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FASST: A Compact Stateless Atmosphere Model for Long-Term Ocean Coupling

Pratham Aggarwal, Duncan Watson-Parris, William E. Chapman

Latestcs.CLcs.LGcs.AIcs.CV
arXiv ID
2610.36297 v1
Submitted
2026-10-26

Abstract

Coupled climate simulations are computationally expensive, largely because of the atmosphere. We introduce FASST, a coupled climate model that retains the prognostic POP2 ocean while replacing the atmosphere and sea ice with lightweight U-Net emulators. The atmospheric emulator is diagnostic, predicting surface fluxes from the evolving ocean-ice state every $6$ simulated hours, while the sea-ice emulator advances the ice state monthly. For $140$ years of unforced integration, FASST maintains a stable global-mean climate without SST restoring, with post-transient global-mean SST of $291.30 \pm 0.22\mathrm{K}$ compared with $291.25 \pm 0.10\mathrm{K}$ in CESM$2$ piControl. FASST also captures interannual Niño-$3.4$ variability, although with weaker amplitude than CESM ($0.62\mathrm{K}$ versus $1.02\mathrm{K}$). These results demonstrate that a stateless learned atmosphere can sustain stable, free-running coupled climate simulations over century-scale integrations.

Comment: Accepted at Tackling Climate Change with Machine Learning: workshop at NeurIPS 2026

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