Primary Drivers of Sea Level Variability in the North – Baltic Sea Transition Using Machine Learning

Abstract

Global mean sea level is rising, however not uniformly. Regional deviations of sea surface height (SSH) are common due to local drivers, including surface winds, ocean density stratifications, vertical land- & crustal movements and more. The contribution of each background driver needs to be better understood to create reliable sea level rise projections, enable effective local policymaking and aid in urban planning decisions. In this study, we assess region-specific historic sea levels along the western Swedish coastline (Kattegat, Skagerrak & South Baltic Sea). We use monthly satellite altimetry observations spanning 26 years and daily observations spanning 6 years, as well as in situ tide gauge measurements to identify SSH covariance between sub-regions. We employed a number of manual statistical methods and found that the North – Baltic Sea transition can be effectively split up into four separate subbasins of sea level covariance. We found that SSH variability in the Skagerrak and Kattegat Seas is different from that of the Belts and south of the Danish Straits. While typically the correlation between SSH time series from different locations declines with distance, this is not seen at the entrance to the Baltic Sea due to the complexity of the region. To investigate this further and identify underlying primary forcings, we quantified the correlation between climatic drivers derived from the ERA5 reanalysis such as 10m-winds, sea surface temperature and sea level pressure, and principle components of the SSH variability signal within these regions. Zonal winds are most important for determining short-term sea level variability throughout the study area. As freshwater discharge from rivers and tributaries to the Baltic Sea is large, pressure- & density gradients may be more important as SSH regulators in this area. Additionally, we used neural networks to try to capture non-linear dependencies between the sea level drivers and sea level that are not apparent from statistical analyses. By predicting sea level at selected locations from different combination of drivers, we can determine which drivers have the highest influence. Since it is important to capture long-term dependencies between variables, we employed a recurrent neural network with a long short-term memory architecture and found that it is possible to predict daily sea level variability within a few cm of error with only a handful of background drivers. We found that excluding the zonal wind component was the most detrimental for model accuracy, which agrees with the statistical analysis.