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Published February 1, 2023 | Version Published
Journal Article Open

Data-Driven Transition Path Analysis Yields a Statistical Understanding of Sudden Stratospheric Warming Events in an Idealized Model

Creators

  • 1. ROR icon Massachusetts Institute of Technology
  • 2. ROR icon California Institute of Technology
  • 3. ROR icon New York University
  • 4. ROR icon University of Chicago

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.

Copyright and License

© 2023 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Acknowledgement

During the time of writing, J.F. was supported by the U.S. DOE, Office of Science, Office of Advanced Scientific Computing Research, Department of Energy Computational Science Graduate Fellowship under Award DE-SC0019323. During the time of writing, R.J.W. was supported by New York University’s Dean’s Dissertation Fellowship and by the Research Training Group in Modeling and Simulation funded by the NSF via Grant RTG/DMS-1646339. E.P.G. acknowledges support from the NSF through Grants AGS-1852727 and OAC-2004572. This work was partially supported by the NASA Astrobiology Program, Grant 80NSSC18K0829, and benefited from participation in the NASA Nexus for Exoplanet Systems Science research coordination network. J.W. acknowledges support from the Advanced Scientific Computing Research Program within the DOE Office of Science through Award DE-SC0020427 and from the NSF through Award DMS-2054306. The computations in the paper were done on the high-performance computing cluster at New York University. We thank John Strahan, Aaron Dinner, and Chatipat Lorpaiboon for many helpful conversations and methodological advice.

Data Availability

The code to produce the dataset and results, either on the Holton–Mass model or on other systems, is publicly available at https://github.com/justinfocus12/SHORT. Interested users are encouraged to contact J.F. for more guidance on usage of the code.

Supplemental Material

Supplemental Materials (467 KB)

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atsc-JAS-D-21-0213.1.pdf

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Additional details

Related works

Is supplemented by
Dataset: https://github.com/justinfocus12/SHORT (URL)

Funding

United States Department of Energy
DE-SC0019323
National Science Foundation
DMS-1646339
National Science Foundation
AGS-1852727
National Science Foundation
OAC-2004572
National Aeronautics and Space Administration
80NSSC18K0829
United States Department of Energy
DE-SC0020427
National Science Foundation
DMS-2054306

Dates

Accepted
2022-10-11
Accepted
Available
2023-01-24
Available Online

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Publication Status
Published