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Regionalizing the sea-level budget with machine learning techniques
Camargo, C.M.L.; Riva, R.E.M.; Hermans, T.H.J.; Schütt, E.M.; Marcos, M.; Hernández-Carrasco, I.; Slangen, A.B.A. (2023). Regionalizing the sea-level budget with machine learning techniques. Ocean Sci. 19: 17-41. https://dx.doi.org/10.5194/os-19-17-2023

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In: Ocean Science. Copernicus: Göttingen. ISSN 1812-0784; e-ISSN 1812-0792, meer
Peer reviewed article  
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  • Camargo, C.M.L., meer
  • Riva, R.E.M.
  • Hermans, T.H.J., meer
  • Schütt, E.M.
  • Marcos, M.
  • Hernández-Carrasco, I.
  • Slangen, A.B.A., meer
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.
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