Using Adjoints of AI-based Weather Models to Study Predictability and Extreme Events

Recently developed AI weather models have been widely recognized for revolutionizing weather prediction, producing forecasts more skillful than traditional models at a fraction of the computational cost. Here I will argue that the next phase of the revolution involves the adjoints of these models, applied to a wide range of problems, including novel exploration of dynamical process in weather and climate variability, extreme events, and new data assimilation systems. Adjoints are derived from gradient operations on the forward model, and are useful for measuring the sensitivity of model outputs to inputs and parameters. Historically adjoints have been derived for a limited set of traditional models, and mainly applied to problems in data assimilation. The ubiquitous availability of adjoints for AI models makes these tools easily accessible and available for a much wider range of applications. Specific examples I will discuss include shadowing trajectories for predictability, "gray swans" and a factory for out-of-sample extreme events, and mechanistic interpretability of specific phenomena.