Abstract
Actual land evapotranspiration (ET) is a key component of the global hydrological cycle and an essential variable determining the evolution of hydrological extreme events under different climate change scenarios. However, recently available ET products show persistent uncertainties that are impeding a precise attribution of human-induced climate change. Here, we aim at comparing a range of independent global monthly land ET estimates with historical model simulations from the global water, agriculture, and biomes sectors participating in the second phase of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2a). Among the independent estimates, we use the EartH2Observe Tier-1 dataset (E2O), two commonly used reanalyses, a pre-compiled ensemble product (LandFlux-EVAL), and an updated collection of recently published datasets that algorithmically derive ET from observations or observations-based estimates (diagnostic datasets). A cluster analysis is applied in order to identify spatio-temporal differences among all datasets and to thus identify factors that dominate overall uncertainties. The clustering is controlled by several factors including the model choice, the meteorological forcing used to drive the assessed models, the data category (models participating in the different sectors of ISIMIP2a, E2O models, diagnostic estimates, reanalysis-based estimates or composite products), the ET scheme, and the number of soil layers in the models. By using these factors to explain spatial and spatio-temporal variabilities in ET, we find that the model choice mostly dominates (24%–40% of variance explained), except for spatio-temporal patterns of total ET, where the forcing explains the largest fraction of the variance (29%). The most dominant clusters of datasets are further compared with individual diagnostic and reanalysis-based estimates to assess their representation of selected heat waves and droughts in the Great Plains, Central Europe and western Russia. Although most of the ET estimates capture these extreme events, the generally large spread among the entire ensemble indicates substantial uncertainties.
Highlights
Climate impact models are frequently used to quantify and analyse the effects of environmental changes in various socio-economic and environmental sectors under a given scenario design
The ensemble spread in tropical rainforest regions is generally largest for the ISIMIP simulations and LFE ALL, whereas the uncertainties in this region are considerably less pronounced in the diagnostic and EartH2Observe Tier-1 dataset (E2O) ensemble
The spatial patterns of the relative ensemble spread in the diagnostic ensemble are very well represented by both ISIMIP sectors and by LFE ALL
Summary
Climate impact models are frequently used to quantify and analyse the effects of environmental changes in various socio-economic and environmental sectors under a given scenario design. For the critical assessment of extreme events, it is absolutely necessary to be aware of these uncertainties, concerning both the spread among the ISIMIP simulations as well as biases of the multi-model mean with respect to independent observation-based estimates from the recent past Key impact variables such as irrigation water demand or agricultural productivity are physically controlled by the partitioning of energy at the land surface, which largely depends on total evapotranspiration As ET accounts for more than half of the precipitation fluxes in many regions, it is an important parameter controlling hydrological extreme events, in particular when considering its potential to amplify droughts and heat waves through coupling with soil moisture (e.g. Seneviratne et al 2010) To analyse such extreme events in greater detail, it is absolutely necessary to be aware of the full range of uncertainties inherent in different estimates of ET. To thoroughly analyse extreme events within the ISIMIP2a framework, it is a prerequisite to precisely assess the magnitude of common ensemble statistics (mean, median and interquartile ranges, IQRs) of presently available ET estimates across datasets/models and sectors, and to further attempt to identify potential causes for differences between these estimates
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