Global Prediction algorithms and predictability of anomalous points in a time series

Abstract

The Indian Ocean Dipole (IOD) event of 1997 had significant impacts on regional climate variability as it produced large Sea Surface Temperature (SST) anomalies in the Indian Ocean (IO). This dipole mode accounted for about 12% of the sea surface temperature variability in the Indian Ocean—and, in its active years, also causes severe rainfall in eastern Africa and droughts in Indonesia. There have been speculations that the El-nino and Intra-Seasonal Oscillations (ISOs) act as stochastic forcing to reinvigorate the natural damped mode and hence contribute to the development of the IOD.  In the present paper we have statistically investigated whether the formation of the dipole was an anomalous phenomenon with respect to the time series generated by the deterministic laws of the system governing the IO SST or was this event a consequence of the dynamics of the system itself. For this, we have used the a global prediction algorithm i.e. linear regression. Prediction errors of global prediction algorithms such as the regression and Artificial Neural Network (ANN) models at various points in time contain important information regarding the statistical nature of the data. On the basis of the error analysis it is found that the occurrence of the IOD is a consequence of the state of the SST system as a whole together with the evolution laws. The role of ISO in providing external forcing to the dipole is investigated by analyzing the prediction errors of the global prediction algorithms for processes at intra-seasonal time scales. It is concluded that the Intra-Seasonal Oscillations may provide the stochastic forcing to the IOD.