1. The GGCMI Phase 2 experiment: global gridded crop model simulations under uniform changes in CO2 temperature, water, and nitrogen levels (protocol version 1.0)
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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., Department of Geosciences, University of Chicago, Chicago, IL 60637, United States, Potsdam Institute for Climate Impact Research (PIK), University of Chicago, NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), International Institute for Applied Systems Analysis [Laxenburg] (IIASA), Comenius University in Bratislava, Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Peking University [Beijing], ICOS-ATC (ICOS-ATC), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Unité de Modélisation du Climat et des Cycles Biogéochimiques (UMCCB), Université de Liège, Met Office Hadley Centre for Climate Change (MOHC), United Kingdom Met Office [Exeter], Ludwig-Maximilians-Universität München (LMU), Tropical Plant Prodution and Agricultural Systems Modelling (TROPAGS), Georg-August-University = Georg-August-Universität Göttingen, INSTITUTE OF LANDSCAPE MATTER DYNAMICS LEIBNIZ CENTRE FOR AGRICULTURAL LANDSCAPE AND LAND USE RESEARCH ZALF MUNCHEBERG DEU, Partenaires IRSTEA, Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), Department of Geographical Sciences [College Park], University of Maryland [College Park], University of Maryland System-University of Maryland System, Tropical Plant Production and Agricultural Systems Modelling (TROPAGS), University of Chicago, Department of Statistics, Eawag, Swiss Federal Institute of Aquatic Science and Technology, CH-8600 Dübendorf, Switzerland and ETH Zürich, Universitätstrasse 16, CH-8092 Zürich, Switzerland, DEPARTMENT OF PHYSICAL GEOGRAPHY AND ECOSYSTEM SCIENCE LUND UNIVERSITY SWE, NASA GODDARD INSTITUTE FOR SPACE STUDIES NEW YORK USA, Columbia University [New York], School of Geography, Earth and Environmental Sciences and Birmingham Institute of Forest Research, University of Birmingham [Birmingham], LUDWIG MAXIMILIANS UNIVERSITAT MUNCHEN DEPARTMENT OF GEOGRAPHY MUNICH DEU, European Project: 641811,H2020,H2020-WATER-2014-two-stage,IMPREX(2015), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), and Georg-August-University [Göttingen]
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[SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere ,[SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment - Abstract
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.
- Published
- 2020