A nice real world case of a deep learning model outperforming physics based simulation, and then being validated by it. Seoul National University’s ENSO model (a convolutional neural network) settled on a very strong El Niño back in April 2026, months ahead of NOAA’s dynamical models. It learned ocean-atmosphere behaviour directly from decades of observations rather than simulating the physics, and crucially it holds forecast skill 18 to 24 months out, past the “spring predictability barrier” that collapses the traditional models beyond about a year. Since April, NOAA’s physics based plume has climbed month by month until it crossed the AI’s number, the two converging from opposite directions. The CNN has quietly trimmed its own estimate as real observations came in, so it’s updating on the data rather than anchoring to a dramatic figure. The CNN model now also projects the follow-on … a flip to La Niña in 2027, a lead time the physics models can’t reach at all. Write-up with the forecast tracking charts (AI vs physics, issuance by issuance): https://4billionyearson.org/posts/the-2026-el-nino-stripped-to-the-science-the-signal-the-warming-and-the-following-la-nina Live tracker comparing both forecasts weekly: https://4billionyearson.org/climate/enso submitted by /u/4billionyearson
Originally posted by u/4billionyearson on r/ArtificialInteligence