Regional climate models (RCMs) are a standard tool for downscaling climate forecasts to finer spatial scales. The evaluation of RCMs against observational data is an important step in building confidence in the use of RCMs for future projection. In addition to model performance in climatological means and marginal distributions, a model’s ability to capture spatiotemporal relationships is important. This study develops two approaches: (1) spatial correlation/variogram for a range of spatial lags, with total monthly precipitation and nonseasonal precipitation components used to assess the spatial variations of precipitation, and (2) spatiotemporal correlation for a wide range of distances, directions, and time lags, with daily precipitation occurrence used to detect the dynamic features of precipitation. These measures of spatial and spatiotemporal dependence are applied to a high-resolution RCM run and to the National Center for Environmental Prediction (NCEP)-U.S. Department of Energy Atmospheric Model Intercomparison Project II reanalysis data (NCEP-R2), which provide initial and lateral boundary conditions for the RCM. The RCM performs significantly better than NCEP-R2 in capturing both the spatial variations of total and nonseasonal precipitation components and the spatiotemporal correlations of daily precipitation occurrences, which are related to dynamic behavior of precipitating systems. The improvements are apparent not only at resolutions finer than that of NCEP-R2 but also when the RCM and observational data are aggregated to the resolution of NCEP-R2.
Areas of Focus: Climate Change
, Climate Science
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