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Asseng, S.* ; Ewert, F.* ; Rosenzweig, C.* ; Jones, J.W.* ; Hatfield, J.L.* ; Ruane, A.C.* ; Boote, K.J.* ; Thorburn, P.J.* ; Rötter, R.P.* ; Cammarano, D.* ; Brisson, N.* ; Basso, B.* ; Martre, P.* ; Aggarwal, P.K.* ; Angulo, C.* ; Bertuzzi, P.* ; Biernath, C.J. ; Challinor, A.J.* ; Doltra, J.* ; Gayler, S.* ; Goldberg, R.* ; Grant, R.* ; Heng, L.* ; Hooker, J.* ; Hunt, L.A.* ; Ingwersen, J.* ; Izaurralde, R.C.* ; Kersebaum, K.C.* ; Müller, C.* ; Naresh Kumar, S.* ; Nendel, C.* ; O'Leary, G.* ; Olesen, J.E.* ; Osborne, T.M.* ; Palosuo, T.* ; Priesack, E. ; Ripoche, D.* ; Semenov, M.A.* ; Shcherbak, I.* ; Steduto, P.* ; Stöckle, C.* ; Stratonovitch, P.* ; Streck,T.* ; Supit, I.* ; Tao, F.* ; Travasso, M.* ; Waha, K.* ; Wallach, D.* ; White, J.W.* ; Williams, J.R.* ; Wolf, J.*

Uncertainty in simulating wheat yields under climate change.

Nat. Clim. Chang. 3, 827-832 (2013)
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Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
Projections of climate change impacts on crop yields are inherently uncertain. Uncertainty is often quantified when projecting future greenhouse gas emissions and their influence on climate. However, multi-model uncertainty analysis of crop responses to climate change is rare because systematic and objective comparisons among process-based crop simulation models are difficult. Here we present the largest standardized model intercomparison for climate change impacts so far. We found that individual crop models are able to simulate measured wheat grain yields accurately under a range of environments, particularly if the input information is sufficient. However, simulated climate change impacts vary across models owing to differences in model structures and parameter values. A greater proportion of the uncertainty in climate change impact projections was due to variations among crop models than to variations among downscaled general circulation models. Uncertainties in simulated impacts increased with CO 2 concentrations and associated warming. These impact uncertainties can be reduced by improving temperature and CO 2 relationships in models and better quantified through use of multi-model ensembles. Less uncertainty in describing how climate change may affect agricultural productivity will aid adaptation strategy development andpolicymaking.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Crop Production ; Models ; Food ; Co2 ; Temperature ; Projections ; Adaptation ; Scenarios ; Ensemble ; Impacts
Sprache englisch
Veröffentlichungsjahr 2013
HGF-Berichtsjahr 2013
ISSN (print) / ISBN 1758-678X
e-ISSN 1758-6798
Zeitschrift Nature Climate Change
Quellenangaben Band: 3, Heft: 9, Seiten: 827-832 Artikelnummer: , Supplement: ,
Verlag Nature Publishing Group
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Soil Ecology (IBOE)
POF Topic(s) 20405 - Terrestrial Systems – from Observation to Prediction
Forschungsfeld(er) Environmental Sciences
PSP-Element(e) G-504400-003
DOI 10.1038/nclimate1916
WOS ID WOS:000326816100022
Scopus ID 84883377040
Erfassungsdatum 2013-09-26
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