A comparison of aircraft-based surface-layer observations over Denmark Strait and the Irminger sea with meteorological analyses and QuikSCAT winds

A compilation of aircraft observations of the atmospheric surface layer is compared with several meteorological analyses and QuikSCAT wind products. The observations are taken during the Greenland Flow Distortion Experiment, in February and March 2007, during cold-air outbreak conditions and moderate to high wind speeds. About 150 data points spread over six days are used, with each data point derived from a 2-min run (equivalent to a 12 km spatial average). The observations were taken 30–50 m above the sea surface and are adjusted to standard heights. Surface-layer temperature, humidity and wind, as well as sea-surface temperature (SST) and surface turbulent fluxes are compared against co-located data from the ECMWF operational analyses, NCEP Global Reanalyses, NCEP North American Regional Reanalyses (NARR), Met Office North Atlantic European (NAE) operational analyses, two MM5 hindcasts, and two QuikSCAT products. In general, the limited-area models are better at capturing the mesoscale high wind speed features and their associated structure; often the models underestimate the highest wind speeds and gradients. The most significant discrepancies are: a poor simulation of relative humidity by the NCEP global and MM5 models, a cold bias in 2 m air temperature near the sea-ice edge in the NAE model, and an overestimation of wind speed above 20 m s-1 in the QuikSCAT wind products. In addition, the NCEP global, NARR and MM5 models all have significant discrepancies associated with the parametrisation of surface turbulent heat fluxes. A high-resolution prescription of the SST field is crucial in this region, although these were not generally used at this time

Probability Distribution Characteristics for Surface Air–Sea Turbulent Heat Fluxes over the Global Ocean

To analyze the probability density distributions of surface turbulent heat fluxes, the authors apply the twoparametric modified Fisher–Tippett (MFT) distribution to the sensible and latent turbulent heat fluxes recomputed from 6-hourly NCEP–NCAR reanalysis state variables for the period from 1948 to 2008. They derived the mean climatology and seasonal cycle of the location and scale parameters of the MFT distribution. Analysis of the parameters of probability distributions identified the areas where similar surface turbulent fluxes are determined by the very different shape of probability density functions. Estimated extreme turbulent heat fluxes amount to 1500–2000 W m22 (for the 99th percentile) and can exceed 2000 W m22 for higher percentiles in the subpolar latitudes and western boundary current regions. Analysis of linear trends and interannual variability in the mean and extreme fluxes shows that the strongest trends in extreme fluxes (more than 15 W m22 decade21) in the western boundary current regions are associated with the changes in the shape of distribution. In many regions changes in extreme fluxes may be different from those for the mean fluxes at interannual and decadal time scales. The correlation between interannual variability of themean and extreme fluxes is relatively low in the tropics, the SouthernOcean, and the Kuroshio Extension region.Analysis of probability distributions in turbulent fluxes has also been used in assessing the impact of sampling errors in theVoluntaryObserving Ship (VOS)-based surface flux climatologies, allowed for the estimation of the impact of sampling in extreme fluxes. Although sampling does not have a visible systematic effect onmean fluxes, sampling uncertainties result in the underestimation of extreme flux values exceeding 100 W m22 in poorly sampled regions

Open-ocean convection: observations, theory and models

We review what is known about the convective process in the open ocean, in which the properties of large volumes of water are changed by intermittent, deep-reaching convection, triggered by winter storms. Observational, laboratory, and modeling studies reveal a fascinating and complex interplay of convective and geostrophic scales, the large-scale circulation of the ocean, and the prevailing meteorology. Two aspects make ocean convection interesting from a theoretical point of view. First, the timescales of the convective process in the ocean are sufficiently long that it may be modified by the Earth's rotation; second, the convective process is localized in space so that vertical buoyancy transfer by upright convection can give way to slantwise transfer by baroclinic instability. Moreover, the convective and geostrophic scales are not very disparate from one another. Detailed observations of the process in the Labrador, Greenland, and Mediterranean Seas are described, which were made possible by new observing technology. When interpreted in terms of underlying dynamics and theory and the context provided by laboratory and numerical experiments of rotating convection, great progress in our description and understanding of the processes at work is being made

A high-resolution simulation of convective roll clouds during a cold-air outbreak

A ubiquitous feature of the high latitude ocean is the occurrence of convective clouds that are organized into two-dimensional structures known as roll clouds or cloud streets. In this paper, we present a simulation of these structures that was performed in a domain large enough to simulate numerous roll clouds and their downstream evolution at a resolution sufficient to resolve the individual convective clouds. The simulations were initialized and validated using observations of roll clouds over the Labrador Sea. The model results indicate that the secondary flow associated with the roll clouds results in significant differences in the temperature, humidity and momentum fields between the updrafts and downdrafts. The model was also able to reproduce the observed downstream evolution of the clouds as the organization of the convection changed from two-dimensional rolls to three-dimensional closed cells

Estimation of the impact of sampling errors in the VOS observations on air-sea fluxes. Part II: Impact on trends and interannual variability

Using the same approach as in Part I, here it is shown how sampling problems in voluntary observing ship (VOS) data affect conclusions about interannual variations and secular changes of surface heat fluxes. The largest uncertainties in linear trend estimates are found in relatively poorly sampled regions like the high-latitude North Atlantic and North Pacific as well as the Southern Ocean, where trends can locally show opposite signs when computed from the regularly sampled and undersampled data. Spatial patterns of shorter-period interannual variability, quantified through the EOF analysis, also show remarkable differences between the regularly sampled and undersampled flux datasets in the Labrador Sea and northwest Pacific. In particular, it is shown that in the Labrador Sea region, in contrast to regularly sampled NCEP–NCAR reanalysis fluxes, VOS-like sampled NCEP–NCAR reanalysis fluxes neither show significant interannual variability nor significant trends. These regions, although quite localized covering small parts of the globe, play a crucial role for the coupled atmosphere–ocean system. In the Labrador Sea, for instance, interannual and decadal-scale changes of the surface net heat fluxes are known to affect oceanic convection and, thus, the meridional overturning circulation of the Atlantic Ocean. From a discussion of current atmospheric data assimilation systems it is argued that in poorly sampled regions reanalysis products are superior to VOS-based products for studying interannual and interdecadal variations of atmosphere–ocean interaction. In well-sampled regions, on the other hand, conclusions about surface heat flux variations are relatively insensitive to the choice of the flux products used (VOS versus reanalysis data). The results are confirmed for two different datasets, that is, ECMWF 40-yr Re-Analysis (ERA-40) data and seasonal integrations with a recent version of the ECMWF model in which no actual data were assimilated

The Labrador Sea Deep Convection Experiment data collection

[1] Between 1996 and 1998, a concerted effort was made to study the deep open ocean convection in the Labrador Sea. Both in situ observations and numerical models were employed with close collaboration between the researchers in the fields of physical oceanography, boundary layer meteorology, and climate. A multitude of different methods were used to observe the state of ocean and atmosphere and determine the exchange between them over the experiment's period. The Labrador Sea Deep Convection Experiment data collection aims to assemble the observational data sets in order to facilitate the exchange and collaboration between the various projects and new projects for an overall synthesis. A common file format and a browsable inventory have been used so as to simplify the access to the data

Estimation of the impact of sampling errors in the VOS observations on air-sea fluxes. Part I: Uncertainties in climate means

Sampling uncertainties in the voluntary observing ship (VOS)-based global ocean–atmosphere flux fields were estimated using the NCEP–NCAR reanalysis and ECMWF 40-yr Re-Analysis (ERA-40) as well as seasonal forecasts without data assimilation. Air–sea fluxes were computed from 6-hourly reanalyzed individual variables using state-of-the-art bulk formulas. Individual variables and computed fluxes were subsampled to simulate VOS-like sampling density. Random simulation of the number of VOS observations and simulation of the number of observations with contemporaneous sampling allowed for estimation of random and total sampling uncertainties respectively. Although reanalyses are dependent on VOS, constituting an important part of data assimilation input, it is assumed that the reanalysis fields adequately reproduce synoptic variability at the sea surface. Sampling errors were quantified by comparison of the regularly sampled (i.e., 6 hourly) and subsampled monthly fields of surface variables and fluxes. In poorly sampled regions random sampling errors amount to 2.5°–3°C for air temperature, 3 m s−1 for the wind speed, 2–2.5 g kg−1 for specific humidity, and 15%–20% of the total cloud cover. The highest random sampling errors in surface fluxes were found for the sensible and latent heat flux and range from 30 to 80 W m−2. Total sampling errors in poorly sampled areas may be higher than random ones by 60%. In poorly sampled subpolar latitudes of the Northern Hemisphere and throughout much of the Southern Ocean the total sampling uncertainty in the net heat flux can amount to 80–100 W m−2. The highest values of the uncertainties associated with the interpolation/extrapolation into unsampled grid boxes are found in subpolar latitudes of both hemispheres for the turbulent fluxes, where they can be comparable with the sampling errors. Simple dependencies of the sampling errors on the number of samples and the magnitude of synoptic variability were derived. Sampling errors estimated from different reanalyses and from seasonal forecasts yield qualitatively comparable spatial patterns, in which the actual values of uncertainties are controlled by the magnitudes of synoptic variability. Finally, estimates of sampling uncertainties are compared with the other errors in air–sea fluxes and the reliability of the estimates obtained is discussed