Articles | Volume 20, issue 2
Research article
22 Mar 2024
Research article |  | 22 Mar 2024

Machine learning methods to predict sea surface temperature and marine heatwave occurrence: a case study of the Mediterranean Sea

Giulia Bonino, Giuliano Galimberti, Simona Masina, Ronan McAdam, and Emanuela Clementi


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-1847', Anonymous Referee #1, 12 Sep 2023
    • AC2: 'Reply on RC1', Giulia Bonino, 20 Dec 2023
  • RC2: 'Comment on egusphere-2023-1847', Anonymous Referee #2, 23 Oct 2023
    • AC1: 'Reply on RC2', Giulia Bonino, 20 Dec 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Giulia Bonino on behalf of the Authors (20 Dec 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (22 Dec 2023) by Matjaz Licer
RR by Anonymous Referee #2 (11 Jan 2024)
RR by Anonymous Referee #1 (22 Jan 2024)
ED: Publish as is (22 Jan 2024) by Matjaz Licer
AR by Giulia Bonino on behalf of the Authors (05 Feb 2024)  Author's response   Manuscript 

Post-review adjustments

AA: Author's adjustment | EA: Editor approval
AA by Giulia Bonino on behalf of the Authors (21 Mar 2024)   Author's adjustment   Manuscript
EA: Adjustments approved (21 Mar 2024) by Matjaz Licer
Short summary
This study employs machine learning to predict marine heatwaves (MHWs) in the Mediterranean Sea. MHWs have far-reaching impacts on society and ecosystems. Using data from ESA and ECMWF, the research develops accurate prediction models for sea surface temperature (SST) and MHWs across the region. Notably, machine learning methods outperform existing forecasting systems, showing promise in early MHW predictions. The study also highlights the importance of solar radiation as a predictor of SST.