Understanding Atmospheric Oscillations and Climate Change Using a Hierarchy of Models

El Niño–Southern Oscillation (ENSO) variability arises from nonlinear interactions between the tropical ocean and atmosphere, combining deterministic recharge–discharge dynamics with episodic stochastic forcing. While conceptual models such as the Cane–Zebiak recharge oscillator capture the core physics of ENSO, forecast skill remains highly intermittent, with pronounced failures during periods of strong atmospheric variability. This study investigates the dynamical origins and predictability limits of ENSO by integrating a hierarchy of models, ranging from idealised oscillators to observationally forced and hybrid forecast frameworks.

Using ERA-20C equatorial zonal wind anomalies and Niño-3.4 sea surface temperature data, we identify and characterise Westerly Wind Bursts (WWBs) as state-dependent atmospheric perturbations that preferentially occur during warm ENSO phases. When imposed on the Cane–Zebiak oscillator, WWBs act as phase-dependent energy injections, modulating growth and decay without fundamentally altering the underlying oscillatory structure. However, these same perturbations substantially reduce the memory of the system, shortening the energy autocorrelation timescale from approximately 18–24 months to about 6 months during WWB-active periods.

A supervised forecast framework at an 8-month lead time reveals strong regime dependence in predictability. While including atmospheric forcing improves mean forecast skill, performance collapses during WWB months, with large, biased, and phase-dependent errors. Linear and machine-learning residual correction models fail under cross-validation, indicating that WWB-induced errors are not deterministically predictable on an event-by-event basis. Instead, forecast error variance exhibits robust phase dependence, enabling the identification of distinct uncertainty regimes.

Building on this structure, we introduce a hybrid, regime-aware forecasting strategy that applies deterministic prediction only during low-uncertainty regimes and adopts conservative alternatives during high-uncertainty WWB conditions. This approach reduces catastrophic forecast errors and improves reliability without overfitting. Overall, the results demonstrate that ENSO is a conditionally predictable system, where atmospheric forcing not only modulates amplitude but imposes intrinsic limits on deterministic forecast skill. These findings argue for forecast systems that explicitly represent uncertainty and regime transitions rather than relying solely on universal deterministic correction.