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LOCATION:Room 5.2D11
DTSTART;TZID=Europe/Stockholm:20250617T153000
DTEND;TZID=Europe/Stockholm:20250617T160000
UID:submissions.pasc-conference.org_PASC25_sess141_msa259@linklings.com
SUMMARY:Harnessing Machine Learning for Long-Term ENSO Predictability: Ins
 ights from Ocean-Atmosphere Interactions and Implications for Sustainable 
 Climate Prediction
DESCRIPTION:Ioana Colfescu (National Center of Atmospheric Research, Unive
 rsity of St Andrews)\n\nAs the demand for high-performance computing (HPC)
  in weather and climate grows, integrating machine learning (ML) technique
 s offers a promising pathway to enhance predictive skill while addressing 
 sustainability. This talk presents an ML-driven approach to identify sourc
 es of long-term predictability for the El Niño-Southern Oscillation (ENSO)
 , focusing on the interplay between ocean (Sea Surface Temperature (SST) a
 nd heat content) and atmosphere (near-surface zonal wind, U10) variables. 
 Our findings reveal that tropical SST serves as the primary source of pred
 ictability, 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 interact
 ions in the Indian Ocean (IO), which propagates across the Pacific via an 
 atmospheric bridge mechanism. By leveraging ML to optimize predictive mode
 ls, we explore how such approaches can reduce computational costs and ener
 gy consumption in HPC systems, contributing to more sustainable climate pr
 ediction frameworks. This work aligns with the broader goal of integrating
  ML into climate modeling to enhance efficiency and scalability while mini
 mizing environmental impact.\n\nDomain: Climate, Weather, and Earth Scienc
 es, Computational Methods and Applied Mathematics\n\nSession Chair: Nick B
 rown (EPCC)\n\n
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