Oceanic Climate Change and Sea Level: Causality and Forecasting with Multivariate Singular Spectrum Analysis
- (Algerian Space Agency, Division of Space Geodesy, Centre of Space Techniques)
The global mean level of the oceans is one of the most important indicators of climate change. It incorporates the reactions from several different components of the climate system. Precise monitoring of changes in the mean level of the oceans, particularly through the use of altimetry satellites, is vitally important, for understanding not just the climate but also the socioeconomic consequences of any rise in sea level.
Long term sea level variations are primarily determined with two different methods. Over the last century, global sea level change has typically been estimated from tide gauge measurements by long-term averaging. Alternatively, satellite altimeter measurements can be combined with precisely known spacecraft orbits to provide an improved measurement of global sea level change.
In this project we aim to overcome many of these difficulties by implementing a different technique for capturing causality between sea level variability from altimetry data and climatic indices (sea surface temperature, concentration in CO2 in the atmosphere, precipitation,...) using multivariate singular spectrum analysis (SSA).