The polar vortex, a swirling mass of cold air encircling the North Pole, is sometimes disrupted by sudden stratospheric warming (SSW) events, unleashing waves of frigid air that penetrate farther south than usual. Temperatures across North America and Europe drop precipitously when this happens, but such events remain hard to predict, meaning we can’t easily warn people when SSW is imminent.

Boscu and team used a machine learning (ML) technique to learn SSW behavior from a physics-based simulation that reproduces important atmospheric dynamics, accelerating polar vortex simulations. They went a step further by analyzing the ML model itself to understand how it represents various aspects of the polar vortex. The work is published in the Journal of Geophysical Research: Machine Learning and Computation.

The authors trained their model on the Holton-Mass model of stratospheric variability, which produces two metastable regimes for the polar vortex: one weak and one strong. A transition from the strong to the weak regime is what causes SSW events.