【气候变化对农业的影响】Michael Glotter^a Joshua Elliott^b1 David McInerney^a2 Neil Best^b Ian Foster^bc and Elisabeth J. Moyer^a. Evaluating the utility of dynamical downscaling in agricultural impacts projections. PNAS 2014 vol. 111 no. 24   8776–8781 doi: 10.1073-pnas.1314787111

Abstract

Interest in estimating the potential socioeconomic costs of climate change has led to the increasing use of dynamical downscaling—nested modeling in which regional climate models (RCMs) are driven with general circulation model (GCM) output—to produce fine-spatial-scale climate projections for impacts assessments. We evaluate here whether this computationally intensive approach significantly alters projections of agricultural yield one of the greatest concerns under climate change. Our results suggest that it does not. We simulate US maize yields under current and future CO₂ concentrations with the widely used Decision Support System for Agrotechnology Transfer crop model driven by a variety of climate inputs including two GCMs each in turn downscaled by two RCMs. We find that no climate model output can reproduce yields driven by observed climate unless a bias correction is first applied. Once a bias correction is applied GCM- and RCM-driven US maize yields are essentially indistinguishable in all scen