The optimization of model ensemble composition and size can enhance the 
robustness of crop yield projections

Li, Linchao; Wang, Bin; Feng, Puyu; Jägermeyr, Jonas; Asseng, Senthold; Müller, Christoph; Macadam, Ian; Liu, De Li; Waters, Cathy; Zhang, Yajie; He, Qinsi; Shi, Yu; Chen, Shang; Guo, Xiaowei; Li, Yi; He, Jianqiang; Feng, Hao; Yang, Guijun; Tian, Hanqin; Yu, Qiang

doi:10.1038/s43247-023-01016-9

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The optimization of model ensemble composition and size can enhance the robustness of crop yield projections

Authors

Li, Linchao
External Organizations;

Wang, Bin
External Organizations;

Feng, Puyu
External Organizations;

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Jägermeyr, Jonas
Potsdam Institute for Climate Impact Research;

Asseng, Senthold
External Organizations;

/persons/resource/Christoph.Mueller

Müller, Christoph
Potsdam Institute for Climate Impact Research;

Macadam, Ian
External Organizations;

Liu, De Li
External Organizations;

Waters, Cathy
External Organizations;

Zhang, Yajie
External Organizations;

He, Qinsi
External Organizations;

Shi, Yu
External Organizations;

Chen, Shang
External Organizations;

Guo, Xiaowei
External Organizations;

Li, Yi
External Organizations;

He, Jianqiang
External Organizations;

Feng, Hao
External Organizations;

Yang, Guijun
External Organizations;

Tian, Hanqin
External Organizations;

Yu, Qiang
External Organizations;

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Citation

Li, L., Wang, B., Feng, P., Jägermeyr, J., Asseng, S., Müller, C., Macadam, I., Liu, D. L., Waters, C., Zhang, Y., He, Q., Shi, Y., Chen, S., Guo, X., Li, Y., He, J., Feng, H., Yang, G., Tian, H., Yu, Q. (2023): The optimization of model ensemble composition and size can enhance the robustness of crop yield projections. - Communications Earth and Environment, 4, 362.
https://doi.org/10.1038/s43247-023-01016-9

Cite as: https://publications.pik-potsdam.de/pubman/item/item_29333

Abstract

Linked climate and crop simulation models are widely used to assess the impact of climate change on agriculture. However, it is unclear how ensemble configurations (model composition and size) influence crop yield projections and uncertainty. Here, we investigate the influences of ensemble configurations on crop yield projections and modeling uncertainty from Global Gridded Crop Models and Global Climate Models under future climate change. We performed a cluster analysis to identify distinct groups of ensemble members based on their projected outcomes, revealing unique patterns in crop yield projections and corresponding uncertainty levels, particularly for wheat and soybean. Furthermore, our findings suggest that approximately six Global Gridded Crop Models and 10 Global Climate Models are sufficient to capture modeling uncertainty, while a cluster-based selection of 3-4 Global Gridded Crop Models effectively represents the full ensemble. The contribution of individual Global Gridded Crop Models to overall uncertainty varies depending on region and crop type, emphasizing the importance of considering the impact of specific models when selecting models for local-scale applications. Our results emphasize the importance of model composition and ensemble size in identifying the primary sources of uncertainty in crop yield projections, offering valuable guidance for optimizing ensemble configurations in climate-crop modeling studies tailored to specific applications.