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首页> 外文期刊>Journal of Climate >A hidden Markov model perspective on regimes and metastability in atmospheric flows.
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A hidden Markov model perspective on regimes and metastability in atmospheric flows.

机译:隐藏的马尔可夫模型对大气流动状态和亚稳态的观点。

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摘要

In this study, data from three atmospheric models are analyzed to investigate the existence of atmospheric flow regimes despite nearly Gaussian statistics of the planetary waves in these models. A hierarchy of models is used, which describes the atmospheric circulation with increasing complexity. To systematically identify atmospheric regimes, the presence of metastable states in the data is searched for by fitting so-called hidden Markov models (HMMs) to the time series. A hidden Markov model is designed to describe the situation in which part of the information of the system is unknown or hidden and another part is observed. Within the context of this study, some representative variable of planetary-scale flow (e.g., mean zonal flow or leading principal component) is known ("observed"), but its dynamics may depend crucially on the overall flow configuration, which is unknown. The behavior of this latter, "hidden" variable is described by a Markov chain. If the Markov chain possesses metastable (or quasi persistent) states, they are identified as regimes. In this perspective, regimes can be present even though the observed data have a nearly Gaussian probability distribution. The parameters of the HMMs are fit to the time series using a maximum-likelihood approach; well-established and robust numerical methods are available to do this. Possible metastability of the Markov chain is assessed by inspecting the eigenspectrum of the associated transition probability matrix. The HMM procedure is first applied to data from a simplified model of barotropic flow over topography with a large-scale mean flow. This model exhibits regime behavior of its large-scale mean flow for sufficiently high topography. In the case of high topography, the authors find three regimes, two of which correspond to zonal flow and the third to blocking. Next, a three-layer quasigeostrophic model is used as a prototype atmospheric general circulation model (GCM). Its first empirical orthogonal function (EOF) is similar to the Arctic Oscillation (AO) and exhibits metastability. For this model, two regime states are found: one corresponding to the positive phase of the AO with large amplitude and decreased variability of the streamfunction field, and another corresponding to the negative AO phase with small amplitude and increased variability. Finally, the authors investigate a comprehensive GCM. The leading four EOFs of this model show no signs of metastability. The results of the barotropic flow over topography and of the quasigeostrophic model suggest that the observed small skewness of planetary wave probability density functions (PDFs) is an imprint of blocked circulation states.
机译:在这项研究中,尽管在这些模型中行星波的统计数据几乎为高斯,但仍分析了来自三种大气模型的数据以调查大气流态的存在。使用了模型层次结构,该模型描述了越来越复杂的大气环流。为了系统地识别大气状态,通过将所谓的隐马尔可夫模型(HMM)拟合到时间序列中,搜索数据中是否存在亚稳态。设计隐马尔可夫模型来描述系统信息的一部分未知或隐藏而观察到另一部分的情况。在本研究的背景下,行星尺度流的某些代表性变量(例如平均纬向流或主导主分量)是已知的(“观察到”),但其动力学可能主要取决于整体流的配置,而未知。后一个“隐藏”变量的行为由马尔可夫链描述。如果马尔可夫链具有亚稳态(或准持久)状态,则将其识别为制度。从这个角度来看,即使观察到的数据几乎具有高斯概率分布,也可以存在各种体制。 HMM的参数使用最大似然法拟合到时间序列。完善且稳健的数值方法可以做到这一点。马尔可夫链的可能亚稳性是通过检查相关跃迁概率矩阵的本征谱来评估的。 HMM过程首先应用于来自地形上具有大尺度平均流量的正压流简化模型的数据。对于足够高的地形,该模型表现出其大规模平均流的态势。对于高地形,作者发现了三种状态,其中两种对应于纬向流,第三种对应于阻塞。接下来,三层拟地转模型被用作原型大气总循环模型(GCM)。它的第一个经验正交函数(EOF)与北极涛动(AO)相似,并表现出亚稳性。对于该模型,发现了两种状态状态:一个状态对应于具有较大振幅且流功能场的可变性减小的AO的正相,而另一种状态对应于具有较小振幅且可变性增加的负的AO相位。最后,作者研究了全面的GCM。该模型的前四个EOF没有显示出亚稳的迹象。地形上的正压流和准地转模型的结果表明,观测到的行星波概率密度函数(PDFs)的小偏斜是阻塞环流状态的印记。

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