Alternating mutual influence of El-Niño/Southern Oscillation and Indian monsoon

Мохов, И. И.; Смирнов, Д. А.; Nakonechny, P. I.; Kozlenko, S. S.; Селезнев, Е. П.; Kurths, Jürgen

doi:10.1029/2010gl045932

Cited by 85 publications

(39 citation statements)

References 19 publications

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“…Typical values of D are about 8 days for SST and 4 days for , depending on the location. GC is a powerful tool for the analysis of climate time series Kaufmann, 1999, 2014;Salvucci et al, 2002;Khokhlov et al, 2006;Mosedale et al, 2006;Mokhov et al, 2011;Attanasio et al, 2012;Pasini et al, 2012). It is especially useful in systems that exhibit feedback and closed loops, for which the lagged correlation can lead to misleading conclusions (Chatfield, 1989;Runge et al, 2014).…”

Section: The Gc Estimatormentioning

confidence: 99%

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A study of the air-sea interaction in the South Atlantic Convergence Zone through Granger causality

2014

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Air-sea interaction in the region of the South Atlantic Convergence Zone (SACZ) is studied using Granger causality (GC) as a measure of directional coupling. Calculation of the area weighted connectivity indicates that the SACZ region is the one with largest mutual air-sea connectivity in the south Atlantic basin during summertime. Focusing on the leading mode of daily coupled variability, GC allows distinguishing four regimes characterized by different coupling: there are years in which the forcing is mainly directed from the atmosphere to the ocean, years in which the ocean forces the atmosphere, years in which the influence is mutual and years in which the coupling is not significant. A composite analysis shows that ocean-driven events have atmospheric anomalies that develop first and are strongest over the ocean, while in events without coupling anomalies develop from the continent where they are strongest and have smaller oceanic extension.

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Section: The Gc Estimatormentioning

confidence: 99%

“…In other words, X is Granger causal of Y if X helps predict Y at some time in the future. Then, the prediction improvement is measured by the Granger causality estimator (GCE) as in Mokhov et al (2011).…”

Section: The Gc Estimatormentioning

confidence: 99%

A study of the air-sea interaction in the South Atlantic Convergence Zone through Granger causality

2014

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“…Analyses of this kind have been applied in climate attribution studies to investigate the influence of one climatic variable on another, e.g. the Granger-causal effect of CO 2 on global temperature (Triacca, 2005;Kodra et al, 2011;Attanasio, 2012), of vegetation and snow coverage on temperature (Kaufmann et al, 2003), of sea surface temperatures on the North Atlantic Oscillation (Mosedale et al, 2006), or of the El Niño-Southern Oscillation on the Indian monsoon (Mokhov et al, 2011). Nonetheless, Granger causality should not be interpreted as "real causality"; one assumes that a time series A Granger causes a time series B if the past of A is helpful in predicting the future of B (see Sect.…”

Section: Introductionmentioning

confidence: 99%

A non-linear Granger-causality framework to investigate climate–vegetation dynamics

et al. 2017

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Abstract. Satellite Earth observation has led to the creation of global climate data records of many important environmental and climatic variables. These come in the form of multivariate time series with different spatial and temporal resolutions. Data of this kind provide new means to further unravel the influence of climate on vegetation dynamics. However, as advocated in this article, commonly used statistical methods are often too simplistic to represent complex climate-vegetation relationships due to linearity assumptions. Therefore, as an extension of linear Granger-causality analysis, we present a novel non-linear framework consisting of several components, such as data collection from various databases, time series decomposition techniques, feature construction methods, and predictive modelling by means of random forests. Experimental results on global data sets indicate that, with this framework, it is possible to detect non-linear patterns that are much less visible with traditional Granger-causality methods. In addition, we discuss extensive experimental results that highlight the importance of considering non-linear aspects of climate-vegetation dynamics.

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“…Also the leading modes of atmospheric variability exhibit nonlinear behavior [43,44] and can Inferring the directionality of coupling with conditional mutual information. in a nonlinear way [45].…”

Section: Discussionmentioning

confidence: 99%

Reliability of Inference of Directed Climate Networks Using Conditional Mutual Information

Hlinka

Hartman

Vejmelka

et al. 2013

Entropy

117

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Across geosciences, many investigated phenomena relate to specific complex systems consisting of intricately intertwined interacting subsystems. Such dynamical complex systems can be represented by a directed graph, where each link denotes an existence of a causal relation, or information exchange between the nodes. For geophysical systems such as global climate, these relations are commonly not theoretically known but estimated from recorded data using causality analysis methods. These include bivariate nonlinear methods based on information theory and their linear counterpart. The trade-off between the valuable sensitivity of nonlinear methods to more general interactions and the potentially higher numerical reliability of linear methods may affect inference regarding structure and variability of climate networks. We investigate the reliability of directed climate networks detected by selected methods and parameter settings, using a stationarized model of dimensionality-reduced surface air temperature data from reanalysis of 60-year global climate records. Overall, all studied bivariate causality methods provided reproducible estimates of climate causality networks, with the linear approximation showing Entropy 2013, 15 2024 higher reliability than the investigated nonlinear methods. On the example dataset, optimizing the investigated nonlinear methods with respect to reliability increased the similarity of the detected networks to their linear counterparts, supporting the particular hypothesis of the near-linearity of the surface air temperature reanalysis data.

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Alternating mutual influence of El-Niño/Southern Oscillation and Indian monsoon

Cited by 85 publications

References 19 publications

A study of the air-sea interaction in the South Atlantic Convergence Zone through Granger causality

A study of the air-sea interaction in the South Atlantic Convergence Zone through Granger causality

A non-linear Granger-causality framework to investigate climate–vegetation dynamics

Reliability of Inference of Directed Climate Networks Using Conditional Mutual Information

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