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Climate

Disentangling the effects of sea surface temperature and CO in global machine learned weather-climate emulators

Spencer K. Clark, Noah D. Brenowitz

Featured June 14, 2026

Original Paper

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Simply

Training climate models on diverse, uncorrelated data allows them to accurately predict how the planet responds to independent changes in temperature and carbon dioxide levels, rather than just historical patterns.

In depth
The paper addresses the inability of previous climate emulators to generalize when sea surface temperature (SST) and CO2 concentrations are decoupled. By introducing random-CO2 reference simulations where these variables vary independently, the authors enable the model to learn their distinct physical effects. The resulting stochastic SFNO architecture further improves the model's ability to represent extreme climate events and maintain global energy conservation.

Key Takeaways

  • 1
    Previous emulators failed because they were trained on correlated SST and CO2 data, preventing the model from learning their independent physical effects.
  • 2
    The authors introduce a new training dataset with uncorrelated SST and CO2 variations, which significantly improves model flexibility and generalization.
  • 3
    The new model, ACE2S-SHiELD+, achieves higher accuracy in extreme scenarios while requiring 25% less training data than previous versions.

Conceptual Flow

HIGH LEVEL
1
Methodology

The researchers created a new training set where temperature and carbon dioxide change independently to teach the model how each factor works on its own.

Historical Data
Randomized Climate Data
Train Model
Flexible Climate Emulator
2
Results

The new model can now correctly predict climate changes even when temperature and carbon dioxide levels do not match historical patterns.

Extreme Climate Scenario
Accurate Prediction
Correct Climate Response

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