Minisymposium Presentation

AI-driven Numerical Weather Prediction (AI-NWP) models, trained on the ERA5 reanalysis are currently our best representation of historical day-to-day weather evolution. They have demonstrated significant skill in forecasting present-day weather, outperforming traditional physics-based forecasting systems. Emerging evidence suggests that AI-NWP models do not merely replicate past atmospheric states but effectively learn the underlying physical dynamics of the atmosphere. We anticipate that, especially at short time scales (on the order of several days), AI-NWP models will outperform most existing climate models in simulating realistic weather conditions, including weather in the future climate scenarios. This improved performance may result from the richer dynamics captured by high-resolution AI-NWP models (typically around 25 km) compared to conventional climate models (usually around 100 km), or simply from their superior representation of atmospheric processes learned from ERA5. Such capabilities could even help to correct biases in current climate models. In this study, we examine the applicability of AI-NWP models to various climate scenarios and demonstrate their potential benefits for contemporary climate research. Specifically, we highlight their capabilities for downscaling coarse-resolution climate simulations and explore their capabilities in investigating extreme weather events through a storyline approach by reproducing present-day extreme events under altered climate conditions.