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Abstract Rare events arising in nonlinear atmospheric dynamics remain hard to predict and attribute. We address the problem of forecasting rare events in a prototypical example, sudden stratospheric warmings (SSWs). Approximately once every other winter, the boreal stratospheric polar vortex rapidly breaks down, shifting midlatitude surface weather patterns for months. We focus on two key quantities of interest: the probability of an SSW occurring, and the expected lead time if it does occur, as functions of initial condition. These optimal forecasts concretely measure the event’s progress. Direct numerical simulation can estimate them in principle but is prohibitively expensive in practice: each rare event requires a long integration to observe, and the cost of each integration grows with model complexity. We describe an alternative approach using integrations that are short compared to the time scale of the warming event. We compute the probability and lead time efficiently by solving equations involving the transition operator, which encodes all information about the dynamics. We relate these optimal forecasts to a small number of interpretable physical variables, suggesting optimal measurements for forecasting. We illustrate the methodology on a prototype SSW model developed by Holton and Mass and modified by stochastic forcing. While highly idealized, this model captures the essential nonlinear dynamics of SSWs and exhibits the key forecasting challenge: the dramatic separation in time scales between a single event and the return time between successive events. Our methodology is designed to fully exploit high-dimensional data from models and observations, and has the potential to identify detailed predictors of many complex rare events in meteorology.
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Data-Driven Transition Path Analysis Yields a Statistical Understanding of Sudden Stratospheric Warming Events in an Idealized Model
Abstract 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, 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 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 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.
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- PAR ID:
- 10444881
- Date Published:
- Journal Name:
- Journal of the Atmospheric Sciences
- Volume:
- 80
- Issue:
- 2
- ISSN:
- 0022-4928
- Page Range / eLocation ID:
- 519 to 534
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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null (Ed.)Abstract The Whole Atmosphere Community Climate Model, version 4 (WACCM4), is used to investigate the influence of stratospheric conditions on the development of sudden stratospheric warmings (SSWs). To this end, targeted experiments are performed on selected modeled SSW events. Specifically, the model is reinitialized three weeks before a given SSW, relaxing the surface fluxes, winds, and temperature below 10 km to the corresponding fields from the free-running simulation. Hence, the tropospheric wave evolution is unaltered across the targeted experiments, but the stratosphere itself can evolve freely. The stratospheric zonal-mean state is then altered 21 days prior to the selected SSWs and rerun with an ensemble of different initial conditions. It is found that a given tropospheric evolution concomitant with the development of an SSW does not uniquely determine the occurrence of an event and that the stratospheric conditions are relevant to the subsequent evolution of the stratospheric flow toward an SSW, even for a fixed tropospheric evolution. It is also shown that interpreting the meridional heat flux at 100 hPa as a proxy of the tropospheric injection of wave activity into the stratosphere should be regarded with caution and that stratospheric dynamics critically influence the heat flux at that altitude.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1175/JAS-D-21-0213.1
