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    State-of-the-art global models underestimate impacts from climate extremes
    ([London] : Nature Publishing Group UK, 2019) Schewe, Jacob; Gosling, Simon N.; Reyer, Christopher; Zhao, Fang; Ciais, Philippe; Elliott, Joshua; Francois, Louis; Huber, Veronika; Lotze, Heike K.; Seneviratne, Sonia I.; van Vliet, Michelle T. H.; Vautard, Robert; Wada, Yoshihide; Breuer, Lutz; Büchner, Matthias; Carozza, David A.; Chang, Jinfeng; Coll, Marta; Deryng, Delphine; de Wit, Allard; Eddy, Tyler D.; Folberth, Christian; Frieler, Katja; Friend, Andrew D.; Gerten, Dieter; Gudmundsson, Lukas; Hanasaki, Naota; Ito, Akihiko; Khabarov, Nikolay; Kim, Hyungjun; Lawrence, Peter; Morfopoulos, Catherine; Müller, Christoph; Müller Schmied, Hannes; Orth, René; Ostberg, Sebastian; Pokhrel, Yadu; Pugh, Thomas A. M.; Sakurai, Gen; Satoh, Yusuke; Schmid, Erwin; Stacke, Tobias; Steenbeek, Jeroen; Steinkamp, Jörg; Tang, Qiuhong; Tian, Hanqin; Tittensor, Derek P.; Volkholz, Jan; Wang, Xuhui; Warszawski, Lila
    Global impact models represent process-level understanding of how natural and human systems may be affected by climate change. Their projections are used in integrated assessments of climate change. Here we test, for the first time, systematically across many important systems, how well such impact models capture the impacts of extreme climate conditions. Using the 2003 European heat wave and drought as a historical analogue for comparable events in the future, we find that a majority of models underestimate the extremeness of impacts in important sectors such as agriculture, terrestrial ecosystems, and heat-related human mortality, while impacts on water resources and hydropower are overestimated in some river basins; and the spread across models is often large. This has important implications for economic assessments of climate change impacts that rely on these models. It also means that societal risks from future extreme events may be greater than previously thought.
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    The GGCMI Phase 2 experiment: Global gridded crop model simulations under uniform changes in CO2, temperature, water, and nitrogen levels (protocol version 1.0)
    (Katlenburg-Lindau : Copernicus, 2020) Franke, James A.; Müller, Christoph; Elliott, Joshua; Ruane, Alex C.; Jägermeyr, Jonas; Balkovic, Juraj; Ciais, Philippe; Dury, Marie; Falloon, Pete D.; Folberth, Christian; François, Louis; Hank, Tobias; Hoffmann, Munir; Izaurralde, R. Cesar; Jacquemin, Ingrid; Jones, Curtis; Khabarov, Nikolay; Koch, Marian; Li, Michelle; Liu, Wenfeng; Olin, Stefan; Phillips, Meridel; Pugh, Thomas A. M.; Reddy, Ashwan; Wang, Xuhui; Williams, Karina; Zabel, Florian; Moyer, Elisabeth J.
    Concerns about food security under climate change motivate efforts to better understand future changes in crop yields. Process-based crop models, which represent plant physiological and soil processes, are necessary tools for this purpose since they allow representing future climate and management conditions not sampled in the historical record and new locations to which cultivation may shift. However, process-based crop models differ in many critical details, and their responses to different interacting factors remain only poorly understood. The Global Gridded Crop Model Intercomparison (GGCMI) Phase 2 experiment, an activity of the Agricultural Model Intercomparison and Improvement Project (AgMIP), is designed to provide a systematic parameter sweep focused on climate change factors and their interaction with overall soil fertility, to allow both evaluating model behavior and emulating model responses in impact assessment tools. In this paper we describe the GGCMI Phase 2 experimental protocol and its simulation data archive. A total of 12 crop models simulate five crops with systematic uniform perturbations of historical climate, varying CO2, temperature, water supply, and applied nitrogen (“CTWN”) for rainfed and irrigated agriculture, and a second set of simulations represents a type of adaptation by allowing the adjustment of growing season length. We present some crop yield results to illustrate general characteristics of the simulations and potential uses of the GGCMI Phase 2 archive. For example, in cases without adaptation, modeled yields show robust decreases to warmer temperatures in almost all regions, with a nonlinear dependence that means yields in warmer baseline locations have greater temperature sensitivity. Inter-model uncertainty is qualitatively similar across all the four input dimensions but is largest in high-latitude regions where crops may be grown in the future.