Estimating decadal variability in sea level from tide gauge records: An application to the North Sea

Abstract

AbstractOne of the primary observational data sets of sea level is represented by the tide gauge record. We propose a new method to estimate variability on decadal time scales from tide gauge data by using a state space formulation, which couples the direct observations to a predefined state space model by using a Kalman filter. The model consists of a time‐varying trend and seasonal cycle, and variability induced by several physical processes, such as wind, atmospheric pressure changes and teleconnection patterns. This model has two advantages over the classical least‐squares method that uses regression to explain variations due to known processes: a seasonal cycle with time‐varying phase and amplitude can be estimated, and the trend is allowed to vary over time. This time‐varying trend consists of a secular trend and low‐frequency variability that is not explained by any other term in the model. As a test case, we have used tide gauge data from stations around the North Sea over the period 1980–2013. We compare a model that only estimates a trend with two models that also remove intra‐annual variability: one by means of time series of wind stress and sea level pressure, and one by using a two‐dimensional hydrodynamic model. The last two models explain a large part of the variability, which significantly improves the accuracy of the estimated time‐varying trend. The best results are obtained with the hydrodynamic model. We find a consistent low‐frequency sea level signal in the North Sea, which can be linked to a steric signal over the northeastern part of the Atlantic.