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Title: Evapotranspiration simulations in ISIMIP2a—Evaluation of spatio-temporal characteristics with a comprehensive ensemble of independent datasets

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 spatiotemporal 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.more » By using these factors to explain spatial and spatio-temporal variabilities in ET, we and 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.« less

Authors:
ORCiD logo [1]; ORCiD logo [1];  [1];  [2];  [3];  [4];  [5];  [6];  [7]; ORCiD logo [1];  [8];  [9];  [10];  [6];  [11];  [12];  [13]; ORCiD logo [14];  [15];  [16] more »; ORCiD logo [17]; ORCiD logo [18];  [10];  [19];  [20]; ORCiD logo [21];  [6];  [17];  [22];  [23];  [24];  [25]; ORCiD logo [14]; ORCiD logo [26]; ORCiD logo [6];  [27]; ORCiD logo [28];  [29]; ORCiD logo [12] « less
+ Show Author Affiliations
  1. ETH Zurich (Switzerland)
  2. National Centre for Scientific Research (CNRS), Gif-sur-Yvette (France). Laboratoire des Sciences du Climat et de l'Environnement; Sorbonne Univ., Paris (France)
  3. National Centre for Scientific Research (CNRS), Gif-sur-Yvette (France). Laboratoire des Sciences du Climat et de l'Environnement
  4. Climate Analytics, Berlin (Germany); Columbia Univ. Center for Climate Systems Research, New York, NY (United States)
  5. Univ. of Chicago, IL (United States)
  6. International Inst. for Applied Systems Analysis (IIASA), Laxenburg (Austria)
  7. Univ. of Nottingham (United Kingdom)
  8. Univ. of Liege, (Belgium)
  9. Goethe Univ., Frankfurt (Germany)
  10. National Inst. for Environmental Studies, Tsukuba (Japan)
  11. Univ. of Tokyo (Japan)
  12. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
  13. International Inst. for Applied Systems Analysis (IIASA), Laxenburg (Austria); South Univ. of Science and Technology of China, Shenzhen (China)
  14. Chinese Academy of Sciences (CAS), Beijing (China)
  15. Hirosaki Univ. (Japan)
  16. Univ. of Exeter (United Kingdom)
  17. Potsdam Inst. for Climate Impact Research (PIK), Potsdam (Germany)
  18. Goethe Univ., Frankfurt (Germany); Senckenberg Biodiversity and Climate Research Centre (SBiK-F), Frankfurt (Germany)
  19. Stockholm Univ. (Sweden); Max Planck Society, Jena (Germany). Max Planck Inst. for Biogeochemistry
  20. Michigan State Univ., East Lansing, MI (United States)
  21. Univ. of Birmingham (United Kingdom); Karlsruhe Inst. of Technology (KIT) Garmisch-Partenkirchen (Germany). IMK-IFU
  22. Univ. of Natural Resources and Life Sciences, Vienna (Austria)
  23. Princeton Univ., NJ (United States); Univ. of Southampton (United Kingdom)
  24. Max Planck Inst. for Meteorology, Hamburg (Germany)
  25. Johannes Gutenberg Univ., Mainz (Germany). Zentrum für Datenverarbeitung
  26. ETH Zurich (Switzerland); Vrije Univ., Amsterdam (Netherlands)
  27. National Centre for Scientific Research (CNRS), Gif-sur-Yvette (France). Laboratoire des Sciences du Climat et de l'Environnement
  28. Met Office, Oxfordshire (United Kingdom)
  29. Eawag, Dubendorf (Switzerland)
Publication Date:
Research Org.:
Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1509479
Report Number(s):
PNNL-SA-135063
Journal ID: ISSN 1748-9326
Grant/Contract Number:  
AC05-76RL01830
Resource Type:
Accepted Manuscript
Journal Name:
Environmental Research Letters
Additional Journal Information:
Journal Volume: 13; Journal Issue: 7; Journal ID: ISSN 1748-9326
Publisher:
IOP Publishing
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES

Citation Formats

Wartenburger, Richard, Seneviratne, Sonia I., Hirschi, Martin, Chang, Jinfeng, Ciais, Philippe, Deryng, Delphine, Elliott, Joshua, Folberth, Christian, Gosling, Simon N., Gudmundsson, Lukas, Henrot, Alexandra-Jane, Hickler, Thomas, Ito, Akihiko, Khabarov, Nikolay, Kim, Hyungjun, Leng, Guoyong, Liu, Junguo, Liu, Xingcai, Masaki, Yoshimitsu, Morfopoulos, Catherine, Müller, Christoph, Schmied, Hannes Müller, Nishina, Kazuya, Orth, Rene, Pokhrel, Yadu, Pugh, Thomas A. M., Satoh, Yusuke, Schaphoff, Sibyll, Schmid, Erwin, Sheffield, Justin, Stacke, Tobias, Steinkamp, Joerg, Tang, Qiuhong, Thiery, Wim, Wada, Yoshihide, Wang, Xuhui, Weedon, Graham P., Yang, Hong, and Zhou, Tian. Evapotranspiration simulations in ISIMIP2a—Evaluation of spatio-temporal characteristics with a comprehensive ensemble of independent datasets. United States: N. p., 2018. Web. doi:10.1088/1748-9326/aac4bb.
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Wartenburger, Richard, Seneviratne, Sonia I., Hirschi, Martin, Chang, Jinfeng, Ciais, Philippe, Deryng, Delphine, Elliott, Joshua, Folberth, Christian, Gosling, Simon N., Gudmundsson, Lukas, Henrot, Alexandra-Jane, Hickler, Thomas, Ito, Akihiko, Khabarov, Nikolay, Kim, Hyungjun, Leng, Guoyong, Liu, Junguo, Liu, Xingcai, Masaki, Yoshimitsu, Morfopoulos, Catherine, Müller, Christoph, Schmied, Hannes Müller, Nishina, Kazuya, Orth, Rene, Pokhrel, Yadu, Pugh, Thomas A. M., Satoh, Yusuke, Schaphoff, Sibyll, Schmid, Erwin, Sheffield, Justin, Stacke, Tobias, Steinkamp, Joerg, Tang, Qiuhong, Thiery, Wim, Wada, Yoshihide, Wang, Xuhui, Weedon, Graham P., Yang, Hong, & Zhou, Tian. Evapotranspiration simulations in ISIMIP2a—Evaluation of spatio-temporal characteristics with a comprehensive ensemble of independent datasets. United States. https://doi.org/10.1088/1748-9326/aac4bb
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Wartenburger, Richard, Seneviratne, Sonia I., Hirschi, Martin, Chang, Jinfeng, Ciais, Philippe, Deryng, Delphine, Elliott, Joshua, Folberth, Christian, Gosling, Simon N., Gudmundsson, Lukas, Henrot, Alexandra-Jane, Hickler, Thomas, Ito, Akihiko, Khabarov, Nikolay, Kim, Hyungjun, Leng, Guoyong, Liu, Junguo, Liu, Xingcai, Masaki, Yoshimitsu, Morfopoulos, Catherine, Müller, Christoph, Schmied, Hannes Müller, Nishina, Kazuya, Orth, Rene, Pokhrel, Yadu, Pugh, Thomas A. M., Satoh, Yusuke, Schaphoff, Sibyll, Schmid, Erwin, Sheffield, Justin, Stacke, Tobias, Steinkamp, Joerg, Tang, Qiuhong, Thiery, Wim, Wada, Yoshihide, Wang, Xuhui, Weedon, Graham P., Yang, Hong, and Zhou, Tian. Thu . "Evapotranspiration simulations in ISIMIP2a—Evaluation of spatio-temporal characteristics with a comprehensive ensemble of independent datasets". United States. https://doi.org/10.1088/1748-9326/aac4bb. https://www.osti.gov/servlets/purl/1509479.
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@article{osti_1509479,
title = {Evapotranspiration simulations in ISIMIP2a—Evaluation of spatio-temporal characteristics with a comprehensive ensemble of independent datasets},
author = {Wartenburger, Richard and Seneviratne, Sonia I. and Hirschi, Martin and Chang, Jinfeng and Ciais, Philippe and Deryng, Delphine and Elliott, Joshua and Folberth, Christian and Gosling, Simon N. and Gudmundsson, Lukas and Henrot, Alexandra-Jane and Hickler, Thomas and Ito, Akihiko and Khabarov, Nikolay and Kim, Hyungjun and Leng, Guoyong and Liu, Junguo and Liu, Xingcai and Masaki, Yoshimitsu and Morfopoulos, Catherine and Müller, Christoph and Schmied, Hannes Müller and Nishina, Kazuya and Orth, Rene and Pokhrel, Yadu and Pugh, Thomas A. M. and Satoh, Yusuke and Schaphoff, Sibyll and Schmid, Erwin and Sheffield, Justin and Stacke, Tobias and Steinkamp, Joerg and Tang, Qiuhong and Thiery, Wim and Wada, Yoshihide and Wang, Xuhui and Weedon, Graham P. and Yang, Hong and Zhou, Tian},
abstractNote = {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 spatiotemporal 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 and 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.},
doi = {10.1088/1748-9326/aac4bb},
journal = {Environmental Research Letters},
number = 7,
volume = 13,
place = {United States},
year = {Thu Jun 21 00:00:00 EDT 2018},
month = {Thu Jun 21 00:00:00 EDT 2018}
}
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