Multiyear statistical prediction of ENSO enhanced by the tropical Pacific observing system

Desislava Petrova*, Joan Ballester, Siem Jan Koopman, Xavier Rodó

*Corresponding author for this work

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Abstract

The theoretical predictability limit of El Niño-Southern Oscillation has been shown to be on the order of years, but long-lead predictions of El Niño (EN) and La Niña (LN) are still lacking. State-of-the-art forecasting schemes traditionally do not predict beyond the spring barrier. Recent efforts have been dedicated to the improvement of dynamical models, while statistical schemes still need to take full advantage of the availability of ocean subsurface variables, provided regularly for the last few decades as a result of the Tropical Ocean-Global Atmosphere Program (TOGA). Here we use a number of predictor variables, including temperature at different depths and regions of the equatorial ocean, in a flexible statistical dynamic components model to make skillful long-lead retrospective predictions (hindcasts) of the Niño-3.4 index in the period 1970-2016. The model hindcasts the major EN episodes up to 2.5 years in advance, including the recent extreme 2015/16 EN. The analysis demonstrates that events are predicted more accurately after the completion of the observational array in the tropical Pacific in 1994, as a result of the improved data quality and coverage achieved by TOGA. Therefore, there is potential to issue long-lead predictions of this climatic phenomenon at a low computational cost.

Original languageEnglish
Pages (from-to)163-174
Number of pages12
JournalJournal of Climate
Volume33
Issue number1
Early online date6 Dec 2019
DOIs
Publication statusPublished - 1 Jan 2020

Funding

Acknowledgments. D.P. acknowledges funding from the European Union’s Horizon 2020 research and innovation programme under grant agreements 727852 (project Blue-Action). J.B. gratefully acknowledges funding from the European Union’s Horizon 2020 research and innovation programme under grant agreements 727852 (project Blue-Action), 730004 (project PUCS), and 737480 (Marie Sklodowska-Curie fellowship ACCLIM). X.R. gratefully acknowledges funding from the Daniel Bravo Foundation (project WINDBIOME), as well as from the PERIS PICAT project (SLT002/ 16/466) and the NEW INDIGO project (PCIN-2013-038).

FundersFunder number
Marie Sklodowska-Curie
Horizon 2020 Framework Programme737480, 727852, 730004
Fundació Privada Daniel Bravo AndreuSLT002/ 16/466, PCIN-2013-038

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