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.
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.
the Creative Commons Attribution 4.0 License.
Research article
| Highlight paper
| 
16 Jan 2023
Research article | Highlight paper |
| 16 Jan 2023
| 16 Jan 2023
Regionalizing the sea-level budget with machine learning techniques
Carolina M. L. Camargo
CORRESPONDING AUTHOR
Department of Estuarine and Delta Systems, NIOZ Royal Netherlands Institute for Sea Research, Yerseke, The Netherlands
Department of Geoscience and Remote Sensing, Delft University of Technology, Delft, The Netherlands
Riccardo E. M. Riva
Department of Geoscience and Remote Sensing, Delft University of Technology, Delft, The Netherlands
Tim H. J. Hermans
Department of Estuarine and Delta Systems, NIOZ Royal Netherlands Institute for Sea Research, Yerseke, The Netherlands
Department of Geoscience and Remote Sensing, Delft University of Technology, Delft, The Netherlands
Eike M. Schütt
Department of Geography, Kiel University, Kiel, Germany
Marta Marcos
Mediterranean Institute for Advanced
Studies (IMEDEA), Spanish National Research Council – University of the Balearic Islands (CSIC-UIB), Esporles, Spain
Ismael Hernandez-Carrasco
Mediterranean Institute for Advanced
Studies (IMEDEA), Spanish National Research Council – University of the Balearic Islands (CSIC-UIB), Esporles, Spain
Aimée B. A. Slangen
Department of Estuarine and Delta Systems, NIOZ Royal Netherlands Institute for Sea Research, Yerseke, The Netherlands
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Cited
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- Leveraging synthetic data to improve regional sea level predictions G. Tong et al. 10.1038/s41598-025-88078-1
- Satellite monitoring for coastal dynamic adaptation policy pathways B. Hamlington et al. 10.1016/j.crm.2023.100555
- Global Sea Level Change Rate, Acceleration and Its Components from 1993 to 2016 F. Wang et al. 10.1080/01490419.2023.2276478
- network-based constraint to evaluate climate sensitivity L. Ricard et al. 10.1038/s41467-024-50813-z
- Unsupervised classification of the northwestern European seas based on satellite altimetry data L. Poropat et al. 10.5194/os-20-201-2024
- Sea Level Budget in the East China Sea Inferred from Satellite Gravimetry, Altimetry and Steric Datasets F. Wang et al. 10.3390/rs17050881
- Global and regional ocean mass budget closure since 2003 C. Ludwigsen et al. 10.1038/s41467-024-45726-w
- Data-driven dimensionality reduction and causal inference for spatiotemporal climate fields F. Falasca et al. 10.1103/PhysRevE.109.044202
- Sea Level Rise in Europe: Observations and projections A. Melet et al. 10.5194/sp-3-slre1-4-2024
- Quantifying the relative contributions of forcings to the variability of estuarine surface suspended sediments using a machine learning framework J. Tavora et al. 10.1016/j.csr.2025.105429
- Predicting Sea Level Rise Using Artificial Intelligence: A Review N. Bahari et al. 10.1007/s11831-023-09934-9
11 citations as recorded by crossref.
- A process-based assessment of the sea-level rise in the northwestern Pacific marginal seas H. Cha et al. 10.1038/s43247-023-00965-5
- Leveraging synthetic data to improve regional sea level predictions G. Tong et al. 10.1038/s41598-025-88078-1
- Satellite monitoring for coastal dynamic adaptation policy pathways B. Hamlington et al. 10.1016/j.crm.2023.100555
- Global Sea Level Change Rate, Acceleration and Its Components from 1993 to 2016 F. Wang et al. 10.1080/01490419.2023.2276478
- network-based constraint to evaluate climate sensitivity L. Ricard et al. 10.1038/s41467-024-50813-z
- Unsupervised classification of the northwestern European seas based on satellite altimetry data L. Poropat et al. 10.5194/os-20-201-2024
- Sea Level Budget in the East China Sea Inferred from Satellite Gravimetry, Altimetry and Steric Datasets F. Wang et al. 10.3390/rs17050881
- Global and regional ocean mass budget closure since 2003 C. Ludwigsen et al. 10.1038/s41467-024-45726-w
- Data-driven dimensionality reduction and causal inference for spatiotemporal climate fields F. Falasca et al. 10.1103/PhysRevE.109.044202
- Sea Level Rise in Europe: Observations and projections A. Melet et al. 10.5194/sp-3-slre1-4-2024
- Quantifying the relative contributions of forcings to the variability of estuarine surface suspended sediments using a machine learning framework J. Tavora et al. 10.1016/j.csr.2025.105429
1 citations as recorded by crossref.
Latest update: 24 Mar 2025
Co-editor-in-chief
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.
Closing the sea level budget (between observed rise and the sum of its causes) has been a...
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.
Sea-level change is mainly caused by variations in the ocean’s temperature and salinity and...
