AbstractENSO is investigated here by considering it as a transition from different states. Transition probability matrices can be defined to describe the evolution of ENSO in this way. Sea Surface Temperature anomalies are classified into four categories, or states, and the probability to move from one state to another has been calculated both for observations and a simulation from a GCM. This could be useful for understanding and diagnosing General Circulation Models elucidating the mechanisms that govern ENSO in models. Furthermore, these matrices have been used to define a predictability index of ENSO based on the entropy concept introduced by Shannon (1948). The index correctly identifies the emergence of the Spring Predictability Barrier and the seasonal variations of the transition probabilities. The Transition Probability Matrices could also be used to formulate a basic prediction model for ENSO that was tested here on a case study.
Journal of Climate – American Meteorological Society
Published: Mar 28, 2017
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