The independent sample predictions, 1981-98 back forecasting, and the 1997-98 ENSO prediction all demonstrate high applicability of our model and scheme that are quite steady during operation. Take NINOs 3.4 and 4 predictions for example. Their 1-4 season leading predictions gave the correlations of greater than 0.85, 0.70, 0.50 and 0.35, respectively.
Our model compares advantageously to the CCA statistical model developed in the US CPC (Climate Prediction Center, Barnston et al., 1992) as regards predictive skill, and part of our predictions has exceeded those of the CPC model. In reference to 2-season leading predictions, for instance, the CCA model gives maximum scoring ranging over 0.85-0.89 just for wintertime months compared to the maximum of 0.85-0.87 for all seasons from our model, with the minimum of 0.30-0.35 (from the CCA) versus 0.37 (from our model). Particularly, it should be pointed out that the CCA model requires a large volume of gridded data, consisting of global sea level pressure (SLP), tropical Pacific SST and sea level height (SLH), and the 20 C isothermal depth in contrast to 20 predictors for our model, leading to the much higher efficiency compared to the CCA model.
The paper presents a canonical mixed regression model which is actually the generalized simple type with scattered coefficients. The predictive scheme includes EEOF technique for the information on previous SST evolution (suggestive of the CCA establishment of a certain similarity relation between the previous SST evolution and the SST index at the prognostic time interval), PRESS sorting out model parameters and factors for independent sample experiments with 1-4 season leading predictions (suggesting a consensus prediction technique). All these have led to successful prediction of, say, the 1997/98 ENSO episode for longer than 6 months in advance and quite good prognosis of the termination of 1998 warm phase by the end of June based on measured data prior to January 1998. Preliminary prediction has been made of a strong La Nina event to occur in October 1998 whose precursors were beginning to emerge in the context of data before May 1998.
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