Understanding and Predicting El Niño via Conceptual Models Nomenclated by Mark Cane

The El Niño–Southern Oscillation (ENSO) provides most of the global seasonal climate forecast skill, yet, quantifying the sources of skillful predictions is a long-standing challenge. Different sources of predictability affect ENSO evolution, leading to distinct global effects. Artificial intelligence (AI) forecasts offer promising advancements but linking their skill to specific physical processes is not yet possible, limiting our understanding of the dynamics underpinning the advancements. We developed a simple approach to quantifying the main sources important for predicting El Niño by utilizing a 2-degree freedom conceptual model known as Recharge Oscillator (RO) which was a term codified by Mark Cane in his review suggestion nearly 30 years ago and RO's recent extension to a low-order model named again by Mark Cane as XRO. This XRO naturally integrates two key theories for climate variability: the recharge oscillator theory for ENSO and the Hasselmann's theory for climate variability outside the equatorial Pacific Ocean by parsimoniously incorporating into ENSO’s RO dynamics with seasonally modulated interactions with about 8 other modes of variability in the global oceans. This XRO exhibits skillful forecasts at lead-times up to 16-18 months, outperforming global climate models and rivalling the best AI forecasts. The contributions of these climate patterns to the magnitude of ENSO events and ENSO predictive skills are quantifiable. This XRO-type framework’s holistic treatment of ENSO’s global multi-timescale interactions highlights promising targets for improving ENSO simulations and seasonal forecasts.