An Advanced Framework for Disentangling Deterministic Dynamics and Stochastic Processes in ENSO Predictability (Invited Presentation)

The El Niño-Southern Oscillation (ENSO) is the dominant mode of climate variability in the tropical Pacific and plays an essential role in modulating the global climate through its numerous teleconnections. As a coupled phenomenon, ENSO is inherently multivariate, and coupled feedbacks are central to generating ENSO events. Debate remains, however, over which initiation mechanisms dominate, and consequently, there is a lack of consensus on ENSO predictability estimates. A key challenge in predicting ENSO lies in its sensitivity to stochastic processes. While coupled feedbacks are critically important in the tropical Pacific, the role of uncoupled atmospheric noise in shaping ENSO growth remains inadequately understood. Previous work demonstrates that suppressing ENSO variability, thereby isolating coupled instabilities, offers a baseline for understanding the tropical system's natural variability in the absence of ENSO. The interactive ensemble (IE) coupling approach, designed to reduce noise from internal atmospheric dynamics at the air-sea interface, allows for quantification of the impact of uncoupled atmospheric noise on variability and predictability. This research seeks to disentangle the contributions of unstable coupled feedbacks and uncoupled atmospheric noise to ENSO variability and identify sources of the loss of predictability in fully coupled models. By integrating the IE approach within a predictability experiment framework, we aim to diagnose the mechanisms driving ENSO growth with minimized interference from uncoupled atmospheric noise. This study addresses a critical gap in understanding the dual influences of deterministic dynamics and stochastic processes on ENSO, with the potential to enhance our ability to forecast ENSO events.