Quantifying the Strength and Delay of Climatic Interactions

The Ambiguities of Cross Correlation and a Novel Measure Based on Graphical Models

Jakob Runge*, Vladimir Petoukhov, Juergen Kurths

*Corresponding author for this work

Research output: Contribution to journalArticle

55 Citations (Scopus)

Abstract

Lagged cross-correlation and regression analysis are commonly used to gain insights into interaction mechanisms between climatological processes, in particular to assess time delays and to quantify the strength of a mechanism. Exemplified on temperature anomalies in Europe and the tropical Pacific and Atlantic, the authors study lagged correlation and regressions analytically for a simple model system. A strong dependence on the influence of serial dependencies or autocorrelation is demonstrated, which can lead to misleading conclusions about time delays and also obscures a quantification of the interaction mechanism.To overcome these possible artifacts, the authors propose a two-step procedure based on the concept of graphical models recently introduced to climate research. In the first step, graphical models are used to detect the existence of (Granger) causal interactions that determine the time delays of a mechanism. In the second step, a certain partial correlation and a regression measure are introduced that allow one to specifically quantify the strength of an interaction mechanism in a well interpretable way that enables the exclusion of misleading effects of serial correlation as well as more general dependencies. The potential of the approach to quantify interactions between two and more processes is demonstrated by investigating teleconnections of ENSO and the mechanism of the Walker circulation.The article is intended to serve as a guideline to interpret lagged correlations and regressions in the presence of autocorrelation and introduces a powerful approach to analyze time delays and the strength of an interaction mechanism.

Original languageEnglish
Pages (from-to)720-739
Number of pages20
JournalJournal of climate
Volume27
Issue number2
DOIs
Publication statusPublished - Jan 2014

Keywords

  • teleconnections
  • Walker circulation
  • data mining
  • regression analysis
  • statistical techniques
  • time series
  • Nino-Southern-Oscillation
  • El-Nino
  • serial-correlation
  • Pacific
  • ocean
  • temperatures
  • variability
  • circulation
  • rainfall
  • project

Cite this

Quantifying the Strength and Delay of Climatic Interactions : The Ambiguities of Cross Correlation and a Novel Measure Based on Graphical Models. / Runge, Jakob; Petoukhov, Vladimir; Kurths, Juergen.

In: Journal of climate, Vol. 27, No. 2, 01.2014, p. 720-739.

Research output: Contribution to journalArticle

@article{1081cf0d4db7461c8ec4cdc16e0ab18e,
title = "Quantifying the Strength and Delay of Climatic Interactions: The Ambiguities of Cross Correlation and a Novel Measure Based on Graphical Models",
abstract = "Lagged cross-correlation and regression analysis are commonly used to gain insights into interaction mechanisms between climatological processes, in particular to assess time delays and to quantify the strength of a mechanism. Exemplified on temperature anomalies in Europe and the tropical Pacific and Atlantic, the authors study lagged correlation and regressions analytically for a simple model system. A strong dependence on the influence of serial dependencies or autocorrelation is demonstrated, which can lead to misleading conclusions about time delays and also obscures a quantification of the interaction mechanism.To overcome these possible artifacts, the authors propose a two-step procedure based on the concept of graphical models recently introduced to climate research. In the first step, graphical models are used to detect the existence of (Granger) causal interactions that determine the time delays of a mechanism. In the second step, a certain partial correlation and a regression measure are introduced that allow one to specifically quantify the strength of an interaction mechanism in a well interpretable way that enables the exclusion of misleading effects of serial correlation as well as more general dependencies. The potential of the approach to quantify interactions between two and more processes is demonstrated by investigating teleconnections of ENSO and the mechanism of the Walker circulation.The article is intended to serve as a guideline to interpret lagged correlations and regressions in the presence of autocorrelation and introduces a powerful approach to analyze time delays and the strength of an interaction mechanism.",
keywords = "teleconnections, Walker circulation, data mining, regression analysis, statistical techniques, time series, Nino-Southern-Oscillation, El-Nino, serial-correlation, Pacific, ocean, temperatures, variability, circulation, rainfall, project",
author = "Jakob Runge and Vladimir Petoukhov and Juergen Kurths",
year = "2014",
month = "1",
doi = "10.1175/JCLI-D-13-00159.1",
language = "English",
volume = "27",
pages = "720--739",
journal = "Journal of climate",
issn = "0894-8755",
publisher = "American Meteorological Society",
number = "2",

}

TY - JOUR

T1 - Quantifying the Strength and Delay of Climatic Interactions

T2 - The Ambiguities of Cross Correlation and a Novel Measure Based on Graphical Models

AU - Runge, Jakob

AU - Petoukhov, Vladimir

AU - Kurths, Juergen

PY - 2014/1

Y1 - 2014/1

N2 - Lagged cross-correlation and regression analysis are commonly used to gain insights into interaction mechanisms between climatological processes, in particular to assess time delays and to quantify the strength of a mechanism. Exemplified on temperature anomalies in Europe and the tropical Pacific and Atlantic, the authors study lagged correlation and regressions analytically for a simple model system. A strong dependence on the influence of serial dependencies or autocorrelation is demonstrated, which can lead to misleading conclusions about time delays and also obscures a quantification of the interaction mechanism.To overcome these possible artifacts, the authors propose a two-step procedure based on the concept of graphical models recently introduced to climate research. In the first step, graphical models are used to detect the existence of (Granger) causal interactions that determine the time delays of a mechanism. In the second step, a certain partial correlation and a regression measure are introduced that allow one to specifically quantify the strength of an interaction mechanism in a well interpretable way that enables the exclusion of misleading effects of serial correlation as well as more general dependencies. The potential of the approach to quantify interactions between two and more processes is demonstrated by investigating teleconnections of ENSO and the mechanism of the Walker circulation.The article is intended to serve as a guideline to interpret lagged correlations and regressions in the presence of autocorrelation and introduces a powerful approach to analyze time delays and the strength of an interaction mechanism.

AB - Lagged cross-correlation and regression analysis are commonly used to gain insights into interaction mechanisms between climatological processes, in particular to assess time delays and to quantify the strength of a mechanism. Exemplified on temperature anomalies in Europe and the tropical Pacific and Atlantic, the authors study lagged correlation and regressions analytically for a simple model system. A strong dependence on the influence of serial dependencies or autocorrelation is demonstrated, which can lead to misleading conclusions about time delays and also obscures a quantification of the interaction mechanism.To overcome these possible artifacts, the authors propose a two-step procedure based on the concept of graphical models recently introduced to climate research. In the first step, graphical models are used to detect the existence of (Granger) causal interactions that determine the time delays of a mechanism. In the second step, a certain partial correlation and a regression measure are introduced that allow one to specifically quantify the strength of an interaction mechanism in a well interpretable way that enables the exclusion of misleading effects of serial correlation as well as more general dependencies. The potential of the approach to quantify interactions between two and more processes is demonstrated by investigating teleconnections of ENSO and the mechanism of the Walker circulation.The article is intended to serve as a guideline to interpret lagged correlations and regressions in the presence of autocorrelation and introduces a powerful approach to analyze time delays and the strength of an interaction mechanism.

KW - teleconnections

KW - Walker circulation

KW - data mining

KW - regression analysis

KW - statistical techniques

KW - time series

KW - Nino-Southern-Oscillation

KW - El-Nino

KW - serial-correlation

KW - Pacific

KW - ocean

KW - temperatures

KW - variability

KW - circulation

KW - rainfall

KW - project

U2 - 10.1175/JCLI-D-13-00159.1

DO - 10.1175/JCLI-D-13-00159.1

M3 - Article

VL - 27

SP - 720

EP - 739

JO - Journal of climate

JF - Journal of climate

SN - 0894-8755

IS - 2

ER -