Regional probabilities of precipitation change: A Bayesian analysis of multimodel simulations

[ 1] Tebaldi et al. [ 2005] present a Bayesian approach to determining probability distribution functions ( PDFs) of temperature change at regional scales, from the output of a multi-model ensemble, run under the same scenario of future anthropogenic emissions. The main characteristic of the method is the formalization of the two criteria of bias and convergence that the REA method [ Giorgi and Mearns, 2002] first quantified as a way of assessing model reliability. Thus, the General Circulation Models ( AOGCMs) of the ensemble are combined in a way that accounts for their performance with respect to current climate and a measure of each model's agreement with the majority of the ensemble. We apply the Bayesian model to a set of transient experiments under two SRES scenarios. We focus on predictions of precipitation change, for land regions of subcontinental size. We highlight differences in the PDFs of precipitation change derived in regions where models find easy agreement, and perform well in simulating present day precipitation, compared to regions where models have large biases, and/or their future projections disagree. We compare results from the two scenarios, thus assessing the consequences of the two alternative hypotheses, and present summaries based on their averaging.