The GGCMI Phase 2 emulators: global gridded crop model responses to changes in CO<sub>2</sub>, temperature, water, and nitrogen (version 1.0)
<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...
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2020-09-01
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| Series: | Geoscientific Model Development |
| Online Access: | https://gmd.copernicus.org/articles/13/3995/2020/gmd-13-3995-2020.pdf |
| _version_ | 1818133073025302528 |
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| author | J. A. Franke J. A. Franke C. Müller J. Elliott J. Elliott A. C. Ruane J. Jägermeyr J. Jägermeyr J. Jägermeyr J. Jägermeyr A. Snyder M. Dury P. D. Falloon C. Folberth L. François T. Hank R. C. Izaurralde R. C. Izaurralde I. Jacquemin C. Jones M. Li M. Li W. Liu W. Liu S. Olin M. Phillips M. Phillips T. A. M. Pugh T. A. M. Pugh A. Reddy K. Williams K. Williams Z. Wang Z. Wang F. Zabel E. J. Moyer E. J. Moyer |
| author_facet | J. A. Franke J. A. Franke C. Müller J. Elliott J. Elliott A. C. Ruane J. Jägermeyr J. Jägermeyr J. Jägermeyr J. Jägermeyr A. Snyder M. Dury P. D. Falloon C. Folberth L. François T. Hank R. C. Izaurralde R. C. Izaurralde I. Jacquemin C. Jones M. Li M. Li W. Liu W. Liu S. Olin M. Phillips M. Phillips T. A. M. Pugh T. A. M. Pugh A. Reddy K. Williams K. Williams Z. Wang Z. Wang F. Zabel E. J. Moyer E. J. Moyer |
| author_sort | J. A. Franke |
| collection | DOAJ |
| description | <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 (<span class="inline-formula">CO<sub>2</sub></span>) 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> |
| first_indexed | 2024-12-11T08:46:55Z |
| format | Article |
| id | doaj.art-70c0cb8f17ea40308cbb142473c564ec |
| institution | Directory Open Access Journal |
| issn | 1991-959X 1991-9603 |
| language | English |
| last_indexed | 2024-12-11T08:46:55Z |
| publishDate | 2020-09-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Geoscientific Model Development |
| spelling | doaj.art-70c0cb8f17ea40308cbb142473c564ec2022-12-22T01:14:07ZengCopernicus PublicationsGeoscientific Model Development1991-959X1991-96032020-09-01133995401810.5194/gmd-13-3995-2020The GGCMI Phase 2 emulators: global gridded crop model responses to changes in CO<sub>2</sub>, temperature, water, and nitrogen (version 1.0)J. A. Franke0J. A. Franke1C. Müller2J. Elliott3J. Elliott4A. C. Ruane5J. Jägermeyr6J. Jägermeyr7J. Jägermeyr8J. Jägermeyr9A. Snyder10M. Dury11P. D. Falloon12C. Folberth13L. François14T. Hank15R. C. Izaurralde16R. C. Izaurralde17I. Jacquemin18C. Jones19M. Li20M. Li21W. Liu22W. Liu23S. Olin24M. Phillips25M. Phillips26T. A. M. Pugh27T. A. M. Pugh28A. Reddy29K. Williams30K. Williams31Z. Wang32Z. Wang33F. Zabel34E. J. Moyer35E. J. Moyer36Department of the Geophysical Sciences, University of Chicago, Chicago, IL, USACenter for Robust Decision-making on Climate and Energy Policy (RDCEP), University of Chicago, Chicago, IL, USAPotsdam Institute for Climate Impact Research, Member of the Leibniz Association, Potsdam, GermanyCenter for Robust Decision-making on Climate and Energy Policy (RDCEP), University of Chicago, Chicago, IL, USANASA Goddard Institute for Space Studies, New York, NY, USACenter for Climate Systems Research, Columbia University, New York, NY 10025, USACenter for Robust Decision-making on Climate and Energy Policy (RDCEP), University of Chicago, Chicago, IL, USAPotsdam Institute for Climate Impact Research, Member of the Leibniz Association, Potsdam, GermanyNASA Goddard Institute for Space Studies, New York, NY, USACenter for Climate Systems Research, Columbia University, New York, NY 10025, USAJoint Global Change Research Institute, Pacific Northwest National Laboratory, College Park, MD, USAUnité de Modélisation du Climat et des Cycles Biogéochimiques, UR SPHERES, Institut d'Astrophysique et de Géophysique, University of Liège, Liège, BelgiumMet Office Hadley Centre, Exeter, UKEcosystem Services and Management Program, International Institute for Applied Systems Analysis, Laxenburg, AustriaUnité de Modélisation du Climat et des Cycles Biogéochimiques, UR SPHERES, Institut d'Astrophysique et de Géophysique, University of Liège, Liège, BelgiumDepartment of Geography, Ludwig-Maximilians-Universität München, Munich, GermanyDepartment of Geographical Sciences, University of Maryland, College Park, MD, USATexas Agrilife Research and Extension, Texas A&M University, Temple, TX, USAUnité de Modélisation du Climat et des Cycles Biogéochimiques, UR SPHERES, Institut d'Astrophysique et de Géophysique, University of Liège, Liège, BelgiumDepartment of Geographical Sciences, University of Maryland, College Park, MD, USACenter for Robust Decision-making on Climate and Energy Policy (RDCEP), University of Chicago, Chicago, IL, USADepartment of Statistics, University of Chicago, Chicago, IL, USAEAWAG, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, SwitzerlandLaboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, FranceDepartment of Physical Geography and Ecosystem Science, Lund University, Lund, SwedenNASA Goddard Institute for Space Studies, New York, NY, USAEarth Institute Center for Climate Systems Research, Columbia University, New York, NY, USASchool of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, UKBirmingham Institute of Forest Research, University of Birmingham, Birmingham, UKDepartment of Geographical Sciences, University of Maryland, College Park, MD, USAMet Office Hadley Centre, Exeter, UKGlobal Systems Institute, University of Exeter, Laver Building, North Park Road, Exeter, UKDepartment of the Geophysical Sciences, University of Chicago, Chicago, IL, USACenter for Robust Decision-making on Climate and Energy Policy (RDCEP), University of Chicago, Chicago, IL, USADepartment of Geography, Ludwig-Maximilians-Universität München, Munich, GermanyDepartment of the Geophysical Sciences, University of Chicago, Chicago, IL, USACenter for Robust Decision-making on Climate and Energy Policy (RDCEP), University of Chicago, Chicago, IL, USA<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 (<span class="inline-formula">CO<sub>2</sub></span>) 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>https://gmd.copernicus.org/articles/13/3995/2020/gmd-13-3995-2020.pdf |
| spellingShingle | J. A. Franke J. A. Franke C. Müller J. Elliott J. Elliott A. C. Ruane J. Jägermeyr J. Jägermeyr J. Jägermeyr J. Jägermeyr A. Snyder M. Dury P. D. Falloon C. Folberth L. François T. Hank R. C. Izaurralde R. C. Izaurralde I. Jacquemin C. Jones M. Li M. Li W. Liu W. Liu S. Olin M. Phillips M. Phillips T. A. M. Pugh T. A. M. Pugh A. Reddy K. Williams K. Williams Z. Wang Z. Wang F. Zabel E. J. Moyer E. J. Moyer The GGCMI Phase 2 emulators: global gridded crop model responses to changes in CO<sub>2</sub>, temperature, water, and nitrogen (version 1.0) Geoscientific Model Development |
| title | The GGCMI Phase 2 emulators: global gridded crop model responses to changes in CO<sub>2</sub>, temperature, water, and nitrogen (version 1.0) |
| title_full | The GGCMI Phase 2 emulators: global gridded crop model responses to changes in CO<sub>2</sub>, temperature, water, and nitrogen (version 1.0) |
| title_fullStr | The GGCMI Phase 2 emulators: global gridded crop model responses to changes in CO<sub>2</sub>, temperature, water, and nitrogen (version 1.0) |
| title_full_unstemmed | The GGCMI Phase 2 emulators: global gridded crop model responses to changes in CO<sub>2</sub>, temperature, water, and nitrogen (version 1.0) |
| title_short | The GGCMI Phase 2 emulators: global gridded crop model responses to changes in CO<sub>2</sub>, temperature, water, and nitrogen (version 1.0) |
| title_sort | ggcmi phase 2 emulators global gridded crop model responses to changes in co sub 2 sub temperature water and nitrogen version 1 0 |
| url | https://gmd.copernicus.org/articles/13/3995/2020/gmd-13-3995-2020.pdf |
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