Preprints
https://doi.org/10.5194/egusphere-2022-876
https://doi.org/10.5194/egusphere-2022-876
 
13 Sep 2022
13 Sep 2022
Status: this preprint is open for discussion.

Regionalizing the Sea-level Budget With Machine Learning Techniques

Carolina M. L. Camargo1,2, Riccardo E. M. Riva2, Tim H. J. Hermans1,2, Eike M. Schütt3, Marta Marcos4, Ismael Hernandez-Carrasco4, and Aimée B. A. Slangen1 Carolina M. L. Camargo et al.
  • 1NIOZ Royal Netherlands Institute for Sea Research, Department of Estuarine and Delta Systems, Yerseke, The Netherlands
  • 2Delft University of Technology, Department of Geoscience and Remote Sensing, Delft, The Netherlands
  • 3Department of Geography, Kiel University, Kiel, Germany
  • 4Mediterranean Institute for Advanced Studies (IMEDEA), Spanish National Research Council-University of Balearic Islands (CSIC-UIB), Esporles, Spain

Abstract. Attribution of sea-level change to its different drivers is typically done using a sea-level budget (SLB) approach. While the global mean SLB is considered closed, closing the SLB 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 SLB has been mainly analysed on a basin-wide scale. Here we investigate the SLB at sub-basin scales, using two machine learning techniques to extract domains of coherent sea-level variability: a neural network approach (Self-Organising Maps) and a network detection approach (δ-MAPS). The extracted domains provide a higher level of spatial detail than entire ocean basins and besides indicating how sea-level variability is connected among different regions. Using these domains we can close the regional SLB world-wide on different spatial scales. Steric variations dominate the temporal sea-level variability and determine a significant part of the total regional change. Sea-level change due to mass transport between ocean and land has a relatively homogeneous contribution to all regions. In highly dynamic regions (e.g., Gulf Stream region) the dynamic mass redistribution is significant. Regions where the SLB 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. Hence, the use of the SLB approach in combination with machine learning techniques leads to new insights into regional sea-level variability and its drivers.

Carolina M. L. Camargo et al.

Status: open (until 08 Nov 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on egusphere-2022-876', Paul PUKITE, 13 Sep 2022 reply
    • AC1: 'Reply on CC1', Carolina M.L. Camargo, 27 Sep 2022 reply

Carolina M. L. Camargo et al.

Carolina M. L. Camargo et al.

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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 show how the different drivers contribute differently to sea-level change depending on the region, highlight how large-scale ocean circulation controls regional sea-level change.