大气转变的路径特性：用低阶突然平流层变暖模式说明
Path properties of atmospheric transitions: illustration with a low-order sudden stratospheric warming model
摘要：
许多罕见的天气事件，包括飓风，干旱和洪水，严重影响人类生活。为了准确地预测这些事件并描述其气候特征，需要专门的数学技术来充分利用现有的有限数据。在这里，我们描述了\emph{transition path theory}（TPT），最初开发的分子模拟的框架，并认为这是一个有用的范式发展机制的理解罕见的气候事件。TPT提供了一种方法来计算进入事件的路径的统计属性。作为TPT效用的初步演示，我们分析了一个低阶模型的平流层突然变暖（SSW），一个戏剧性的扰动的极涡，可以诱导极端寒冷的法术在中纬度地区的表面。由于SSW事件的快速、复杂的发生和发展，对季节性天气预报提出了重大挑战。由于SSW的多样性和间歇性，气候模式很难捕捉SSW的长期统计数据。我们使用一个随机强迫的Holton-Mass型模型，该模型具有两个稳定的状态，对应于辐射平衡和摇摆的SSW状制度。在这种随机的随机环境中，从某些概率预测TPT有利于估计占主导地位的过渡途径和过渡的返回时间。这些“动态统计”是通过求解模型相空间中的偏微分方程获得的。随着未来应用于更复杂的模型，TPT及其组成数量有望通过生成和原则性评估预测来提高极端天气事件的可预测性。
英文摘要：
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 data 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 model's 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.
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