Improving Statistical Downscaling of General Circulation Models

We present a method for the statistical downscaling of coarse-resolution General Circulation Model (GCM) fields to predict local climate change. Most atmospheric variables have strong seasonal cycles. We show that the prediction of the non-seasonal variability of maximum and minimum daily surface temperature is improved if the seasonal cycle is removed prior to the statistical analysis. The new method consists of three major steps. First, the average seasonal cycles of both predictands and predictors are removed. Second, a principal component-based multiple linear regression model between the deseasonalized predictands and predictors is developed and validated. Finally, the regression is used to make projections of future changes in maximum and minimum daily surface temperature at Shearwater, Nova Scotia. This projection is made using the local grid-scale variables of the Canadian General Circulation Model Version 3 (CGCM3) climate model as predictors. Our statistical downscaling method indicates significant skill in predicting the observed distribution of temperature using GCM predictors. Future projections suggest minimum and maximum temperatures at Shearwater will be up to about five degrees warmer by 2100 under the current ``business as usual'' scenario.