Mean Sea Level Variability in the North Sea: Processes and Implications

• Published in: Journal of Geophysical Research: Oceans
• Main Authors: Dangendorf, Sönke, Calafat, Francisco M., Arns, Arne, Wahl, Thomas, Haigh, Ivan D., Jensen, Jürgen
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: Digital Commons @ University of South Florida 2014
• Subjects: regional mean sea level; North Sea; tide gauge; atmospheric forcing; Life Sciences; North Atlantic
• Online Access: https://digitalcommons.usf.edu/msc_facpub/2554; https://doi.org/10.1002/2014JC009901

Mean sea level (MSL) variations across a range of time scales are examined for the North Sea under the consideration of different forcing factors since the late 19th century. We use multiple linear regression models, which are validated for the second half of the 20th century against the output of a tide+surge model, to determine the barotropic response of the ocean to fluctuations in atmospheric forcing. We find that local atmospheric forcing mainly initiates MSL variability on time scales up to a few years, with the inverted barometric effect dominating the variability along the UK and Norwegian coastlines and wind controlling the MSL variability in the south from Belgium up to Denmark. On decadal time scales, MSL variability mainly reflects steric changes, which are largely forced remotely. A spatial correlation analysis of altimetry observations and gridded steric heights suggests evidence for a coherent signal extending from the Norwegian shelf down to the Canary Islands. This fits with the theory of longshore wind forcing along the eastern boundary of the North Atlantic causing coastally trapped waves to propagate over thousands of kilometers along the continental slope. Implications of these findings are assessed with statistical Monte-Carlo experiments. It is demonstrated that the removal of known variability increases the signal to noise ratio with the result that: (i) linear trends can be estimated more accurately; (ii) possible accelerations (as expected, e.g., due to anthropogenic climate change) can be detected much earlier. Such information is of crucial importance for anticipatory coastal management, engineering, and planning.