Centers and Institutes
Published September 3, 2020 | Version v1
Journal article Open

The GGCMI Phase 2 emulators: global gridded crop model responses to changes in CO2, temperature, water, and nitrogen (version 1.0)

Creators

  • 1. University of Chicago
  • 2. Potsdam Institute for Climate Impact Research
  • 3. Columbia University
  • 4. Pacific Northwest National Laboratory
  • 5. University of Liège
  • 6. Met Office Hadley Centre
  • 7. International Institute for Applied Systems Analysis
  • 8. Ludwig-Maximilians-Universität München
  • 9. University of Maryland
  • 10. Swiss Federal Institute of Aquatic Science and Technology
  • 11. Lund University
  • 12. NASA Goddard Institute for Space Studies
  • 13. University of Birmingham
  • 14. University of Exeter

Description

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 2. The GGCMI Phase 2 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 of all models with a simple polynomial in mean growing-season values. Climatological-mean yields are a central metric in climate change impact analysis; we show here that they can be captured without relying on interannual variations. In general, emulation errors are negligible relative to differences across crop models or even across climate model scenarios; errors become significant only in some marginal lands where crops are not currently grown. We demonstrate that the resulting GGCMI emulators can reproduce yields under realistic future climate simulations, even though the GGCMI Phase 2 dataset is constructed with uniform CTWN offsets, suggesting that the 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.

Data availability

The polynomial parameters for crop model emulators are available at https://doi.org/10.5281/zenodo.3592453 (Franke, 2019) and https://doi.org/10.5281/zenodo.3994593 (Franke et al., 2020b).

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gmd-13-3995-2020.pdf

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Additional details

Identifiers

DOI
10.5194/gmd-13-3995-2020
Other
oai:uchicago.tind.io:14005

Funding

National Science Foundation
SES-1463644
National Science Foundation
DGE-1735359
National Science Foundation
DGE-1746045
German Federal Ministry of Education and Research
01LN1317A
NASA
NNX16AK38G
European Research Council
ERC-2013-SynG-610028
Newton Fund
European Commission
641811
Lund University
Texas A&M University
U.S. Department of Energy

UChicago Information

Division(s)
Physical Sciences Division
Department(s)
Geophysical Sciences, Statistics
Center(s) or Institute(s)
Center for Robust Decision Making on Climate and Energy Policy