Toward Mechanistic Interpretability of an AI Foundation Model Fine-Tuned for Atmospheric Chemistry

Weather forecasting foundation models (FMs) are increasingly fine-tuned to predict air quality, offering fast global pollution forecasts at lower computational cost than conventional chemical transport models.

These FMs are typically trained on reanalysis data and generate forecasts through autoregressive rollout.

They do not explicitly represent governing physical or chemical processes.

Therefore, high forecast skill does not reveal whether a model has learned physical mechanisms or exploits statistical regularities in its training data.

Here, we present the first study of what a FM fine-tuned for atmospheric chemistry has learned by examining Microsoft's Aurora model.

We impose controlled chemical perturbations on its forecasts and test them against known photochemical relationships.

We then examine the internal representations that generate these forecasts.

We find that Aurora captures a first-order ozone response to reactive nitrogen but does not enforce the chemical constraints that a process-based model encodes.

It generates chemically inconsistent combinations of related species and relaxes localized emission features such as wildfire plumes toward background.

Internally, its representations remain largely organized around the meteorology inherited during pretraining, with little structure specific to chemistry.

Using sparse autoencoders, we identify internal components that causally control the chemical forecast but do not map cleanly onto individual atmospheric processes.

This work provides a framework for testing whether AI forecasting systems learn atmospheric chemistry from reanalysis data.

As these models are increasingly positioned to inform environmental policy decisions, we argue that composition forecasts should also be judged by their internal mechanisms rather than by benchmark skill alone.