The GGCMI Phase 2 experiment: global gridded crop model simulations under uniform changes in CO2, temperature, water, and nitrogen levels (protocol version 1.0)

James Franke, Christoph Müller, Joshua Elliott, Alex Ruane, Jonas Jagermayer, Thomas Pugh

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6 Citations (Scopus)
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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.
Original languageEnglish
Pages (from-to)2315–2336
Number of pages22
JournalGeoscientific Model Development
Issue number5
Publication statusPublished - 18 May 2020

Bibliographical note

Funding Information:
Financial support. RDCEP is funded by NSF (grant no. SES-

Funding Information:
1463644) through the Decision Making Under Uncertainty program. James Franke was supported by the NSF NRT program (grant no. DGE-1735359) and the NSF Graduate Research Fellowship Program (grant no. DGE-1746045). Christoph Müller was supported by the MACMIT project (grant no. 01LN1317A) funded through the German Federal Ministry of Education and Research (BMBF). Alex C. Ruane was supported by NASA NNX16AK38G (INCA) and the NASA Earth Sciences Directorate/GISS Climate Impacts Group. Christian Folberth was supported by the European Research Council Synergy (grant no. ERC-2013-SynG-610028) Imbalance-P. Pete Falloon and Karina Williams were supported by the Newton Fund through the Met Office program Climate Science for Service Partnership Brazil (CSSP Brazil). Karina Williams was supported by the IMPREX research project supported by the European Commission under the Horizon 2020 Framework program (grant no. 641811). Stefan Olin acknowledges support from the Swedish strong research areas BECC and MERGE, together with support from LUCCI (Lund University Centre for studies of Carbon Cycle and Climate Interactions). R. Cesar Izaurralde acknowledges support from the Texas Agrilife Research and Extension, Texas A & M University.

Publisher Copyright:
© Author(s) 2020.


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