| Popis: |
Global gridded climate–crop model ensembles are increasingly used to make projections of how climate change will affect future crop yield. However, the level of certainty that can be attributed to such simulations is unknown. Here, using currently available geospatial datasets and a widely employed simulation procedure, we created a wheat model ensemble of 1,440 global simulations of 20 climate scenarios, 3 crop models, 4 parameterization strategies and 3 management inputs of sowing date. We quantified the contributions of climate, model, parameterization and management to the overall uncertainty to predicted responses of yield to warming, then related the results to the latitude of the grid cells. For all warming scenarios, the total uncertainty for mid- and high latitudes is much larger than for low latitudes. Uncertainty arising from crop models was larger than that from the other sources combined. Parameterizing crop models with grid-specific information on wheat cultivars tended to decrease the crop model uncertainty, particularly for low latitudes. Crop model improvements and better-quality spatial input data more closely representing the wide range of growing conditions around the world will be needed to reduce the uncertainty of climate change impact assessment of crop yields. Global gridded crop models simulate the impact of climate change on crop yield, but uncertainty is difficult to quantify, which reduces their reliability. Xiong et al. explored how parameterization strategies can reduce uncertainty and identified greater uncertainty in future wheat yields grown at mid-to-high latitudes. |
