Data from 21 years of satellite altimeter measurements are used to identify and understand the major contributing components of sea surface height variability (SSV) on monthly time-scales in the North East Atlantic. A number of SSV drivers is considered, which are categorised into two groups; local (wind and sea surface temperature) and remote (sea level pressure and the North Atlantic oscillation index). A multiple linear regression model is constructed to model the SSV for a specific target area in
the North Sea basin. Cross-correlations between candidate regressors potentially lead to ambiguity in the interpretation of the results. We therefore use an objective hierarchical selection method based on variance inflation factors to select the optimal number of regressors for the target area and accept these into the regression model if they can be associated to SSV through a direct underlying physical forcing mechanism. Results show that a region of high SSV exists off the west coast of Denmark and that it can be represented well with a regression model that uses local wind, sea surface temperature and sea level pressure as primary regressors. The regression model developed here helps to understand sea level change in the North East Atlantic. The methodology is generalised and easily applied to other regions.
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