Print Email Facebook Twitter Regionalizing the sea-level budget with machine learning techniques Title Regionalizing the sea-level budget with machine learning techniques Author Machado Lima de Camargo, C. (TU Delft Physical and Space Geodesy; NIOZ Royal Netherlands Institute for Sea Research) Riva, R.E.M. (TU Delft Physical and Space Geodesy) Hermans, T.H.J. (TU Delft Physical and Space Geodesy) Schütt, Eike M. (University of Kiel) Marcos, Marta (University of the Balearic Islands) Hernandez-Carrasco, Ismael (University of the Balearic Islands) Slangen, Aimée B.A. (NIOZ Royal Netherlands Institute for Sea Research) Date 2023 Abstract Attribution of sea-level change to its different drivers is typically done using a sea-level budget approach. While the global mean sea-level budget is considered closed, closing the budget on a finer spatial scale is more complicated due to, for instance, limitations in our observational system and the spatial processes contributing to regional sea-level change. Consequently, the regional budget has been mainly analysed on a basin-wide scale. Here we investigate the sea-level budget at sub-basin scales, using two machine learning techniques to extract domains of coherent sea-level variability: a neural network approach (self-organizing map, SOM) and a network detection approach (δ-MAPS). The extracted domains provide more spatial detail within the ocean basins and indicate how sea-level variability is connected among different regions. Using these domains we can close, within 1σ uncertainty, the sub-basin regional sea-level budget from 1993–2016 in 100 % and 76 % of the SOM and δ-MAPS regions, respectively. Steric variations dominate the temporal sea-level variability and determine a significant part of the total regional change. Sea-level change due to mass exchange between ocean and land has a relatively homogeneous contribution to all regions. In highly dynamic regions (e.g. the Gulf Stream region) the dynamic mass redistribution is significant. Regions where the budget cannot be closed highlight processes that are affecting sea level but are not well captured by the observations, such as the influence of western boundary currents. The use of the budget approach in combination with machine learning techniques leads to new insights into regional sea-level variability and its drivers. To reference this document use: http://resolver.tudelft.nl/uuid:5f067bfe-d07c-4abd-b46c-55e47afbc7ae DOI https://doi.org/10.5194/os-19-17-2023 ISSN 1812-0784 Source Ocean Science, 19 (1), 17-41 Part of collection Institutional Repository Document type journal article Rights © 2023 C. Machado Lima de Camargo, R.E.M. Riva, T.H.J. Hermans, Eike M. Schütt, Marta Marcos, Ismael Hernandez-Carrasco, Aimée B.A. Slangen Files PDF os_19_17_2023.pdf 12.91 MB Close viewer /islandora/object/uuid:5f067bfe-d07c-4abd-b46c-55e47afbc7ae/datastream/OBJ/view