Path properties of atmospheric transitions: illustration with a low-order sudden stratospheric warming model

TL;DR

This paper introduces transition path theory (TPT) as a novel framework for understanding rare climate events, demonstrated through a low-order model of sudden stratospheric warming, with potential to improve weather prediction accuracy.

Contribution

It applies TPT to a climate model to analyze transition pathways and times, offering a new mechanistic approach to rare event prediction in climate science.

Findings

TPT effectively estimates transition pathways and return times in a bistable climate model.

The approach provides dynamical statistics by solving PDEs in phase space.

Potential for improved predictability of extreme weather events in future models.

Abstract

Many rare weather events, including hurricanes, droughts, and floods, dramatically impact human life. To accurately forecast these events and characterize their climatology requires specialized mathematical techniques to fully leverage the limited data that are available. Here we describe \emph{transition path theory} (TPT), a framework originally developed for molecular simulation, and argue that it is a useful paradigm for developing mechanistic understanding of rare climate events. TPT provides a method to calculate statistical properties of the paths into the event. As an initial demonstration of the utility of TPT, we analyze a low-order model of sudden stratospheric warming (SSW), a dramatic disturbance to the polar vortex which can induce extreme cold spells at the surface in the midlatitudes. SSW events pose a major challenge for seasonal weather prediction because of their rapid, complex onset and development. Climate models struggle to capture the long-term statistics of SSW, owing to their diversity and intermittent nature. We use a stochastically forced Holton-Mass-type model with two stable states, corresponding to radiative equilibrium and a vacillating SSW-like regime. In this stochastic bistable setting, from certain probabilistic forecasts TPT facilitates estimation of dominant transition pathways and return times of transitions. These "dynamical statistics" are obtained by solving partial differential equations in the model's phase space. With future application to more complex models, TPT and its constituent quantities promise to improve the predictability of extreme weather events, through both generation and principled evaluation of forecasts.