Global Climate Models (GCMs) use our understanding of atmospheric physics and other earth processes to simulate potential future changes in climate on a global scale. However, these large scale models are not fit for predicting smaller scale, local changes. Downscaling methods can be applied to the outputs of GCMs to give guidance appropriate for a more regional level. No standard approach to downscaling currently exists, however, and the process often results in climate projections that suggest a wide array of possible futures. It is critical that decision-makers looking to incorporate climate information understand the uncertainties associated with different downscaling approaches and can evaluate downscaled data to determine which datasets are appropriate for addressing their questions.
The goal of this project is to provide decision-makers with this information by evaluating the uncertainties associated with different downscaled datasets. Materials will then be developed to communicate these uncertainties to managers and explore how they can be incorporated into risk decision-making. The results will enable managers across the country to better understand possible climate futures in their jurisdictions, allowing them to make more informed planning decisions in the face of uncertainty.