Seasonal Characteristics and Mechanisms of Evaporation Variation Uncertainty over the Tropical Indian Ocean in Four Datasets

Evaporation is a key component of air–sea coupling processes and understanding the uncertainty in its estimation is essential for climate research and prediction. Based on four widely used datasets (OAFlux, NCEP2, MERRA2 and ERA5), this study systematically analyzes the seasonal evolution of inter-dataset uncertainty in evaporation variation over the tropical Indian Ocean using an evaporation decomposition method. Our main contribution is to show that evaporation variation uncertainty is not seasonally uniform but organized into distinct seasonal regimes with different dominant controlling factors and sensitivity structures. The results reveal significant seasonal dependence of evaporation variation uncertainty: the uncertainty is relatively small in boreal spring and autumn but larger in boreal summer and winter. The evaporation variation is primarily controlled by the relative humidity term (RH*) in boreal summer and by the wind speed term (U*) in other seasons. More importantly, the sources of uncertainty differ fundamentally between seasons: the large uncertainty of RH* in boreal summer mainly originates from the high and variable sensitivity of evaporation to relative humidity, whereas the large uncertainty of U* in boreal winter primarily stems from substantial inter-dataset discrepancies in wind speed data itself. These findings reveal that evaporation variation uncertainty arises from both input data discrepancies and the nonlinear sensitivity of evaporation processes, with their relative contributions varying seasonally. This study provides a physically based explanation for evaporation uncertainty and offers a useful basis for evaporation dataset selection and climate model evaluation.