Explained predictions of strong eastern Pacific El Niño events using deep learning

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dc.contributor.authorRivera Tello, Gerardo A.
dc.contributor.authorTakahashi, Ken
dc.contributor.authorKaramperidou, Christina
dc.date.accessioned2023-12-12T19:50:07Z
dc.date.available2023-12-12T19:50:07Z
dc.date.issued2023-11-30
dc.description.abstractGlobal and regional impacts of El Niño-Southern Oscillation (ENSO) are sensitive to the details of the pattern of anomalous ocean warming and cooling, such as the contrasts between the eastern and central Pacific. However, skillful prediction of such ENSO diversity remains a challenge even a few months in advance. Here, we present an experimental forecast with a deep learning model (IGP-UHM AI model v1.0) for the E (eastern Pacific) and C (central Pacific) ENSO diversity indices, specialized on the onset of strong eastern Pacific El Niño events by including a classification output. We find that higher ENSO nonlinearity is associated with better skill, with potential implications for ENSO predictability in a warming climate. When initialized in May 2023, our model predicts the persistence of El Niño conditions in the eastern Pacific into 2024, but with decreasing strength, similar to 2015–2016 but much weaker than 1997–1998. In contrast to the more typical El Niño development in 1997 and 2015, in addition to the ongoing eastern Pacific warming, an eXplainable Artificial Intelligence analysis for 2023 identifies weak warm surface, increased sea level and westerly wind anomalies in the western Pacific as precursors, countered by warm surface and southerly wind anomalies in the northern Atlantic.es_ES
dc.description.peer-reviewPor pareses_ES
dc.formatapplication/pdfes_ES
dc.identifier.citationRivera Tello, G. A., Takahashi, K., & Karamperidou, C. (2023). Explained predictions of strong eastern Pacific El Niño events using deep learning.==$Scientific Reports, 13,$==(1), 21150. https://doi.org/10.1038/s41598-023-45739-3es_ES
dc.identifier.doihttps://doi.org/10.1038/s41598-023-45739-3es_ES
dc.identifier.govdocindex-oti2018
dc.identifier.journalScientific Reportses_ES
dc.identifier.urihttp://hdl.handle.net/20.500.12816/5497
dc.language.isoenges_ES
dc.publisherNature Researches_ES
dc.relation.ispartofurn:issn:2045-2322
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/es_ES
dc.subjectClimate scienceses_ES
dc.subjectOcean scienceses_ES
dc.subjectEl Niñoes_ES
dc.subject.ocdehttps://purl.org/pe-repo/ocde/ford#1.05.09es_ES
dc.titleExplained predictions of strong eastern Pacific El Niño events using deep learninges_ES
dc.typeinfo:eu-repo/semantics/articlees_ES

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