This study focuses on the evaluation of the SUNYA regional climate model (ReCM) and its application in an investigation into the impact of the Pacific North American (PNA) pattern on the winter climate of the Northeast United States. SUNYA ReCM is dynamically based on the hydrostatic version of PSU/NCAR MM5 with the inclusion of a land surface model and GCM parameterization for clouds and radiation (Dudek et al., 1996; Gong and Wang,2000; Wang et al., 2000). In this study, the model was run with a 20-km horizontal resolution for the period of November 1998 through March 1999, originally applying 12-hourly ECMWF TOGA atmospheric and NCEP SST data for initial and lateral boundary conditions. Subsequent model runs used 6-hourly NCEP-NCAR Reanalysis atmospheric data. The model successfully captures the large-scale circulation pattern and the spatial and temporal patterns of surface temperature and precipitation, although a 2-3C cold bias and too little precipitation are evident. Synoptic and mesoscale features, such as fronts, are reasonably produced at the correct time and position, further supporting the judgement that the SUNYA ReCM can successfully reproduce the winter climate of the Northeast.

The PNA pattern significantly influences the temperature, precipitation, and circulation across the Northeast during the winter. Correlation coefficients range from -0.34 (Maine) to -0.64 (West Virginia) between observational mean temperature and PNA for December 1958-2000. Correlations between precipitation and PNA typically are more variable, ranging from -0.11 (Maine) to -0.65 (Michigan). The observational data therefore suggests that a positive phase of PNA typically coincides with cooler and drier conditions across the Northeast, particularly in the western side of the model domain. The SUNYA ReCM was run for ten Decembers during 1980-1999, five with the most positive PNA and five with the most negative PNA. These simulations are being composited to determine the impact of PNA on the winter climate. The results will be compared to the observational study while also gaining further insight at the regional scale.

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