Exploring Storm Tides Projections and Their Return Levels Around the Baltic Sea Using a Machine Learning Approach

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.

Authors

Publication Details

Journal
Tellus A Dynamic Meteorology and Oceanography
Published
2025-04-07
DOI
https://doi.org/10.16993/tellusa.4101
Citations
2
Primary Topic
Oceanographic and Atmospheric Processes
Type
article
Field-Weighted Citation Impact
3.59
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article

Exploring Storm Tides Projections and Their Return Levels Around the Baltic Sea Using a Machine Learning Approach

Magnus Hieronymus, Martin Drews, Erik Nilsson, Anna Rutgersson et al.
2 citations
Tellus A Dynamic Meteorology and Oceanography
Oceanographic and Atmospheric Processes
3.59
article

Exploring Storm Tides Projections and Their Return Levels Around the Baltic Sea Using a Machine Learning Approach

Magnus Hieronymus, Martin Drews, Erik Nilsson, Anna Rutgersson, Mehdi Pasha Karami, Morten Andreas Dahl Larsen, Kévin Dubois
article en
2 citations

Abstract

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.

Tellus A Dynamic Meteorology and OceanographyVol. 77(1)
Openalex Percentile: Top 9%
Oceanographic and Atmospheric Processes
3.59
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Exploring Storm Tides Projections and Their Return Levels Around the Baltic Sea Using a Machine Learning Approach — Magnus Hieronymus, Martin Drews, et al. · Tellus A Dynamic Meteorology and Oceanography (2025) | TGRS Research Map | TGRS