Application of Singular Spectrum Analysis for Investigating Chaos in Sea Surface Temperature

The goal of this study is to explore the chaotic behavior of sea surface temperature (SST) in the Indian Ocean and in the equatorial Pacific Ocean. The SST time series is analyzed for Bay of Bengal, Arabian Sea and South Indian Ocean as well as for two extreme phenomena: El Nino and Indian Ocean Dipole (IOD). The analysis is based on Singular spectrum analysis, and singular value decomposition (SVD). Our analysis reveals that the dynamics of SST is chaotic in varying degrees in all the studied cases, since Lyapunov exponent, an indicator of chaoticity, is positive in each case. To study the degree of predictability of these SST series, we search for embedded periodic component(s) using two different approaches: Orthogonal functions extracted from the Singular spectrum analysis and Periodicity spectrum analysis based on SVD. Both the methods reveal presence of a strong periodic component(s) for the SST signals in the Arabian Sea, Bay of Bengal and South Indian Ocean, whereas no periodicity is found for El Nino and IOD. Therefore, it can be concluded that the dynamics of SST is more complex in the El Nino and IOD region compared to Bay of Bengal, Arabian Sea and South Indian Ocean; hence it is much more difficult to predict El Nino and IOD.