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.
This page is maintaind by Jason Evans |
Last updated 23 January 2018