Evapotranspiration simulations in ISIMIP2a—Evaluation of spatio-temporal characteristics with a comprehensive ensemble of independent datasets
Wartenburger,Richard1,37; Seneviratne,Sonia I1; Hirschi,Martin1; Chang,Jinfeng24,30; Ciais,Philippe24; Deryng,Delphine2,3; Elliott,Joshua26; Folberth,Christian9; Gosling,Simon N20; Gudmundsson,Lukas1; Henrot,Alexandra-Jane14; Hickler,Thomas25,29; Ito,Akihiko23; Khabarov,Nikolay4; Kim,Hyungjun22; Leng,Guoyong8; Liu,Junguo4,12; Liu,Xingcai7; Masaki,Yoshimitsu18; Morfopoulos,Catherine28; Müller,Christoph17; Schmied,Hannes Müller5,6; Nishina,Kazuya16; Orth,Rene31,34; Pokhrel,Yadu13; Pugh,Thomas A M10,11; Satoh,Yusuke4; Schaphoff,Sibyll17; Schmid,Erwin19; Sheffield,Justin32,33; Stacke,Tobias15; Steinkamp,Joerg36; Tang,Qiuhong7; Thiery,Wim1,35; Wada,Yoshihide4; Wang,Xuhui24; Weedon,Graham P21; Yang,Hong27; Zhou,Tian8
Source PublicationEnvironmental Research Letters
AbstractAbstract 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.
KeywordISIMIP2a evapotranspiration uncertainty cluster analysis hydrological extreme events
WOS IDIOP:1748-9326-13-7-aac4bb
PublisherIOP Publishing
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Document Type期刊论文
Affiliation1.Institute for Atmospheric and Climate Science, ETH Zurich, Universitaetstrasse 16, CH-8092 Zurich, Switzerland
2.Climate Analytics, 10969 Berlin, Germany
3.Columbia University Center for Climate Systems Research, New York, NY 10025, United States of America
4.International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria
5.Institute of Physical Geography, Goethe-University Frankfurt, Frankfurt, Germany
6.Senckenberg Biodiversity and Climate Research Centre (SBiK-F), Frankfurt, Germany
7.Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, People’s Republic of China
8.Atmospheric Sciences & Global Change Division, Pacific Northwest National Laboratory, Richland, WA 99352, United States of America
9.Ecosystem Services and Management Program, International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria
10.School of Geography, Earth & Environmental Sciences and Birmingham Institute of Forest Research, University of Birmingham, Birmingham, United Kingdom
11.Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research - Atmospheric Environmental Research (IMK-IFU), Garmisch-Partenkirchen, Germany
12.School of Environmental Science and Engineering, South University of Science and Technology of China, Shenzhen, People’s Republic of China
13.Department of Civil and Environmental Engineering, Michigan State University, East Lansing, MI 48824 United States of America
14.Unité de Modèlisation du climat et des Cycles Biogéochimiques, UR SPHERES, Université de Liège, Quartier Agora, Liège, Belgium
15.Max Planck Institute for Meteorology, Hamburg, Germany
16.National Institute for Environmental Studies, Tsukuba, Japan
17.Potsdam Institute for Climate Impact Research (PIK), Telegraphenberg A31, 14473 Potsdam, Germany
18.Hirosaki University, Aomori, Japan
19.University of Natural Resources and Life Sciences, Department of Economics and Social Sciences, Feistmantelstrasse 4, A-1180 Vienna, Austria
20.School of Geography, University of Nottingham, Nottingham NG7 2RD, United Kingdom
21.Met Office (JCHMR), Maclean Building, Benson Lane, Crowmarsh Gifford, Wallingford, Oxfordshire OX10 8BB, United Kingdom
22.Institute of Industrial Science, The University of Tokyo, Tokyo, Japan
23.National Institute for Environmental Studies, Tsukuba, Japan
24.Laboratoire des Sciences du Climat et de l’Environnement, UMR8212, CEA-CNRS-UVSQ, Gif-sur-Yvette, France
25.Institute of Physical Geography, Geosciences, Goehte University, Frankfurt am Main, Germany
26.The University of Chicago, 5757 S. University Avenue, Chicago IL 60637, United States of America
27.Department of Systems Analysis, Integrated Assessment and Modelling, Eawag, 8600 Dübendorf, Switzerland
28.College of Life and Environmental Sciences, University of Exeter, Exeter, United Kingdom
29.Senckenberg Biodiversity and Climate Research Centre (BiK-F) & Goethe-University Frankfurt, Senckenberganlage 25, D-60325 Frankfurt am Main, Germany
30.Sorbonne Universités (UPMC, Univ Paris 06)-CNRS-IRD-MNHN, LOCEAN/IPSL, Paris, France
31.Department of Physical Geography, Bolin Centre for Climate Research, Stockholm University, SE-10691 Stockholm, Sweden
32.Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey, United States of America
33.Geography and Environment, University of Southampton, Southampton, United Kingdom
34.Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry, D-07745 Jena, Germany
35.Department of Hydrology and Hydraulic Engineering, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium
36.Zentrum für Datenverarbeitung, Johannes Gutenberg-Universit?t Mainz, Germany
37.Author to whom any correspondence should be addressed.
Recommended Citation
GB/T 7714
Wartenburger,Richard,Seneviratne,Sonia I,Hirschi,Martin,等. Evapotranspiration simulations in ISIMIP2a—Evaluation of spatio-temporal characteristics with a comprehensive ensemble of independent datasets[J]. Environmental Research Letters,2018,13(7).
APA Wartenburger,Richard.,Seneviratne,Sonia I.,Hirschi,Martin.,Chang,Jinfeng.,Ciais,Philippe.,...&Zhou,Tian.(2018).Evapotranspiration simulations in ISIMIP2a—Evaluation of spatio-temporal characteristics with a comprehensive ensemble of independent datasets.Environmental Research Letters,13(7).
MLA Wartenburger,Richard,et al."Evapotranspiration simulations in ISIMIP2a—Evaluation of spatio-temporal characteristics with a comprehensive ensemble of independent datasets".Environmental Research Letters 13.7(2018).
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