Sea level variability at Adriatic coast and its relationship to atmospheric forcing

Sea level (SLH) variability at the Adriatic coast was investigated for the period 1872-2001 using monthly average values of observations at 13 tide gauge stations. Linear trends and seasonal cycles were investigated first and removed afterwards from the data. Empirical Orthogonal Functions (EOF) analysis was used further on remaining anomalies (SLA) to extract the regional intermonthly variability of SLH. It was found that the leading EOF and its principal component (PC) explain a major part of SLA variability (92%). The correlation between the reconstructed SLA, based on leading EOF and its PC, and overlapping observed SLA values for selected tide gauge stations is between 0.93 and 0.99. Actual SLH values at tide gauge stations can be reconstructed and some gaps in the data can be filled in on the basis of estimated SLA values if reasonable estimates of long-term trends and seasonal cycles are also available. A strong, seasonally dependent relationship between SLA at the Adriatic coast and atmospheric forcing, represented by sea level pressure (SLP) fields, was also found. Comparing the time series of leading PC and gridded SLP data for the period 1948-2001, the highest correlation coefficients (r) of -0.92 in winter, -0.84 in spring, -0.66 in summer, and -0.91 in autumn were estimated for a SLP grid point located in northern Italy. The SLP variability on this grid point contains information about the isostatic response of the sea level at the Adriatic coast, but can also be treated as a sort of teleconnection index representing the large-scale SLP variability across central and southern Europe. To some extent the large-scale SLP variability that affects the SLA at the Adriatic coast can be related to the North Atlantic Oscillation (NAO), because significant correlations were found between the NAO index and the first PC of SLA (r(winter)=-0.56, r(spring)=-0.45, r(summer)=-0.48, and r(autumn)=-0.43) for the period 1872-2001. The use of partial least-squares (PLS) regression between large-scale SLP fields and SLA only slightly improved the description of the SLA dependence on SLP forcing in comparison to the single grid point approach. A strong relationship between atmospheric pressure and the sea level could represent an additional possibility for filling in the gaps in the tide gauge data.