Articles | Volume 13, issue 5
https://doi.org/10.5194/gmd-13-2315-2020
© 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-13-2315-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model experiment description paper
| 
18 May 2020
Model experiment description paper |
| 18 May 2020
| 18 May 2020
The GGCMI Phase 2 experiment: global gridded crop model simulations under uniform changes in CO2, temperature, water, and nitrogen levels (protocol version 1.0)
James A. Franke
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
Joshua Elliott
Center for Robust Decision-making on Climate and Energy Policy (RDCEP), University of Chicago, Chicago, IL, USA
Department of Computer Science, University of Chicago, Chicago, IL, USA
Alex C. Ruane
NASA Goddard Institute for Space Studies, New York, NY, USA
Jonas Jägermeyr
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
Department of Computer Science, University of Chicago, Chicago, IL, USA
NASA Goddard Institute for Space Studies, New York, NY, USA
Juraj Balkovic
Ecosystem Services and Management Program, International Institute for Applied Systems Analysis, Laxenburg, Austria
Department of Soil Science, Faculty of Natural Sciences, Comenius University in Bratislava, Bratislava, Slovak Republic
Philippe Ciais
Laboratoire des Sciences du Climat et de l'Environnement, CEA-CNRS-UVSQ, Gif-sur-Yvette, France
Sino-French Institute of Earth System Sciences, College of Urban and Env. Sciences, Peking University, Beijing, China
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
Munir Hoffmann
Georg-August-University Göttingen, Tropical Plant Production and Agricultural Systems Modeling, Göttingen, Germany
Leibniz Centre for Agricultural Landscape Research (ZALF), Müncheberg, 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
Nikolay Khabarov
Ecosystem Services and Management Program, International Institute for Applied Systems Analysis, Laxenburg, Austria
Marian Koch
Georg-August-University Göttingen, Tropical Plant Production and Agricultural Systems Modeling, Göttingen, Germany
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
Laboratoire des Sciences du Climat et de l'Environnement, CEA-CNRS-UVSQ, Gif-sur-Yvette, France
EAWAG, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland
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
Xuhui Wang
Laboratoire des Sciences du Climat et de l'Environnement, CEA-CNRS-UVSQ, Gif-sur-Yvette, France
Sino-French Institute of Earth System Sciences, College of Urban and Env. Sciences, Peking University, Beijing, China
Karina Williams
Met Office Hadley Centre, Exeter, UK
Global Systems Institute, University of Exeter, Laver
Building, North Park Road, Exeter, EX4 4QE, UK
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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Cited
34 citations as recorded by crossref.
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- 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
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34 citations as recorded by crossref.
- Climate impacts on global agriculture emerge earlier in new generation of climate and crop models J. Jägermeyr et al. 10.1038/s43016-021-00400-y
- Virtual nitrogen and phosphorus flow associated with interprovincial crop trade and its effect on grey water stress in China D. Ren et al. 10.1088/1748-9326/ac3604
- Effects of combined nitrogen-nutrient sources on lowland rice straw yield (a potential dairy feed) in a derived savannah ecology O. Iyanda et al. 10.1088/1755-1315/1219/1/012016
- Water footprints and crop water use of 175 individual crops for 1990–2019 simulated with a global crop model O. Mialyk et al. 10.1038/s41597-024-03051-3
- Exploring the uncertainty in projected wheat phenology, growth and yield under climate change in China H. Liu et al. 10.1016/j.agrformet.2022.109187
- Retrospective Predictions of Rice and Other Crop Production in Madagascar Using Soil Moisture and an NDVI-Based Calendar from 2010–2017 A. Rigden et al. 10.3390/rs14051223
- Climate change impacts on potential maize yields in Gambella Region, Ethiopia A. Degife et al. 10.1007/s10113-021-01773-3
- Future climate change significantly alters interannual wheat yield variability over half of harvested areas W. Liu et al. 10.1088/1748-9326/ac1fbb
- Large potential for crop production adaptation depends on available future varieties F. Zabel et al. 10.1111/gcb.15649
- Agricultural breadbaskets shift poleward given adaptive farmer behavior under climate change J. Franke et al. 10.1111/gcb.15868
- 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
- Trends and climate change analysis for common climate variables in Gelgel Belese Watershed, Upper Blue Nile Basin, Ethiopia K. Shitu et al. 10.1007/s00704-023-04568-0
- Modelling crop yield and harvest index: the role of carbon assimilation and allocation parameters H. Camargo-Alvarez et al. 10.1007/s40808-022-01625-x
- The statistical emulators of GGCMI phase 2: responses of year-to-year variation of crop yield to CO2, temperature, water, and nitrogen perturbations W. Liu et al. 10.5194/gmd-16-7203-2023
- Input database related uncertainty of Biome-BGCMuSo agro-environmental model outputs N. Fodor et al. 10.1080/17538947.2021.1953161
- How climate change and international trade will shape the future global soybean security pattern C. Qiao et al. 10.1016/j.jclepro.2023.138603
- Aggravation of global maize yield loss risk under various hot and dry scenarios using multiple types of prediction approaches X. Yin et al. 10.1002/joc.8371
- Seasonal Predictability of Four Major Crop Yields Worldwide by a Hybrid System of Dynamical Climate Prediction and Eco-Physiological Crop-Growth Simulation T. Doi et al. 10.3389/fsufs.2020.00084
- Future changes in crop yield over Poland driven by climate change, increasing atmospheric CO2 and nitrogen stress P. Marcinkowski & M. Piniewski 10.1016/j.agsy.2023.103813
- Disentangling the separate and confounding effects of temperature and precipitation on global maize yield using machine learning, statistical and process crop models X. Yin et al. 10.1088/1748-9326/ac5716
- Heterogeneous impacts of excessive wetness on maize yields in China: Evidence from statistical yields and process-based crop models W. Liu et al. 10.1016/j.agrformet.2022.109205
- Heat stress to jeopardize crop production in the US Corn Belt based on downscaled CMIP5 projections M. Yang & G. Wang 10.1016/j.agsy.2023.103746
- Optimisation of vertically mounted agrivoltaic systems P. Campana et al. 10.1016/j.jclepro.2021.129091
- Role of Indigenous and local knowledge in seasonal forecasts and climate adaptation: A case study of smallholder farmers in Chiredzi, Zimbabwe L. Zvobgo et al. 10.1016/j.envsci.2023.03.017
- Mapping the Global-Scale Maize Drought Risk Under Climate Change Based on the GEPIC-Vulnerability-Risk Model Y. Yin et al. 10.1007/s13753-021-00349-3
- Assessing and addressing the global state of food production data scarcity E. Kebede et al. 10.1038/s43017-024-00516-2
- Regional assessment and uncertainty analysis of carbon and nitrogen balances at cropland scale using the ecosystem model LandscapeDNDC O. Sifounakis et al. 10.5194/bg-21-1563-2024
- 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
- Assessment of Crop Yield in China Simulated by Thirteen Global Gridded Crop Models D. Yin et al. 10.1007/s00376-023-2234-3
- The GGCMI Phase 2 emulators: global gridded crop model responses to changes in CO<sub>2</sub>, temperature, water, and nitrogen (version 1.0) J. Franke et al. 10.5194/gmd-13-3995-2020
- Observational constraint of process crop models suggests higher risks for global maize yield under climate change X. Yin & G. Leng 10.1088/1748-9326/ac7ac7
- 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
- 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
- Machine Learning Crop Yield Models Based on Meteorological Features and Comparison with a Process-Based Model Q. Liu et al. 10.1175/AIES-D-22-0002.1
Latest update: 17 Apr 2024
Short summary
Concerns about food security under climate change motivate efforts to better understand future changes in crop yields. Crop models, which represent plant biology, are necessary tools for this purpose since they allow representing future climate, farmer choices, and new agricultural geographies. The Global Gridded Crop Model Intercomparison (GGCMI) Phase 2 experiment, under the Agricultural Model Intercomparison and Improvement Project (AgMIP), is designed to evaluate and improve crop models.
Concerns about food security under climate change motivate efforts to better understand future...
