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

Franke, James A. ; Müller, Christoph LU ; Elliott, Joshua ; Ruane, Alex C. ; Jägermeyr, Jonas ; Snyder, Abigail ; Dury, Marie ; Falloon, Pete D. ; Folberth, Christian and François, Louis , et al. (2020) In Geoscientific Model Development 13(9). p.3995-4018
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 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... (More)

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.

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@article{30898cc7-7e06-4d93-b9cb-8ac85321ce54,
  abstract     = {{<p>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.</p>}},
  author       = {{Franke, James A. and Müller, Christoph and Elliott, Joshua and Ruane, Alex C. and Jägermeyr, Jonas and Snyder, Abigail and Dury, Marie and Falloon, Pete D. and Folberth, Christian and François, Louis and Hank, Tobias and Cesar Izaurralde, R. and Jacquemin, Ingrid and Jones, Curtis and Li, Michelle and Liu, Wenfeng and Olin, Stefan and Phillips, Meridel and Pugh, Thomas A.M. and Reddy, Ashwan and Williams, Karina and Wang, Ziwei and Zabel, Florian and Moyer, Elisabeth J.}},
  issn         = {{1991-959X}},
  language     = {{eng}},
  month        = {{09}},
  number       = {{9}},
  pages        = {{3995--4018}},
  publisher    = {{Copernicus GmbH}},
  series       = {{Geoscientific Model Development}},
  title        = {{The GGCMI Phase 2 emulators : Global gridded crop model responses to changes in CO2, temperature, water, and nitrogen (version 1.0)}},
  url          = {{http://dx.doi.org/10.5194/gmd-13-3995-2020}},
  doi          = {{10.5194/gmd-13-3995-2020}},
  volume       = {{13}},
  year         = {{2020}},
}