The contribution of atmospheric pressure and wind to sea level variability at Goa (West coast of India) for the period 2007-2008 is investigated. Sea level data from a tide gauge are compared with measured local surface meteorological as well as oceanographic data. Multilinear regression analysis is used to resolve the dependence of sea level on various forcing parameters. The multilinear regression analysis performed over ~two years data shows that the local surface meteorological data and

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... r temperature accounts the sea level variability only up to 6%. The accounted sea level variability increases to 25%, when the local wind and the surface currents obtained from satellite altimetry in the near vicinity of the study area are incorporated in the regression analysis. The contribution of local wind increases substantially, when the regression is performed over two month duration and it is variable within the year. During the summer monsoon season (May-September), the sea level variability attributable to wind is up to 47% (75%) respectively for 2007 (2008) ; however it reduces to less than 20% during the winter monsoon (November-February) season. A significant part of the variability observed in sea level remains unaccounted for and is attributed to remote forcing.