Articles | Volume 10, issue 6
https://doi.org/10.5194/gmd-10-2321-2017
https://doi.org/10.5194/gmd-10-2321-2017
Methods for assessment of models
 | 
23 Jun 2017
Methods for assessment of models |  | 23 Jun 2017

A Bayesian posterior predictive framework for weighting ensemble regional climate models

Yanan Fan, Roman Olson, and Jason P. Evans

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Yanan Fan on behalf of the Authors (10 Mar 2017)  Author's response   Manuscript 
ED: Referee Nomination & Report Request started (20 Mar 2017) by James Annan
RR by Anonymous Referee #1 (06 Apr 2017)
RR by Hans R Künsch (26 Apr 2017)
ED: Publish subject to minor revisions (Editor review) (08 May 2017) by James Annan
AR by Yanan Fan on behalf of the Authors (17 May 2017)  Author's response   Manuscript 
ED: Publish as is (22 May 2017) by James Annan
AR by Yanan Fan on behalf of the Authors (23 May 2017)
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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.