Regional climate model assessment using statistical upscaling and downscaling techniques

Climate models are mathematical models that describe the temporal evolution of climate, oceans, atmosphere, ice, and land-use processes, across a spatial domain via systems of partial differential equations. Because these models cannot be solved analytically, the model output is generated numerically over grid boxes. Regional climate models (RCMs), or the dynamic downscaling of global climate models to regional scales, are often used for planning purposes, and it is important to assess carefully the uncertainty of such models. We evaluate the Swedish Meteorological and Hydrological Institute (SMHI) RCM by comparing its model output at the grid box level, with the predictions obtained from two observation-driven spatio-temporal statistical models. The “downscaling model” combines the spatially and temporally smoothed climate model output with temperature observations at synoptic stations in a spatio-temporal linear statistical model. The “upscaling model” describes the observational temperature alone at the daily scale, via a spatio-temporal model that includes a wavelet-based trend, spatially varying seasonality, along with volatility and long-range dependence terms. Both statistical models have the ability to make predictions at a seasonal scale, both at point and grid box level. In the years 1962–2007 in South Central Sweden, we show that the climate model performs well in predicting the annual and seasonal average temperature at three reserved stations, but there are interesting differences among the model output and the statistical model-based predictions at the grid box level.