Keeping Us Honest: Examining Climate States and Transition Probabilities of Precipitation Projections in General Circulation Models

Abstract

There is a vast amount of research on downscaling and bias-correcting general circulation model (GCM) data to a regional scale, but research is lacking on whether these techniques alter precipitation signals embedded in these models or reproduce climate states that are viable for water resource planning and management. Using the Tampa, Florida, region, this case study investigates: (1) whether GCM and the downscaled, bias-corrected data are able to replicate important historical climate states, and (2) if climate state or transition probabilities in raw GCMs are preserved or lost in translation in the corrected downscaled data. This has important implications in understanding the limitations of bias-correction methods and shortcomings of future projection scenarios. Results showed that the GCM and downscaled and bias-corrected data did a poor job in capturing historical climate states for wet or dry states as well as variability in precipitation, including some extremes associated with El Niño events. Furthermore, the corrected products ended up creating different cycles compared to the original GCMs. Because the corrected products did not preserve GCM historical transition probabilities, more than likely, similar types of deviations will occur for “future” predictions, and therefore another correction could be applied if desired to reproduce the degree of spatial persistence of atmospheric features and climatic states that are hydrologically important.