Goddard Satellite-Based Surface Turbulent Fluxes Research Articles

An Improved Near-Surface Specific Humidity and Air Temperature Climatology for the SSM/I Satellite Period

AbstractA near-surface specific humidity (Qa) and air temperature (Ta) climatology on daily and 0.25° grids was constructed by the objectively analyzed air–sea fluxes (OAFlux) project by objectively merging two recent satellite-derived high-resolution analyses, the OAFlux existing 1° analysis, and atmospheric reanalyses. The two satellite products include the multi-instrument microwave regression (MIMR) Qa and Ta analysis and the Goddard Satellite-Based Surface Turbulent Fluxes, version 3 (GSSTF3), Qa analysis. This study assesses the degree of improvement made by OAFlux using buoy time series measurements at 137 locations and a global empirical orthogonal function (EOF) analysis. There are a total of 130 855 collocated daily values for Qa and 283 012 collocated daily values for Ta in the buoy evaluation. It is found that OAFlux Qa has a mean difference close to 0 and a root-mean-square (RMS) difference of 0.73 g kg−1, and Ta has a mean difference of −0.03°C and an RMS difference of 0.45°C. OAFlux shows no major systematic bias with respect to buoy measurements over all buoy locations except for the vicinity of the Gulf Stream boundary current, where the RMS difference exceeds 1.8°C in Ta and 1.2 g kg−1 in Qa. The buoy evaluation indicates that OAFlux represents an improvement over MIMR and GSSTF3. The global EOF-based intercomparison analysis indicates that OAFlux has a similar spatial–temporal variability pattern with that of three atmospheric reanalyses including MERRA, NCEP-1, and ERA-Interim, but that it differs from GSSTF3 and the Climate Forecast System Reanalysis (CFSR).

On the net surface water exchange rate estimated from remote-sensing observation and reanalysis

This study compares the net surface water exchange rates, or surface precipitation (P) minus evapotranspiration (ET), and atmospheric water vapour sinks calculated from various observations and reanalyses, and investigates whether they are physically consistent. We use the observed precipitation from the Global Precipitation Climatology Project (GPCP) and the Tropical Rainfall Measuring Mission (TRMM) 3B43, ocean evaporation from Goddard Satellite-based Surface Turbulent Fluxes Version 2c (GSSTF2c), and land ET from the Moderate Resolution Imaging Spectroradiometer (MODIS) global ET project (MOD16) and PT-JPL products to calculate observed P minus observed ET. P–ET is also obtained from atmospheric water vapour sink calculated using Atmospheric Infrared Sounder (AIRS)/Advanced Microwave Sounding Unit observation specific humidity observation and wind fields from the Modern-Era Retrospective Analysis for Research and Applications (MERRA) and ERA-interim, denoted as AIRSM and AIRSE, respectively. MERRA and ERA-interim water vapour budgets are also calculated for cross-comparison and consistency check. The period of study is between 2003 and 2006 based on the availability of all of the data sets. Averaged water vapour sinks from AIRS and reanalysis are consistent over the global ocean and are close to zero (range: 0.02–0.06 mm day−1), but range between 0.14 and 0.23 mm day−1 when land is included. Over ocean within 50oS--50oN, averaged observed P minus observed evaporation shows a much larger negative number than that obtained from AIRS and reanalysis. The differences mainly occur over subtropical oceans, especially in the southern hemisphere in summer and the northern hemisphere in winter. Over land, generally higher agreement between observed P minus observed ET and atmospheric water vapour sinks (calculated from AIRS and reanalysis) is found. However, large regional differences, often with strong seasonal dependence, are also observed over land. Estimates of atmospheric water vapour sinks are influenced by both winds and biases in water vapour data, especially over tropics and subtropical oceans, thereby calling for the need for further investigations and consistency checks of satellite-based and reanalysis water vapour, reanalysis winds, P observations, and surface evaporation estimates. In higher latitudes, atmospheric water vapour sinks calculated from AIRSM, AIRSE, MERRA, and ERA-interim are more consistent with each other.

Validation of Satellite-Derived Daily Latent Heat Flux over the South China Sea, Compared with Observations and Five Products

Abstract This study describes the development of the South China Sea (SCS) daily satellite-derived latent heat flux (SCSSLH) for the period of 1998–2011 at 0.25° × 0.25° resolution using data mainly from the Tropical Rain Measuring Mission (TRMM) Microwave Imager (TMI). Flux-related variables of daily TMI data smoothed with 3-day running mean were finally chosen because of the best fit with the 1727 high-quality observations from seven moored stations and 24 ship surveys. Near-surface air specific humidity was computed using the global relationship based on satellite precipitable water. Verification against 1016 high-resolution radiosonde profiles from 1998 to 2012 and the time series from the Xisha automatic weather station during 2008–10 indicate that this satellite-derived air specific humidity can reasonably capture observed mean condition and temporal variability. They are therefore used to derive SCSSLH based on the Coupled Ocean–Atmosphere Response Experiment version 3.0 (COARE 3.0) algorithm. Compared with five other latent heat flux products—the Goddard Satellite-Based Surface Turbulent Fluxes version 2 (GSSTF2), the objectively analyzed air–sea heat fluxes (OAFlux), the Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data version 3 (HOAPS3), the National Centers for Environmental Prediction/Department of Energy Global Reanalysis 2 (NCEP-2), and the European Centre for Medium-Range Weather Forecasts (ECMWF)—the daily SCSSLH shows the highest spatial resolution and realistic values in the SCS, with an exception along the northern continental shelf. More importantly, the other five products seem to overestimate the latent heat flux systematically. The flux representation in this study comes not only with a better flux algorithm but also with the improved estimates of bulk variables based on in situ measurements, which further highlights the unique role of high-quality meteorological measurements and atmospheric weather stations in evaluating the air–sea interaction in the SCS.

Trends and variations of ocean surface latent heat flux: Results from GSSTF2c data set

Trends and variations of Goddard Satellite‐based Surface Turbulent Fluxes (GSSTF) version 2c (GSSTF2c) latent heat flux (LHF) are examined. This version of LHF takes account of the correction in Earth incidence angle. The trend of global mean LHF for GSSTF2c is much reduced relative to GSSTF version 2b Set 1 and Set 2 for the same period 1988–2008. Temporal increase of GSSTF2c LHF in the two decades is 11.0%, in which 3.1%, 5.8%, and 2.1% are attributed to the increase in wind, the increase in sea surface saturated air humidity, and the decrease in near‐surface air humidity, respectively. The first empirical orthogonal function of LHF is a conventional El Niño Southern Oscillation (ENSO) mode. However, the trends in LHF are independent of conventional ENSO phenomena. After removing ENSO signal, the pattern of LHF trends is primarily determined by the pattern of air‐sea humidity difference trends.

Closing the Global Water Vapor Budget with AIRS Water Vapor, MERRA Reanalysis, TRMM and GPCP Precipitation, and GSSTF Surface Evaporation

Abstract The authors investigate if atmospheric water vapor from remote sensing retrievals obtained from the Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit (AIRS) and the water vapor budget from the NASA Goddard Space Flight Center (GSFC) Modern Era Retrospective-analysis for Research and Applications (MERRA) are physically consistent with independently synthesized precipitation data from the Tropical Rainfall Measuring Mission (TRMM) or the Global Precipitation Climatology Project (GPCP) and evaporation data from the Goddard Satellite-based Surface Turbulent Fluxes (GSSTF). The atmospheric total water vapor sink (Σ) is estimated from AIRS water vapor retrievals with MERRA winds (AIRS–MERRA Σ) as well as directly from the MERRA water vapor budget (MERRA–MERRA Σ). The global geographical distributions as well as the regional wavelet amplitude spectra of Σ are then compared with those of TRMM or GPCP precipitation minus GSSTF surface evaporation (TRMM–GSSTF and GPCP–GSSTF P − E, respectively). The AIRS–MERRA and MERRA–MERRA Σs reproduce the main large-scale patterns of global P − E, including the locations and variations of the ITCZ, summertime monsoons, and midlatitude storm tracks in both hemispheres. The spectra of regional temporal variations in Σ are generally consistent with those of observed P − E, including the annual and semiannual cycles, and intraseasonal variations. Both AIRS–MERRA and MERRA–MERRA Σs have smaller amplitudes for the intraseasonal variations over the tropical oceans. The MERRA P − E has spectra similar to that of MERRA–MERRA Σ in most of the regions except in tropical Africa. The averaged TRMM–GSSTF and GPCP–GSSTF P − E over the ocean are more negative compared to the AIRS–MERRA, MERRA–MERRA Σs, and MERRA P − E.

An Assessment of the Uncertainties in Ocean Surface Turbulent Fluxes in 11 Reanalysis, Satellite-Derived, and Combined Global Datasets

Abstract Ocean surface turbulent fluxes play an important role in the energy and water cycles of the atmosphere–ocean coupled system, and several flux products have become available in recent years. Here, turbulent fluxes from 6 widely used reanalyses, 4 satellite-derived flux products, and 2 combined product are evaluated by comparison with direct covariance latent heat (LH) and sensible heat (SH) fluxes and inertial-dissipation wind stresses measured from 12 cruises over the tropics and mid- and high latitudes. The biases range from −3.0 to 20.2 W m−2 for LH flux, from −1.4 to 6.0 W m−2 for SH flux, and from −7.6 to 7.9 × 10−3 N m−2 for wind stress. These biases are small for moderate wind speeds but diverge for strong wind speeds (>10 m s−1). The total flux biases are then further evaluated by dividing them into uncertainties due to errors in the bulk variables and the residual uncertainty. The bulk-variable-caused uncertainty dominates many products’ SH flux and wind stress biases. The biases in the bulk variables that contribute to this uncertainty can be quite high depending on the cruise and the variable. On the basis of a ranking of each product’s flux, it is found that the Modern-Era Retrospective Analysis for Research and Applications (MERRA) is among the “best performing” for all three fluxes. Also, the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim) and the National Centers for Environmental Prediction–Department of Energy (NCEP–DOE) reanalysis are among the best performing for two of the three fluxes. Of the satellite-derived products, version 2b of the Goddard Satellite-Based Surface Turbulent Fluxes (GSSTF2b) is among the best performing for two of the three fluxes. Also among the best performing for only one of the fluxes are the 40-yr ERA (ERA-40) and the combined product objectively analyzed air–sea fluxes (OAFlux). Direction for the future development of ocean surface flux datasets is also suggested.

An inter-comparison of six latent and sensible heat flux products over the Southern Ocean

The latent heat fluxes (LHF) and sensible heat fluxes (SHF) over the Southern Ocean from six different data sets are inter-compared for the period 1988- 2000. The six data sets include three satellite-based products, namely, the second version of the Goddard Satellite-Based Surface Turbulent Fluxes data set (GSSTF-2), the third version of the Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data (HOAPS-3) and the Japanese Ocean Fluxes Data Sets with Use of Remote Sensing Observations (J-OFURO); two global reanalysis products, namely, the National Centers for Environmental Prediction-Department of Energy Reanalysis 2 data set (NCEP-2) and the European Centre for Medium-Range Weather Forecasts 40 Year Re-analysis data set (ERA-40); and the Objectively Analyzed Air-Sea Fluxes for the Global Oceans data set (OAFlux). All these products reveal a similar pattern in the averaged flux fields. The zonal mean LHF fields all exhibit a continuous increase equatorward. With an exception of HOAPS-3, the zonal mean SHF fields display a minimum value near 50°S, increasing both pole- and equatorward. The differences in the standard deviation for LHF are larger among the six data products than the differences for SHF. Over the regions where the surface fluxes are significantly influenced by the Antarctic Oscillation and the Pacific-South American teleconnection, the values and distributions of both LHF and SHF are consistent among the six products. It was found that the spatial patterns of the standard deviations and trends of LHF and SHF can be explained primarily by sea-air specific humidity and temperature differences; wind speed plays a minor role. Keywords: Latent heat flux; sensible heat flux; Southern Ocean (Published: 17 November 2011) Citation: Polar Research 2011, 30 , 10167, DOI: 10.3402/polar.v30i0.10167

A note on reviving the Goddard Satellite-based Surface Turbulent Fluxes (GSSTF) dataset

Accurate sea surface flux measurements are crucial for understanding the global water and energy cycles. The oceanic evaporation, which is a major component of the global oceanic fresh water flux, is useful for predicting oceanic circulation and transport. The global Goddard Satellite-based Surface Turbulent Fluxes Version-2 (GSSTF2; July 1987–December 2000) dateset that was officially released in 2001 has been widely used by scientific community for global energy and water cycle research, and regional and short period data analyses. We have recently been funded by NASA to resume processing the GSSTF dataset with an objective of continually producing a uniform dataset of sea surface turbulent fluxes, derived from remote sensing data. The dataset is to be reprocessed and brought up-to-date (GSSTF2b) using improved input datasets such as a recently upgraded NCEP/DOE sea surface temperature reanalysis, and an upgraded surface wind and microwave brightness temperature V6 dataset (Version 6) from the Special Sensor Microwave Imager (SSM/I) produced by Remote Sensing Systems (RSS). A second new product (GSSTF3) is further proposed with a finer temporal (12-h) and spatial (0.25° × 0.25°) resolution. GSSTF2b (July 1987–December 2008) and GSSTF3 (July 1999–December 2009) will be released for the research community to use by late 2009 and early 2011, respectively.

Evaluation of a satellite-derived latent heat flux product in the South China Sea: A comparison with moored buoy data and various products

Latent heat flux in the South China Sea (SCS) mainly based on the Tropical Rain Measuring Mission Microwave Imager (TMI) is compared with the Objectively Analyzed air-sea heat Fluxes (OAFlux), the Goddard Satellite-Based Surface Turbulent Fluxes, version 2 (GSSTF2), the Japanese Ocean Flux Data Sets with use of Remote Sensing Observations (J-OFURO), the European Centre for Medium-Range Weather Forecasts (ECMWF), the National Centers for Environmental Prediction/Department of Energy reanalysis 2 (NCEP2), and International Comprehensive Ocean-Atmosphere Data Set data (ICOADS). These products did not all compare well to in situ data. It is found that TMI latent heat flux and OAFlux approximately reproduce the time variations of buoy data. However, ECMWF and ICOADS were found that had moderate systematic errors with respect to in situ data. All products qualitatively reveal a similar pattern in climatological fields. Although the satellite-derived products are quite similar, TMI latent heat is smaller than those of OAFlux, GSSTF2 and J-OFURO in the northern SCS. The difference between TMI latent heat and ICOADS is large, and the temporal correlation is extremely low in the entire basin except the Luson Strait. This is owned to that the ICOADS hardly reproduces accurate variability in the data-sparse regions. Also the temporal cross-correlation coefficients of between TMI latent heat and ECMWF or NCEP2 are considerably lower in the central SCS, may be affected by the lack of ship observations of ECMWF and NCEP2 there. The present study also compares meridional structures of the zonal averaged latent heat. The TMI latent heat and OAFlux overestimate the other two satellite-derived products during summer, whereas in winter, the TMI latent heat underestimates the other products in the northern regions. Though the examination of the influence of input variables, we note that the underestimation of TMI latent heat flux than other three satellite derived products in the northern region is mainly caused by larger near-surface air humidity there. While, large sea surface saturation humidity contributes to larger ECMWF latent heat flux. On the other hand, lager latent heat flux of NCEP2 in central parts and ICOADS in eastern bands is possibly mainly caused by the difference in winds.

Decadal variations of global energy and ocean heat budget and meridional energy transports inferred from recent global data sets

We use the most recent global, decades‐long data sets, consisting of two satellite‐derived top‐of‐atmosphere (TOA) and surface radiative flux data sets from the International Satellite Cloud Climatology Project Flux product (ISCCP‐FD) and the Global Energy and Water Cycle Experiment Surface Radiation Budget project (GEWEX‐SRB), three ocean surface turbulent flux data sets from Goddard Satellite‐based Surface Turbulent Fluxes (GSSTF), Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite data (HOAPS) and Woods Hole Oceanographic Institution Objectively Analyzed air‐sea Fluxes (WHOI) and one ocean heat content (or energy storage rate) data set from Willis et al. to investigate what can be learned about how decadal‐scale variations of the global energy budget at TOA are partitioned between the atmosphere and ocean and their mean meridional heat transports. Although the mean differences among the TOA radiative flux data sets are large enough that direct measurements of planetary energy imbalances are still unreliable, comparison of the interannual anomalies of the ocean heat content with the two (satellite‐derived) planetary energy imbalances converted to accumulated ocean heat content (or equivalently comparison of the anomalies of ocean heat content converted to ocean heat storage rate with the planetary energy imbalances) show excellent quantitative agreement. These data sets essentially show a heating of the upper ocean since the 1990s but different “trends” at the beginning of this century that need further investigation. The comparison of interannual anomalies of total ocean surface energy fluxes converted to accumulated ocean heat content do not show such good agreement, the former generally indicating a cooling over the past decade. The fact that the anomalies in surface net radiative heating are slightly too large suggests that the latent heat flux anomalies are also too large (causing an overall cooling). The interannual anomalies of the mean meridional heat transport by the atmosphere‐ocean system inferred from the two TOA radiative flux data sets all show similar patterns of weakened poleward transport associated with El Niño events. Each event is different in character. The most interesting difference suggested by the latitudinal patterns is that some heat transport anomalies appear in the atmosphere and some in the ocean, but the current quality of the surface turbulent energy flux data sets precludes confirmation. Although not completely successful, we believe that this analysis indicates that these products are somewhat better than might have been expected and that the goal of further work should now be to reduce their uncertainties enough to diagnose the variations of the coupling of the atmosphere and ocean heat exchanges and transports over decadal timescales.

Comparison of Five Satellite-Derived Latent Heat Flux Products to Moored Buoy Data

Abstract Five satellite products of latent heat flux at the sea surface were compared to bulk fluxes calculated with data from 75 moored buoys, on almost 36 successive months from 1998 to 2000. The five products compared are the Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Dataset (HOAPS-2), the Japanese Ocean Flux Datasets with Use of Remote Sensing Observations (J-OFURO), the Jones dataset, the Goddard Satellite-Based Surface Turbulent Fluxes, version 2 (GSSTF-2), and the Bourras–Eymard–Liu dataset (BEL). The comparisons were performed under tropical and midlatitude environmental conditions, with three datasets based on 66 Tropical Atmosphere–Ocean array (TAO) buoys in the tropical Pacific, nine National Data Buoy Center (NDBC) buoys off the U.S. coasts, and four Met Office/Météo-France (UK–MF) moorings west of the United Kingdom and France, respectively. The satellite products did not all compare well to surface data. However, for each in situ dataset (TAO, NDBC, or UK–MF) at least one satellite product was found that had a good fit to surface data, that is, an rms deviation of 15–30 W m−2. It was found that HOAPS-2, J-OFURO, GSSTF-2, and BEL satellite products had moderate systematic errors with respect to surface data, from −13 to 26 W m−2, and small biases at midlatitudes (6–8 W m−2). Most of the satellite products were able to render the seasonal cycle of the latent heat flux calculated with surface data. The estimation of near-surface specific humidity was found to be problematic in most products, but it was best estimated in the HOAPS-2 product. GSSTF-2 and J-OFURO strongly overestimated the surface flux variations in time and space compared to surface data and to a flux climatology. With respect to TAO data, Jones fluxes yielded good results in terms of rms deviation (27 W m−2) but also presented a large systematic deviation. Overall, for application of the satellite fluxes to the world oceans, it was found that HOAPS-2 was the most appropriate product, whereas for application to the Tropics, BEL fluxes had the best performance in rms with respect to TAO data (24 W m−2).

Monthly Climatologies of Oceanic Friction Velocity Cubed

Abstract Different measures of wind influence the ocean in different ways. In particular, the time-averaged mixed layer turbulent energy production rate is proportional to 〈u3*〉, where u* is the “oceanic friction velocity” that is based on wind stress. Estimating 〈u3*〉 from monthly averages of wind stress or wind speed may introduce large biases due to the day-to-day variability of the direction and magnitude of the wind. The authors create monthly climatologies of 〈u3*〉 from daily wind stress measurements obtained from the Goddard Satellite-based Surface Turbulent Fluxes version 2 (GSSTF2; based on satellite microwave measurements), the Quick Scatterometer (QuikSCAT; based on satellite scatterometry measurements), and the National Centers for Environmental Prediction (NCEP) reanalysis wind. The differences among zonal averages of these climatologies and of a similar climatology based on the da Silva version of the Comprehensive Ocean–Atmosphere Data Set (COADS) have a complex dependence on latitude. These differences are typically 10%–30% of the climatological values. The GSSTF2 data confirm that 〈u3*〉 is much larger than estimates from monthly averaged wind stress or wind speed, especially outside the Tropics.

Decorrelation Scales of High-Resolution Turbulent Fluxes at the Ocean Surface and a Method to Fill in Gaps in Satellite Data Products

Abstract In the first part of the paper, a high space–time resolution (1° latitude/longitude and daily) dataset of the turbulent fluxes at the ocean surface is used to estimate and study the seasonal to annual near-global maps of the decorrelation scales of the latent and sensible heat fluxes. The decorrelation scales describe the temporal and spatial patterns that dominate the flux fields (within a bandpass window) and hence reveal the dominant variability in the air–sea interaction. Regional comparison to the decorrelation scales of the flux-related variables such as the wind stress, the humidity difference, and the SST identifies the main mechanism responsible for the variability in each flux field. In the second part of the paper, the decorrelation scales are used to develop a method for filling missing values in the dataset that result from the incomplete satellite coverage. Weight coefficients in a linear regression function are determined from the spatial and temporal decorrelations and are functions of zonal and meridional distance and time. Therefore they account for all spatial and temporal patterns on scales greater than 1 day and 1° latitude/longitude and less than 1 yr and the ocean basin scale. The method is evaluated by simulating the missing-value distribution of the Goddard Satellite-Based Surface Turbulent Fluxes, version 2 (GSSTF2) dataset in the NCEP SST, the International Satellite Climatology Project (ISCCP)-FD (fluxes calculated using D1 series) surface radiation, and the Global Precipitation Climatology Project (GPCP) datasets and by comparing the filled datasets to the original ones. Main advantages of the method are that the decorrelation scales are unrestricted functions of space and time; only information internal to the flux field is used in the interpolation scheme, and the computation cost of the method is low enough to facilitate its use in similar large datasets.

A Comparison of Latent Heat Fluxes over Global Oceans for Four Flux Products

The ocean surface latent heat flux (LHF) plays an essential role in global energy and water cycle variability. In this study, monthly LHF over global oceans during 1992–93 are compared among Goddard Satellite-Based Surface Turbulent Fluxes, version 2 (GSSTF2), Hamburg Ocean–Atmosphere Parameters and Fluxes from Satellite Data (HOAPS), NCEP–NCAR reanalysis (NCEP), and da Silva et al. (da Silva). To find the causes for discrepancies of LHF, monthly 10-m wind speed (U10m), 10-m specific humidity (Q10m), and sea–air humidity difference (QS − Q10m) are also compared during the same period. The mean differences, standard deviations of differences, and temporal correlation of these monthly variables over global oceans during 1992–93 between GSSTF2 and each of the other three datasets are analyzed. The large-scale patterns of the 2-yr-mean fields for these variables are similar among these four datasets, but significant quantitative differences are found. The temporal correlation is higher in the northern extratropics than in the south for all variables, with the contrast being especially large for da Silva as a result of more missing ship observations in the south. The da Silva dataset has extremely low temporal correlation and large differences with GSSTF2 for all variables in the southern extratropics, indicating that da Silva hardly produces a realistic variability in these variables. The NCEP has extremely low temporal correlation (0.27) and large spatial variations of differences with GSSTF2 for QS − Q10m in the Tropics, which causes the low correlation for LHF. Over the Tropics, the HOAPS mean LHF is significantly smaller than GSSTF2 by ∼31% (37 W m−2), whereas the other two datasets are comparable to GSSTF2. This is because the HOAPS has systematically smaller LHF than GSSTF2 in space, while the other two datasets have very large spatial variations of large positive and negative LHF differences with GSSTF2 to cancel and to produce smaller regional-mean differences. Based on comparison with high-quality flux observations, we conclude that the GSSTF2 latent heat flux, surface air humidity, and winds are likely to be more realistic than the other three flux datasets examined, although those of GSSTF2 are still subject to regional biases.

Surface Turbulent Heat and Momentum Fluxes over Global Oceans Based on the Goddard Satellite Retrievals, Version 2 (GSSTF2)

Information on the turbulent fluxes of momentum, latent heat, and sensible heat at the air–sea interface is essential in improving model simulations of climate variations and in climate studies. A 13.5-yr (July 1987–December 2000) dataset of daily surface turbulent fluxes over global oceans has been derived from the Special Sensor Microwave Imager (SSM/I) radiance measurements. This dataset, Goddard Satellite-based Surface Turbulent Fluxes, version 2 (GSSTF2), has a spatial resolution of 1° × 1° latitude–longitude and a temporal resolution of 1 day. Turbulent fluxes are derived from the SSM/I surface winds and surface air humidity, as well as the 2-m air and sea surface temperatures (SST) of the NCEP–NCAR reanalysis, using a bulk aerodynamic algorithm based on the surface layer similarity theory. The GSSTF2 bulk flux model is validated by comparing hourly turbulent fluxes computed from ship data using the model with those observed fluxes of 10 field experiments over the tropical and midlatitude oceans during 1991–99. In addition, the GSSTF2 daily wind stress, latent heat flux, wind speed, surface air humidity, and SST compare reasonably well with those of the collocated measurements of the field experiments. The global distributions of 1988–2000 annual- and seasonal-mean turbulent fluxes show reasonable patterns related to the atmospheric general circulation and seasonal variations. Zonal averages of latent heat fluxes and input parameters over global oceans during 1992–93 have been compared among several flux datasets: GSSTF1 (version 1), GSSTF2, the Hamburg Ocean–Atmosphere Parameters and Fluxes from Satellite Data (HOAPS), NCEP–NCAR reanalysis, and one based on the Comprehensive Ocean–Atmosphere Data Set (COADS). Significant differences are found among the five. These analyses suggest that the GSSTF2 latent heat flux, surface air humidity, and winds are likely to be more realistic than the other four flux datasets examined, although those of GSSTF2 are still subject to regional biases. The GSSTF2 is useful for climate studies and has been submitted to the sea surface turbulent flux project (SEAFLUX) for intercomparison studies.

Intercomparison of Various Surface Latent Heat Flux Fields

Abstract The Japanese Ocean Flux Data Sets with use of Remote Sensing Observations (J-OFURO) latent heat flux field is compared with the Hamburg Ocean–Atmosphere Parameters and Fluxes from Satellite Data (HOAPS), the Goddard Satellite-Based Surface Turbulent Fluxes (GSSTF), ECMWF, NCEP–NCAR reanalysis (NCEP1), and da Silva et al.'s fields. All products qualitatively reveal a similar pattern in the average fields. Although the latent heat fluxes of J-OFURO and GSSTF are quite similar, they are larger than those of HOAPS in the tropical regions. The difference between J-OFURO and the da Silva data is large, and the temporal correlation is extremely low in the Southern Hemisphere. This suggests that the da Silva product hardly reproduces accurate variability in the data-sparse regions. Also the time correlation between J-OFURO and ECMWF or NCEP1 is considerably lower in the Southern Hemisphere than in the Northern Hemisphere. The ECMWF and NCEP1 fields may be affected by the lack of ship observations there. ...