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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/gmd-2019-237
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2019-237
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: model experiment description paper 25 Oct 2019

Submitted as: model experiment description paper | 25 Oct 2019

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This discussion paper is a preprint. It is a manuscript under review for the journal Geoscientific Model Development (GMD).

The GGCMI Phase II experiment: global gridded crop model simulations under uniform changes in CO2, temperature, water, and nitrogen levels (protocol version 1.0)

James Franke1,2, Christoph Müller3, Joshua Elliott2,4, Alex C. Ruane5, Jonas Jagermeyr2,3,4,5, Juraj Balkovic6,7, Philippe Ciais8,9, Marie Dury10, Peter Falloon11, Christian Folberth6, Louis Francois10, Tobias Hank12, Munir Hoffmann13,22, R. Cesar Izaurralde14,15, Ingrid Jacquemin10, Curtis Jones14, Nikolay Khabarov6, Marian Koch13, Michelle Li2,16, Wenfeng Liu8,17, Stefan Olin18, Meridel Phillips5,19, Thomas A. M. Pugh20,21, Ashwan Reddy14, Xuhui Wang8,9, Karina Williams11, Florian Zabel12, and Elisabeth Moyer1,2 James Franke et al.
  • 1Department of the Geophysical Sciences, University of Chicago, Chicago, IL, USA
  • 2Center for Robust Decision-making on Climate and Energy Policy (RDCEP), University of Chicago, Chicago, IL, USA
  • 3Potsdam Institute for Climate Impact Research, Member of the Leibniz Association, Potsdam, Germany
  • 4Department of Computer Science, University of Chicago, Chicago, IL, USA
  • 5NASA Goddard Institute for Space Studies, New York, NY, USA
  • 6Ecosystem Services and Management Program, International Institute for Applied Systems Analysis, Laxenburg, Austria
  • 7Department of Soil Science, Faculty of Natural Sciences, Comenius University in Bratislava, Bratislava, Slovak Republic
  • 8Laboratoire des Sciences du Climat et de l’Environnement, CEA-CNRS-UVSQ, 91191 Gif-sur-Yvette, France
  • 9Sino-French Institute of Earth System Sciences, College of Urban and Env. Sciences, Peking University, Beijing, China
  • 10Unité de Modélisation du Climat et des Cycles Biogéochimiques, UR SPHERES, Institut d’Astrophysique et de Géophysique, University of Liège, Belgium
  • 11Met Office Hadley Centre, Exeter, UK
  • 12Department of Geography, Ludwig-Maximilians-Universität, Munich, Germany
  • 13Georg-August-University Göttingen, Tropical Plant Production and Agricultural Systems Modeling, Göttingen, Germany
  • 14Department of Geographical Sciences, University of Maryland, College Park, MD, USA
  • 15Texas Agrilife Research and Extension, Texas A&M University, Temple, TX, USA
  • 16Department of Statistics, University of Chicago, Chicago, IL, USA
  • 17EAWAG, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland
  • 18Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden
  • 19Earth Institute Center for Climate Systems Research, Columbia University, New York, NY, USA
  • 20School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, UK
  • 21Birmingham Institute of Forest Research, University of Birmingham, Birmingham, UK
  • 22Leibniz Centre for Agricultural Landscape Research (ZALF), 15374 Müncheberg, Germany

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 II 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 II experimental protocol and its simulation data archive. Twelve 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 II archive. For example, modeled yields show robust decreases to warmer temperatures in almost all regions, with a nonlinear dependence that indicates 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.

James Franke et al.
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Status: open (until 20 Dec 2019)
Status: open (until 20 Dec 2019)
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Data sets

GGCMI Phase II simulation outputs J. Franke et al. https://zenodo.org/search?page=1&size=20&q=AgMIP

James Franke et al.
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Short summary
Concerns about food security under climate change motivate efforts to better understand future changes in crop yields. Crop models, which represent plant biology, are necessary tools for this purpose since they allow representing future climate, farmer choices, and new agricultural geographies. The Global Gridded Crop Model Intercomparison (GGCMI) Phase II experiment, under the Agricultural Model Intercomparison and Improvement Project (AgMIP), is designed to evaluate and improve crop models.
Concerns about food security under climate change motivate efforts to better understand future...
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