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AIMIP Phase 1: systematic evaluations of AI weather and climate models

Brian Henn, Christopher S. Bretherton, Nikolay Koldunov, Christian Lessig, Maria J. Molina

Featured May 20, 2026

Original Paper

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Simply

Scientists created a new way, AIMIP Phase 1, to fairly test different AI models that predict weather and climate, finding they are good at historical patterns but struggle with future warming trends.

In depth
The paper introduces the AI Model Intercomparison Project (AIMIP) Phase 1, a standardized framework for systematically evaluating diverse AI weather and climate models (AIWCMs). This initiative, inspired by traditional climate model intercomparisons, specifies common experiments, data formats, and training constraints to identify architectural differences influencing model behavior and build trust in AI-driven climate predictions.

Key Takeaways

  • 1
    AIMIP Phase 1 establishes a crucial standardized protocol for evaluating AI weather and climate models, adapting successful methodologies from physics-based modeling.
  • 2
    The study demonstrates that AIWCMs can achieve lower climate biases and accurately simulate ENSO response compared to a conventional physics-based model in historical simulations.
  • 3
    Significant divergence exists among AIWCMs in capturing warming trends and generalizing to out-of-sample perturbed SST scenarios, highlighting critical areas for future development.

Conceptual Flow

HIGH LEVEL
1
Methodology: How was it done?

Researchers set up a shared playground where different AI weather models could all play by the same rules, making it easy to see which ones were best at predicting the weather and climate.

Different AI Models
Run Common Tests
Compare Results
2
Results: What did they find?

The AI models were really good at matching past weather, but they had trouble predicting how the climate would change in the future, especially with big temperature shifts.

AI Models
Good at Past, Struggle with Future
Mixed Performance