Abstract
Based on the updated relative sea level rise rates, 21st-century projections are made for the west coast of Portugal Mainland. The mean sea level from Cascais tide gauge and North Atlantic satellite altimetry data have been analyzed. Through bootstrapping linear regression and polynomial adjustments, mean sea level time series were used to calculate different empirical projections for sea level rise, by estimating the initial velocity and its corresponding acceleration. The results are consistent with an accelerated sea level rise, showing evidence of a faster rise than previous century estimates. Based on different numerical methods of second order polynomial fitting, it is possible to build a set of projection models of relative sea level rise. Applying the same methods to regional sea level anomaly from satellite altimetry, additional projections are also built with good consistency. Both data sets, tide gauge and satellite altimetry data, enabled the development of an ensemble of projection models. The relative sea level rise projections are crucial for national coastal planning and management since extreme sea level scenarios can potentially cause erosion and flooding. Based on absolute vertical velocities obtained by integrating global sea level models, neo-tectonic studies, and permanent Global Positioning System (GPS) station time series, it is possible to transform relative into absolute sea level rise scenarios, and vice-versa, allowing the generation of absolute sea level rise projection curves and its comparison with already established global projections. The sea level rise observed at the Cascais tide gauge has always shown a significant correlation with global sea level rise observations, evidencing relatively low rates of vertical land velocity and residual synoptic regional dynamic effects. An ensemble of sea level projection models for the 21st century is proposed with its corresponding probability density function, both for relative and absolute sea level rise for the west coast of Portugal Mainland. A mean sea level rise of 1.14 m was obtained for the epoch of 2100, with a likely range of 95% of probability between 0.39 m and 1.89 m.
Highlights
After almost two decades of continuous observations of the digital acoustic Cascais tide gauge (TG), operated under the responsibility of the actual national authority, the Directorate-General for Territorial Development (DGT), it is possible to estimate sea level rise rates with lower uncertainties and increased reliability
Cascais TG data have shown very similar searise level rise caused (SLR) rates when compared with the ones retrieved by GMSL models, either by considering global network TG or satellite altimetry models, denoting very low tectonic and post-glacial isostatic adjustment (GIA) velocities and low ocean dynamics on a decadal scale
Cascais was downloaded from the NASA Sea Level Change Data Analysis Tool (DAT), while data for a region in the North Atlantic Ocean was downloaded from the AVISO + CNES Data Centre website
Summary
After almost two decades of continuous observations of the digital acoustic Cascais tide gauge (TG), operated under the responsibility of the actual national authority, the Directorate-General for Territorial Development (DGT), it is possible to estimate sea level rise rates with lower uncertainties and increased reliability. Cascais TG data have shown very similar SLR rates when compared with the ones retrieved by GMSL models, either by considering global network TG or satellite altimetry models, denoting very low tectonic and post-glacial isostatic adjustment (GIA) velocities and low ocean dynamics on a decadal scale. Instead of a single estimation of SLR velocity and acceleration for Cascais, as proposed in [3,4], the present work provides an ensemble of SLR projections estimated and combined to assess the probability density function (PDF) of relative SLR from the present up to 2100 Such PDF is intended to be used for the estimation of the probability of a certain level of SLR being exceeded and for the evaluation of the probability of a certain projection model. The possibility of greater global warming than expected [8] must be considered
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