UCSC-SOE-13-22: Observation-based Blended Projections from Ensembles of Regional Climate Models

Esther Salazar, Dorit Hammerling, Xia Wang, Bruno Sanso, Andrew O. Finley, and Linda Mearns
12/20/2013 11:08 AM
Applied Mathematics & Statistics
We consider the problem of projecting future climate from ensembles of regional climate model (RCM) simulations using results from the North American Regional Climate Change Assessment Program (NARCCAP). To this end, we develop a hierarchical Bayesian space-time model that quantifies the discrepancies between different members of an ensemble of RCMs corresponding to present day conditions, and observational records. Discrepancies are then propagated into the future to obtain high resolution blended projections of 21st century climate. In addition to blended projections, the proposed method provides location-dependent comparisons between the different simulations by estimating the different modes of spatial variability, and using the climate model-specific coefficients of the spatial factors for comparisons. The approach has the flexibility to provide projections at any spatial scale of potential interest to stakeholders while correctly accounting for the uncertainties associated with projections at that scale. We demonstrate the methodology with simulations from WRF using three different forcings: NCEP, CCSM 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 and project yearly mean summer and winter temperatures for a domain in the South West of the United States.