Benjamin Zaitchik (Dept. of Earth & Planetary Sciences)

Seth Guikema (Dept. of Geography & Engineering )

Dr. Sharon Gourdji (International Center for Tropical Agriculture (CIAT) Cali, Colombia)

One of the greatest challenges in climate science today is the call to provide actionable information for adaptation to climate change. This is a particularly difficult problem because Global Climate Models (GCMs) are poorly suited for predicting climate impacts of interest at local scale. This means that GCM projections must be “downscaled” to the local environment, often through statistical methods. This seed grant is motivated by the recognition that existing statistical downscaling systems suffer from subjective and incomplete selection of predictor fields. To address this limitation we are implementing an automated statistical downscaling system that employs a combination of optimization and statistical learning theory driven predictive modeling. This system will generate predictive models informed by multiple modeling approaches and a diverse and expandable library of gridded predictor fields.