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

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. A random selection of six global crop grid models and ten global climate models is sufficient to determine the uncertainty of a model ensemble, but the contribution of each crop model to this uncertainty varies by region and crop type, according to a cluster analysis of future crop yield projections.

Automated summary

This study investigates the influence of ensemble configurations on crop yield projections and modeling uncertainty. The findings suggest that specific ensemble compositions and sizes can effectively capture modeling uncertainty and represent the full ensemble. The contribution of individual crop models to the overall uncertainty varies by region and crop type, emphasizing the importance of considering specific models in local-scale applications.