Journal cover Journal topic
Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
doi:10.5194/gmd-2016-291
© Author(s) 2017. This work is distributed
under the Creative Commons Attribution 3.0 License.
Methods for assessment of models
04 Jan 2017
Review status
A revision of this discussion paper is under review for the journal Geoscientific Model Development (GMD).
A Bayesian posterior predictive framework for weighting ensemble regional climate models
Yanan Fan1, Roman Olson2, and Jason P. Evans3 1School 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
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.

Citation: 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.
Yanan Fan et al.
Yanan Fan et al.
Yanan Fan et al.

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Short summary
We develop a novel and principled Bayesian statistical approach to computing model weights in climate change projection ensembles of regional climate models.

The approach accounts for uncertainty in model bias, trend and internal variability. The weights are easily interpretable and the ensemble weighted models are shown to provide the correct coverage and improve upon existing methods in terms of providing narrower confidence intervals for climate change projections.
We develop a novel and principled Bayesian statistical approach to computing model weights in...
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