Geophysical Research Letters · 2024 · 11 citations · 55 references
Natural SciencesEarth SciencesMeteorologyClimate ScienceClimatologyTime Series AnalysisClimate ExtremesClimate VariabilityEl Niño-southern OscillationExtreme Climate EventsSea Surface TemperatureSea Surface Temperature VariabilityClusteringEl Niño OscillationEl Niño Sea Surface Temperature VariabilityEl Niño-southern Oscillation VariabilityEnso Decadal VariabilityEnso DiversityLa Niña Sea Surface Temperature VariabilitySea Surface Temperature Anomalies
Abstract El Niño Southern Oscillation (ENSO) diversity is characterized based on the longitudinal location of maximum sea surface temperature anomalies (SSTA) and amplitude in the tropical Pacific, as Central Pacific events are typically weaker than Eastern Pacific events. SSTA pattern and intensity undergo low‐frequency modulations, affecting ENSO prediction skill and remote impacts, and resulting in low‐frequency changes in ENSO variance. Yet, how different ENSO types contribute to these decadal variance changes remains unclear. Here, we decompose the low‐frequency changes of ENSO variance into contributions from ENSO diversity categories. We propose a fuzzy clustering of monthly SSTA to allow for non‐binary event category memberships, where each event can belong to different clusters. Our approach identifies two La Niña and three El Niño categories and shows that the major shift of ENSO variance in the mid‐1970s was associated with an increasing likelihood of strong La Niña and extreme El Niño events.
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