Articles | Volume 16, issue 23
https://doi.org/10.5194/gmd-16-7203-2023
https://doi.org/10.5194/gmd-16-7203-2023
Model description paper
 | 
12 Dec 2023
Model description paper |  | 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, Tao Ye, Christoph Müller, Jonas Jägermeyr, James A. Franke, Haynes Stephens, and Shuo Chen

Related authors

Spatiotemporal variation of growth-stage specific concurrent climate extremes and their yield impacts for rice in southern China
Ran Sun, Tao Ye, Yiqing Liu, Weihang Liu, and Shuo Chen
EGUsphere, https://doi.org/10.5194/egusphere-2025-1393,https://doi.org/10.5194/egusphere-2025-1393, 2025
This preprint is open for discussion and under review for Earth System Dynamics (ESD).
Short summary
Long-term Ruminant Livestock Distribution Datasets in Grazing Livestock Production Systems in China from 2000 to 2021 (CLRD-GLPS)
Ning Zhan, Tao Ye, Mario Herrero, Jian Peng, Weihang Liu, and Heng Ma
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-534,https://doi.org/10.5194/essd-2024-534, 2024
Manuscript not accepted for further review
Short summary
Spatiotemporal variation of growth-stage specific compound climate extremes for rice in South China: Evidence from concurrent and consecutive compound events
Ran Sun, Tao Ye, Yiqing Liu, Weihang Liu, and Shuo Chen
Earth Syst. Dynam. Discuss., https://doi.org/10.5194/esd-2024-8,https://doi.org/10.5194/esd-2024-8, 2024
Manuscript not accepted for further review
Short summary
Event-based probabilistic risk assessment of livestock snow disasters in the Qinghai–Tibetan Plateau
Tao Ye, Weihang Liu, Jidong Wu, Yijia Li, Peijun Shi, and Qiang Zhang
Nat. Hazards Earth Syst. Sci., 19, 697–713, https://doi.org/10.5194/nhess-19-697-2019,https://doi.org/10.5194/nhess-19-697-2019, 2019
Short summary

Related subject area

Biogeosciences
Including the phosphorus cycle into the LPJ-GUESS dynamic global vegetation model (v4.1, r10994) – global patterns and temporal trends of N and P primary production limitation
Mateus Dantas de Paula, Matthew Forrest, David Warlind, João Paulo Darela Filho, Katrin Fleischer, Anja Rammig, and Thomas Hickler
Geosci. Model Dev., 18, 2249–2274, https://doi.org/10.5194/gmd-18-2249-2025,https://doi.org/10.5194/gmd-18-2249-2025, 2025
Short summary
A comprehensive land-surface vegetation model for multi-stream data assimilation, D&B v1.0
Wolfgang Knorr, Matthew Williams, Tea Thum, Thomas Kaminski, Michael Voßbeck, Marko Scholze, Tristan Quaife, T. Luke Smallman, Susan C. Steele-Dunne, Mariette Vreugdenhil, Tim Green, Sönke Zaehle, Mika Aurela, Alexandre Bouvet, Emanuel Bueechi, Wouter Dorigo, Tarek S. El-Madany, Mirco Migliavacca, Marika Honkanen, Yann H. Kerr, Anna Kontu, Juha Lemmetyinen, Hannakaisa Lindqvist, Arnaud Mialon, Tuuli Miinalainen, Gaétan Pique, Amanda Ojasalo, Shaun Quegan, Peter J. Rayner, Pablo Reyes-Muñoz, Nemesio Rodríguez-Fernández, Mike Schwank, Jochem Verrelst, Songyan Zhu, Dirk Schüttemeyer, and Matthias Drusch
Geosci. Model Dev., 18, 2137–2159, https://doi.org/10.5194/gmd-18-2137-2025,https://doi.org/10.5194/gmd-18-2137-2025, 2025
Short summary
Sources of uncertainty in the SPITFIRE global fire model: development of LPJmL-SPITFIRE1.9 and directions for future improvements
Luke Oberhagemann, Maik Billing, Werner von Bloh, Markus Drüke, Matthew Forrest, Simon P. K. Bowring, Jessica Hetzer, Jaime Ribalaygua Batalla, and Kirsten Thonicke
Geosci. Model Dev., 18, 2021–2050, https://doi.org/10.5194/gmd-18-2021-2025,https://doi.org/10.5194/gmd-18-2021-2025, 2025
Short summary
The unicellular NUM v.0.91: a trait-based plankton model evaluated in two contrasting biogeographic provinces
Trine Frisbæk Hansen, Donald Eugene Canfield, Ken Haste Andersen, and Christian Jannik Bjerrum
Geosci. Model Dev., 18, 1895–1916, https://doi.org/10.5194/gmd-18-1895-2025,https://doi.org/10.5194/gmd-18-1895-2025, 2025
Short summary
FESOM2.1-REcoM3-MEDUSA2: an ocean–sea ice–biogeochemistry model coupled to a sediment model
Ying Ye, Guy Munhoven, Peter Köhler, Martin Butzin, Judith Hauck, Özgür Gürses, and Christoph Völker
Geosci. Model Dev., 18, 977–1000, https://doi.org/10.5194/gmd-18-977-2025,https://doi.org/10.5194/gmd-18-977-2025, 2025
Short summary

Cited articles

Blanc, É.: Statistical emulators of maize, rice, soybean and wheat yields from global gridded crop models, Agr. For. Meteorol., 236, 145–161, https://doi.org/10.1016/j.agrformet.2016.12.022, 2017. 
Blanc, É.: Statistical emulators of irrigated crop yields and irrigation water requirements, Agr. For. Meteorol., 284, 107828, https://doi.org/10.1016/j.agrformet.2019.107828, 2020. 
Blanc, E. and Sultan, B.: Emulating maize yields from global gridded crop models using statistical estimates, Agr. Forest Meteorol., 214–215, 134–147, https://doi.org/10.1016/j.agrformet.2015.08.256, 2015. 
Campbell, B. M., Vermeulen, S. J., Girvetz, E., Loboguerrero, A. M., and Ramirez-Villegas, J.: Reducing risks to food security from climate change, Glob. Food Secur.-AGR., 11, 34–43, https://doi.org/10.1016/j.gfs.2016.06.002, 2016. 
Chen, S., Liu, W., Feng, P., Ye, T., Ma, Y., and Zhang, Z.: Improving Spatial Disaggregation of Crop Yield by Incorporating Machine Learning with Multisource Data: A Case Study of Chinese Maize Yield, Remote Sens.-Basel, 14, 2340, https://doi.org/10.3390/rs14102340, 2022. 
Download
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.
Share