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北大西洋可预报性和极值的动力代理。

Dynamical proxies of North Atlantic predictability and extremes.

机构信息

Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, F-91191 Gif-sur-Yvette, France.

Department of Meteorology, Stockholm University and Bolin Centre for Climate Research Science, Stockholm, Sweden.

出版信息

Sci Rep. 2017 Jan 25;7:41278. doi: 10.1038/srep41278.

DOI:10.1038/srep41278
PMID:28120899
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5264183/
Abstract

Atmospheric flows are characterized by chaotic dynamics and recurring large-scale patterns. These two characteristics point to the existence of an atmospheric attractor defined by Lorenz as: "the collection of all states that the system can assume or approach again and again, as opposed to those that it will ultimately avoid". The average dimension D of the attractor corresponds to the number of degrees of freedom sufficient to describe the atmospheric circulation. However, obtaining reliable estimates of D has proved challenging. Moreover, D does not provide information on transient atmospheric motions, such as those leading to weather extremes. Using recent developments in dynamical systems theory, we show that such motions can be classified through instantaneous rather than average properties of the attractor. The instantaneous properties are uniquely determined by instantaneous dimension and stability. Their extreme values correspond to specific atmospheric patterns, and match extreme weather occurrences. We further show the existence of a significant correlation between the time series of instantaneous stability and dimension and the mean spread of sea-level pressure fields in an operational ensemble weather forecast at lead times of over two weeks. Instantaneous properties of the attractor therefore provide an efficient way of evaluating and informing operational weather forecasts.

摘要

大气流具有混沌动力学和反复出现的大规模模式的特点。这两个特点表明存在一个大气吸引子,由 Lorenz 定义为:“系统可以一次又一次地假设或接近的所有状态的集合,而不是那些系统最终会避免的状态”。吸引子的平均维度 D 对应于足以描述大气环流的自由度数量。然而,获得可靠的 D 估计值一直具有挑战性。此外,D 并没有提供有关瞬态大气运动的信息,例如导致极端天气的运动。利用动力系统理论的最新进展,我们表明可以通过吸引子的瞬时而不是平均特性对这些运动进行分类。瞬时特性由瞬时维度和稳定性唯一确定。它们的极值对应于特定的大气模式,并与极端天气事件相匹配。我们进一步表明,在超过两周的时间内,瞬时稳定性和维度的时间序列与海平面压力场的平均分布之间存在显著的相关性。因此,吸引子的瞬时特性提供了一种评估和告知业务天气预报的有效方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0a22/5264183/5e1d87d8a744/srep41278-f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0a22/5264183/d2be95cbc580/srep41278-f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0a22/5264183/c35ae3d74652/srep41278-f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0a22/5264183/5a6c10efb6a4/srep41278-f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0a22/5264183/3359c2a779d7/srep41278-f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0a22/5264183/5e1d87d8a744/srep41278-f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0a22/5264183/d2be95cbc580/srep41278-f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0a22/5264183/c35ae3d74652/srep41278-f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0a22/5264183/5a6c10efb6a4/srep41278-f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0a22/5264183/3359c2a779d7/srep41278-f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0a22/5264183/5e1d87d8a744/srep41278-f5.jpg

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