Sea level prediction based on the long time series satellite observations

In recent decades, global sea level is rising year by year and some low and close to the coastal regions will face to be swallowed up by the sea. It is necessary and important to monitor and even forecast the sea level change to study the evolution of land-ocean system and its impact on human activities. In this study, satellite altimeter retrieved monthly average sea level height products from 1993 to 2017 are used to extract the components of their trends, seasonal and residual. Its trends which are obtained by the linear regression method in South China Sea increases about 4.03 mm annually within 25 years. Using the Fourier spectrum analysis and Harmonic analysis, its period mainly has some cycles of 12, 30 and 150 months. The residual component is analyzed in temporal and spatial by Empirical Orthogonal Function and the changes of their temporal coefficient are forecasted by Auto-Regressive Moving Average model. The result shows that the contribution of residual components is mainly to display the details of sea level height, and they affect the accuracy of the sea level prediction. By some post-prediction experiments, the estimated sea level in South China Sea, which are the linear combination of their trends, seasonal and residual components forecasting, are consistent with satellite observations. In addition, there are some deviations in details on some regions which are decided by the prediction of residual components. In the future, those conclusions are helpful in assessing the hazards of sea level rise from long time-series by satellite altimeter.