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.
Nationally and globally, humans are going to have to learn how to respond to climate change. One of the biggest impacts of climate change will be how extreme events change. This work focuses on extreme, 1-day precipitation events. Global climate models (GCMs) potentially can tell us how these extreme events will change in the future, but first one must know if GCMs can replicate the observed frequency of extreme daily precipitation. One also needs to know if GCMs produce extremes for the same physical behavior as occurs in real-world extremes. This project analyzed how well several state-of-the-science GCMs satisfied these goals for winter precipitation in the central United States. Extremes of winter precipitation often occur as rain falling on frozen ground, promoting flooding, or as deep snow, which poses its own hazards. Figure 1 shows the observed and simulated frequency of precipitation over a range of intensity categories, with UW denoting observations obtained from a University of Washington data set. The GCMs generally reproduced well the observed (UW) behavior for daily intensities as strong as 30 mm/day. This is a little over an inch/day if rain or up to 10 inches if snow). Although most models do not produce events seen in observations exceeding 90 mm/day (about 3.5 inches if rain or up to 3 feet if snow), models that can simulate finer details of atmospheric behavior tend to do better. More important, the models produce their extremes for the same reasons as the real world. For example, Figure 2 shows the typical wind speed and direction near the surface during extreme events in the real world [panel a)] and in the simulations (the remaining panels). The flow pattern shows that the extreme precipitation occurs when air flows over the Gulf of Mexico, bring substantial moist air into the central U.S. Overall, the models reproduce observations well enough that one can use them to understand the causes of extreme precipitation changes in future climate and to indicate how much extreme precipitation will change and impact human society and natural ecosystems.