# AI Emulation of Stochastic Sudden Stratospheric Warming with Interpretable Latent Structure

arXiv: https://arxiv.org/abs/2610.02069 · HTML: https://mlsanity.com/paper/2610.02069/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 38 references that are in the map.
- First posted on arXiv 1 Oct 2026.

## Builds on (most cited first, 10 of 10)

- [FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators](https://mlsanity.com/paper/2202.11214/) (2022)
- [Can AI weather models predict out-of-distribution gray swan tropical cyclones?](https://mlsanity.com/paper/2410.14932/) (2024)
- [Dynamical Tests of a Deep-Learning Weather Prediction Model](https://mlsanity.com/paper/2309.10867/) (2023)
- [Numerical models outperform AI weather forecasts of record-breaking extremes](https://mlsanity.com/paper/2508.15724/) (2025)
- [Long-term stability and generalization of observationally-constrained stochastic data-driven models for geophysical turbulence](https://mlsanity.com/paper/2205.04601/) (2022)
- [On interpretability and proper latent decomposition of autoencoders](https://mlsanity.com/paper/2211.08345/) (2022)
- [Lazy Diffusion: Mitigating spectral collapse in generative diffusion-based stable autoregressive emulation of turbulent flows](https://mlsanity.com/paper/2512.09572/) (2025)
- [LUCIE-3D: A three-dimensional climate emulator for forced responses](https://mlsanity.com/paper/2509.02061/) (2025)
- [Efficient Generative AI Boosts Probabilistic Forecasting of Sudden Stratospheric Warmings](https://mlsanity.com/paper/2510.26376/) (2025)
- [Generative forecasting with joint probability models](https://mlsanity.com/paper/2512.24446/) (2025)

## Abstract (arXiv)

Rare weather regime transitions pose a challenge for data-driven modeling due to class imbalance. In this study, we develop a probabilistic deep learning emulator for a prototypical system with regime transitions, the stochastic Holton--Mass model of stratospheric variability, and analyze the structure of its learned latent space. The Holton--Mass model exhibits two metastable regimes, a strong and a weak polar vortex, maintained by nonlinear wave--mean flow interactions, with weak stochastic forcing intermittently triggering rare transitions between these regimes that qualitatively represent SSW events. We employ a ResNet-inspired Conditional Variational Autoencoder with six-layer encoder and decoder layers and explicit current-state conditioning to model the distribution of the system's state at the next time step (one day). The emulator accurately reproduces short-term dynamics, steady-state probability distributions, regime persistence statistics, rare transition rates, the transition committor function, and the transition expected lead time of the physical model. Beyond emulation fidelity, we interrogate the learned latent representation to understand how the model internalizes the underlying metastable structure of the dynamics. Principal Component Analysis of the 32-dimensional latent space reveals a clear and unsupervised separation into four physically interpretable clusters corresponding to strong versus weak vortex regimes and stable versus transition-prone configurations. Such emergent regime separation in latent space is hard to identify for deep generative models applied to high-dimensional stochastic systems. Our results show that carefully designed probabilistic emulators can uncover physically meaningful manifolds governing extreme-event dynamics, potentially aiding the development of improved operational advanced warning systems.