This study uses the Weather Research and Forecasting (WRF) model to evaluate three dynamical downscaling methodologies for extreme precipitation events in the western US. The first methodology uses North American Regional Climate Change Assessment Program (NARCCAP) model simulations for direct downscaling of the most extreme (i.e., top 1%) precipitation events over the area of interest (in this study, the Colorado Front Range). In this approach, initial conditions are constructed from the raw model output provided by the NARCCAP dataset to simulate a set of cases in the WRF model. High-resolution simulations (1-km gridspacing) of the top future and past events are compared in order to evaluate environmental and storm-scale differences in both kinematic and thermodynamic fields.
The second methodology uses extreme event composites, again constructed from NARCCAP data, as initial conditions in the WRF model. The composites are comprised of the top events in the past and future simulations respectively, and the WRF model output from these composite initial conditions is again analyzed for differences between the future and past events.
The third methodology mimics the climate perturbation, or pseudo-global-warming approach used by several recent studies. This tactic begins by modifying the initial conditions of a single extreme precipitation event by adding an average climate change signal to the thermodynamic fields of the original event. This technique is often used in order to see how an event with the same dynamics might evolve under altered temperature and humidity fields as dictated by a specific climate change projection.
These three methods are employed and their results compared for extreme precipitation events across central and eastern Colorado, with the ultimate objective of identifying the respective strengths and weaknesses of each.