Thursday 25 August 2005

P2

PTH0107
Variability in sea level seasonal pattern from tide gauge records in the North Atlantic
Barbosa, Susana¹, Silva, Eduarda¹, Fernandes, Joana¹
1 FCUP, Portugal
Author email: susana.barbosa@fc.up.pt
Sea level seasonality, although including a small gravitational contribution, is dominated by forcing processes. Climate change-induced sea level variability is expected to affect not only the mean sea level but also the amplitude and phase of the seasonal cycle. Changes in coastal sea level seasonality are anticipated to seriously impact marine ecosystems and coastal management. This study addresses the temporal variability of sea level seasonal pattern from long and continuous tide gauge records in the extra-tropical North Atlantic. A non-stationary description of the sea level annual cycle, in the form of a time-varying sinusoid, is obtained through decomposition into latent components of an autoregressive process fitted to each record. The use of an explicit stochastic model provides an adequate inferential framework since uncertainty is handled in a Bayesian context through simulation from posterior distributions. Sea level annual seasonality is found to exhibit considerable spatial variability both in the mean amplitude and in the temporal pattern, reflecting the diversity of local conditions affecting coastal sea level. The annual cycle amplitude is not constant over the analysed period exhibiting substantial inter-annual variability. For the longest records in the west side, the amplitude pattern is adequately described by a decreasing linear trend associated to a decrease in maximum levels during summer / early autumn and an increase in sea level values for winter / early spring. For the longest records in the east side, the amplitude of the sea level annual cycle exhibits an increasing trend driven by an increase of sea level values in autumn and a decrease in spring, associated to steric forcing. The application of statistical methodology to the estimation of non-stationary seasonal signals and associated uncertainty may play a significant role in the analysis of climate and oceanographic variables.

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