Data-Driven Transition Path Analysis Yields a Statistical Understanding of Sudden Stratospheric Warming Events in an Idealized Model
Data-Driven Transition Path Analysis Yields a Statistical Understanding of Sudden Stratospheric Warming Events in an Idealized Model
复制标题
数据驱动的转变路径分析可以在理想化模型中对平流层突然变暖事件产生统计了解
DOI：
10.1175/jas-d-21-0213.1
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发表时间：
2023
影响因子：
3.1
通讯作者：
Weare, Jonathan
中科院分区：
文献类型：
--
作者：
Finkel, Justin;Webber, Robert J.;Gerber, Edwin P.;Abbot, Dorian S.;Weare, Jonathan
大气状况转变作为极端天气事件的驱动因素具有高度影响力，但也带来了两个巨大的建模挑战：预测下一个事件（天气预报）和描述给定严重程度事件的统计数据（风险气候学）。每个事件都有不同的持续时间和空间结构，因此很难定义客观的“平均事件”。我们在此认为，过渡路径理论（TPT）是一种随机过程框架，是完成该任务的合适工具。我们在 Holton 和 Mass 开发的平流层突然变暖 (SSW) 的波均流模型上展示了 TPT 的能力，该模型对于透明的 TPT 分析来说足够理想化，但也足够复杂，足以证明计算可扩展性。最近的一篇文章研究了近期 SSW 可预测性，而本文使用 TPT 将可预测性与长期 SSW 频率联系起来。这不仅需要从初始条件向前预测时间，还需要向后预测时间以评估初始条件本身的概率。 TPT 使人们能够调节发生状态转变的动态，从而通过称为无功电流的矢量场可视化其物理驱动因素。无功电流表明，在 SSW 发生之前，耗散和随机强迫导致较低海拔处涡流强度缓慢衰减。高层风的反应迟缓且突然，从概率的角度来看，只有在转变几乎完成之后才会发生。本案例研究表明，在具有物理意义的变量空间中可视化的 TPT 量可以帮助人们理解状态转变的动态。
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 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 alsobackward in timeto 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 thereactive 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.