The weather is said to have "a mind of its own", as a chaotic system with limited predictability. Yet its fluctuations remain largely confined to a familiar probability distribution, to which the built and natural environment is well adapted. More impactful are the extreme, intermittent fluctuations of temperature, moisture, and wind, which can seriously threaten ill-equipped human and ecological communities. Extreme events have "dynamics of their own" which are difficult to quantify because they play out in a far tail of the probability distribution, and so sample sizes are limited in both observations and simulations.
Nonetheless, non-equilibrium statistical mechanics provide some general laws for the progression of rare events. In particular, transition path theory (TPT) formulates the ensemble of rare events as a flow of probability current through a high-dimensional phase space, climbing a landscape called the committor function which quantifies progress toward the event and distinguishes the dominant physical mechanisms at each step. In this talk I will present a TPT analysis of a particular atmospheric phenomenon, sudden stratospheric warming (SSW): a premature collapse of the stratospheric polar vortex whose impacts propagate down to the surface and manifest in midlatitude cold air outbreaks. In an idealized model setting, we use enhanced sampling and coarse-graining to piece together the probability current from a patchwork of short simulations. Equipped with physical features informed by wave-mean flow interaction theory, our data-driven procedure makes precise the role of stochastic forcing in driving the enstrophy transfer from mean flow to waves that destroys the polar vortex, and the altitude dependence of each process. Our computation and visualization methods, we believe, can enhance our understanding of many kinds of extreme events in the climate system.