Articles | Volume 13, issue 9
https://doi.org/10.5194/gmd-13-3995-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-13-3995-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
03 Sep 2020
Model description paper |
| 03 Sep 2020
| 03 Sep 2020
The GGCMI Phase 2 emulators: global gridded crop model responses to changes in CO2, temperature, water, and nitrogen (version 1.0)
James A. Franke
CORRESPONDING AUTHOR
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
Christoph Müller
Potsdam Institute for Climate Impact Research, Member of the Leibniz Association, Potsdam,
Germany
Joshua Elliott
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
Alex C. Ruane
Center for Climate Systems Research, Columbia University, New York, NY 10025, USA
Jonas Jägermeyr
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
Abigail Snyder
Joint Global Change Research Institute, Pacific Northwest National Laboratory, College Park, MD, USA
Marie Dury
Unité de Modélisation du Climat et des Cycles Biogéochimiques, UR SPHERES, Institut d'Astrophysique et de Géophysique, University of Liège, Liège, Belgium
Pete D. Falloon
Met Office Hadley Centre, Exeter, UK
Christian Folberth
Ecosystem Services and Management Program, International Institute for Applied Systems Analysis, Laxenburg, Austria
Louis François
Unité de Modélisation du Climat et des Cycles Biogéochimiques, UR SPHERES, Institut d'Astrophysique et de Géophysique, University of Liège, Liège, Belgium
Tobias Hank
Department of Geography, Ludwig-Maximilians-Universität München, Munich,
Germany
R. Cesar Izaurralde
Department of Geographical Sciences, University of Maryland, College Park, MD, USA
Texas Agrilife Research and Extension, Texas A&M University, Temple, TX, USA
Ingrid Jacquemin
Unité de Modélisation du Climat et des Cycles Biogéochimiques, UR SPHERES, Institut d'Astrophysique et de Géophysique, University of Liège, Liège, Belgium
Curtis Jones
Department of Geographical Sciences, University of Maryland, College Park, MD, USA
Michelle Li
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
Wenfeng Liu
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
Stefan Olin
Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden
Meridel Phillips
NASA Goddard Institute for Space Studies, New York, NY, USA
Earth Institute Center for Climate Systems Research, Columbia University, New York, NY, USA
Thomas A. M. Pugh
School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, UK
Birmingham Institute of Forest Research, University of Birmingham, Birmingham, UK
Ashwan Reddy
Department of Geographical Sciences, University of Maryland, College Park, MD, USA
Karina Williams
Met Office Hadley Centre, Exeter, UK
Global Systems Institute, University of Exeter, Laver Building, North Park Road, Exeter, UK
Ziwei Wang
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
Florian Zabel
Department of Geography, Ludwig-Maximilians-Universität München, Munich,
Germany
Elisabeth J. Moyer
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
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- Global-to-local-to-global interactions and climate change U. Baldos et al. 10.1088/1748-9326/acc95c
- Explaining population booms and busts in Mid-Holocene Europe D. Kondor et al. 10.1038/s41598-023-35920-z
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- Redistribution of nitrogen to feed the people on a safer planet H. Kahiluoto et al. 10.1093/pnasnexus/pgae170
- Landscape of fear: indirect effects of conflict can account for large-scale population declines in non-state societies D. Kondor et al. 10.1098/rsif.2024.0210
- Reduction of uncertainties in rice yield response to elevated CO2 by experiment-model integration: A case study in East China Z. Wang et al. 10.1016/j.cj.2024.06.012
- The GGCMI Phase 2 experiment: global gridded crop model simulations under uniform changes in CO<sub>2</sub>, temperature, water, and nitrogen levels (protocol version 1.0) J. Franke et al. 10.5194/gmd-13-2315-2020
26 citations as recorded by crossref.
- How reliable are current crop models for simulating growth and seed yield of canola across global sites and under future climate change? E. Wang et al. 10.1007/s10584-022-03375-2
- Exploring uncertainties in global crop yield projections in a large ensemble of crop models and CMIP5 and CMIP6 climate scenarios C. Müller et al. 10.1088/1748-9326/abd8fc
- Pangeo-Enabled ESM Pattern Scaling (PEEPS): A customizable dataset of emulated Earth System Model output B. Kravitz et al. 10.1371/journal.pclm.0000159
- Integrating machine learning and environmental variables to constrain uncertainty in crop yield change projections under climate change L. Li et al. 10.1016/j.eja.2023.126917
- Enhancing Maize Yield Simulations in Regional China Using Machine Learning and Multi-Data Resources Y. Zou et al. 10.3390/rs16040701
- Rising temperatures can negate CO2 fertilization effects on global staple crop yields: A meta-regression analysis C. Zhu et al. 10.1016/j.agrformet.2023.109737
- Extreme Events and Production Shocks for Key Crops in Southern Africa Under Climate Change T. Thomas et al. 10.3389/fclim.2022.787582
- Spatiotemporal patterns of winter wheat phenology and its climatic drivers based on an improved pDSSAT model Y. Luo et al. 10.1007/s11430-020-9821-0
- Trade can buffer climate-induced risks and volatilities in crop supply I. Haqiqi 10.1088/2976-601X/ad7d12
- Predicting spatiotemporal soil organic carbon responses to management using EPIC-IIASA meta-models T. Ippolito et al. 10.1016/j.jenvman.2023.118532
- Adaptation Strategies Strongly Reduce the Future Impacts of Climate Change on Simulated Crop Yields R. Abramoff et al. 10.1029/2022EF003190
- The water-energy-food-ecosystem nexus in the Danube River Basin: Exploring scenarios and implications of maize irrigation E. Probst et al. 10.1016/j.scitotenv.2023.169405
- Agricultural breadbaskets shift poleward given adaptive farmer behavior under climate change J. Franke et al. 10.1111/gcb.15868
- Modelling Agroforestry’s Contributions to People—A Review of Available Models P. Kraft et al. 10.3390/agronomy11112106
- Enhanced nitrous oxide emission factors due to climate change increase the mitigation challenge in the agricultural sector L. Li et al. 10.1111/gcb.17472
- Grid based monitoring and forecasting system of cropping conditions and risks by agrometeorological indicators in Austria – Agricultural Risk Information System ARIS J. Eitzinger et al. 10.1016/j.cliser.2024.100478
- CLASH – Climate-responsive Land Allocation model with carbon Storage and Harvests T. Ekholm et al. 10.5194/gmd-17-3041-2024
- osiris: An R package to process climate impacts on agricultural yields for the Global Change Analysis Model H. Ahsan et al. 10.21105/joss.05226
- The optimization of model ensemble composition and size can enhance the robustness of crop yield projections L. Li et al. 10.1038/s43247-023-01016-9
- ChinaRiceCalendar – seasonal crop calendars for early-, middle-, and late-season rice in China H. Li et al. 10.5194/essd-16-1689-2024
- Global-to-local-to-global interactions and climate change U. Baldos et al. 10.1088/1748-9326/acc95c
- Explaining population booms and busts in Mid-Holocene Europe D. Kondor et al. 10.1038/s41598-023-35920-z
- Historical simulation of maize water footprints with a new global gridded crop model ACEA O. Mialyk et al. 10.5194/hess-26-923-2022
- Redistribution of nitrogen to feed the people on a safer planet H. Kahiluoto et al. 10.1093/pnasnexus/pgae170
- Landscape of fear: indirect effects of conflict can account for large-scale population declines in non-state societies D. Kondor et al. 10.1098/rsif.2024.0210
- Reduction of uncertainties in rice yield response to elevated CO2 by experiment-model integration: A case study in East China Z. Wang et al. 10.1016/j.cj.2024.06.012
Latest update: 13 Dec 2024
Short summary
Improving our understanding of the impacts of climate change on crop yields will be critical for global food security in the next century. The models often used to study the how climate change may impact agriculture are complex and costly to run. In this work, we describe a set of global crop model emulators (simplified models) developed under the Agricultural Model Intercomparison Project. Crop model emulators make agricultural simulations more accessible to policy or decision makers.
Improving our understanding of the impacts of climate change on crop yields will be critical for...
