Projecting climate change impacts on grain maize based on three different crop model approaches

Abstract

Decision making in climate change adaptation planning depends on the quantification and broad understanding of uncertainties in projected climate impacts. In a case study, we estimated impacts of climate change on potential grain maize yield up to the time horizon 2036–2065 for three climatic regions in Switzerland using – for the first time – three fundamentally different impact modelling approaches: a process-based, a statistical and an expert-based approach. The aim was to quantify uncertainties originating from climate model chains, downscaling weather generator choice, and impact model parameterization. We find that while estimated climate impacts on yields are subject to large uncertainties originating from both climate model chains and impact model approaches, estimates of changes in crop-specific climate limitations are less ambiguous. We conclude that by subtracting the layer of uncertainty related to the aggregation of different climate influences on yield estimates and by focusing on estimated changes in climate limitations, more decision-relevant information can be provided to support crop-specific adaptation planning.