Spatial Generalization Tests for Machine Learning-based Weather Models to Assess Physical Consistency

Machine learning-based weather prediction is revolutionizing weather forecasting by learning from weather data in present-day climate.

However, generalization to other climates remains a major challenge.

With melting sea ice, land-use change, and increasing ocean temperatures, boundary conditions are changing.

Therefore, generalization in time depends on generalization in space.

Here, we present three test cases to evaluate whether machine learning-based weather and climate models generalize in space and apply them to GraphCast and NeuralGCM.

We reverse or rotate the planet in longitude or latitude under the model's coordinate system and adapt all boundary conditions and forcings accordingly.

Physics-based general circulation models simulate a rotated/reversed planet with only rounding errors, but GraphCast and NeuralGCM fail these tests.

The analyses furthermore revealed unphysical variable mappings based on correlation rather than causation.

We argue that machine learning-based climate models should be designed to pass generalization tests to prevent overfitting on present-day regional climate.