Pathways to identify and reduce uncertainties in agricultural climate impact assessments

Abstract

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. Accurately assessing the impacts of climate change on agricultural productivity is key to the development of effective and sustainable adaptation strategies. This Perspective discusses the main sources of uncertainty in such impact assessments and proposes strategies for improved crop modelling.