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We use a multi‐model, multi‐scenario climate model ensemble to identify climate change hotspots in the continental United States. Our ensemble consists of the CMIP3 atmosphere‐ocean general circulation models, along with a high‐resolution nested climate modeling system. We test both high (A2) and low (B1) greenhouse gas emissions trajectories, as well as two different statistical metrics for identifying regional climate change hotspots. We find that the pattern of peak responsiveness in the CMIP3 ensemble is persistent across variations in GHG concentration, GHG trajectory, and identification method. Areas of the southwestern United States and northern Mexico are the most persistent hotspots. The high‐resolution climate modeling system produces highly localized hotspots within the basic GCM structure, but with a higher sensitivity to the identification method. Across the ensemble, the pattern of relative climate change hotspots is shaped primarily by changes in interannual variability of the contributing variables rather than by changes in the long‐term means.
Diffenbaugh et al. (Fri,) studied this question.