Downscaling is the collective term for the methods used to regionalize information from global climate models and create fine-spatial-scale projections of climate change. Our group is active in the development, evaluation, and application of downscaling techniques.
Until recently, there were two main types of downscaling methods: dynamical methods, which involve the use of high-resolution regional climate models, and statistical methods, which use mathematical relationships between local climate variables and their large-scale predictors. Dynamical downscaling is highly physically realistic, but computationally very expensive, making comprehensive regional studies impractical. Statistical downscaling is computationally cheap, but not necessarily physically realistic.
In recent years, our group has focused on pioneering a third type of downscaling technique, which we call hybrid downscaling. As the name implies, it combines aspects of dynamical and statistical downscaling. We perform a limited set of dynamical downscaling simulations, analyze them in order to diagnose physical relationships between large-scale and fine-scale climate variables, and then build a statistical model that incorporates those physical relationships. With this statistical model, we are able to enlarge our set of simulations. This work enables us to assess climate change impacts on the scales that matter most to those making decisions about climate change adaptation.