Spatial patterns of probabilistic temperature change projections from a multivariate Bayesian analysis

We present probabilistic projections for spatial patterns of future temperature change using a multivariate Bayesian analysis. The methodology is applied to the output from 21 global coupled climate models used for the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. The statistical technique is based on the assumption that spatial patterns of climate change can be separated into a large scale signal related to the true forced climate change and a small scale signal due to model bias and variability. The different scales are represented via dimension reduction techniques in a hierarchical Bayesian model. Posterior probabilities are obtained with a Markov chain Monte Carlo simulation. We show that with 66% (90%) probability 79% (48%) of the land areas warm by more than 2°C by the end of the century for the SRES A1B scenario. DOI: https://doi.org/10.1029/2006GL027754 Posted at the Zurich Open Repository and Archive, University of Zurich ZORA URL: https://doi.org/10.5167/uzh-21539 Published Version Originally published at: Furrer, R; Knutti, R; Sain, S; Nychka, D; Meehl, G A (2007). Spatial patterns of probabilistic temperature change projections from a multivariate Bayesian analysis. Geophysical Research Letters, 34(06711):1-4. DOI: https://doi.org/10.1029/2006GL027754 Spatial patterns of probabilistic temperature change projections from a multivariate Bayesian analysis R. Furrer, R. Knutti, S. R. Sain, D. W. Nychka, and G. A. Meehl Received 2 August 2006; revised 18 January 2007; accepted 4 February 2007; published 31 March 2007. [1] We present probabilistic projections for spatial patterns of future temperature change using a multivariate Bayesian analysis. The methodology is applied to the output from 21 global coupled climate models used for the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. The statistical technique is based on the assumption that spatial patterns of climate change can be separated into a large scale signal related to the true forced climate change and a small scale signal due to model bias and variability. The different scales are represented via dimension reduction techniques in a hierarchical Bayesian model. Posterior probabilities are obtained with a Markov chain Monte Carlo simulation. We show that with 66% (90%) probability 79% (48%) of the land areas warm by more than 2 C by the end of the century for the SRES A1B scenario. Citation: Furrer, R., R. Knutti, S. R. Sain, D. W. Nychka, and G. A. Meehl (2007), Spatial patterns of probabilistic temperature change projections from a multivariate Bayesian analysis, Geophys. Res. Lett., 34, L06711, doi:10.1029/