AbstractDynamic downscaling with regional-scale climate models is used widely for increasing the spatial resolution of global-scale climate model projections. One uncertainty in generating these projections is the choice of boundary forcing applied. In this study the Nested Regional Climate Model (NRCM) is used with a grid spacing of 12 km over the United States (excluding Hawaii) to dynamically downscale 2.5° National Centers for Environmental Prediction–U.S. Department of Energy Reanalysis-2 data, with different applications of spectral nudging (SN) for the boundary conditions. Nine numerical experiments for July 2005—each with different wavenumbers and nudging duration periods, applied to different model layers—evaluated the performance of SN in downscaling near-surface fields. The calculations were compared with the North America Regional Reanalysis dataset over four subregions of the contiguous 48 states. Results show significant differences with different wavenumbers, nudging duration periods, and nudging altitudes. The short-period SN with three waves, applied above 850 hPa, showed the highest skill in simulating precipitation, whereas whole-period SN produced a higher skill level and performed slightly better than short-period SN for surface temperature and 10-m wind, respectively. Differences in the performance of SN applied at different altitudes were not significant. On the basis of the comparisons for precipitation, surface temperature, and wind fields over entire contiguous states, whole-period nudging with six waves starting above 850 hPa for downscaling calculations for climate-related variables is recommended. This method improved the performance of the NRCM in predicting near-surface fields by more than 30.5% relative to a case with no nudging.
Cloud Parameterizations in SUNYA Regional Climate Model for the East Asia Summer Monsoon Simulations
