  Atmospheric regime transitions are highly impactful as drivers of extreme
weather events, but pose two formidable modeling challenges: predicting the
next event (weather forecasting), and characterizing the statistics of events
of a given severity (the risk climatology). Each event has a different duration
and spatial structure, making it hard to define an objective "average event."
We argue here that transition path theory (TPT), a stochastic process
framework, is an appropriate tool for the task. We demonstrate TPT's capacities
on a wave-mean flow model of sudden stratospheric warmings (SSWs) developed by
Holton and Mass (1976), which is idealized enough for transparent TPT analysis
but complex enough to demonstrate computational scalability. Whereas a recent
article (Finkel et al. 2021) studied near-term SSW predictability, the present
article uses TPT to link predictability to long-term SSW frequency. This
requires not only forecasting forward in time from an initial condition, but
also \emph{backward in time} to assess the probability of the initial
conditions themselves. TPT enables one to condition the dynamics on the regime
transition occurring, and thus visualize its physical drivers with a vector
field called the \emph{reactive current}. The reactive current shows that
before an SSW, dissipation and stochastic forcing drive a slow decay of vortex
strength at lower altitudes. The response of upper-level winds is late and
sudden, occurring only after the transition is almost complete from a
probabilistic point of view. This case study demonstrates that TPT quantities,
visualized in a space of physically meaningful variables, can help one
understand the dynamics of regime transitions.
