Ocean Science
Ocean Science
OS
Articles | Volume 21, issue 4
Articles | Volume 21, issue 4
https://doi.org/10.5194/os-21-1813-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/os-21-1813-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 21, issue 4
Research article
 | 
26 Aug 2025
Research article |  | 26 Aug 2025

Drivers of high-frequency extreme sea levels around northern Europe – synergies between recurrent neural networks and random forest

Céline Heuzé, Linn Carlstedt, Lea Poropat, and Heather Reese

Viewed

Total article views: 4,282 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
3,574 512 196 4,282 213 283
  • HTML: 3,574
  • PDF: 512
  • XML: 196
  • Total: 4,282
  • BibTeX: 213
  • EndNote: 283
Views and downloads (calculated since 21 Feb 2025)
Cumulative views and downloads (calculated since 21 Feb 2025)

Viewed (geographical distribution)

Total article views: 4,282 (including HTML, PDF, and XML) Thereof 4,140 with geography defined and 142 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Saved (final revised paper)

Latest update: 06 Sep 2026
Download
Short summary
Extreme sea levels will worsen under climate change. In northern Europe, what drives these extreme events will not change, so determining these drivers is of use for planning coastal defences. Here, using two machine learning methods on hourly tide gauge and weather data at nine locations around the North and Baltic seas, we determine that the drivers of prolonged periods of high sea level are westerly winds, whereas the drivers of the most extreme peaks depend on the coastline geometry.
Read more
Share
Special issue
Special issue on ocean extremes (55th International Liége...