%0 Journal Article %1 Ruane2016Multiwheatmodel %A Ruane, Alex C. %A Hudson, Nicholas I. %A Asseng, Senthold %A Camarrano, Davide %A Ewert, Frank %A Martre, Pierre %A Boote, Kenneth J. %A Thorburn, Peter J. %A Aggarwal, Pramod K. %A Angulo, Carlos %A Basso, Bruno %A Bertuzzi, Patrick %A Biernath, Christian %A Brisson, Nadine %A Challinor, Andrew J. %A Doltra, Jordi %A Gayler, Sebastian %A Goldberg, Richard %A Grant, Robert F. %A Heng, Lee %A Hooker, Josh %A Hunt, Leslie A. %A Ingwersen, Joachim %A Izaurralde, Roberto C. %A Kersebaum, Kurt C. %A Kumar, Soora N. %A Müller, Christoph %A Nendel, Claas %A O'Leary, Garry %A Olesen, Jørgen E. %A Osborne, Tom M. %A Palosuo, Taru %A Priesack, Eckart %A Ripoche, Dominique %A Rötter, Reimund P. %A Semenov, Mikhail A. %A Shcherbak, Iurii %A Steduto, Pasquale %A Stöckle, Claudio O. %A Stratonovitch, Pierre %A Streck, Thilo %A Supit, Iwan %A Tao, Fulu %A Travasso, Maria %A Waha, Katharina %A Wallach, Daniel %A White, Jeffrey W. %A Wolf, Joost %D 2016 %J Environmental Modelling & Software %K euporias %P 86--101 %R 10.1016/j.envsoft.2016.03.008 %T Multi-wheat-model ensemble responses to interannual climate variability %U http://dx.doi.org/10.1016/j.envsoft.2016.03.008 %V 81 %X Compares interannual climate response of 27 wheat models at four locations. Calculates the diminishing return of constructing multi-model ensembles for assessment. Identifies similarities and major differences of model responses. Differentiates between interannual temperature sensitivity and climate change response. 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.

@article{Ruane2016Multiwheatmodel, abstract = { Compares interannual climate response of 27 wheat models at four locations. Calculates the diminishing return of constructing multi-model ensembles for assessment. Identifies similarities and major differences of model responses. Differentiates between interannual temperature sensitivity and climate change response. 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.}, added-at = {2019-08-14T18:35:35.000+0200}, author = {Ruane, Alex C. and Hudson, Nicholas I. and Asseng, Senthold and Camarrano, Davide and Ewert, Frank and Martre, Pierre and Boote, Kenneth J. and Thorburn, Peter J. and Aggarwal, Pramod K. and Angulo, Carlos and Basso, Bruno and Bertuzzi, Patrick and Biernath, Christian and Brisson, Nadine and Challinor, Andrew J. and Doltra, Jordi and Gayler, Sebastian and Goldberg, Richard and Grant, Robert F. and Heng, Lee and Hooker, Josh and Hunt, Leslie A. and Ingwersen, Joachim and Izaurralde, Roberto C. and Kersebaum, Kurt C. and Kumar, Soora N. and M\"{u}ller, Christoph and Nendel, Claas and O'Leary, Garry and Olesen, J{\o}rgen E. and Osborne, Tom M. and Palosuo, Taru and Priesack, Eckart and Ripoche, Dominique and R\"{o}tter, Reimund P. and Semenov, Mikhail A. and Shcherbak, Iurii and Steduto, Pasquale and St\"{o}ckle, Claudio O. and Stratonovitch, Pierre and Streck, Thilo and Supit, Iwan and Tao, Fulu and Travasso, Maria and Waha, Katharina and Wallach, Daniel and White, Jeffrey W. and Wolf, Joost}, biburl = {https://www.bibsonomy.org/bibtex/2d1a5ecfeff8e888bb7ef0b5567f884f1/karinawilliams}, citeulike-article-id = {13998882}, citeulike-linkout-0 = {http://dx.doi.org/10.1016/j.envsoft.2016.03.008}, comment = {(private-note)AGMIP maize file from Exeter library}, doi = {10.1016/j.envsoft.2016.03.008}, interhash = {b736baf2b57642fc85f0d3a6eeb8703e}, intrahash = {d1a5ecfeff8e888bb7ef0b5567f884f1}, issn = {13648152}, journal = {Environmental Modelling \& Software}, keywords = {euporias}, month = jul, pages = {86--101}, posted-at = {2016-04-05 13:13:57}, priority = {2}, timestamp = {2019-08-14T18:35:35.000+0200}, title = {Multi-wheat-model ensemble responses to interannual climate variability}, url = {http://dx.doi.org/10.1016/j.envsoft.2016.03.008}, volume = 81, year = 2016 }