Surface Temperature Probability Distributions and Extremes in the NARCCAP Hindcast Experiment: Evaluation Methodology and Metrics, Results, and Associated Atmospheric Mechanisms

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Wednesday, 5 February 2014: 4:00 PM
Room C101 (The Georgia World Congress Center )
Paul C. Loikith, JPL, Pasadena, CA; and D. E. Waliser, J. Kim, H. Lee, B. R. Lintner, J. D. Neelin, S. A. McGinnis, C. Mattmann, and L. O. Mearns

Daily surface temperature probability distribution functions (PDFs) for a suite of regional climate model (RCM) hindcast experiments participating in the North American Regional Climate Change Assessment Program (NARCCAP) are evaluated against two state-of-the-art high resolution reanalysis products over North America. While all models exhibit some level of temperature bias throughout the PDF, the majority of models capture higher moment statistics like variance and skewness with reasonable fidelity in the winter. More substantial disagreement exists in the simulation of the tails of the summer temperature distributions, with notable observational uncertainty, indicative of difficulty in properly simulating temperature extremes here. To better understand the mechanisms behind model-reanalysis disagreement, potential mechanisms affecting PDF shape are evaluated, focusing on events in the tails of the distribution. Composite analysis is employed to investigate differences in atmospheric circulation patterns associated with extreme temperature days at select locations where station data are available to reconcile reanalysis PDF uncertainty. In general, the models simulate large-scale atmospheric circulation patterns well where the shape of the PDF most resembles reanalysis, especially in the winter. Summertime patterns tend to be smaller in scale and lower in magnitude where extreme temperature occurrence may also be associated with local processes such as unusual soil moisture content and convective precipitation and cloudiness, introducing additional possibilities for model disagreement. From this comprehensive multi-model evaluation, it is possible to identify which models may be best suited to simulate temperature extremes under global warming conditions.