Netherlands Polar Data Center

Abstract

Data from 21 years of satellite altimeter meas-
urements 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 catego-
rised into two groups; local (wind and sea surface tempera-
ture) 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 interpreta-
tion of the results. We therefore use an objective hierarchi-
cal 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 rep-
resented 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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Keywords

• North Sea
• Sea level variability