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Using climate model data to determine spatial synoptic classifications

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Thursday, 27 January 2011
Using climate model data to determine spatial synoptic classifications
Washington State Convention Center
Neil Davis, University of North Carolina, Chapel Hill, NC; and A. Hanna, A. Xiu, K. Talgo, and S. C. Sheridan

The Spatial Synoptic Classification (SSC) is a hybrid classification scheme which typically uses surface observations to create daily weather type classifications for individual stations across North America. This study utilized the SSC technique to identify changes in climate at a particular point, with a focus on how resolution affects the modeled SSC. WRF was run as a Regional Climate Model for 3 periods 2001-2003, 2018-2020, and 2048-2050 using CCSM a1b scenarios as the ICBCs for 3 resolutions, 108km, 36km, and 12km. The SSC was run for all 3 resolutions, as well as for the CCSM itself, for all 3 periods and observations for the contemporary period. We examined the impact of grid resolution on model performance in matching the climatological SSCs in the observed data, and also investigated the changes to the SCC in the future climate scenarios. The future climate scenarios were investigated both in terms of the changes to the number and patterns of SSCs and in terms of how the input meteorology changed for the various SSC classifications.