ENSO prediction with Markov models: The impact of sea level

A series of seasonally varying linear Markov models are constructed in a re duced multivariate empirical orthogonal function (MEOF) space of observed s ea surface temperature, surface wind stress, and sea level analysis. The Ma rkov models are trained in the 1980-95 period and are verified in the 1964- 79 period. It is found that the Markov models that include seasonality fit to the data better in the training period and have a substantially higher s kill in the independent period than the models without seasonality. The aut hors conclude that seasonality is an important component of ENSO and should be included in Markov models. This conclusion is consistent with that of s tatistical models that take seasonality into account using different method s. The impact of each variable on the prediction skill of Markov models is inv estigated by varying the weightings among the three variables in the MEOF s pace. For the training period the Markov models that include sea level info rmation fit the data better than the models without sea level information. For the independent 1964-79 period, the Markov models that include sea leve l information have a much higher skill than the Markov models without sea l evel information. The authors conclude that sea level contains the most ess ential information for ENSO since it contains the filtered response of the ocean to noisy wind forcing. The prediction skill of the Markov model with three MEOFs is competitive fo r both the training and independent periods. This Markov model successfully predicted the 1997/98 El Nino and the 1998/99 La Nina.