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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-2019-365
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2019-365
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: model description paper 13 Feb 2020

Submitted as: model description paper | 13 Feb 2020

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This preprint is currently under review for the journal GMD.

The GGCMI phase II emulators: global gridded crop model responses to changes in CO2, temperature, water, and nitrogen (version 1.0)

James Franke1,2, Christoph Müller3, Joshua Elliott2,4, Alex C. Ruane5, Jonas Jägermeyr4,2,3,5, Abigail Snyder6, Marie Dury7, Pete Falloon8, Christian Folberth9, Louis François7, Tobias Hank10, R. Cesar Izaurralde11,12, Ingrid Jacquemin7, Curtis Jones11, Michelle Li2,13, Wenfeng Liu14,15, Stefan Olin16, Meridel Phillips5,17, Thomas A. M. Pugh18,19, Ashwan Reddy11, Karina Williams8, Ziwei Wang1,2, Florian Zabel10, 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
  • 6Joint Global Change Research Institute, Pacific Northwest National Laboratory, College Park, MD, USA
  • 7Unité de Modélisation du Climat et des Cycles Biogéochimiques, UR SPHERES, Institut d’Astrophysique et de Géophysique, University of Liège, Belgium
  • 8Met Office Hadley Centre, Exeter, UK
  • 9Ecosystem Services and Management Program, International Institute for Applied Systems Analysis, Laxenburg, Austria
  • 10Department of Geography, Ludwig-Maximilians-Universität, Munich, Germany
  • 11Department of Geographical Sciences, University of Maryland, College Park, MD, USA
  • 12Texas Agrilife Research and Extension, Texas A&M University, Temple, TX, USA
  • 13Department of Statistics, University of Chicago, Chicago, IL, USA
  • 14EAWAG, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland
  • 15Laboratoire des Sciences du Climat et de l’Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, 91191 Gif-sur-Yvette, France
  • 16Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden
  • 17Earth Institute Center for Climate Systems Research, Columbia University, New York, NY, USA
  • 18School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, UK
  • 19Birmingham Institute of Forest Research, University of Birmingham, Birmingham, UK

Abstract. Statistical emulation allows combining advantageous features of statistical and process-based crop models for understanding the effects of future climate changes on crop yields. We describe here the development of emulators for nine process-based crop models and five crops using output from the Global Gridded Model Intercomparison Project (GGCMI) Phase II. The GGCMI Phase II experiment is designed with the explicit goal of producing a structured training dataset for emulator development that samples across four dimensions relevant to crop yields: atmospheric carbon dioxide (CO2) concentrations, temperature, water supply, and nitrogen inputs (CTWN). Simulations are run under two different adaptation assumptions: that growing seasons shorten in warmer climates, and that cultivar choice allows growing seasons to remain fixed. The dataset allows emulating the climatological mean yield response without relying on interannual variations; we show that these are quantitatively different. Climatological mean yield responses can be readily captured with a simple polynomial in nearly all locations, with errors significant only in some marginal lands where crops are not currently grown. In general, emulation errors are negligible relative to differences across crop models or even across climate model scenarios. We demonstrate that the resulting GGCMI emulators can reproduce yields under realistic future climate simulations, even though the GGCMI Phase II dataset is constructed with uniform CTWN offsets, suggesting that effects of changes in temperature and precipitation distributions are small relative to those of changing means. The resulting emulators therefore capture relevant crop model responses in a lightweight, computationally tractable form, providing a tool that can facilitate model comparison, diagnosis of interacting factors affecting yields, and integrated assessment of climate impacts.

James Franke et al.

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James Franke et al.

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AgMIP's GGCMI Phase II: Crop model Emulators at 0.5 degree global resolution J. Franke https://doi.org/10.5281/zenodo.3592453

James Franke et al.

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Short summary
Improving our understanding of the impacts of climate change on crop yields will be critical for global food security in the next century. The models often used to study the how climate change may impact agriculture are complex and costly to run. In this work we describe a set of global crop model emulators (simplified models) developed under the Agricultural Model Intercomparison Project. Crop model emulators make agricultural simulations more accessible to policy or decision makers.
Improving our understanding of the impacts of climate change on crop yields will be critical for...
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