We review prediction efforts of El Niño events in the tropical Pacific with particular focus on using modern machine learning (ML) methods based on artificial neural networks. With current classical prediction methods using both statistical and dynamical models, the skill decreases substantially for lead times larger than about 6 months. Initial ML results have shown enhanced skill for lead times larger than 12 months. The search for optimal attributes in these methods is described, in particular those derived from complex network approaches, and a critical outlook on further developments is given.

The paper originated from a visit of HD to IFISC in January 2019 and was funded by the University of the Balearic Islands. EH-G and CL were supported by the Spanish Research Agency, through grant MDM-2017-0711 from the Maria de Maeztu Program for Units of Excellence in R&D. HD also acknowledges support from the Netherlands Earth System Science Centre (NESSC), financially supported by the Ministry of Education, Culture and Science (OCW), grant no. 024.002.001.

Peer reviewed