RAPID: Physical Behavior of Precipitation Extremes in CMIP5 GCMs and Observations

Iowa State University, Ames IA

Investigators

Abstract

This is one of 16 Rapid Response (RAPID) projects funded as the result of a Dear Colleague Letter (NSF 11-006) encouraging diagnostic analyses of climate model simulations prepared for the Intergovernmental Panel on Climate Change Fifth Assessment Report (IPCC AR5). Research conducted in these projects is expected to lead to more detailed model intercomparisons, better understanding of robust model behaviors, and better understanding and quantification of uncertainty in future climate simulations. This project examines extreme precipitation events in present-day climate simulations prepared for the AR5. The central question motivating the project is this: to what extent do extreme events simulated by the models result from physical behavior that occurs when extreme events actually happen? This question is addressed through two main activities: 1) extracting extreme daily precipitation events for select, climatologically homogeneous regions in simulations and observations, and 2) evaluating the behavior of the events as depicted in circulation, temperature, humidity and water/energy flux fields on the days leading up to the extreme event. The statistical analysis tool known as Self-organizing maps (SOMs) is used to extract the spatial patterns and evolution of the synoptic conditions that accompany extreme precipitation events in both simulations and observations, so that the two can be compared for consistency. The region of focus for the project is North America, where observations are dense enough to allow in-depth physical comparison. The broader impact of the project lies in its support of the IPCC AR5, which is intended to provide information on climate change and its consequences to decision makers worldwide. The climate models evaluated in this study will be used for assessing projected changes in extreme precipitation events due to global warming, and this analysis is important for determining the level of confidence we can have in model projections of future changes in extremes.