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The role of South Pacific atmospheric variability in the development of different types of ENSO
Author(s) -
You Yujia,
Furtado Jason C.
Publication year - 2017
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1002/2017gl073475
Subject(s) - climatology , predictability , el niño southern oscillation , pacific decadal oscillation , anomaly (physics) , subtropics , multivariate enso index , environmental science , la niña , oceanography , geology , physics , condensed matter physics , quantum mechanics , fishery , biology
Recent advances in tropical Pacific climate variability have focused on understanding the development of El Niño–Southern Oscillation (ENSO) events, specifically the types or “flavors” of ENSO (i.e., central versus eastern Pacific events). While precursors to ENSO events exist, distinguishing the particular flavor of the expected ENSO event remains unresolved. This study offers a new look at ENSO predictability using South Pacific atmospheric variability during austral winter as an indicator. The positive phase of the leading mode of South Pacific sea level pressure variability, which we term the South Pacific Oscillation (SPO), exhibits a meridional dipole with with a(n) (anti)cyclonic anomaly dominating the subtropics (extratropics/high latitudes). Once energized, the cyclonic anomalies in the subtropical node of the SPO weaken the southeasterly trade winds and promote the charging of the eastern equatorial Pacific Ocean, giving rise to eastern Pacific ENSO events. Indeed, the type of ENSO event can be determined accurately using only the magnitude and phase of the SPO during austral winter as a predictor (17 out of 23 cases). The SPO may also play a role in explaining the asymmetry of warm and cold events. Collectively, our findings present a new perspective on ENSO‐South Pacific interactions that can advance overall understanding of the ENSO system and enhance its predictability across multiple timescales.