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تسجيل مستخدم جديدExploring stratospheric rare events with transition path theory and short simulations
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Extreme weather events are simultaneously the least likely and the most impactful features of the climate system, increasingly so as climate change proceeds. Extreme events are multi-faceted, highly variable processes which can be characterized in many ways: return time, worst-case severity, and predictability are all sought-after quantities for various kinds of rare events. A unifying framework is needed to define and calculate the most important quantities of interest for the purposes of near-term forecasting, long-term risk assessment, and benchmarking of reduced-order models. Here we use Transition Path Theory (TPT) for a comprehensive analysis of sudden stratospheric warming (SSW) events in a highly idealized wave-mean flow interaction system with stochastic forcing. TPT links together probabilities, dynamical behavior, and other risk metrics associated with rare events that represents their full statistical variability. At face value, fulfilling this promise demands extensive direct simulation to generate the rare event many times. Instead, we implement a highly parallel computational method that launches a large ensemble of short simulations, estimating long-timescale rare event statistics from short-term tendencies. We specifically investigate properties of SSW events including passage time distributions and large anomalies in vortex strength and heat flux. We visualize high-dimensional probability densities and currents, obtaining a nuanced picture of critical altitude-dependent interactions between waves and the mean flow that fuel SSW events. We find that TPT more faithfully captures the statistical variability between events as compared to the more conventional minimum action method.
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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 emph{optimal forecasts} concretely measure the events 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 emph{short} compared to the timescale 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 (1976) 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 timescales 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.
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 da
ta 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 models 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.
Using meteor wind data from the Super Dual Auroral Radar Network (SuperDARN) in the Northern Hemisphere, we (1) demonstrate that the migrating (Sun-synchronous) tides can be separated from the nonmigrating components in the mesosphere and lower therm
osphere (MLT) region and (2) use this to determine the response of the different components of the semidiurnal tide (SDT) to sudden stratospheric warming (SSW) conditions. The radars span a limited range of latitudes around 60$^{circ}$ N and are located over nearly 180$^{circ}$ of longitude. The migrating tide is extracted from the nonmigrating components observed in the meridional wind recorded from meteor ablation drift velocities around 95-km altitude, and a 20-year climatology of the different components is presented. The well-documented late summer and wintertime maxima in the semidiurnal winds are shown to be due primarily to the migrating SDT, whereas during late autumn and spring the nonmigrating components are at least as strong as the migrating SDT. The robust behavior of the SDT components during SSWs is then examined by compositing 13 SSW events associated with an elevated stratopause recorded between 1995 and 2013. The migrating SDT is seen to reduce in amplitude immediately after SSW onset and then return anomalously strongly around 10-17 days after the SSW onset. We conclude that changes in the underlying wind direction play a role in modulating the tidal amplitude during the evolution of SSWs and that the enhancement in the midlatitude migrating SDT (previously reported in modeling studies) is observed in the MLT at least up to 60$^{circ}$ N.
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