Multi-model, multi-sensor estimates of global evapotranspiration: climatology, uncertainties and trends

Estimating evapotranspiration (ET) at continental to global scales is central to understanding the partitioning of energy and water at the earth's surface and the feedbacks with the atmosphere and biosphere, especially in the context of climate change. Recent evaluations of global estimates from remote sensing, upscaled observations, land surface models and atmospheric reanalyses indicate large uncertainty across the datasets of the order of 50% of the global annual mean value. In this paper, we explore the uncertainties in global land ET estimates using three process-based ET models and a set of remote sensing and observational based radiation and meteorological forcing datasets. Input forcings were obtained from International Satellite Cloud Climatology Project (ISCCP) and Surface Radiation Budget (SRB). The three process-based ET models are: a surface energy balance method (SEBS), a revised PenmanMonteith (PM) model, and a modified Priestley-Taylor model. Evaluations of the radiation products from ISCCP and SRB show large differences in the components of surface radiation, and temporal inconsistencies that relate to changes in satellite sensors and retrieval algorithms. In particular, step changes in the ISCCP surface temperature and humidity data lead to spurious increases in downward and upward longwave radiation that contributes to a step change in net radiation, and the ISCCP data are not used further. An ensemble of global estimates of land surface ET are generated at daily time scale and 0.5 degree spatial resolution for 19842007 using two SRB radiation products (SRB and SRBqc) and the three models. Uncertainty in ET from the models is much larger than the uncertainty from the radiation data. The largest uncertainties relative to the mean annual ET are in transition zones between dry and humid regions and monsoon regions. Comparisons with previous studies and an inferred estimate of ET from long-term inferred ET indicate that the ensemble mean value is reasonable, but generally biased high globally. Long-term changes over 19842007 indicate a slight increase over 19841998 and decline thereafter, although uncertainties in the forcing radiation data and lack of direct linkage with soil moisture limitations in the models prevents attribution of these changes. Copyright (C) 2011 John Wiley & Sons, Ltd.