Skip to main content
U.S. flag

An official website of the United States government

Multivariate spatio-temporal clustering of wind–wave variability across European seas

August 11, 2026

This study presents a multivariate spatio-temporal clustering framework to characterise joint wind–wave regimes across European seas using the fifth-generation atmospheric reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ERA5; 1979–2014). Seasonal and annual statistics of significant wave height, mean wave period, wind speed, and wave/wind direction were computed at 0.5° resolution. Principal component analysis was used to reduce dimensionality, retaining 30 components that captured 99% of the variance. K-means clustering was then used to identify nine coherent dynamical regimes with persistent spatio-temporal signatures. These regimes were grouped into open-ocean, transitional, and enclosed/semi-enclosed categories based on internal variability, directional spread, and geographic exposure. Open-Atlantic regimes are found to be energy-rich, exhibiting clear December–February maxima in significant wave height (𝐻𝑠), mean wave period (𝑇02), and 10 m wind speed (𝑊𝑠10); enclosed and semi-enclosed basins show lower amplitudes and reduced variability, while transitional shelves and the southern Mediterranean display intermediate conditions, characterised by moderate 𝑇02 levels and seasonal rotation of wave and wind directions, reflecting a mixed influence of locally generated seas and remotely forced swell. Dispersion analysis highlights a clear Atlantic–Mediterranean partition, with transitional shelves forming a dynamical bridge between open-ocean and enclosed basins. Teleconnection analysis shows that the North Atlantic Oscillation and Arctic Oscillation dominate Atlantic regimes, while the Scandinavia, East Atlantic, and Polar/Eurasia patterns modulate variability and directional persistence in transitional and enclosed seas. The classification defines a climatological framework of European wind–wave conditions and establishes a practical basis for renewable energy assessment, engineering design, and long-term change analysis, with methods transferable to other basins.

Publication Year 2026
Title Multivariate spatio-temporal clustering of wind–wave variability across European seas
DOI 10.3390/atmos17080776
Authors Ponni Maya, José A. Antolínez, Kai Alexander Parker, Laura Cagigal, Andrei Metrikine
Publication Type Article
Publication Subtype Journal Article
Series Title Atmosphere
Index ID 70283123
Record Source USGS Publications Warehouse
USGS Organization Pacific Coastal and Marine Science Center
Was this page helpful?