Towards reducing uncertainties of global evapotranspiration due to the energy balance closure gap in flux tower data

Accurate quantification of evapotranspiration (ET) is crucial for understanding variability in the global water cycle, yet state-of-the-art estimates of ET derived from models and remote sensing products contain large uncertainties. Taking the advantage of extensive eddy covariance measurements and machine learning algorithms, ET can be upscaled from globally distributed in-situ observations by combining them with global meteorological and satellite data (e.g., FLUXCOM ensembles, Jung et al., 2019). However, eddy covariance measurements suffer from well-known energy balance non-closure problems, and those uncertainties are further propagated to the global ET estimates. Here, we first estimate the energy balance non-closure within dynamic sliding windows for flux tower site, then we compute correction factors for ET measurements following different hypothesis (that assign errors to latent and/or sensible heat fluxes) according to insights from large eddy simulation studies. Then energy balance closure corrected ET data are used in FLUXCOM to estimate global ET. The upscaled ET then is evaluated by comparison with water-balance-drived ET at the catchment level. This comparison helps to determine the most consistent correction of ET for different regions and conditions. By providing improved global ET estimates, water-related studies can be further facilitated, and model parameterizations can be further optimized to address the challenges posed by climate change on ecosystems and water resources.