Robert Nicholas (lead), Chris Forest, Murali Haran, Klaus Keller, Robert Lempert, Alan Robock, Ian Sue Wing, Nancy Tuana

Climate impacts analyses and decision support applications often demand climate data with a high degree of spatial and temporal resolution. However, in many circumstances, potential changes in relevant features of the climate system are poorly constrained even at seasonal and regional scales. Assessments of potential agricultural, infrastructure, and economic impacts due to future climate changes that fail to account for relevant uncertainties could be vastly overconfident and ultimately dangerous if translated into policy. This project focuses on the development of approaches to empirical-statistical downscaling that capture differences in model structure, uncertainties in model parameters, and account for natural variability in the climate system.