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HOME > Outreach > Meetings > 33rd Annual Climate Diagnostics & Prediction Workshop > Abstracts

Review of Recent Climate Conditions and Forecasts


Abstract Author: Raymond W. Arritt

Abstract Title: Regional drought in the North American Regional Climate Change Assessment Program (NARCCAP)

Abstract: The North American Regional Climate Change Assessment Program (NARCCAP) is using six nested regional climate models (RCMs) at 50 km node spacing to develop climate change projections for North America. In NARCCAP Phase I, ability of these RCMs to reproduce current climate is assessed using simulations for 1979 through 2004 that take initial and boundary condition data from the NCEP-DOE reanalysis. We examine the ability of the models to represent regional drought and flood using correlation of monthly observed and simulated time series of precipitation as a performance metric. The models performed best in coastal California, where the correlation between the simulated and observed monthly time series exceeded 0.94 for all models. The correlation for the ensemble mean was 0.97. The models successfully reproduced the multi-year drought experienced in that region during the late 1980s, as well as the strong El Nino-induced heavy precipitation events that occurred there in 1982-83 and 1997-98. Model performance tended to deteriorate from west to east across the domain, or roughly from the inflow boundary toward the outflow boundary. Nonetheless, the models captured the strong regional drought over the north-central U.S. during the spring and summer of 1988. The deterioration of model performance with distance from the inflow boundary is ameliorated to some extent in models that are formulated to include large-scale information in the model solution (as by spectral nudging or use of a perturbation form of the governing equations). These results suggest that regional climate models are able to reproduce seasonal and multi-year drought when provided with adequate large-scale information at their lateral boundaries. Furthermore, there is value in using an ensemble of such models: the correlations for the ensemble were comparable to or greater than those for the best model in every region that was examined.

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