Understanding the Low Predictability of the 2015/16 El Ni?o Event Based on a Deep Learning Model

The 2015/16 El Ni?o event ranks among the top three of the last 100 years in terms of intensity, but most dynamical models had a relatively low prediction skill for this event before the summer months. Therefore, the attribution of this particular event can help us to understand the cause of super El Ni?o–Southern Oscillation events and how to forecast them skillfully. The present study applies attribute methods based on a deep learning model to study the key factors related to the formation of this event. A deep learning model is trained using historical simulations from 21 CMIP6 models to predict the Ni?o-3.4 index. The integrated gradient method is then used to identify the key signals in the North Pacific that determine the evolution of the Ni?o-3.4 index. These crucial signals are then masked in the initial conditions to verify their roles in the prediction. In addition to confirming the key signals inducing the super El Ni?o event revealed in previous attribution studies, we identify the combined contribution of the tropical North Atlantic and the South Pacific oceans to the evolution and intensity of this event, emphasizing the crucial role of the interactions among them and the North Pacific. This approach is also applied to other El Ni?o events, revealing several new precursor signals. This study suggests that the deep learning method is useful in attributing the key factors inducing extreme tropical climate events.