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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Learning Forecasts of Rare Stratospheric Transitions from Short Simulations
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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- Award ID(s):
- 1852727
- NSF-PAR ID:
- 10329444
- Date Published:
- Journal Name:
- Monthly Weather Review
- Volume:
- 149
- Issue:
- 11
- ISSN:
- 0027-0644
- Page Range / eLocation ID:
- 3647 to 3669
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Extreme weather events have significant consequences, dominating the impact of climate on society. While high‐resolution weather models can forecast many types of extreme events on synoptic timescales, long‐term climatological risk assessment is an altogether different problem. A once‐in‐a‐century event takes, on average, 100 years of simulation time to appear just once, far beyond the typical integration length of a weather forecast model. Therefore, this task is left to cheaper, but less accurate, low‐resolution or statistical models. But there is untapped potential in weather model output: despite being short in duration, weather forecast ensembles are produced multiple times a week. Integrations are launched with independent perturbations, causing them to spread apart over time and broadly sample phase space. Collectively, these integrations add up to thousands of years of data. We establish methods to extract climatological information from these short weather simulations. Using ensemble hindcasts by the European Center for Medium‐range Weather Forecasting archived in the subseasonal‐to‐seasonal (S2S) database, we characterize sudden stratospheric warming (SSW) events with multi‐centennial return times. Consistent results are found between alternative methods, including basic counting strategies and Markov state modeling. By carefully combining trajectories together, we obtain estimates of SSW frequencies and their seasonal distributions that are consistent with reanalysis‐derived estimates for moderately rare events, but with much tighter uncertainty bounds, and which can be extended to events of unprecedented severity that have not yet been observed historically. These methods hold potential for assessing extreme events throughout the climate system, beyond this example of stratospheric extremes.
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Abstract. Stratospheric circulation is a critical part of the Arctic ozone cycle.Sudden stratospheric warming events (SSWs) manifest the strongest alterationof stratospheric dynamics. During SSWs, changes in planetary wavepropagation vigorously influence zonal mean zonal wind, temperature, andtracer concentrations in the stratosphere over the high latitudes. In thisstudy, we examine six persistent major SSWs from 2004 to 2020 using theModern-Era Retrospective analysis for Research and Applications, Version 2(MERRA-2). Using the unique density of observations around the Greenlandsector at high latitudes, we perform comprehensive comparisons of high-latitude observations with the MERRA-2 ozone dataset during the six majorSSWs. Our results show that MERRA-2 captures the high variability of mid-stratospheric ozone fluctuations during SSWs over high latitudes. However,larger uncertainties are observed in the lower stratosphere and troposphere.The zonally averaged stratospheric ozone shows a dramatic increase of9 %–29 % in total column ozone (TCO) near the time of each SSW, which lastsup to 2 months. This study shows that the average shape of the Arcticpolar vortex before SSWs influences the geographical extent, timing, andmagnitude of ozone changes. The SSWs exhibit a more significant impact onozone over high northern latitudes when the average polar vortex is mostlyelongated as seen in 2009 and 2018 compared to the events in which the polarvortex is displaced towards Europe. Strong correlation (R2=90 %) isobserved between the magnitude of change in average equivalent potentialvorticity before and after SSWs and the associated averaged total columnozone changes over high latitudes. This paper investigates the differentterms of the ozone continuity equation using MERRA-2 circulation, whichemphasizes the key role of vertical advection in mid-stratospheric ozoneduring the SSWs and the magnified vertical advection in elongated vortexshape as seen in 2009 and 2018.more » « less
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null (Ed.)Although sudden stratospheric warmings (SSWs) can improve subseasonal-to-seasonal forecasts, it is unclear whether the two types of SSW - displacements and splits - have different near- surface effects. To examine the longer-term (i.e., multi-week lead) tropospheric response to displacements and splits, we utilize an intermediate-complexity model and impose wave-1 and wave-2 stratospheric heating perturbations spun-off from a control run. At longer lags, the tropospheric response is found to be insensitive to both the wavenumber and location of the imposed heating, in agreement with freely evolving displacements and splits identified in the control run. At shorter lags, however, large differences are found between displacements and splits in both the control run and the different wavenumber- forced events. In particular, in the control run, the free-running splits have an immediate barotropic response throughout the stratosphere and troposphere whereas displacements take 1–2 weeks before a near-surface response becomes evident. Interestingly, this barotropic response found during CTRL splits is not captured by the barotropically forced wave-2 events, indicating that the zonal-mean tropospheric circulation is somehow coupled with the generation of the wave-2 splits. It is also found that in the control run, displacements yield stronger Polar-Cap temperature anomalies than splits, yet both still yield similar magnitude tropospheric responses. Hence, the strength of the stratospheric warming is not the only governing factor in the surface response. Overall, SSW classification based on vortex morphology may be useful for subseasonal but not seasonal tropospheric prediction.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1175/MWR-D-21-0024.1
