Global Modeling and Assimilation Office Annual Report and Research Highlights 2011-2012

Over the last year, the Global Modeling and Assimilation Office (GMAO) has continued to advance our GEOS-5-based systems, updating products for both weather and climate applications. We contributed hindcasts and forecasts to the National Multi-Model Ensemble (NMME) of seasonal forecasts and the suite of decadal predictions to the Coupled Model Intercomparison Project (CMIP5)

Frontogenesis in the North Pacific Oceanic Frontal Zones--A Numerical Simulation

A primitive equation model [Geophysical Fluid Dynamics Laboratory\u27s (GFDL\u27s) MOM 2] with one degree horizontal resolution is used to simulate the seasonal cycle of frontogenesis in the subarctic frontal zone (SAFZ) and the subtropical frontal zone (STFZ) of the North Pacific Ocean. The SAFZ in the model contains deep (greater than 500 m in some places) regions with seasonally varying high gradients in temperature and salinity. The gradients generally weaken toward the east. The STFZ consists of a relatively shallow (less than 200 m in most places) region of high gradient in temperature that disappears in the summer/fall. The high gradient in salinity in the STFZ maintains its strength year round and extends across almost the entire basin. The model simulates the location and intensity of the frontal zones in good agreement with climatological observations: generally to within two degrees of latitude and usually at the same or slightly stronger intensity. The seasonal cycle of the frontal zones also marches observations well, although the subarctic front is stronger than observed in winter and spring. The model balances are examined to identify the dominant frontogenetic processes. The seasonal cycle of temperature frontogenesis in the surface level of the model is governed by both the convergence of the wind-driven Ekman transport and differential heating/cooling. In the STFZ, the surface Ekman convergence is frontogenetic throughout the year as opposed to surface heating, which is frontogenetic during winter and strongly frontolytic during late spring and summer. The subarctic front at 40 degrees N in the central Pacific (not the maximum wintertime gradient in the model, but its location in summer and the location where variability is in best agreement with the observations) undergoes frontogenesis during spring and summer due to surface Ekman convergence and differential horizontal shear. The frontolysis during winter is due to the joint influence of differential heat flux and vertical convection in opposition to frontogenetic Ekman convergence. The seasonal cycle of salinity frontogenesis in the surface level is governed by Ekman convergence, differential surface freshwater flux, and differential vertical convection (mixing). For salinity, the differential convection is primarily forced by Ekman convergence and differential cooling, thereby linking the salinity and temperature frontogenesis/frontolysis. Below the surface level, the seasonal frontogenesis/frontolysis is only significant in the western and central SAFZ where ii is due primarily to differential mixing (mostly in winter and early spring) with contributions from convergence and shearing advection during fall and winter. The shearing advection in the model western SAFZ is likely a result of the Kuroshio overshooting its observed separation latitude. The model\u27s vertical mixing through convective adjustment is found to be very important in controlling much of the frontogenesis/frontolysis. Thus, the seasonal cycle of the surface frontal variability depends strongly on the subsurface structure

A note on frictional effects in Taylor\u27s problem

Taylor\u27s tidal problem of the reflection of a Kelvin wave in a semi-infinite rotating channel is modified here by considering the effect of the inclusion of fri ction in the analysis. Results are obtained using Galerkin and Collocation methods to satisfy the end boundary condition, and these are compared with results given by other authors for the nonfriction case

An Assessment of the Skill of GEOS-5 Seasonal Forecasts

The seasonal forecast skill of the NASA Global Modeling and Assimilation Office coupled global climate model (CGCM) is evaluated based on an ensemble of 9-month lead forecasts for the period 1993 to 2010. The results from the current version (V2) of the CGCM consisting of the GEOS-5 AGM coupled to the MOM4 ocean model are compared with those from an earlier version (V1) in which the AGCM (the NSIPP model) was coupled to the Poseidon Ocean Model. It was found that the correlation skill of the Sea Surface Temperature (SST) forecasts is generally better in V2, especially over the sub-tropical and tropical central and eastern Pacific, Atlantic, and Indian Ocean. Furthermore, the improvement in skill in V2 mainly comes from better forecasts of the developing phase of ENSO from boreal spring to summer. The skill of ENSO forecasts initiated during the boreal winter season, however, shows no improvement in terms of correlation skill, and is in fact slightly worse in terms of root mean square error (RMSE). The degradation of skill is found to be due to an excessive ENSO amplitude. For V1, the ENSO amplitude is too strong in forecasts starting in boreal spring and summer, which causes large RMSE in the forecast. For V2, the ENSO amplitude is slightly stronger than that in observations and V1 for forecasts starting in boreal winter season. An analysis of the terms in the SST tendency equation, shows that this is mainly due to an excessive zonal advective feedback. In addition, V2 forecasts that are initiated during boreal winter season, exhibit a slower phase transition of El Nino, which is consistent with larger amplitude of ENSO after the ENSO peak season. It is found that this is due to weak discharge of equatorial Warm Water Volume (WWV). In both observations and V1, the discharge of equatorial WWV leads the equatorial geostrophic easterly current so as to damp the El Nino starting in January. This process is delayed by about 2 months in V2 due to the slower phase transition of the equatorial zonal current from westerly to easterly

Ensemble Data Assimilation Without Ensembles: Methodology and Application to Ocean Data Assimilation

Two methods to estimate background error covariances for data assimilation are introduced. While both share properties with the ensemble Kalman filter (EnKF), they differ from it in that they do not require the integration of multiple model trajectories. Instead, all the necessary covariance information is obtained from a single model integration. The first method is referred-to as SAFE (Space Adaptive Forecast error Estimation) because it estimates error covariances from the spatial distribution of model variables within a single state vector. It can thus be thought of as sampling an ensemble in space. The second method, named FAST (Flow Adaptive error Statistics from a Time series), constructs an ensemble sampled from a moving window along a model trajectory. The underlying assumption in these methods is that forecast errors in data assimilation are primarily phase errors in space and/or time

Background Error Covariance Estimation Using Information from a Single Model Trajectory with Application to Ocean Data Assimilation

An attractive property of ensemble data assimilation methods is that they provide flow dependent background error covariance estimates which can be used to update fields of observed variables as well as fields of unobserved model variables. Two methods to estimate background error covariances are introduced which share the above property with ensemble data assimilation methods but do not involve the integration of multiple model trajectories. Instead, all the necessary covariance information is obtained from a single model integration. The Space Adaptive Forecast error Estimation (SAFE) algorithm estimates error covariances from the spatial distribution of model variables within a single state vector. The Flow Adaptive error Statistics from a Time series (FAST) method constructs an ensemble sampled from a moving window along a model trajectory.SAFE and FAST are applied to the assimilation of Argo temperature profiles into version 4.1 of the Modular Ocean Model (MOM4.1) coupled to the GEOS-5 atmospheric model and to the CICE sea ice model. The results are validated against unassimilated Argo salinity data. They show that SAFE and FAST are competitive with the ensemble optimal interpolation (EnOI) used by the Global Modeling and Assimilation Office (GMAO) to produce its ocean analysis. Because of their reduced cost, SAFE and FAST hold promise for high-resolution data assimilation applications

The GEOS-iODAS: Description and Evaluation

This report documents the GMAO's Goddard Earth Observing System sea ice and ocean data assimilation systems (GEOS iODAS) and their evolution from the first reanalysis test, through the implementation that was used to initialize the GMAO decadal forecasts, and to the current system that is used to initialize the GMAO seasonal forecasts. The iODAS assimilates a wide range of observations into the ocean and sea ice components: in-situ temperature and salinity profiles, sea level anomalies from satellite altimetry, analyzed SST, and sea-ice concentration. The climatological sea surface salinity is used to constrain the surface salinity prior to the Argo years. Climatological temperature and salinity gridded data sets from the 2009 version of the World Ocean Atlas (WOA09) are used to help constrain the analysis in data sparse areas. The latest analysis, GEOS ODAS5.2, is diagnosed through detailed studies of the statistics of the innovations and analysis departures, comparisons with independent data, and integrated values such as volume transport. Finally, the climatologies of temperature and salinity fields from the Argo era, 2002-2011, are presented and compared with the WOA09

Ocean data assimilation using optimal interpolation with a quasi-geostrophic model

Optimal interpolation (OI) has been used to produce analyses of quasi-geostrophic (QG) stream function over a 59-day period in a 150-km-square domain off northern California. Hydrographic observations acquired over five surveys, each of about 6 days' duration, were assimilated into a QG open boundary ocean model. Since the true forecast error covariance function required for the OI is unknown, assimilation experiments were conducted separately for individual surveys to investigate the sensitivity of the OI analyses to parameters defining the decorrelation scale of an assumed error covariance function. The analyses were intercompared through dynamical hindcasts between surveys, since there were too few independent data for other verification of the various analyses. For the hindcasts, the QG model was initialized with an analysis for one survey and then integrated according to boundary data supplied by the corresponding analysis for the next survey. Two sets of such hindcasts were conducted, since there were only three statistically independent realizations of the stream function field for the entire observing period. For the irregular sampling strategy of the first half of the observing period, the best hindcast was obtained using the smooth analyses produced with assumed error decorrelation scales identical to those of the observed stream function (about 80 km): the root mean square difference between the hindcast stream function and the final analysis was only 23% of the observation standard deviation. The best hindcast (with a 31% error) for the second half of the observing period was obtained using an initial analysis based on an 80-km decorrelation scale and a final analysis based on a 40-km decorrelation scale. The change in decorrelation scale was apparently associated with a change in sampling strategy and the importance of the resolution of small-scale vorticity input across the open boundary. The last survey used a regular sampling scheme with good coverage (about 20-km resolution) of the entire domain so that smaller-scale features were resolved by the data. The earlier surveys used a coarser (about 75 km) sampling resolution, and smaller-scale features that were not well-resolved could not be inferred correctly even with short error covariance scales. During the hindcast integrations, the dynamical model effectively filtered the stream function fields to reduce differences between the various initial fields. Differences between the analyses near inflow boundary points ultimately dominated the differences between dynamical hindcasts. Analyses for the entire 59-day observing period of the five independent surveys were produced using continuous assimilation. A modified form of OI in which the forecast error variances were updated according to the analysis error variances and an assumed model error growth rate was also used, allowing the OI to retain information about prior assimilation. The analyses using the updated variances were spatially smoother and often in better agreement with the observations than the OI analyses using constant variances. The two sets of OI analyses were temporally smoother than the fields from statistical objective analysis (OA) and in good agreement with the only independent data available for comparison. Unfortunately, the limiting factor in the validation of the assimilation methodology remains the paucity of observations

Atmospheric Reanalyses-Recent Progress and Prospects for the Future. A Report from a Technical Workshop, April 2010

In April 2010, developers representing each of the major reanalysis centers met at Goddard Space Flight Center to discuss technical issues - system advances and lessons learned - associated with recent and ongoing atmospheric reanalyses and plans for the future. The meeting included overviews of each center s development efforts, a discussion of the issues in observations, models and data assimilation, and, finally, identification of priorities for future directions and potential areas of collaboration. This report summarizes the deliberations and recommendations from the meeting as well as some advances since the workshop

Methodology

© The Author(s) 2019. A detailed overview of the methodologies used to develop the 2.0 °C and 1.5 °C scenario presented in this book. Starting with the overall modelling approach, the interaction of seven different models is explained which are used to calculate and developed detailed scenarios for greenhouse gas emission and energy pathways to stay within a 2.0 °C and 1.5 °C global warming limit. The following models are presented: For the non-energy GHG emission pathways, the Generalized Equal Quantile Walk (GQW)method, the land-based sequestration design method and the Carbon cycle and climate (MAGICC) model. For the energy pathways, a renewable energy resources assessment for space constrained environments ([R]E-SPACE, the transport scenario model (TRAEM), the Energy System Model (EM) and the power system model [R]E 24/7. The methodologies of an employment analysis model, and a metal resource assessment tool are outlined. These models have been used to examine the analysis of the energy scenario results