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A simple method for Bayesian model averaging of regional climate model projections: Application to southeast Australian temperatures
Author(s) -
Olson Roman,
Fan Yanan,
Evans Jason P.
Publication year - 2016
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1002/2016gl069704
Subject(s) - probabilistic logic , climate model , bayesian probability , bayesian inference , statistical model , climatology , computer science , simple (philosophy) , ensemble average , econometrics , projection (relational algebra) , climate change , environmental science , mathematics , geology , algorithm , artificial intelligence , philosophy , oceanography , epistemology
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.