Intercomparison of Global Ocean Surface Latent Heat Flux Fields

To improve our understanding of global energy and water cycle variability, and to improve model simulations of climate variations, it is vital to have accurate latent heat fluxes (LHF) over global oceans. We have compared monthly LHF over global oceans during 1992-93 among GSSTF2 (Goddard Satellite-based Surface Turbulent Fluxes, version 2), HOAPS (Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data), NCEP (NCEP/NCAR reanalysis), and da Silva (da Silva et al.). To find the causes for discrepancies of LHF, we have also compared monthly 10-m wind speed (U10m), 10-m specific humidity (Q10m), and sea-air humidity difference (QS-Q10m) 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 three other datasets are analyzed. The large-scale patterns of the 2yr-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 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.