Keywords: El Niño-Southern Oscillation, Prediction Skill, Sea Level

Summary

A series of seasonally varying linear Markov models are constructed in a reduced multivariate empirical orthogonal function (MEOF) space of observed sea surface temperature (SST), surface wind stress and sea level analysis. The Markov models are trained in the 1980- 1995 period and are verified in the 1964-1979 period. It is found that the Markov models which include seasonality fit to the data better in the training period and have a substantially higher skill in the independent period than the models without seasonality. We conclude that seasonality is an important component of ENSO and should be included in Markov models. This conclusion is consistent with that of statistical models which take seasonality into account using different methods.

The impact of each variable on the prediction skill of Markov models is investigated by varying the weightings among the three variables in the MEOF space. For the training period the Markov models which include sea level information fit the data better than the models without sea level information. For the independent 1964-1979 period, the Markov models which include sea level information have a much higher skill than the Markov models without sea level information. We conclude that sea level contains the most essential information for ENSO since it contains the filtered response of the ocean to noisy wind forcing.

The prediction skill of the Markov model with 3 MEOFs is competitive for both the training and independent periods. This Markov model successfully predicted the 1997/98 El Nino and the 1998/99 La Nina.

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