Articles | Volume 14, issue 6
Geosci. Model Dev., 14, 3539–3551, 2021
https://doi.org/10.5194/gmd-14-3539-2021
Geosci. Model Dev., 14, 3539–3551, 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 et al.

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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 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
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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.