacefluxes might be merged within a single consistent system using as complete as necessaryphysical parameterisations. Satellite products have been demonstrated to have great potential to improve theoverall situation especially concerning coverage, time sampling, and data consistency. Inaddition, the time period covered by satellite data is approaching a length where such databecome useful for climate variability studies (at least on the interannual scale). However up tonow it was not possible to construct a product that included the four relevant energy fluxes andthe freshwater flux. This was of high importance given the requirements of the data user12 - Conclusions 273 12.4 Perspectives12.5 RecommendationsReport of the Working Group on Air Sea Fluxes June, 2000community. Thus the following recommendations concerning the future use of satellite data canbe drawn. Firstly, to approach at least a climatological net flux estimate from satellite dataalone, projects must be established on the combination of different sensors. Future satellitemissions should follow the strategy of the TRMM satellite, where instruments for measuringradiative fluxes, basic variables for heat flux estimation, and precipitation have been installed onone platform. Secondly, to take full advantage of the satellite data there is a definite need forflux reference sites. These should deliver highly accurate measurements of all the heat fluxesfor both error estimation and long term calibration. This is particularly important for theconstruction of long time series from similar sensors on different satellite platforms. Thirdly,the use of satellite data in conjunction with in situ data products should be carefully evaluated toestablish a data set that takes full advantage of each data source and which is independent fromthe models. Finally, within mission related programs, international and national fundingagencies should more strongly support activities targeted on the use of the data for generation orcontinuation of user friendly flux products.The report has shown that all existing flux estimates have deficiencies and that they areonly suitable for some of the described applications. Nevertheless, despite the various problemswith the range of flux products that were reviewed in this report, it is evident that the productsare complementary rather than being in competition, and that the ensemble of air-sea fluxestimates that they represent cover the time and space scales relevant to present ocean sciencestudies.12.5. Specific RecommendationsThe main conclusions from the Working Group's endeavours appear throughout thebody of this report. They have been summarised in the preceding sections of this Chapter whichincludes a number of recommendations. At its final meeting, the Working Group also adoptedthe following specific recommendations:❍ Reanalyses should be performed every 5-10 years by more than one centre; adequateresources should be provided to the reanalysis efforts to improve their surface fluxes, tocarry out and evaluate the reanalyses and to ensure that they are easily available to theentire scientific community. Surface fields should be output every 3 hours .❍ Evaluation of the fluxes from global operational NWP systems will benefit futurereanalyses as well as provide critical guidance and product uncertainty estimates tousers of these flux products. The WGNE's plans to archive and evaluate the fluxes froma number of global NWP systems should therefore be supported.❍ A network of high quality flux reference platforms (combination of long-term mooringsand ships) should be established to deliver highly accurate values of stress and allcomponents of the air-sea heat fluxes for, inter alia, verification of flux estimates fromsatellites and models, and the long term calibration of satellite sensors.❍ There is continuing need to compare and assess the quality of fluxes derived fromvarious sources, and to evaluate the parameterisations used. Encouragement should begiven to efforts to enhance the reliability of momentum, net heat and freshwater fluxesby combining the best estimates from these various sources.❍ Support should be provided for the continuing assembly of flux and flux-related datasets (in particular Voluntary Observing Ship-based collections such as COADS andother historical data). Continued efforts are needed to remove non-stationaryobservational biases in historical data. Basic meteorological variables should beincluded as well as uncertainties, error estimates and adequate documentation for allflux data sets. A catalogue of flux data sets should be maintained on the Internet.12 - Conclusions 274 12.5 RecommendationsReport of the Working Group on Air Sea Fluxes June 2000❍ Research and field experiments are needed to improve boundary layerparameterisations and bulk formulae, especially in regions where our physicalunderstanding is poor. Adequate resources for complete analysis of the resulting dataare necessary to realise the full benefits of the field experiments.ACKNOWLEDGEMENTSSections of this report have been based on contributions written by Working Groupmembers to recent WCRP sponsored workshops. We wish to thank the following individuals:Ken Campana for the paragraph on Air Force nephanalyses; Mike Freilich for contributionsregarding scatterometer data; Bill Rossow for contributions to the radiation section; DetlefStammer and Ichiro Fukumori for input to the section on data assimilation. The Working Groupgratefully acknowledges the sponsorship received from both SCOR and the WCRP and the helpand support from the officers of those bodies.Reference list 275Report of the Working Group on Air Sea Fluxes June, 2000REFERENCEAagaard, K., and P.Greismann, 1975: Toward new massand heat budgets for the Arctic Ocean. J.Geophys.Res., 80, 3821-3827.Alados-Arboledas, L., J. Vida and J.I.Jimenez, 1988:Effects of solar radiation on the performance ofpyrgeometers with silicon domes. J. Atmos. OceanicTechnol., 5, 666-670Alberta, T. L., and T. P. Charlock, 1999: Acomprehensive resource for the investigation ofshort-wave fluxes in clear conditions: CAGEXVersion 3. Proc. 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MEMBERS OF THE WORKING GROUP ON AIR-SEA FLUXESDr. Sergey Gulev (co-chair),P.P.Shirshov Institute of Oceanology,RAS36 Nakhimovsky Ave.,117218 Moscow,RussiaPeter K. Taylor (co-chair),James Rennell Division (254/27)Southampton Oceanography Centre,European Way,Southampton, SO14 3ZH,United KingdomDr. Bernard BarnierLEGIInstitut de Mécanique de GrenobleBP53, 38041, Grenoble Cedex,FranceDr. E. Frank BradleyCSIRO, Land and Water,GPO Box 1666,Canberra, ACT 2601,AustraliaDr. Tom CharlockAtmospheric Sciences DivisionMail Stop 420NASA Langley Research CenterHampton, VA 23681-0001,USADr. Peter GlecklerPCMDI,Lawrence Livermore National Lab,P.O. Box 808, L-264University of California,Livermore, CA, 94551,USAProf. Masahisa KubotaSchool of Marine Science andTechnology,Tokai UniversityOrido,Shimizu,Shizuoka 424-8610.JAPANDr. Kunio Kutsuwada,School of Marine Science and Technology,Tokai University3-20-1, Orido,Shimizu, Shizuoka 424-8610,JapanDr. David LeglerCOAPS, Florida State University2035 E.Paul Dirac DriveR.M.Johnson Bldg. - Suite 200Tallahassee, FL 32306-2840,USADr. Ralf LindauInstitut für MeereskundeDuesternbrooker Weg. 20,D-24105 Kiel,GermanyDr. Drew RothrockApplied Physics Laboratory,University of Washington,1013 NE 40th Streeet,Seattle, WA 98105,USADr. Joerg SchulzDeutsches Zentrum für Luft- und RaumfahrtDFD-US KPLinder HoeheD-51170 Koeln,GermanyDr. Arlindo da SilvaData Assimilation OfficeGoddard Space Flight Center,Greenbelt,Maryland, 20771,USADr. Andreas SterlKNMI,PO Box 2013730 AE De Bilt,The NetherlandsDr. Glenn WhiteW/NMC2, WWB, Rm.207National Centers for EnvironmentalPrediction5200 Auth Rd.Washington, D.C. 20233, USA300Report of the Working Group on Air Sea Fluxes June, 2000Appendix B. 301 The CatalogueReport of the Working Group on Air Sea Fluxes June, 2000APPENDIX B. CATALOGUE OF FLUX DATA SETS AND FLUX RELATEDPRODUCTSB.1 IntroductionThis Appendix give details of how to access the catalogue, the catalogue contents,and the information which should be submitted for each catalogue entry.B.2 Accessing the CatalogueThe Catalogue of Flux products is available on the Internet athttp://www-pcmdi.llnl.gov/airseawg/and also mirrored athttp://www.lmd.jussieu.fr/pcmdi-mirror/airseawg/and athttp://www.bom.gov.au/bmrc/clch/pcmdi-mirror/airseawg/It is intended that this catalogue should be continued to be maintained following the end ofthe Working Groups term of office.B.3 Preliminary Initial Contents ListArctic Global Radiation (AGR-1) Data Set ofpoint observationsArctic Global Radiation (AGR-2) GriddedData SetArctic Ocean Radiative FluxesAtlas and Ardizzone SSMI-derived windsCERSATECMWF Operational AnalysesERA-15 ReanalysisFSU Tropical pseudo stress analysesGEOS1 ReanalysisGridded Daily and Monthly ArcticAtmospheric and Surface Data from TOVSHOAPS fluxes from satelliteIABP/POLES 2-m air temperature data setIfM Fluxes and Flux Related Data SetIORAS/IFM North Atlantic wave and wavedependent wind stressJapanese Ocean Flux data sets with Use ofRemote sensing ObservationsNCEP-1 ReanalysisNCEP-2 ReanalysisNMC/NCEP Operational AnalysesOCEANOR wave fieldsORSTOM Tropical Atlantic Pseudo-stressand Sea Surface Temperature AnalysesPolar Radiation FluxesSOC Satellite altimeter significant waveheight global climatologySOC ClimatologyUWM/COADS ClimatologyWAM cycle 4 data obtained with ECMWFRe-Analysis windsWAM wave hindcasts using ERA windsWOCE Surface Meteorology DataB.4 Information to be SubmittedTable B.1 (overleaf) shows the categories of information which are required for aproduct entry and for a verification data set such as an air-sea interaction experiment.Appendix B. 302 The CatalogueReport of the Working Group on Air Sea Fluxes June, 2000PRODUCT DATA SET EXPERIMENT DATA SETName: Area:Geographical area: Time Period:Time period: VARIABLES:Grid: (space/time) platformsResolution:(space/time; note that this maybe less than the grid spacing)instruments (accuracies)VARIABLES: FLUXES:Formulae used measured (method)Data sources calculated (formulae)Corrections applied AVAILABILITYAVAILABILITY: How/WhereHow/where: FormatsFormat(s) ReferencesDOCUMENTATION: DOCUMENTATIONProduct documentation: CommentsReference list:COMMENTS:(including notes on strengths/weaknessesand results of quality tests where these areavailable)Appendix C. 303 Acronyms and AbreviationsReport of the Working Group on Air Sea Fluxes June 2000APPENDIX C. ACRONYMS AND ABBREVIATIONS4DVAR 4D variational assimilationADM Angular Distribution ModelsADCP Acoustic Doppler Current ProfilerAERI Atmospheric Emitted RadianceInterferometerAES (Canadian) Atmospheric EnvironmentServiceAGCM Atmospheric General Circulation ModelAIDJEX Artic Ice Dynamics Joint ExperimentAIP Algorithm Intercomparison Projects ( ofGPCP)AMIP Atmospheric Model IntercomparisonProjectAMSR Advanced Scanning MicrowaveRadiometerAOGCM Atmospheric Ocean General CirculationModel (coupled model)ARESE ARM Enhanced Shortwave ExperimentARGO (experiment to deploy drifting buoys forocean data assimilation)ARKTIS'93 Polarstern Arctic cruisesARM Atmospheric Radiation MeasurementprogramARPEGE Action de Recherche Petite EchelleGrande Echelle - French climate modelASTEX Atlantic Stratocumulus TransformationExperimentATLAS Autonomous Temperature LineAcquisition SystemATOVS Advanced TIROS Operational VerticalSounderATSR Along-Track Scanning RadiometerAVHRR Advanced Very High ResolutionRadiometerBOMEX Barbados Oceanographic MeteorologicalExperimentBDRF Bi-Directional Reflectance FunctionBSRN Baseline Surface Radiation NetworkCAGEX CERES/ARM/GEWEX experimentCART (ARM) Cloud and Radiation TestbedCASP Canadian Atlantic Storm ProjectCASPR Cloud and Surface Parameter RetrievalsystemCCN Cloud Condensation NucleiCDAS Climate Data Assimilation SystemCDC (US) Climate Diagnostics CenterCEAREX Coordinated Eastern Arctic ExperimentCEPEX Central Equatorial Pacific ExperimentCERES Clouds and the Earth's Radiant EnergySystemCERSAT Centre ERS-1 d'Archivage et deTraitementCFD Computational Fluid DynamicsCIMO WMO Commission for Instruments andMethods of ObservationCLASS (ARM radiosonde type)CMAP CPC Merged Analysis of PrecipitationCMDL (NOAA) Climate Monitoring andDiagnostics LaboratoryCMMIV Beaufort Scale defined by Commision forMarine Meteorology 4th SessionCMR-5 Compressed Marine Reports--Product 5(of COADS)COADS Comprehensive Ocean-Atmosphere DataSetCOAPS Center for Ocean Atmosphere PredictionStudies (FSU)COARE Coupled Ocean-Atmosphere ResponseExperimentCOLA . Centre for Ocean-Land-AtmosphereStudiesCOMPARE CERES Ocean Measurement Platform forAerosol and Radiation EvaluationCPC Climate Prediction CenterCSIRO Commonwealth Scientific and IndustrialReserch Organisation (Australia)CTD Conductivity Temperature and Depth(instrument)CTT Convective Transport TheoryDAAC Distributed Active Archive CenterDAO Data Assimilation OfficeDMSP . Defense Meteorological Satellite ProgramDOE Department of EnergyDSD Drop Size DistributionECHAM3 European Centre model with HAMburgphysics (version 3)ECMWF European Centre for Medium RangeWeather ForecastingENSO El Niño / Southern OscillationEOF Empirical Orthogonal FunctionEOS-AM1 Earth Observation System AM1 satellite(now Terra)ERA15 ECMWF 15 year reanalysisERBE Earth Radiation Budget ExperimentERI Engine Room IntakeERICA Experiment on Rapidly IntensifyingCyclones over the AtlanticERS-1 European Remote Sensing satelliteESA European Space AgencyESTAR Electrically Scanning Thinned ArrayRadiometerEUMETSAT European Meteorological Satellite agencyEUROWAVE (system for diagnosis and forecasting ofwaves in European coastal areas)FASINEX Frontal Air-Sea Interaction ExperimentFIFE First ISCCP Field ExperimentFNOC Fleet Numerical Oceongraphy CenterFSU Florida State UniversityFTIR Fourier Transform Infrared InstrumentFTP File Transfer ProtocolGALE Genesis of Atlantic Lows ExperimentGARP Global Atmospheric Research ProgrammeGATE GARP Atlantic Tropical ExperimentGCI Global Cloud ImageryGCM's General Circulation ModelsGCOS Global Climate Observing SystemGEBA Global Energy Balance ArchiveGEOS1 first Goddard Earth Observation SystemreanalysisGEOSAT (altimetric satelllite)GEWEX Global Energy and Water CycleExperimentAppendix C. 304 Acronyms and AbreviationsReport of the Working Group on Air Sea Fluxes June, 2000GIN Greenland/Iceland/Norwegian SeasGISS Godard Institute for Space StudiesGISST Global Ice and Sea Surface Temperature(project)GMS [Japanese] Geostationary MeteorologicalSatelliteGODAE Global Ocean Data AssimilationExperimentGOES-E/W [US] geostationary meteorologicalsatellitesGOOS Global Ocean Observing SystemGPCP Global Precipitation Climatology ProjectGPI Geostationary Precipitation IndexGPS Global Positioning SystemGRDC Global Runoff Data CentreGSFC Goddard Space Flight CentreGTS Global Telecommunications SystemHadCM3 Hadley Centre Climate Model (version 3)HEXMAX (main HEXOS field experiment)HEXOS Humidity Exchange Over the Sea(experiment program)HIRS High-resolution Infrared RadiationSounderHOAPS Hamburg Ocean Atmosphere Parametersand Fluxes from Satellite DataIABP/POLES International Arctic BuoyProgramme/Polar Exchange at the SeaSurfaceICE International Cirrus ExperimentICRCCM Intercomparison of Radiation Codes inClimate ModelsIFREMER Institut Français de la Researche sur laMerIfM Institut für Meereskunde KielIHP International Hydrography Program IMET Improved Meteorology (WHOIinstrumentation package)IOP Intensive Observation PhaseIPCC International Panel on Climate ChangeIR InfraRedISCCP International Satellite Cloud ClimatologyProjectITCZ Inter-Tropical Convergence ZoneJASIN Joint Air-Sea Interaction ExperimentJASMINE Joint Air Sea Monsoon ExperimentJMA . Japan Meteorological AgencyJONSWAP Joint North Sea Wave ProjectJSC Joint Scientific Committee (of the WCRP)KNMI Koninklijk Nederlands MeteorologischInstituutKWAJEX TRMM validation Experiment aroundKwajalein islandLabSea Labrador Sea Deep ConvectionExperimentLBLRTM Line-By-Line Radiative Transfer ModelsLEADEX (Remote Sensing of Lead Properties in theArctic)LMD Laboratoire de Mètèorologie Dynamique(Paris, france)LMR Long Marine RecordsLOWTRAN7 (radiation transfer code)LSG Large Scale Geostrophic modelLW LongwaveMBT Mechanical BathythermographMCSST Multi Channel Sea Surface TemperatureMCTEX Maritime Continent ThunderstormExperiment.METEOSAT [European] geostationary meteorologicalsatelliteMFWT . Meridional Fresh Water TransportMHT Meridional Heat TransportMJO Madden-Julian oscillationMODIS MODerate resolution Imaging Spectro-radiometerMSTG COADS Monthly Summary TrimmedGroupsMSU Microwave Sounding UnitMWSR Microwave Water Substance RadiometerNAO North Atlantic OscillationNASA (US) National Aeronautics and SpaceAdministrationNCAR (US) National Center for AtmosphericResearchNCAR CCM3 NCAR Community Climate Model(version 3)NCAR-ATD NCAR Atmospheric Technology DivisionNCAR-CSM NCAR Climate System ModelNCEP (US) National Centers for EnvironmentalPredictionNCEP1/NCEP2 NCEP/NCAR reanalysesNDBC (US) National Data Buoy CenterNESDIS (US) National Environmental SatelliteData and Information ServiceNHF Net Heat FluxNIP Normal Incidendence PyrheliometerNLW Net Long WaveNMC National Meteorological Centre (nowNCEP)NOAA (US) National Oceanic and AtmosphericAdministrationNOAA/ETL NOAA Environment TechnologyLaboratoryNODC National Oceanographic Data CenterNPO North Pacific OscillationNSCAT NASA ScatterometerNSW Net Short WaveNWP Numerical Weather PredictionOCL Ocean Climate LaboratoryODA Ocean Data AssimilationODBS (Japan) Ocean data Buoy SystemODGP Ocean Data Gathering ProgrammeOGCM Ocean General Circulation ModelOI Optimal InterpolationOLR Outgoing Longwave RadiationOPAICE (French Ocean and ICE model)OPI OLR based Precipitation IndexORG Optical RaingaugeORSTOM (Institut français de recherche scientifiquepour le développement en coopération)OWS Ocean Weather ShipP-ALACE Profiling Autonomous LagrangianCirculation ExplorerPCMDI Program for Climate Model Diagnosis andIntercomparisonPI Principal InvestigatorPIP Precipitation Intercomparison ProjectPIR Eppley precision infrared radiometerPIRATA Pilot Research Moored Array in theTropical AtlanticPNA Pacific-North American patternPR Precipitation RadarPROBE Pilot Radiation Observation ExperimentPROVOR (FRENCH PROFILING FLOAT)PRT platinum resistance thermometerAppendix C. 305 Acronyms and AbreviationsReport of the Working Group on Air Sea Fluxes June 2000QuikScat (NASA Scatterometry Satellite)RADARSAT (Canadian Scatterometry Satellite)RAMS (Valero radiometer)RAPS Rotating Azimuth Plane ScanningRGPS RADARSAT Geophysical ProcessorSystemRH Relative HumidityRHS Right Hand SideRMS/rms Root Mean SquareRRTM Rapid Radiative Transfer ModelRTM Radiative Transfer ModelRTNEPH (United States Air Force) Real-TimeNephanalysisSAR Synthetic Apperture RadarSBUV Solar Backscatter UltraVioletSBWR Ship Borne Wave RecorderScaRaB Scanner for Radiation BudgetSCOPE San Clemente Ocean Probing ExperimentSCOR Scientific Committee on Ocean ResearchSCSMEX South China Sea Monsoon ExperimentSECTIONS Energetically Active Zones of the Ocean(EAZO) and Climate Variabilityexperiment programmeSEMAPHORE Structure des Echanges Mer-Atmosphere, Proprietes des HeterogeneitesOceaniques: researche ExperimentaleSHEBA Surface Heat Budget of the Arctic OceanSMMR Scanning Multichannel MicrowaveRadiometerSMOS Soil Moisture and Ocean SalinitySOC Southampton Oceanography CentreSOFIA Surface of the Ocean, Fluxes andInteractions with the AtmosphereSRB surface radiation budgetSRB Surface Radiation BudgetSSI spectral statistical interpolationSSM/I Special Sensor Microwave/ImagerSSS Sea Surface SalinitySSST Sea Surface SkinTemperatureSST Sea Surface TemperatureSVD Singular Vector DecompositionSW ShortwaveSWH Significant Wave HeightSWS-2 Storm Wave Study - 2TAO Tropical Atmosphere Ocean ArrayTIROS Television InfraRed ObservationalSatelliteTMI TRMM Microwave ImagerTOA Top Of the AtmosphereTOGA Tropical Ocean Global AtmosphereTOMS Total Ozone Measuring SystemTOPEX/POSEIDON (Altimetric satellite system)TOVS TIROS Operational Vertical SounderTRITON (West Pacific buoy array)TRMM Tropical Rainfall Measuring MissionTWP ARM Tropical West PacificUCAR University Corporation for AtmosphericResearchUKMO United Kingdom Meteorological OfficeURL Internet address (Uniform ResourceLocator)UWM University of Wisconsin-MilwaukeeVIRS Visible and Infrared ScannerVISSR Visible Infra Red Spin-Scan Radiometer(on GMS)VOS Voluntary Observing ShipsVSOP-NA VOS Special Observing Programme -North AtlanticVTPR Vertical temperature Profile RadiometerWAM Wave ModelWAMDI Wave Model Development andImplementation groupWCRP World Climate Research ProgrammeWGASF Working Group on Air-Sea FluxesWGNE Working Group on NumericalExperimentationWHOI Woods Hole Oceanographic InstitutionWMO World Meteorological OrganisationWOCE World Ocean Circulation ExperimentWOCE AIMS WOCE Analysis Interpretation Modellingand SynthesisWRR World Radiation ReferenceXBT Expendable Bathy-ThermographXCTD Expendable CTD306Report of the Working Group on Air Sea Fluxes June, 2000

Intercomparison and validation of ocean–atmosphere energy flux fields. Final report of the Joint WCRP/SCOR Working Group on Air–Sea Fluxes (SCOR Working Group 110)

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