Comparison of some statistical methods of probabilistic forecasting of ENSO

Mason, Simon J.; Mimmack, Gillian M.

Numerous models have been developed in recent years to provide predictions of the state of the El Niño–Southern Oscillation (ENSO) phenomenon. Predictions of the ENSO phenomenon are usually presented in deterministic form, but because of the inherent uncertainty involved probabilistic forecasts should be provided. In this paper, various statistical methods are used to calculate probabilities for monthly Niño-3.4 anomalies within predefined ranges, or categories. The statistical methods used are predictive discriminant analysis, canonical variate analysis, and various forms of generalized linear models. In addition, probabilistic forecasts are derived from a multiple regression model by using contingency tables and from the model's prediction intervals. By using identical sets of predictors and predictands, the methods are compared in terms of their performance over an independent retroactive forecast period, which includes the 1980s and 1990s. The models outperform persistence and damped persistence as reference forecast strategies at some times of the year. The models have greatest skill in predicting El Niño, although La Niña is predicted with greater skill at longer lead times and with greater reliability. The forecasts for the ENSO extremes are reasonably well calibrated, and so the forecast probabilities are reliable estimates of forecast uncertainty.


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International Research Institute for Climate and Society
Published Here
March 23, 2020