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Pathways to identify and reduce uncertainties in agricultural climate impact assessments

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Wang,  Bin
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Jägermeyr,  Jonas       
Potsdam Institute for Climate Impact Research;

O’Leary,  Garry J.
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Wallach,  Daniel
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Ruane,  Alex C.
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Feng,  Puyu
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Li,  Linchao
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Liu,  De Li
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Waters,  Cathy
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Yu,  Qiang
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Asseng,  Senthold
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Rosenzweig,  Cynthia
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Wang, B., Jägermeyr, J., O’Leary, G. J., Wallach, D., Ruane, A. C., Feng, P., Li, L., Liu, D. L., Waters, C., Yu, Q., Asseng, S., Rosenzweig, C. (2024): Pathways to identify and reduce uncertainties in agricultural climate impact assessments. - Nature Food, 5, 550-556.
https://doi.org/10.1038/s43016-024-01014-w


???ViewItemOverview_lblCiteAs???: https://publications.pik-potsdam.de/pubman/item/item_30221
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Both climate and impact models are essential for understanding and quantifying the impact of climate change on agricultural productivity. Multi-model ensembles have highlighted considerable uncertainties in these assessments, yet a systematic approach to quantify these uncertainties is lacking. We propose a standardized approach to attribute uncertainties in multi-model ensemble studies, based on insights from the Agricultural Model Intercomparison and Improvement Project. We find that crop model processes are the primary source of uncertainty in agricultural projections (over 50%), excluding unquantified hidden uncertainty that is not explicitly measured within the analyses. We propose multidimensional pathways to reduce uncertainty in climate change impact assessments.