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Abstract:
Robust estimates of climate change impacts, such as future changes in crop yields, are crucial for mitigation and adaptation decision-making. A key source of uncertainty in future climate impact estimates is the spread in projections of temperature and other climatic variables across the global climate models (GCMs) which are used to drive impact models. Here, we demonstrate the importance of this source of uncertainty for agricultural impact modelling using the latest generation of GCM-driven global gridded crop models, and explore the utility of global warming levels (GWLs) for aligning trajectories of agricultural yield projections. To do this, we compare the spread in distributions of spatially aggregated yield change projections across GCMs using the GWL and the standard fixed time window approaches. We find that the GWL approach is particularly effective in aligning impact trajectories across GCMs at the global scale in projections of interannual yield variability changes, and that this effect is robust across crops. In contrast, for changes in mean yield, the effectiveness of GWLs is strongly crop-dependent; we show that the differences in effectiveness of GWLs result from different responses to increasing CO2 concentrations across crops and yield metrics. For C4 crops and for changes in yield variability, where the underlying drivers scale with warming, the GWL approach enables the analysis of impacts from a multi-model ensemble in a physically consistent manner and the investigation of the components of GCM uncertainty. The relevance of these findings also extends to the broader impact modelling community, particularly in settings where an ensemble of climate models is used, such as studies based on the Inter-Sectoral Impact Model Intercomparison Project framework.
