A Bayesian posterior predictive framework for weighting ensemble
regional climate models
Yanan Fan1, Roman Olson2, and Jason P. Evans31School of Mathematics and Statistics, UNSW, Australia 2Department of Atmospheric Sciences, Yonsei University, South Korea 3Climate Change Research Centre and ARC Centre of Excellence for Climate System Science, UNSW, Australia
Received: 28 Nov 2016 – Accepted: 02 Jan 2017 – Published: 04 Jan 2017
Abstract. We present a novel Bayesian statistical approach to computing model weights in climate change We present a novel Bayesian statistical approach to computing model weights in climate change projection ensembles. The weight of each climate model is obtained by weighting the current day observed data under the posterior distribution admitted under competing climate models. We use a linear model to describe the model output and observations. The approach accounts for uncertainty in model bias, trend and internal variability, as well as including error in the observations used. Our framework is general, requires very little problem specific input, and works well with default priors. We carry out cross-validation checks that confirm that the method produces the correct coverage.
Fan, Y., Olson, R., and Evans, J. P.: A Bayesian posterior predictive framework for weighting ensemble
regional climate models, Geosci. Model Dev. Discuss., doi:10.5194/gmd-2016-291, in review, 2017.