Articles | Volume 16, issue 23
https://doi.org/10.5194/gmd-16-7203-2023
© Author(s) 2023. 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-16-7203-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
12 Dec 2023
Model description paper |
| 12 Dec 2023
| 12 Dec 2023
The statistical emulators of GGCMI phase 2: responses of year-to-year variation of crop yield to CO2, temperature, water, and nitrogen perturbations
Weihang Liu
State Key Laboratory of Earth Surface Processes and Resource Ecology (ESPRE), Beijing Normal University, Beijing 100875, China
Key Laboratory of Environmental Change and Natural Disasters, Ministry of Education, Beijing Normal University, Beijing 100875, China
Academy of Disaster Reduction and Emergency Management, Ministry of Emergency Management and Ministry of Education, Beijing 100875, China
Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
State Key Laboratory of Earth Surface Processes and Resource Ecology (ESPRE), Beijing Normal University, Beijing 100875, China
Key Laboratory of Environmental Change and Natural Disasters, Ministry of Education, Beijing Normal University, Beijing 100875, China
Academy of Disaster Reduction and Emergency Management, Ministry of Emergency Management and Ministry of Education, Beijing 100875, China
Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Christoph Müller
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, Potsdam, Germany
Jonas Jägermeyr
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, Potsdam, Germany
NASA Goddard Institute for Space Studies, New York City, New York, USA
Center for Climate Systems Research, Columbia University, New York City, New York, USA
James A. Franke
Department of the Geophysical Sciences, University of Chicago, Chicago, Illinois, USA
Center for Robust Decision-Making on Climate and Energy Policy (RDCEP), University of Chicago, Chicago, Illinois, USA
Haynes Stephens
Department of the Geophysical Sciences, University of Chicago, Chicago, Illinois, USA
Center for Robust Decision-Making on Climate and Energy Policy (RDCEP), University of Chicago, Chicago, Illinois, USA
Shuo Chen
State Key Laboratory of Earth Surface Processes and Resource Ecology (ESPRE), Beijing Normal University, Beijing 100875, China
Key Laboratory of Environmental Change and Natural Disasters, Ministry of Education, Beijing Normal University, Beijing 100875, China
Academy of Disaster Reduction and Emergency Management, Ministry of Emergency Management and Ministry of Education, Beijing 100875, China
Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
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Cited
11 citations as recorded by crossref.
- The representation of climate impacts in the FRIDAv2.1 Integrated Assessment Model C. Wells et al. https://doi.org/10.5194/gmd-19-1229-2026
- Evaluating the effects of climate variability on banana yields in Ngazidja Island, Comoros using statistical modeling A. Abdoussalami et al. https://doi.org/10.1007/s00704-026-06351-3
- A food crop yield emulator for integration in the compact Earth system model OSCAR (OSCAR-crop v1.0) X. Liu et al. https://doi.org/10.5194/gmd-19-5857-2026
- Projected climate change impacts on crop yields in China's drylands by statistical models and emulators S. Zi et al. https://doi.org/10.1016/j.agsy.2026.104949
- Machine-Learning Surrogate Modeling of SOC and N2O Responses to Diversified Crop Rotations in the Texas High Plains A. Attia et al. https://doi.org/10.3390/agronomy16171679
- copan:LPJmL: a new hybrid modelling framework for dynamic land use and agricultural management J. Breier et al. https://doi.org/10.5194/gmd-19-6829-2026
- Localized thresholds for smarter crop risk management T. Ye et al. https://doi.org/10.1038/s43016-026-01321-4
- Transdisciplinary coordination is essential for advancing agricultural modeling with machine learning L. Sweet et al. https://doi.org/10.1016/j.oneear.2025.101233
- A role for regression trees in the calibration of a new process-based crop model M. Oliveira et al. https://doi.org/10.1016/j.eja.2025.127805
- Predicting weather impacts on corn production in a data-limited region using a transfer learning approach S. Vishwakarma et al. https://doi.org/10.1088/1748-9326/ade728
- A data-driven method for identifying climate drivers of agricultural yield failure from daily weather data L. Sweet et al. https://doi.org/10.5194/gmd-19-6687-2026
11 citations as recorded by crossref.
- The representation of climate impacts in the FRIDAv2.1 Integrated Assessment Model C. Wells et al. https://doi.org/10.5194/gmd-19-1229-2026
- Evaluating the effects of climate variability on banana yields in Ngazidja Island, Comoros using statistical modeling A. Abdoussalami et al. https://doi.org/10.1007/s00704-026-06351-3
- A food crop yield emulator for integration in the compact Earth system model OSCAR (OSCAR-crop v1.0) X. Liu et al. https://doi.org/10.5194/gmd-19-5857-2026
- Projected climate change impacts on crop yields in China's drylands by statistical models and emulators S. Zi et al. https://doi.org/10.1016/j.agsy.2026.104949
- Machine-Learning Surrogate Modeling of SOC and N2O Responses to Diversified Crop Rotations in the Texas High Plains A. Attia et al. https://doi.org/10.3390/agronomy16171679
- copan:LPJmL: a new hybrid modelling framework for dynamic land use and agricultural management J. Breier et al. https://doi.org/10.5194/gmd-19-6829-2026
- Localized thresholds for smarter crop risk management T. Ye et al. https://doi.org/10.1038/s43016-026-01321-4
- Transdisciplinary coordination is essential for advancing agricultural modeling with machine learning L. Sweet et al. https://doi.org/10.1016/j.oneear.2025.101233
- A role for regression trees in the calibration of a new process-based crop model M. Oliveira et al. https://doi.org/10.1016/j.eja.2025.127805
- Predicting weather impacts on corn production in a data-limited region using a transfer learning approach S. Vishwakarma et al. https://doi.org/10.1088/1748-9326/ade728
- A data-driven method for identifying climate drivers of agricultural yield failure from daily weather data L. Sweet et al. https://doi.org/10.5194/gmd-19-6687-2026
Saved (final revised paper)
Latest update: 03 Sep 2026
Short summary
We develop a machine-learning-based crop model emulator with the inputs and outputs of multiple global gridded crop model ensemble simulations to capture the year-to-year variation of crop yield under future climate change. The emulator can reproduce the year-to-year variation of simulated yield given by the crop models under CO2, temperature, water, and nitrogen perturbations. Developing this emulator can provide a tool to project future climate change impact in a simple way.
We develop a machine-learning-based crop model emulator with the inputs and outputs of multiple...
