Recent studies using regional climate models to make probabilistic projections break important
new ground. However, they typically lack cross validation, pull the projections toward agreeing models
(which can agree due to shared biases), and ignore model skill at reproducing internal variability when
weighing the models. Here we conduct the first, to our knowledge, application of Bayesian model averaging
(BMA) to make probabilistic projections using regional climate models (RCMs). We weigh the RCMs from the
NARCliM project based on their skill at representing temperature over 12 southeast Australian regions in
terms of trend, bias, and internal variability. The weights do not depend on model agreement with other
models. Using the weighted ensemble, we provide probabilistic seasonal temperature projections. We cross
validate the method, and demonstrate that weighted projections are well calibrated and more precise than
the unweighted ones. We find considerable differences between the weighted and the unweighted
projections in some cases.
Figure 4. Weighted (solid thick red lines) and unweighted (black lines) probabilistic projections of DJF temperature change
in 2060–2079 compared to 1990–2009 for regions in southeast Australia. The 90% credible intervals are denoted by vertical
red dotted lines. Red circles represent changes derived from the raw bias-corrected output for each of the 12 models.
This page is maintaind by Jason Evans |
Last updated 29 November 2013