El Nino and La Nina events have predictable patterns governed by ocean-atmosphere coupling.
El Niño and La Niña events (ENSO) exhibit predictable patterns and evolutions that are fundamentally governed and modulated by complex ocean-atmosphere coupling and feedbacks.
The retrieved papers consistently demonstrate that ENSO events have predictable patterns that are governed by ocean-atmosphere coupling, air-sea feedbacks, and tropical basin interactions. None of the papers refute this foundational concept in climate science, though some discuss challenges like the spring predictability barrier or the influence of specific modes, which are ultimately managed through improved coupled modeling.
Stuecker MF, Zhao S, Timmermann A, Ghosh R, Semmler T, Lee SS, Moon JY, Jin FF, Jung T. Global climate mode resonance due to rapidly intensifying El Niño-Southern Oscillation.. 2025. https://doi.org/10.1038/s41467-025-64619-0
Paper 0 demonstrates that ENSO represents a key predictable climate signal whose regularity and predictability are governed by air-sea feedbacks.
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Lin Z, Jin Y, He C, Zhang S, McPhaden MJ, Lin X. Deep learning reveals enhanced ENSO predictability under historical anthropogenic forcing.. 2026. https://doi.org/10.1126/sciadv.aec9518
Paper 4 shows that ENSO predictability is tied to key ocean-atmosphere feedbacks such as advective and thermocline processes.
Mei Z, Lin S, Fang K, Tang W, Zhou F, Liu F, Zhao S, Wu H, Li J, Zhao Z, Ou T, Xie X, Chen D. Identifying key convection-sensitive oceanic regions to weaken the ENSO spring predictability barrier.. 2026. https://doi.org/10.1073/pnas.2512725123
Paper 5 highlights that ENSO evolution depends on air-sea coupling and convection-sensitive oceanic regions, which provide predictable patterns.
Zhou L, Zhang RH. Tropical basin interactions reduce spring predictability barrier of ENSO in a deep learning model.. 2026. https://doi.org/10.1126/sciadv.aeb0901
Paper 6 illustrates that tropical basin interactions involving ocean-atmosphere dynamics enable skillful ENSO predictions.
Kim JH, Kang D, Yang YM, Park JH, Ham YG. Data-driven global ocean model resolving atmospherically forced ocean dynamics.. 2026. https://doi.org/10.1126/sciadv.aed1225
Paper 8 confirms that global ocean-atmosphere models successfully capture the key ocean dynamics underlying ENSO.
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