A Bayesian posterior predictive framework for weighting ensemble regional climate models.

We present a novel Bayesian statistical approach to computing model weights in climate change projection ensembles in order to create probabilistic projections. 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, 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.

Figure 7. Bootstrapped weighted projections of DJF temperature change in 2060–2079 compared to 1990–2009 for regions in south-eastern Australia. Black lines correspond to wm weights, green lines to wm,I weights and blue lines to wm,T weights. Red lines are results from Olson et al. (2016a). Black vertical lines represent 95 % credible intervals, and red vertical lines represent the 95 % credible intervals obtained by Olson et al. (2016a). Circles represent the difference between the changes in temperature using the individual models. Black crosses indicate the simple ensemble mean of the changes in temperature.