A seasonal threshold on El Niño amplitude set by the Pacific zonal temperature gradient

Abstract. El Niño is the largest year-to-year fluctuation of Earth’s climate, and its strongest events disrupt rainfall across much of the planet. Each event weakens the Pacific trade winds, which are maintained by the west–east sea-surface temperature (SST) contrast that El Niño itself erodes. Whether this relationship imposes an upper limit on event amplitude remains unknown. Across three SST reconstructions for 1950–2025, we find that the zonal SST contrast decreases by about 1°C per degree of Niño3.4 warming within each calendar month. Extrapolating this relationship to zero contrast gives a seasonal threshold ranging from +1.8°C in April to +4.3°C in November; no month since 1950 has exceeded its corresponding threshold. Relative to the tropical mean SST, the threshold has changed little since 1870, as strengthening of the climatological gradient and tropical warming nearly offset each other. In historical near-zero-gradient months, the ocean advective terms associated with the Bjerknes feedback reversed, although these cases occurred only during the decay season. Across 57 coupled climate models, a near-zero gradient during September–December is associated with a lower probability of further Niño3.4 growth: 29%, compared with 62% when 1–2°C of gradient remains. In 2026, the gradient reached a record September low while El Niño was still developing. We define prospective observational tests to assess whether the event continues to grow as the gradient approaches zero, providing a direct test of the proposed constraint.

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Publication Details

Published
2026-10-27
DOI
https://doi.org/10.5194/egusphere-2026-6517
Primary Topic
Climate variability and models
Type
preprint

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preprint

A seasonal threshold on El Niño amplitude set by the Pacific zonal temperature gradient

Massimo Alberto Bollasina, Nick Dunstone, Ioana Colfescu, Edwin Schneider et al.
Climate variability and models
preprint

A seasonal threshold on El Niño amplitude set by the Pacific zonal temperature gradient

Massimo Alberto Bollasina, Nick Dunstone, Ioana Colfescu, Edwin Schneider, Matt Patterson, Mat Collins
preprint en

Abstract

Abstract. El Niño is the largest year-to-year fluctuation of Earth’s climate, and its strongest events disrupt rainfall across much of the planet. Each event weakens the Pacific trade winds, which are maintained by the west–east sea-surface temperature (SST) contrast that El Niño itself erodes. Whether this relationship imposes an upper limit on event amplitude remains unknown. Across three SST reconstructions for 1950–2025, we find that the zonal SST contrast decreases by about 1°C per degree of Niño3.4 warming within each calendar month. Extrapolating this relationship to zero contrast gives a seasonal threshold ranging from +1.8°C in April to +4.3°C in November; no month since 1950 has exceeded its corresponding threshold. Relative to the tropical mean SST, the threshold has changed little since 1870, as strengthening of the climatological gradient and tropical warming nearly offset each other. In historical near-zero-gradient months, the ocean advective terms associated with the Bjerknes feedback reversed, although these cases occurred only during the decay season. Across 57 coupled climate models, a near-zero gradient during September–December is associated with a lower probability of further Niño3.4 growth: 29%, compared with 62% when 1–2°C of gradient remains. In 2026, the gradient reached a record September low while El Niño was still developing. We define prospective observational tests to assess whether the event continues to grow as the gradient approaches zero, providing a direct test of the proposed constraint.

Met Office (GB), George Mason University (US), University of St Andrews (GB), University of Exeter (GB), National Centre for Atmospheric Science (GB), University of Edinburgh (GB)
University of St Andrews, Met Office, National Oceanic and Atmospheric Administration, Natural Environment Research Council
Climate action
Climate variability and models
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