Articles | Volume 23, issue 6
https://doi.org/10.5194/acp-23-3609-2023
https://doi.org/10.5194/acp-23-3609-2023
Technical note
 | 
24 Mar 2023
Technical note |  | 24 Mar 2023

Technical note: Unsupervised classification of ozone profiles in UKESM1

Fouzia Fahrin, Daniel C. Jones, Yan Wu, James Keeble, and Alexander T. Archibald

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on acp-2022-423', Anonymous Referee #1, 29 Aug 2022
    • AC1: 'Reply on RC1', Fouzia Fahrin, 25 Jan 2023
  • RC2: 'Comment on acp-2022-423', Anonymous Referee #2, 03 Sep 2022
    • AC2: 'Reply on RC2', Fouzia Fahrin, 25 Jan 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Fouzia Fahrin on behalf of the Authors (26 Jan 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (28 Jan 2023) by Jianzhong Ma
RR by Anonymous Referee #1 (09 Feb 2023)
RR by Anonymous Referee #2 (14 Feb 2023)
ED: Publish subject to minor revisions (review by editor) (14 Feb 2023) by Jianzhong Ma
AR by Fouzia Fahrin on behalf of the Authors (22 Feb 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (23 Feb 2023) by Jianzhong Ma
AR by Fouzia Fahrin on behalf of the Authors (25 Feb 2023)  Author's response   Manuscript 
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Short summary
We use a machine learning technique called Gaussian mixture modeling (GMM) to classify vertical ozone profiles into groups based on how the ozone concentration changes with pressure. Even though the GMM algorithm was not provided with spatial information, the classes are geographically coherent. We also detect signatures of tropical broadening in UKESM1 future climate scenarios. GMM may be useful for understanding ozone structures in modeled and observed datasets.
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