Prediction of Maize Yield Response to Climate Change with Climate and Crop Model Uncertainties

Abstract

rojections of climate change impacts on crop yields are subject to uncertainties, and quantification of such uncertainty is essential for the effective use of the projection results for adaptation and mitigation purposes. This work analyzes the uncertainties in maize yield predictions using two crop models together with three climate projections downscaled with one regional climate model nested with three global climate models under the A1B emission scenario in northeast China (NEC). Projections were evaluated for the Zhuanghe agrometeorological station in NEC for the 2021?50 period, taking 1971?2000 as the baseline period. The results indicated a yield reduction of 13% during 2021?50, with 95% probability intervals of (?41%, +12%) relative to 1971?2000. Variance decomposition of the yield projections showed that uncertainty in the projections caused by climate and crop models is likely to change with prediction period, and climate change uncertainty generally had a larger impact on projections than did crop model uncertainty during the 2021?50 period. In addition, downscaled climate projections had significant bias that can introduce significant uncertainties in yield projections. Therefore, they have to be bias corrected before use.