Abstract
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.
Current affiliation: Department of Atmospheric Sciences, National Taiwan University, Taipei, Taiwan
Corresponding author address: Dr. Shu-Hsien Chou, Department of Atmospheric Sciences, National Taiwan University, 1, Section 4, Roosevelt Road, Taipei 106, Taiwan. Email: shchou@atmos1.as.ntu.edu.tw
