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首页> 外文期刊>Climate dynamics >Non-linear dependence and teleconnections in climate data: sources, relevance, nonstationarity
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Non-linear dependence and teleconnections in climate data: sources, relevance, nonstationarity

机译：气候数据中的非线性依存关系和遥相关性：来源，相关性，非平稳性

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摘要

Quantification of relations between measured variables of interest by statistical measures of dependence is a common step in analysis of climate data. The choice of dependence measure is key for the results of the subsequent analysis and interpretation. The use of linear Pearson's correlation coefficient is widespread and convenient. On the other side, as the climate is widely acknowledged to be a nonlinear system, nonlinear dependence quantification methods, such as those based on information-theoretical concepts, are increasingly used for this purpose. In this paper we outline an approach that enables well informed choice of dependence measure for a given type of data, improving the subsequent interpretation of the results. The presented multi-step approach includes statistical testing, quantification of the specific non-linear contribution to the interaction information, localization of areas with strongest nonlinear contribution and assessment of the role of specific temporal patterns, including signal nonstationarities. In detail we study the consequences of the choice of a general nonlinear dependence measure, namely mutual information, focusing on its relevance and potential alterations in the discovered dependence structure. We document the method by applying it to monthly mean temperature data from the NCEP/NCAR reanalysis dataset as well as the ERA dataset. We have been able to identify main sources of observed non-linearity in inter-node couplings. Detailed analysis suggested an important role of several sources of nonstationarity within the climate data. The quantitative role of genuine nonlinear coupling at monthly scale has proven to be almost negligible, providing quantitative support for the use of linear methods for monthly temperature data.
机译：通过相关性的统计测量来量化感兴趣的测量变量之间的关系是气候数据分析中的一个常见步骤。依赖性度量的选择对于后续分析和解释的结果至关重要。线性皮尔逊相关系数的使用广泛且方便。另一方面，由于气候被广泛认为是一种非线性系统，因此非线性依赖量化方法（例如基于信息理论的方法）正越来越多地用于此目的。在本文中，我们概述了一种方法，该方法可为给定类型的数据提供明智的依赖性度量选择，从而改善结果的后续解释。提出的多步骤方法包括统计测试，对交互信息的特定非线性贡献的量化，具有最强非线性贡献的区域的定位以及对特定时间模式（包括信号非平稳性）的作用的评估。详细地，我们研究选择通用非线性依赖度量（即互信息）的后果，重点是其相关性和所发现依赖结构中的潜在变化。我们通过将其应用于NCEP / NCAR再分析数据集以及ERA数据集的月平均温度数据来记录该方法。我们已经能够确定节点间耦合中观察到的非线性的主要来源。详细分析表明，气候数据中几种非平稳性来源的重要作用。事实证明，月度范围内真正的非线性耦合的定量作用几乎可以忽略不计，这为使用线性方法获取月度温度数据提供了定量支持。

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  • 来源
    《Climate dynamics》 |2014年第8期|1873-1886|共14页
  • 作者

    Jaroslav Hlinka; David Hartman; Martin Vejmelka; Dagmar Novotna; Milan Palus;

  • 作者单位

    Institute of Computer Science, Academy of Sciences of the Czech Republic, Pod Vodarenskou vezi 2, 182 07 Prague 8, Czech Republic;

    Institute of Computer Science, Academy of Sciences of the Czech Republic, Pod Vodarenskou vezi 2, 182 07 Prague 8, Czech Republic;

    Institute of Computer Science, Academy of Sciences of the Czech Republic, Pod Vodarenskou vezi 2, 182 07 Prague 8, Czech Republic;

    Institute of Atmospheric Physics, Academy of Sciences of the Czech Republic, Prague 8, Czech Republic;

    Institute of Computer Science, Academy of Sciences of the Czech Republic, Pod Vodarenskou vezi 2, 182 07 Prague 8, Czech Republic;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Climate networks; Nonlinearity; Mutual information; Teleconnections; Seasonality in variance; Nonstationarity;

    机译：气候网络;非线性;相互信息;远程连接;季节性差异;非平稳性;

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