Machine learning for the physics of climate
From Earthly Machine Learning by Amirpasha
May 3, 2026 · 20 min · Season 2 · Episode 8
About this episode
This episode discusses the advancements in machine learning applications for climate physics, particularly in forecasting El Niño and improving weather prediction models.
Machine learning for the physics of climate Citation: Bracco, A., Brajard, J., Dijkstra, H. A., Hassanzadeh, P., Lessig, C., & Monteleoni, C. (2025). Machine learning for the physics of climate. Nature Reviews Physics , 7, 6–20. https://doi.org/10.1038/s42254-024-00776-3 Main Takeaways: Breaking the El Niño Spring Barrier : For decades, forecasts of the El Niño Southern Oscillation hit a hard wall at roughly 6 months lead time — a limit known as the spring predictability barrier. Convolutional neural networks trained on a mix of climate model and reanalysis data have shattered this ceiling, delivering skillful forecasts at 17 months out, with newer architectures pushing to 21–24 months. ML models can also now anticipate which type of El Niño will develop (eastern vs. central Pacific), which matters enormously because the two flavors produce very different regional impacts around the world. Weather Forecasting at a Fraction of the Cost : A new generation of ML weather emulators — Pangu-Weather, GraphCast, FourCastNet, FuXi, NeuralGCM — now match or beat the European Centre's flagship physics-based forecasting system on most variables, including hurricane tracks, while running…
People in this episode
Host: Amirpasha
Topics covered
- machine learning
- climate physics
- weather forecasting
- El Niño
- predictability barrier
- ML models
Keywords
- machine learning
- climate
- El Niño
- weather forecasting
- predictability barrier
- neural networks
- Pangu-Weather
- GraphCast
- FourCastNet
Mentioned in this episode
Organizations: Nature Reviews Physics, European Centre
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