Global climate models (GCMs) are a tool used to model historical climate and project future conditions. In order to apply these global-scale datasets to answer local- and regional-scale climate questions, GCMs undergo a process known as “downscaling”. Since there are many different approaches to downscaling there associated sources of uncertainty; however, downscaled data can be highly valuable for management decision-making if used with a knowledge of its limitations and appropriate applications.
In order to use downscaled data appropriately, scientists and managers need to understand how the climate projections made by various downscaling methods are affected by uncertainties in the climate system (such as greenhouse gas emissions and observed data). This project will produce 243 climate projections using three different downscaling methods, giving researchers insight into how each of these methods responds to various sources of climate uncertainty. This analysis will allow researchers to assist managers in selecting the best downscaled data for their specific management questions. This project will also result in foundational downscaled climate projections for the South Central region, assisting stakeholders in identifying the potential impacts of climate on a range of systems, from water to ecosystems to agriculture.