Abstract
The El Nino-Southern Oscillation (ENSO) is an ocean – atmosphere phenomenon that involves sustained sea surface temperature fluctuations in the Pacific Ocean. This causes disruption in the behavior of the ocean and the atmosphere having important consequences on the global weather. This paper develops a stochastic model to describe El Nino-Southern Oscillation patterns. A Markov Switching Autoregressive model (MS-AR) was implemented to fit the Southern Oscillation Index (SOI), a variable that explains the phenomenon. The model consists of two autoregressive processes describing the time evolution of SOI, each of which associated with a specific phase of ENSO (El Nino and la Nina). The switching between these two models is governed by a discrete time Markov chain. We study the advantages of incorporating time-varying transition probabilities between them and show that the fitted model provides an adequate description of the time series, and demonstrate its utility in analysis and evaluation of the phenomenon
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