Temperature Extremes and Associated Large-Scale Meteorological Patterns in NARCCAP Regional Climate Models: Towards a framework for generalized model evaluation

Wednesday, 17 December 2014
Paul Loikith1, Duane Edward Waliser1, Huikyo Lee1, Jinwon Kim2, J David Neelin2, Seth A McGinnis3, Benjamin R Lintner4 and Linda O Mearns5, (1)Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, United States, (2)University of California Los Angeles, Los Angeles, CA, United States, (3)National Center for Atmospheric Research, Boulder, CO, United States, (4)Rutgers, New Brunswick, NJ, United States, (5)NCAR, Boulder, CO, United States
Large-scale meteorological patterns associated with extreme temperatures are evaluated across a suite of regional climate model (RCM) simulations produced as a part of the North American Regional Climate Change Assessment Program (NARRCAP). Evaluation is performed on six hindcast simulations and eleven simulations driven by four global climate models (GCMs). In places removed from the influence of complex topography in the winter, such as the Midwest of the United States, extremes and associated patterns are generally simulated with high fidelity. In other cases, such as for much of the Gulf of Mexico Coast in summer, the RCMs have notable difficulty in reproducing temperature extremes and associated meteorological patterns. In some cases, the temperature extremes appear to be well reproduced, but for the wrong reasons, making this analysis particularly valuable for diagnosing and interpreting RCM skill in making future projections of temperature extremes. An RCM skill score is developed, based on pattern agreement at all grid cells, to identify the RCM-GCM combinations that may be best suited for making future projections of temperature extremes. Cases identified as having low RCM skill will be the subject of further investigations with a focus on understanding key processes that are contributing to model error and helping to guide future model development. It is anticipated that this work will be implemented as part of a framework for evaluating temperature extremes in RCMs, providing generalized performance metrics based on mechanistic and process-oriented diagnostics.