Articles | Volume 14, issue 6
https://doi.org/10.5194/gmd-14-3539-2021
https://doi.org/10.5194/gmd-14-3539-2021
Development and technical paper
 | 
11 Jun 2021
Development and technical paper |  | 11 Jun 2021

A Markov chain method for weighting climate model ensembles

Max Kulinich, Yanan Fan, Spiridon Penev, Jason P. Evans, and Roman Olson

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AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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AR: Author's response | RR: Referee report | ED: Editor decision
AR by Max Kulinich on behalf of the Authors (18 Feb 2021)  Author's response   Manuscript 
ED: Referee Nomination & Report Request started (19 Feb 2021) by Steven Phipps
RR by Ben Sanderson (04 Mar 2021)
RR by Anonymous Referee #2 (11 Mar 2021)
ED: Publish subject to minor revisions (review by editor) (28 Mar 2021) by Steven Phipps
AR by Max Kulinich on behalf of the Authors (07 Apr 2021)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (02 May 2021) by Steven Phipps
AR by Max Kulinich on behalf of the Authors (07 May 2021)
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Short summary
We present a novel stochastic approach based on Markov chains to estimate climate model weights of multi-model ensemble means. This approach showed improved performance (better correlation with observations) over existing alternatives during cross-validation and model-as-truth tests. The results of this comparative analysis should serve to motivate further studies in applications of Markov chain and other nonlinear methods to find optimal model weights for constructing ensemble means.