Temperature Extremes and Associated Large-Scale Meteorological Patterns in NARCCAP Regional Climate Models
Motivated by the need to thoroughly evaluate the fidelity with which regional climate models (RCMs) simulate temperature extremes, large-scale meteorological patterns (LSMPs) associated with extreme temperature days are evaluated for a suite of RCMs contributing to the North American Regional Climate Change Assessment Program (NARCCAP).
LSMPs are defined as composites of surface air temperature, sea level pressure, and 500 hPa geopotential height anomalies concurrent with extreme temperature days. Six of the RCMs are from a hindcast experiment and are driven by observed boundary conditions while 11 RCMs are driven by one of four global climate models (GCMs). Four case studies are analyzed in detail. Model fidelity is high for cold winter extremes near Chicago, and weaker for extreme cool summer extremes near Houston and extreme heat events in the Ohio Valley. The RCMs have fundamentally different LSMPs associated with extreme warm days over much of California in the winter suggesting that higher resolution is needed here.
There is some indication that the ability of an RCM to reproduce realistic temperature distribution shape, especially at the tails, is also related to model fidelity in simulating LMSPs. The entire suite of simulations is evaluated over the entire domain and each ensemble member ranked. Overall, the multi-RCM ensemble mean LSMPs resemble observations better than any individual ensemble member. The methodology developed here provides a framework for identifying places where further process-based evaluation would improve the understanding of simulation error and help guide future model improvement and downscaling efforts.