Ocean Science
Ocean Science
OS
Articles | Volume 19, issue 1
Articles | Volume 19, issue 1
https://doi.org/10.5194/os-19-17-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/os-19-17-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 19, issue 1
Research article
 | Highlight paper
 | 
16 Jan 2023
Research article | Highlight paper |  | 16 Jan 2023

Regionalizing the sea-level budget with machine learning techniques

Carolina M. L. Camargo, Riccardo E. M. Riva, Tim H. J. Hermans, Eike M. Schütt, Marta Marcos, Ismael Hernandez-Carrasco, and Aimée B. A. Slangen

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Closing the sea level budget (between observed rise and the sum of its causes) has been a challenge, is an ongoing effort and has primarily concerned the global mean. Here, the authors use machine learning to identify sub-areas with similar trends to close the sea level budget on a regional level, with much reduced errors compared with 1-degree grid points.
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Short summary
Sea-level change is mainly caused by variations in the ocean’s temperature and salinity and land ice melting. Here, we quantify the contribution of the different drivers to the regional sea-level change. We apply machine learning techniques to identify regions that have similar sea-level variability. These regions reduce the observational uncertainty that has limited the regional sea-level budget so far and highlight how large-scale ocean circulation controls regional sea-level change.
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