Understanding the spatial variation of sea level rise in the North Sea using satellite altimetry
(Journal of Geophysical Research: Oceans, 2017. Read full article here.)
Abstract: This paper examines the spatial variation of sea surface height trends in the North Sea Basin as seen by satellite altimetry and assesses its underlying causes. Changes in the potential temperature and salinity of the North Sea are transposed into corresponding changes in sea surface height and regional anomalies of linear sea level trend calculated. The same is carried out for the meteorological processes which act on the sea surface. The steric and meteorological regional sea level rise anomalies are summed with those from contributions from land ice and compared against the values seen by satellite altimetry over the period 1993–2014. Results show that there is good agreement between the observations and the reconstruction. The local meteorological contribution appears to be most important in describing regional variation in linear sea level rise and is reinforced with a local halosteric contribution which shows a similar spatial pattern.
Sea surface height variability in the North East Atlantic from satellite altimetry
(Climate Dynamics, 2015. Read full article here.)
Abstract: 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.