Eleventh International Conference on Climate Change: Impacts & Responses

Abstract

The estimation of sea level variability is of primary importance for a climate change assessment. In the present work, the direct scaling analysis approach (DSA) is proposed in order to model the long-term variability of the sea level fluctuations, detrended with respect to the secular growth. The DSA analysis was performed at 12 worldwide selected tide gauge stations. For each of these stations, it has been shown that the sea level fluctuations probability density function (pdf) exhibits a scaling behavior, which can be modeled by a power-law with different shape and scale parameters. This approach allowed to get insights about the scale invariance of these parameters and the local predictions of the sea level variability. The overall analysis, further to highlight scaling features, shows the possibility of adopting the same approach to predict the future sea level behavior. These predictions, obtained from the analysis of the historical (from min 1843 to max 2016) data regarding the local sea level change, can be used to improve climate change models in order to highlight the scenario that best fits the examined data. Moreover, the analysis provides a general framework to characterize, at a global scale, what is the uncertainty in the prediction of the sea level change.