Simulation, precursor analysis and targeted observation sensitive area identification for two types of ENSO using ENSO-MC v1.0

Abstract

Abstract. The global impact of an El Niño–Southern Oscillation (ENSO) event can differ greatly depending on whether it is an eastern Pacific (EP)-type event or a central Pacific (CP)-type event. Reliable predictions of the two types of ENSO are therefore of critical importance. Here we construct a deep neural network with multichannel structure for ENSO (named ENSO-MC) to simulate the spatial evolution of sea surface temperature (SST) anomalies for the two types of events. We select SST, heat content and wind stress (i.e., three key ingredients of Bjerknes feedback) to represent coupled ocean–atmosphere dynamics that underpin ENSO, achieving skilful forecasts for the spatial patterns of SST anomalies out to 1 year ahead. Furthermore, it is of great significance to analyse the precursors of EP-type or CP-type events and identify targeted observation sensitive areas for the understanding and prediction of ENSO. Precursors analysis is to determine what type of initial perturbations will develop into EP-type or CP-type events. Sensitive area identification is to determine the regions where initial states tend to have the greatest impacts on the evolution of ENSO. We use the saliency map method to investigate the subsurface precursors and identify the sensitive areas of ENSO. The results show that there are pronounced signals in the equatorial subsurface before EP events, while the precursory signals of CP events are located in the northern Pacific. It indicates that the subtropical precursors seem to favour the generation of the CP-type El Niño and that the EP-type El Niño is more related to the tropical thermocline dynamics. Furthermore, the saliency maps show that the sensitive areas of the surface and the subsurface are located in the equatorial central Pacific and the equatorial western Pacific respectively. The sensitivity experiments imply that additional observations in the identified sensitive areas can improve forecasting skills. Our results of precursors and sensitive areas are consistent with the previous theories of ENSO, demonstrating the potential usage and advantages of the ENSO-MC model in improving the simulation, understanding and observations of the two ENSO types.