Conditional Controls on Kelvin-Wave-Driven Initiation of Historical El Nino Events

Overview

El Nino events are Pacific-originating oceanic-atmospheric phenomena that usher in shifts in global circulation and weather–shifts that in the past have spiked global grain prices 40%, sparked wildfires of 10 million hectares in Indonesia, and triggered deadly epidemics in Southeast Asia. Yet for most months of the year, prediction of El Nino variability is well below 50%, worse than random chance. One prominent reason for this lies in Kelvin waves, precursors of El Nino that produce vastly differing El Nino events even when they, themselves, are similar in magnitude. Such variability can be attributed to the background state, and as such, this project investigates the role of background oceanic and atmospheric conditions in modulating the effect of equatorial downwelling Kelvin waves on El Nino development, with the aim of informing better predictive models. The initial hypothesis states that if atmospheric-oceanic conditions do not exceed quantifiable thresholds, then a Kelvin wave, regardless of strength, will fail to initiate an El Nino event. The first ever comprehensive catalogue of equatorial Kelvin waves was constructed using 2004-2024 satellite and buoy data, which was refined with climatology and temporal filters to isolate possible Kelvin wave signals. Each of the 79 Kelvin waves identified was then categorized by success in triggering a subsequent El Nino, and key background variables–upper-ocean heat content, zonal wind anomalies, westerly wind bursts, and thermocline depth–were quantified for the time frame between Kelvin wave onset and El Nino onset. Such variables were then grouped with each other into regime based categories (ie low-low, low-high) to quantify conditional effects, which were further affirmed with logistic regression. Results affirm the hypothesis and reveal that Kelvin-wave-driven El Nino initiation is fundamentally controlled by thresholds. Crucially, levels of wind anomalies and thermocline slope act as primary gatekeepers that determine the role of secondary variables, sometimes diminishing their effects almost entirely. Interestingly, wind anomalies and thermocline slope in general were also the best predicting variables, with just the pairing of Kelvin-wave-coupled wind anomalies and thermocline slope above their respective median values correlating to a resultant El Nino 46.67% of the time. The nonlinearity researched here explains numerous past failed predictions and enables a better quantification of the nuanced role of the background state in Kelvin wave development, paving the way for more reliable El Nino prediction.

Competition history

• CSEF 2026 Earth & Environmental Sciences(Senior Division) · Entry S-08-06

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