Representational Similarity Across AI Weather Models

AI weather models produce skilful forecasts competitive with traditional Numerical Weather Prediction, but it remains unclear whether these independently developed models learn similar internal representations. We investigate representational similarity across six AI weather models, which comprise three variants of WeatherNext 1 Graph (GraphCast), AIFS-CRPS, Aurora and ArchesWeather, using five similarity metrics which include CKA and orthogonal Procrustes. Spatial and temporal variability in the atmosphere is often dominated by climatological latitude and seasonal variations that obscure the effects of synoptic weather systems. To account for this, we construct a null calibration of the metrics that preserves the large-scale structure, making the metrics more responsive to similarity in the representation of weather systems. We find non-zero calibrated similarity between all model pairs, strongest and most consistent within the GraphCast family, while rankings among the other models depend on the metric and spatial data used. Global CKA relationships remain stable throughout the year, whereas regional comparisons show greater seasonal variability, particularly in the midlatitudes. We also assessed representational similarity across processor depth in individual models. GraphCast and AIFS-CRPS representations evolve relatively smoothly, whereas Aurora and ArchesWeather change more strongly due to processor-layer boundaries. Together, these results suggest partially shared representations of atmospheric states, whose organisation and construction differ across models, and demonstrate the importance of calibrated, multi-metric analysis.