NeuralGCM: Google's hybrid physics-ML atmosphere model matches top weather forecasts and runs decades-long climate simulations
In Nature (Kochkov et al., 22 July 2024) Google introduced NeuralGCM. It pairs a differentiable spectral dynamical core with neural-network physics parameterisations trained end-to-end. It was competitive with ECMWF for 1–15-day forecasts, reproduced four decades of observed temperatures in AMIP-style runs, and needed 3–5 orders of magnitude less compute than conventional models.
Key facts
- Paper: 'Neural general circulation models for weather and climate', Nature 632, 1060–1066 (2024); arXiv 2311.07222
- Hybrid: physics-based dynamical core + learned column physics, trained end-to-end through the solver
- Runs at 8–40× coarser horizontal resolution than ECMWF IFS and global cloud-resolving models, giving 3–5 orders of magnitude compute savings
- Stable multi-decade climate simulations, unlike pure-ML weather emulators at the time
Science result
- Field
- climate-weather / atmospheric modelling
- Problem
- Fast, accurate general circulation models for both weather and climate
- Result
- Hybrid differentiable GCM competitive with ECMWF on medium-range forecasts and able to run decades-long climate simulations at a fraction of the cost.
- AI system
- NeuralGCM
- Human role
- Human-led research
- Verification
- Peer-reviewed in Nature
- Status
- confirmed
What happened
Unlike GraphCast-style end-to-end emulators, NeuralGCM kept a numerical dynamical core and learned only the unresolved physics. That made it stable enough for climate-length runs.
Why it matters
It showed that ML can reach climate modelling, not only weather forecasting, and made differentiable hybrid GCMs a serious research direction.
Changelog
- 2026-09-29: created
Related events
- GraphCast: ML weather model beats the world's best physics-based 10-day forecast on 90% of targets ★★★★
- GenCast: diffusion-based ensemble forecast beats ECMWF's ENS on 97% of targets ★★★
Sources (3)
- paperNature: Neural general circulation models for weather and climate
- paperarXiv 2311.07222
- officialGoogle Research: NeuralGCM harnesses AI to better simulate long-range global precipitation
id: 2024-07-22-neuralgcm · updated 2026-09-29 · open in the interactive timeline