Esther Salazar, Bruno Sanso, Andrew O. Finley, Dorit Hammerling, Ingelin Steinsland, Xia Wang and Paul Delamater
05/09/2011 09:00 AM
Applied Mathematics & Statistics
We consider the problem of forecasting future regional climate. Our method is based on blending different members of an ensemble of regional climate model (RCM) simulations while accounting for the discrepancies between these simulations, under present day conditions, and observational records for the recent past. To this end, we develop Bayesian space-time models that assess the discrepancies between climate model simulations and observational records. Those discrepancies are then propagated into the future to obtain blended forecasts of 21st century climate. The model allows for location-dependent spatial heterogeneities, providing local comparisons between the different simulations. Additionally, we estimate the different modes of spatial variabil- ity, and use the climate model-specific coefficients of the spatial factors for comparisons. We focus on regional climate model simulations performed in the context of the North American Regional Climate Change Assessment Program (NARCCAP). We consider, in particular, simulations from RegCM3 using three different forcings: NCEP, GFDL and CGCM3. We use simulations for two time periods: current climate conditions, covering 1971 to 2000, and future climate conditions under the SRES A2 emissions scenario, covering 2041 to 2070. We investigate yearly mean summer temperature for a domain in the South West of the United States. The results indicated the RCM sim- ulations underestimate the mean summer temperature increase for most of the domain compared to our model.
