Articles | Volume 18, issue 5
https://doi.org/10.5194/os-18-1491-2022
https://doi.org/10.5194/os-18-1491-2022
Research article
 | 
20 Oct 2022
Research article |  | 20 Oct 2022

Surface circulation properties in the eastern Mediterranean emphasized using machine learning methods

Georges Baaklini, Roy El Hourany, Milad Fakhri, Julien Brajard, Leila Issa, Gina Fifani, and Laurent Mortier

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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 egusphere-2022-202', Anonymous Referee #1, 31 May 2022
  • RC2: 'Comment on egusphere-2022-202', Anonymous Referee #2, 01 Jun 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Georges Baaklini on behalf of the Authors (16 Jul 2022)  Author's response   Author's tracked changes 
EF by Sarah Buchmann (18 Jul 2022)  Manuscript 
ED: Referee Nomination & Report Request started (12 Aug 2022) by Aida Alvera-Azcárate
RR by Anonymous Referee #2 (24 Aug 2022)
RR by Anonymous Referee #1 (07 Sep 2022)
ED: Publish subject to minor revisions (review by editor) (13 Sep 2022) by Aida Alvera-Azcárate
AR by Georges Baaklini on behalf of the Authors (19 Sep 2022)  Author's response   Author's tracked changes   Manuscript 
ED: Publish subject to technical corrections (28 Sep 2022) by Aida Alvera-Azcárate
AR by Georges Baaklini on behalf of the Authors (29 Sep 2022)  Author's response   Manuscript 
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Short summary
We use machine learning to analyze the long-term variation of the surface currents in the Levantine Sea, located in the eastern Mediterranean Sea. We decompose the circulation into groups based on their physical characteristics and analyze their spatial and temporal variability. We show that most structures of the Levantine Sea are becoming more energetic over time, despite those of the western area remaining the most dominant due to their complex bathymetry and strong currents.