Author: ["S. Asseng","F. Ewert","C. Rosenzweig","J. W. Jones","J. L. Hatfield","A. C. Ruane","K. J. Boote","P. J. Thorburn","R. P. Rötter","D. Cammarano","N. Brisson","B. Basso","P. Martre","P. K. Aggarwal","C. Angulo","P. Bertuzzi","C. Biernath","A. J. Challinor","J. Doltra","S. Gayler","R. Goldberg","R. Grant","L. Heng","J. Hooker","L. A. Hunt","J. Ingwersen","R. C. Izaurralde","K. C. Kersebaum","C. Müller","S. Naresh Kumar","C. Nendel","G. O’Leary","J. E. Olesen","T. M. Osborne","T. Palosuo","E. Priesack","D. Ripoche","M. A. Semenov","I. Shcherbak","P. Steduto","C. Stöckle","P. Stratonovitch","T. Streck","I. Supit","F. Tao","M. Travasso","K. Waha","D. Wallach","J. W. White","J. R. Williams","J. Wolf"]
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Abstract
Large standardized model intercomparison projects enable the quantification of uncertainty in projecting the impacts of climate change. One of the largest studies so far indicates that individual crop models are able to simulate wheat yields accurately under a range of environments, but that differences between crop models are a major source of uncertainty. Projections of climate change impacts on crop yields are inherently uncertain1. Uncertainty is often quantified when projecting future greenhouse gas emissions and their influence on climate2. However, multi-model uncertainty analysis of crop responses to climate change is rare because systematic and objective comparisons among process-based crop simulation models1,3 are difficult4. 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 CO2 concentrations and associated warming. These impact uncertainties can be reduced by improving temperature and CO2 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.Cite this article
Asseng, S., Ewert, F., Rosenzweig, C. et al. Uncertainty in simulating wheat yields under climate change. Nature Clim Change 3, 827–832 (2013). https://doi.org/10.1038/nclimate1916 