Minisymposium Presentation

As the demand for high-performance computing (HPC) in weather and climate grows, integrating machine learning (ML) techniques offers a promising pathway to enhance predictive skill while addressing sustainability. This talk presents an ML-driven approach to identify sources of long-term predictability for the El Niño-Southern Oscillation (ENSO), focusing on the interplay between ocean (Sea Surface Temperature (SST) and heat content) and atmosphere (near-surface zonal wind, U10) variables. Our findings reveal that tropical SST serves as the primary source of predictability, while U10 alone exhibits comparable predictive skill to SST at lead times of 11 to 21 months, particularly from late fall to late spring. We uncover a long-lead signal originating from coupled wind-SST interactions in the Indian Ocean (IO), which propagates across the Pacific via an atmospheric bridge mechanism. By leveraging ML to optimize predictive models, we explore how such approaches can reduce computational costs and energy consumption in HPC systems, contributing to more sustainable climate prediction frameworks. This work aligns with the broader goal of integrating ML into climate modeling to enhance efficiency and scalability while minimizing environmental impact.