Downscaling is a way of turning large-scale information (like comes out of a global climate model) into local information with resolution of a few kilometers. Statistical downscaling involves using advanced mathematical tools to estimate local information from global information. Dynamical downscaling involves taking large-scale information and feeding it into a regional model to produce local information. Each has its advantages and disadvantages for particular situations.
Accurate, local information is critical for making decisions. Dynamical downscaling is a way to provide that information, but actually producing the dataset and verifying its accuracy is difficult. One needs to conduct a series of bias correction and extrapolation steps that are often unique to the location and require local expertise. Research to determine the best way to conduct downscaling for the particular region of interest is necessary to ensure a high-quality product.
We are currently conducting a major downscaling effort under Indiana University’s Prepared for Environmental Change Grand Challenge Initiative.
