Multi-wheat-model ensemble responses to interannual climate variability

Alex C. Ruane*, Nicholas I. Hudson, Senthold Asseng, Davide Camarrano, Frank Ewert, Pierre Martre, Kenneth J. Boote, Peter J. Thorburn, Pramod K. Aggarwal, Carlos Angulo, Bruno Basso, Patrick Bertuzzi, Christian Biernath, Nadine Brisson, Andrew J. Challinor, Jordi Doltra, Sebastian Gayler, Richard Goldberg, Robert F. Grant, Lee HengJosh Hooker, Leslie A. Hunt, Joachim Ingwersen, Roberto C. Izaurralde, Kurt Christian Kersebaum, Soora Naresh Kumar, Christoph Müller, Claas Nendel, Garry O'Leary, Jørgen E. Olesen, Tom M. Osborne, Taru Palosuo, Eckart Priesack, Dominique Ripoche, Reimund P. Rötter, Mikhail A. Semenov, Iurii Shcherbak, Pasquale Steduto, Claudio O. Stöckle, Pierre Stratonovitch, Thilo Streck, Iwan Supit, Fulu Tao, Maria Travasso, Katharina Waha, Daniel Wallach, Jeffrey W. White, Joost Wolf

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

33 Citations (Scopus)


We compare 27 wheat models' yield responses to interannual climate variability, analyzed at locations in Argentina, Australia, India, and The Netherlands as part of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Pilot. Each model simulated 1981-2010 grain yield, and we evaluate results against the interannual variability of growing season temperature, precipitation, and solar radiation. The amount of information used for calibration has only a minor effect on most models' climate response, and even small multi-model ensembles prove beneficial. Wheat model clusters reveal common characteristics of yield response to climate; however models rarely share the same cluster at all four sites indicating substantial independence. Only a weak relationship (R2 ≤ 0.24) was found between the models' sensitivities to interannual temperature variability and their response to long-term warming, suggesting that additional processes differentiate climate change impacts from observed climate variability analogs and motivating continuing analysis and model development efforts.

Original languageEnglish
Pages (from-to)86-101
JournalEnvironmental Modelling & Software
Publication statusPublished - 2016


  • AgMIP
  • Climate impacts
  • Crop modeling
  • Interannual variability
  • Multi-model ensemble
  • Precipitation
  • Temperature
  • Uncertainty
  • Wheat

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