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Evapotranspiration simulations in ISIMIP2a-Evaluation of spatio-temporal characteristics with a comprehensive ensemble of independent datasets
Wartenburger, Richard1; Seneviratne, Sonia, I1; Hirschi, Martin1; Chang, Jinfeng24,31; Ciais, Philippe24; Deryng, Delphine2,3; Elliott, Joshua26; Folberth, Christian9; Gosling, Simon N.21; Gudmundsson, Lukas1; Henrot, Alexandra-Jane15; Hickler, Thomas25,29,30; Ito, Akihiko17; Khabarov, Nikolay4; Kim, Hyungjun23; Leng, Guoyong8; Liu, Junguo4,13; Liu, Xingcai7; Masaki, Yoshimitsu19; Morfopoulos, Catherine28; Mueller, Christoph18; Schmied, Hannes Mueller5,6; Nishina, Kazuya17; Orth, Rene32,35; Pokhrel, Yadu14; Pugh, Thomas A. M.10,11,12; Satoh, Yusuke4; Schaphoff, Sibyll18; Schmid, Erwin20; Sheffield, Justin33,34; Stacke, Tobias16; Steinkamp, Joerg37; Tang, Qiuhong7; Thiery, Wim1,36; Wada, Yoshihide4; Wang, Xuhui24; Weedon, Graham P.22; Yang, Hong27; Zhou, Tian8
2018-07-01
Source PublicationENVIRONMENTAL RESEARCH LETTERS
ISSN1748-9326
Volume13Issue:7Pages:20
Corresponding AuthorWartenburger, Richard(richard.wartenburger@env.ethz.ch)
AbstractActual 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
DOI10.1088/1748-9326/aac4bb
WOS KeywordSURFACE PARAMETERIZATION SCHEMES ; GLOBAL TERRESTRIAL EVAPOTRANSPIRATION ; MODEL DESCRIPTION ; WATER-RESOURCES ; SOIL-MOISTURE ; POTENTIAL EVAPOTRANSPIRATION ; HYDROLOGICAL MODELS ; FUTURE PROJECTIONS ; VEGETATION MODEL ; REANALYSIS DATA
Indexed BySCI
Language英语
Funding ProjectEuropean Research Council DROUGHT-HEAT project[617518] ; Office of Science of the US Department of Energy as part of the Integrated Assessment Research Program ; US DOE[DE-AC05-76RLO1830] ; DECC[GA01101] ; National Natural Science Foundation of China[41625001] ; National Natural Science Foundation of China[41571022] ; Southern University of Science and Technology[G01296001] ; Defra Integrated Climate Program - DECC/Defra[GA01101]
Funding OrganizationEuropean Research Council DROUGHT-HEAT project ; Office of Science of the US Department of Energy as part of the Integrated Assessment Research Program ; US DOE ; DECC ; National Natural Science Foundation of China ; Southern University of Science and Technology ; Defra Integrated Climate Program - DECC/Defra
WOS Research AreaEnvironmental Sciences & Ecology ; Meteorology & Atmospheric Sciences
WOS SubjectEnvironmental Sciences ; Meteorology & Atmospheric Sciences
WOS IDWOS:000436020600001
PublisherIOP PUBLISHING LTD
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/54614
Collection中国科学院地理科学与资源研究所
Corresponding AuthorWartenburger, Richard
Affiliation1.Swiss Fed Inst Technol, Inst Atmospher & Climate Sci, Univ Str 16, CH-8092 Zurich, Switzerland
2.Climate Analyt, D-10969 Berlin, Germany
3.Columbia Univ, Ctr Climate Syst Res, New York, NY 10025 USA
4.IIASA, Laxenburg, Austria
5.Goethe Univ Frankfurt, Inst Phys Geog, Frankfurt, Germany
6.Senckenberg Biodivers & Climate Res Ctr SBiK, Frankfurt, Germany
7.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China
8.Pacific Northwest Natl Lab, Atmospher Sci & Global Change Div, Richland, WA 99352 USA
9.IIASA, Ecosyst Serv & Management Program, Laxenburg, Austria
10.Univ Birmingham, Sch Geog Earth & Environm Sci, Birmingham, W Midlands, England
11.Univ Birmingham, Birmingham Inst Forest Res, Birmingham, W Midlands, England
12.Karlsruhe Inst Technol, Inst Meteorol & Climate Res Atmospher Environm Re, Garmisch Partenkirchen, Germany
13.South Univ Sci & Technol China, Sch Environm Sci & Engn, Shenzhen, Peoples R China
14.Michigan State Univ, Dept Civil & Environm Engn, E Lansing, MI 48824 USA
15.Univ Liege, Unite Modelisat Climat Cycles Biogeochim, UR SPHERES, Liege, Belgium
16.Max Planck Inst Meteorol, Hamburg, Germany
17.Natl Inst Environm Studies, Tsukuba, Ibaraki, Japan
18.Potsdam Inst Climate Impact Res PIK, Telegraphenberg A31, D-14473 Potsdam, Germany
19.Hirosaki Univ, Aomori, Japan
20.Univ Nat Resources & Life Sci, Dept Econ & Social Sci, Feistmantelstr 4, A-1180 Vienna, Austria
21.Univ Nottingham, Sch Geog, Nottingham NG7 2RD, England
22.Met Off JCHMR, Maclean Bldg,Benson Lane, Wallingford OX10 8BB, Oxon, England
23.Univ Tokyo, Inst Ind Sci, Tokyo, Japan
24.UVSQ, CEA, CNRS, Lab Sci Climat & Environm,UMR8212, Gif Sur Yvette, France
25.Goehte Univ, Inst Phys Geog, Geosci, Frankfurt, Germany
26.Univ Chicago, 5757 S Univ Ave, Chicago, IL 60637 USA
27.Eawag, Dept Syst Anal Integrated Assessment & Modelling, CH-8600 Dubendorf, Switzerland
28.Univ Exeter, Coll Life & Environm Sci, Exeter, Devon, England
29.Senckenberg Biodivers & Climate Res Ctr BiK F, Senckenberganlage 25, D-60325 Frankfurt, Germany
30.Goethe Univ Frankfurt, Senckenberganlage 25, D-60325 Frankfurt, Germany
31.Univ Paris 06, UPMC, Sorbonne Univ, LOCEAN IPSL,CNRS,IRD,MNHN, Paris, France
32.Stockholm Univ, Bolin Ctr Climate Res, Dept Phys Geog, SE-10691 Stockholm, Sweden
33.Princeton Univ, Dept Civil & Environm Engn, Princeton, NJ 08544 USA
34.Univ Southampton, Geog & Environm, Southampton, Hants, England
35.Max Planck Inst Biogeochem, Dept Biogeochem Integrat, D-07745 Jena, Germany
36.Vrije Univ Brussel, Dept Hydrol & Hydraul Engn, Pl Laan 2,1050, B-1050 Brussels, Belgium
37.Johannes Gutenberg Univ Mainz, Zentrum Datenverarbeitung, Mainz, Germany
Recommended Citation
GB/T 7714
Wartenburger, Richard,Seneviratne, Sonia, I,Hirschi, Martin,et al. Evapotranspiration simulations in ISIMIP2a-Evaluation of spatio-temporal characteristics with a comprehensive ensemble of independent datasets[J]. ENVIRONMENTAL RESEARCH LETTERS,2018,13(7):20.
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),20.
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):20.
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