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© 2020. This work is published under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

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 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.

Details

Title
The GGCMI Phase 2 emulators: global gridded crop model responses to changes in CO2, temperature, water, and nitrogen (version 1.0)
Author
Franke, James A 1 ; Müller, Christoph 2   VIAFID ORCID Logo  ; Elliott, Joshua 3 ; Ruane, Alex C 4   VIAFID ORCID Logo  ; Jägermeyr, Jonas 5   VIAFID ORCID Logo  ; Snyder, Abigail 6 ; Dury, Marie 7 ; Falloon, Pete D 8 ; Folberth, Christian 9   VIAFID ORCID Logo  ; Louis, François 7 ; Tobias, Hank 10 ; Izaurralde, R Cesar 11 ; Jacquemin, Ingrid 7 ; Jones, Curtis 12 ; Li, Michelle 13 ; Liu, Wenfeng 14   VIAFID ORCID Logo  ; Olin, Stefan 15 ; Phillips, Meridel 16 ; Pugh, Thomas A M 17   VIAFID ORCID Logo  ; Reddy, Ashwan 12 ; Williams, Karina 18   VIAFID ORCID Logo  ; Wang, Ziwei 1   VIAFID ORCID Logo  ; Zabel, Florian 10 ; Moyer, Elisabeth J 1 

 Department of the Geophysical Sciences, University of Chicago, Chicago, IL, USA; Center for Robust Decision-making on Climate and Energy Policy (RDCEP), University of Chicago, Chicago, IL, USA 
 Potsdam Institute for Climate Impact Research, Member of the Leibniz Association, Potsdam, Germany 
 Center for Robust Decision-making on Climate and Energy Policy (RDCEP), University of Chicago, Chicago, IL, USA; NASA Goddard Institute for Space Studies, New York, NY, USA 
 Center for Climate Systems Research, Columbia University, New York, NY 10025, USA 
 NASA Goddard Institute for Space Studies, New York, NY, USA; Center for Robust Decision-making on Climate and Energy Policy (RDCEP), University of Chicago, Chicago, IL, USA; Potsdam Institute for Climate Impact Research, Member of the Leibniz Association, Potsdam, Germany; Center for Climate Systems Research, Columbia University, New York, NY 10025, USA 
 Joint Global Change Research Institute, Pacific Northwest National Laboratory, College Park, MD, USA 
 Unité de Modélisation du Climat et des Cycles Biogéochimiques, UR SPHERES, Institut d'Astrophysique et de Géophysique, University of Liège, Liège, Belgium 
 Met Office Hadley Centre, Exeter, UK 
 Ecosystem Services and Management Program, International Institute for Applied Systems Analysis, Laxenburg, Austria 
10  Department of Geography, Ludwig-Maximilians-Universität München, Munich, Germany 
11  Department of Geographical Sciences, University of Maryland, College Park, MD, USA; Texas Agrilife Research and Extension, Texas A&M University, Temple, TX, USA 
12  Department of Geographical Sciences, University of Maryland, College Park, MD, USA 
13  Center for Robust Decision-making on Climate and Energy Policy (RDCEP), University of Chicago, Chicago, IL, USA; Department of Statistics, University of Chicago, Chicago, IL, USA 
14  EAWAG, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland; Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, France 
15  Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden 
16  NASA Goddard Institute for Space Studies, New York, NY, USA; Earth Institute Center for Climate Systems Research, Columbia University, New York, NY, USA 
17  School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, UK; Birmingham Institute of Forest Research, University of Birmingham, Birmingham, UK 
18  Met Office Hadley Centre, Exeter, UK; Global Systems Institute, University of Exeter, Laver Building, North Park Road, Exeter, UK 
Pages
3995-4018
Publication year
2020
Publication date
2020
Publisher
Copernicus GmbH
ISSN
1991962X
e-ISSN
19919603
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2439582699
Copyright
© 2020. This work is published under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.