Gridded Lagrangian surface drifter observations in the North Sea: An overview on high resolution tidal dynamics and surface currents

A data set detected by a high-resolution Lagrangian surface drifter is presented for the North Sea from 2017-2021. The North Sea is a marginal sea and is considered a shallow water shelf for most of its area, making its oceanographic dynamics quite different from those in the deep ocean. In contrast, it is less dominated by baroclinic dynamics but is strongly driven by tides, which results in a significant difference in the circulation patterns of particle motions. Numerous Lagrangian drifter observations have been deployed to gain a better understanding of the current behavior at the sea surface. Compared to Eulerian approaches and remote sensing methods, such as high-frequency radars and satellite altimeters, the Lagrangian measurement method can resolve fine current structures at spatial and temporal scales while covering a large region. In addition, Lagrangian methods are variable in space and time, allowing analysis of fine submesoscale fluid dynamics, such as divergence and eddy dynamics, by using clusters of drifters.

The methods suitable for calculating surface and tidal currents using drifter position data are presented. Thus, using the large amount of data collected from 2017 to 2021, this study provides a high-resolution mean surface current map and gridded representation of tidal dynamcis in the North Sea. Significant differences between the shallow water shelf and the deep area of the North Sea become apparent. While tidal currents dominate the shallow coastal areas, deep areas such as the Skagerrak register a high mean residual circulation driven by high density gradients. Comparison with other measured data proves that the chosen methods to calculate the currents are reliable. This presents the potential for Lagrangian measurements by surface drifters and the capability of the already detected data set, as it can be used for further analysis and to advance and calibrate numerical models.