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Exploring Storm Tides Projections and Their Return Levels Around the Baltic Sea Using a Machine Learning Approach
Uppsala Univ, Dept Earth Sci, Ctr Nat Hazards & Disaster Sci CNDS, S-75236 Uppsala, Sweden.;Uppsala Univ, Ctr Nat Hazards & Disaster Sci CNDS, S-75236 Uppsala, Sweden..
Uppsala Univ, Dept Earth Sci, Ctr Nat Hazards & Disaster Sci CNDS, S-75236 Uppsala, Sweden.;Uppsala Univ, Ctr Nat Hazards & Disaster Sci CNDS, S-75236 Uppsala, Sweden..
Danish Meteorol Off, DK-2100 Copenhagen, Denmark..
Tech Univ Denmark, Dept Technol Management & Econ, DK-2800 Lyngby, Denmark..
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2025 (English)In: Tellus. Series A, Dynamic meteorology and oceanography, ISSN 0280-6495, E-ISSN 1600-0870, Vol. 77, no 1, p. 79-97Article in journal (Refereed) Published
Abstract [en]

Extreme sea levels are a major global concern due to their potential to cause fatalities and significant economic losses in coastal areas. Consequently, accurate projections of these extremes for the coming century are crucial for effective coastal planning. While it is well established that relative sea level rise driven by ongoing climate change is a key factor influencing future extreme sea levels, changes in storm surges resulting from shifts in storm climatology may also play a critical role. In this study, we project future daily maximum storm tides (the combination of storm surge and tides) using a random forest machine learning approach for 59 stations around the Baltic Sea, based on atmospheric variables such as surface pressure, wind speed, and wind direction derived from climate datasets. The results suggest both positive and negative changes, with sub-regional variations, in 50-year storm tide return levels across the Baltic Sea when comparing the period of 2070-2099 to 1850-1879. Localized increases of up to 10 cm are projected along the west coast of Sweden and the northern Baltic Sea, while decreases of up to 6 cm are anticipated along the south coast of Sweden, the Gulf of Riga, and the mouth of the Gulf of Finland. Negligible levels of change are expected in other parts of the Baltic Sea. The variability in atmospheric drivers across the four climate models contributes to a high degree of uncertainty in future climate projections.

Place, publisher, year, edition, pages
STOCKHOLM UNIV PRESS , 2025. Vol. 77, no 1, p. 79-97
Keywords [en]
Machine Learning, Baltic Sea, Coastal flooding, Extreme Sea Levels
National Category
Oceanography, Hydrology and Water Resources
Research subject
Climate; Oceanography
Identifiers
URN: urn:nbn:se:smhi:diva-6755DOI: 10.16993/tellusa.4101ISI: 001470879500001OAI: oai:DiVA.org:smhi-6755DiVA, id: diva2:1956635
Available from: 2025-05-06 Created: 2025-05-06 Last updated: 2025-05-06Bibliographically approved

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Hieronymus, MagnusKarami, Pasha

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