Title: 'Hierarchical Models to Assess Climate Predictions'
We consider a general framework for the analysis of ensembles of simulations produced by climate models. The methods are based on hierarchical Bayesian models. We use summaries of the simulated variables, to obtain sensible comparisons between model simulations and historical records, and quantify possible discrepancies. These are used as the basis for the averaging of the simulation ensembles. We are particularly interested in obtaining blended results for time varying factors that can summarize the variability of large spatio-temporal fields. We consider examples for predictions of oceanic indexes in the North Pacific. We also consider the problem of comparing and blending regional climate model simulations for the American Southwest.
Professor Bruno Sanso is from The University of California in Santa Cruz, USA, and will be with the UQ Centre for Applications in Natural Resource Mathematics (CARM) from October 29 to November 2.