Quantifying the Predictability of ENSO Complexity Using a Statistically Accurate Multiscale Stochastic Model and Information Theory

An information theory based framework is developed to assess the predictability of the ENSO complexity, which includes different types of the ENSO events with diverse characteristics in spatial patterns, peak intensities and temporal evolutions. The information theory advances a unique way to quantify the forecast uncertainty and allows to distinguish the predictability limit of each type of event. With the assistance of a recently developed multiscale stochastic conceptual model that succeeds in capturing both the large-scale dynamics and many crucial statistical properties of the observed ENSO complexity, it is shown that different ENSO events possess very distinct predictability limits. Beyond the ensemble mean value, the spread of the ensemble members also has remarkable contributions to the predictability. Specifically, while the result indicates that predicting the onset of the eastern Pacific (EP) El Ninos is challenging, it reveals a universal tendency to convert strong predictability to skillful forecast for predicting many central Pacific (CP) El Ninos about two years in advance. In addition, strong predictability is found for the La Nina events, corresponding to the effectiveness of the El Nino to La Nina transitions. In the climate change scenario with the strengthening of the background Walker circulation, the predictability of sea surface temperature in the CP region has a significant response with a notable increase in summer and fall. Finally, the Gaussian approximation exhibits to be accurate in computing the information gain, which facilitates the use of more sophisticated models to study the ENSO predictability.