A Markov chain method for weighting climate model ensembles

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

doi:https://doi.org/10.5194/gmd-14-3539-2021

Articles | Volume 14, issue 6

https://doi.org/10.5194/gmd-14-3539-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/gmd-14-3539-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 14, issue 6

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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Final revised paper (published on 11 Jun 2021)
Preprint (discussion started on 01 Oct 2020)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC1: 'Review of Kulinich et al', Ben Sanderson, 02 Nov 2020
RC2: 'Reviewer Comment', Anonymous Referee #2, 10 Nov 2020
AC1: 'Comment on gmd-2020-253', Max Kulinich, 14 Jan 2021

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

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.