Halide, Halmar, and Ridd, Peter (2008) Complicated ENSO models do not significantly outperform very simple ENSO models. International Journal of Climatology, 28 (2). pp. 219-233.

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Abstract

An extremely simple univariate statistical model called IndOzy was developed to predict El Niño-Southern Oscillation (ENSO) events. The model uses five delayed-time inputs of the Niño 3.4 sea surface temperature anomaly (SSTA) index to predict up to 12 months in advance. The prediction skill of the model was assessed using both short- and long-term indices and compared with other operational dynamical and statistical models. Using ENSO-CLIPER(climatology and persistence) as benchmark, only a few statistical models including IndOzy are considered skillful for short-range prediction. All models, however, do not differ significantly from the benchmark model at seasonal Lead-3-6. None of the models show any skill, even against a no-skill random forecast, for seasonal Lead-7. When using the Niño 3.4 SSTA index from 1856 to 2005, the ultra simple IndOzy shows a useful prediction up to 4 months lead, and is slightly less skillful than the best dynamical model LDEO5. That such a simple model such as IndOzy, which can be run in a few seconds on a standard office computer, can perform comparably with respect to the far more complicated models raises some philosophical questions about modelling extremely complicated systems such as ENSO. It seems evident that much of the complexity of many models does little to improve the accuracy of prediction. If larger and more complex models do not perform significantly better than an almost trivially simple model, then perhaps future models that use even larger data sets, and much greater computer power may not lead to significant improvements in both dynamical and statistical models. Investigating why simple models perform so well may help to point the way to improved models. For example, analysing dynamical models by successively stripping away their complexity can focus in on the most important parameters for a good prediction.

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