Geoscientific Model Development (May 2020)
The GGCMI Phase 2 experiment: global gridded crop model simulations under uniform changes in CO<sub>2</sub>, temperature, water, and nitrogen levels (protocol version 1.0)
- J. A. Franke,
- J. A. Franke,
- C. Müller,
- J. Elliott,
- J. Elliott,
- A. C. Ruane,
- J. Jägermeyr,
- J. Jägermeyr,
- J. Jägermeyr,
- J. Jägermeyr,
- J. Balkovic,
- J. Balkovic,
- P. Ciais,
- P. Ciais,
- M. Dury,
- P. D. Falloon,
- C. Folberth,
- L. François,
- T. Hank,
- M. Hoffmann,
- M. Hoffmann,
- R. C. Izaurralde,
- R. C. Izaurralde,
- I. Jacquemin,
- C. Jones,
- N. Khabarov,
- M. Koch,
- M. Li,
- M. Li,
- W. Liu,
- W. Liu,
- S. Olin,
- M. Phillips,
- M. Phillips,
- T. A. M. Pugh,
- T. A. M. Pugh,
- A. Reddy,
- X. Wang,
- X. Wang,
- K. Williams,
- K. Williams,
- F. Zabel,
- E. J. Moyer,
- E. J. Moyer
Affiliations
- J. A. Franke
- Department of the Geophysical Sciences, University of Chicago, Chicago, IL, USA
- J. A. Franke
- Center for Robust Decision-making on Climate and Energy Policy (RDCEP), University of Chicago, Chicago, IL, USA
- C. Müller
- Potsdam Institute for Climate Impact Research, Member of the Leibniz Association, Potsdam, Germany
- J. Elliott
- Center for Robust Decision-making on Climate and Energy Policy (RDCEP), University of Chicago, Chicago, IL, USA
- J. Elliott
- Department of Computer Science, University of Chicago, Chicago, IL, USA
- A. C. Ruane
- NASA Goddard Institute for Space Studies, New York, NY, USA
- J. Jägermeyr
- Center for Robust Decision-making on Climate and Energy Policy (RDCEP), University of Chicago, Chicago, IL, USA
- J. Jägermeyr
- Potsdam Institute for Climate Impact Research, Member of the Leibniz Association, Potsdam, Germany
- J. Jägermeyr
- Department of Computer Science, University of Chicago, Chicago, IL, USA
- J. Jägermeyr
- NASA Goddard Institute for Space Studies, New York, NY, USA
- J. Balkovic
- Ecosystem Services and Management Program, International Institute for Applied Systems Analysis, Laxenburg, Austria
- J. Balkovic
- Department of Soil Science, Faculty of Natural Sciences, Comenius University in Bratislava, Bratislava, Slovak Republic
- P. Ciais
- Laboratoire des Sciences du Climat et de l'Environnement, CEA-CNRS-UVSQ, Gif-sur-Yvette, France
- P. Ciais
- Sino-French Institute of Earth System Sciences, College of Urban and Env. Sciences, Peking University, Beijing, China
- M. Dury
- Unité de Modélisation du Climat et des Cycles Biogéochimiques, UR SPHERES, Institut d'Astrophysique et de Géophysique, University of Liège, Liège, Belgium
- P. D. Falloon
- Met Office Hadley Centre, Exeter, UK
- C. Folberth
- Ecosystem Services and Management Program, International Institute for Applied Systems Analysis, Laxenburg, Austria
- L. François
- Unité de Modélisation du Climat et des Cycles Biogéochimiques, UR SPHERES, Institut d'Astrophysique et de Géophysique, University of Liège, Liège, Belgium
- T. Hank
- Department of Geography, Ludwig-Maximilians-Universität München, Munich, Germany
- M. Hoffmann
- Georg-August-University Göttingen, Tropical Plant Production and Agricultural Systems Modeling, Göttingen, Germany
- M. Hoffmann
- Leibniz Centre for Agricultural Landscape Research (ZALF), Müncheberg, Germany
- R. C. Izaurralde
- Department of Geographical Sciences, University of Maryland, College Park, MD, USA
- R. C. Izaurralde
- Texas Agrilife Research and Extension, Texas A&M University, Temple, TX, USA
- I. Jacquemin
- Unité de Modélisation du Climat et des Cycles Biogéochimiques, UR SPHERES, Institut d'Astrophysique et de Géophysique, University of Liège, Liège, Belgium
- C. Jones
- Department of Geographical Sciences, University of Maryland, College Park, MD, USA
- N. Khabarov
- Ecosystem Services and Management Program, International Institute for Applied Systems Analysis, Laxenburg, Austria
- M. Koch
- Georg-August-University Göttingen, Tropical Plant Production and Agricultural Systems Modeling, Göttingen, Germany
- M. Li
- Center for Robust Decision-making on Climate and Energy Policy (RDCEP), University of Chicago, Chicago, IL, USA
- M. Li
- Department of Statistics, University of Chicago, Chicago, IL, USA
- W. Liu
- Laboratoire des Sciences du Climat et de l'Environnement, CEA-CNRS-UVSQ, Gif-sur-Yvette, France
- W. Liu
- EAWAG, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland
- S. Olin
- Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden
- M. Phillips
- NASA Goddard Institute for Space Studies, New York, NY, USA
- M. Phillips
- Earth Institute Center for Climate Systems Research, Columbia University, New York, NY, USA
- T. A. M. Pugh
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, UK
- T. A. M. Pugh
- Birmingham Institute of Forest Research, University of Birmingham, Birmingham, UK
- A. Reddy
- Department of Geographical Sciences, University of Maryland, College Park, MD, USA
- X. Wang
- Laboratoire des Sciences du Climat et de l'Environnement, CEA-CNRS-UVSQ, Gif-sur-Yvette, France
- X. Wang
- Sino-French Institute of Earth System Sciences, College of Urban and Env. Sciences, Peking University, Beijing, China
- K. Williams
- Met Office Hadley Centre, Exeter, UK
- K. Williams
- Global Systems Institute, University of Exeter, Laver Building, North Park Road, Exeter, EX4 4QE, UK
- F. Zabel
- Department of Geography, Ludwig-Maximilians-Universität München, Munich, Germany
- E. J. Moyer
- Department of the Geophysical Sciences, University of Chicago, Chicago, IL, USA
- E. J. Moyer
- Center for Robust Decision-making on Climate and Energy Policy (RDCEP), University of Chicago, Chicago, IL, USA
- DOI
- https://doi.org/10.5194/gmd-13-2315-2020
- Journal volume & issue
-
Vol. 13
pp. 2315 – 2336
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.