Regional and inter-regional effects in evolving climate networks

Hlinka, Jaroslav; Hartman, David; Jajcay, Nikola; Vejmelka, Martin; Donner, Reik V.; Marwan, Norbert; Kurths, Jürgen; Paluš, Milan

doi:10.5194/npg-21-451-2014

Cited by 24 publications

(23 citation statements)

References 44 publications

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“…The network is represented by its binary adjacency matrix A with entries A ij = 1 if two nodes i and j are linked and A ij = 0 otherwise [Donges et al, 2009b;Boers et al, 2013;Stolbova et al, 2014]. An extension of this procedure is the usage of an edge-weighted adjacency matrix W where W ij = 0 denotes the absence of a link, but W ij > 0 denotes its strength (e.g., the pairwise correlation) [Barrat et al, 2004;Hlinka et al, 2014;Zemp et al, 2014].…”

Section: Methodsmentioning

confidence: 99%

A climate network‐based index to discriminate different types of El Niño and La Niña

Wiedermann

Radebach²,

Donges

et al. 2016

Geophysical Research Letters

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El Niño exhibits distinct Eastern Pacific (EP) and Central Pacific (CP) types which are commonly, but not always consistently, distinguished from each other by different signatures in equatorial climate variability. Here we propose an index based on evolving climate networks to objectively discriminate between both flavors by utilizing a scalar‐valued measure that quantifies spatial localization and dispersion in global teleconnections of surface air temperature. Our index displays a sharp peak (high localization) during EP events, whereas during CP events (larger dispersion) it remains close to the values observed during normal periods. In contrast to previous classification schemes, our approach specifically accounts for El Niño's global impacts. We confirm recent El Niño classifications for the years 1951 to 2014 and assign types to those cases where former works yielded ambiguous results. Ultimately, we demonstrate that our index provides a similar discrimination of La Niña episodes into two distinct types.

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Section: Methodsmentioning

confidence: 99%

A climate network‐based index to discriminate different types of El Niño and La Niña

Wiedermann

Radebach²,

Donges

et al. 2016

Geophysical Research Letters

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“…The issue of interdependent predictors is not limited to pair-wise relationships: it has been shown that various variability modes in the climate system are intertwined in quite complex networks, with nontrivial time-delayed relations among oscillations in different regions (e.g., Wyatt et al, 2012). Intricacy of such structures becomes even more apparent when generalized links are studied, unrestricted to just the conventional variability modes (e.g., Hlinka et al, 2013Hlinka et al, , 2014a.…”

Section: Discussionmentioning

confidence: 99%

Imprints of climate forcings in global gridded temperature data

2016

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Abstract. Monthly near-surface temperature anomalies from several gridded data sets (GISTEMP, Berkeley Earth, MLOST, HadCRUT4, 20th Century Reanalysis) were investigated and compared with regard to the presence of components attributable to external climate forcings (associated with anthropogenic greenhouse gases, as well as solar and volcanic activity) and to major internal climate variability modes (El Niño/Southern Oscillation, North Atlantic Oscillation, Atlantic Multidecadal Oscillation, Pacific Decadal Oscillation and variability characterized by the Trans-Polar Index). Multiple linear regression was used to separate components related to individual explanatory variables in local monthly temperatures as well as in their global means, over the 1901-2010 period. Strong correlations of temperature and anthropogenic forcing were confirmed for most of the globe, whereas only weaker and mostly statistically insignificant connections to solar activity were indicated. Imprints of volcanic forcing were found to be largely insignificant in the local temperatures, in contrast to the clear volcanic signature in their global averages. Attention was also paid to the manifestations of short-term time shifts in the responses to the forcings, and to differences in the spatial fingerprints detected from individual temperature data sets. It is shown that although the resemblance of the response patterns is usually strong, some regional contrasts appear. Noteworthy differences from the other data sets were found especially for the 20th Century Reanalysis, particularly for the components attributable to anthropogenic forcing over land, but also in the response to volcanism and in some of the teleconnection patterns related to the internal climate variability modes.

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“…The spatial scales for climate subsystems can be inferred from climate data using classical approaches of rotated principal component analysis combined with more recent surrogate data testing approach [58], or the very modern approach of complex networks [59]. Evolving climate networks can track important short-term climate influencing events such as El Niño episodes or large volcanic eruptions [60] and discern regional from global effects [61]. Besides considering specific spatial scales, a more detailed look on repetitive patterns on specific temporal scales in the air temperature and other meteorological data has led to an identification of oscillatory phenomena possibly possessing a nonlinear origin and exhibiting phase synchronization between oscillatory modes extracted either from different types of climate-related data or data recorded at different locations on the Earth [9][10][11][12][13]37].…”

Section: Cross-scale Information Transfer In Atmospheric Dynamicsmentioning

confidence: 99%

Cross-Scale Interactions and Information Transfer

Paluš

2014

Entropy

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An information-theoretic approach for detecting interactions and information transfer between two systems is extended to interactions between dynamical phenomena evolving on different time scales of a complex, multiscale process. The approach is demonstrated in the detection of an information transfer from larger to smaller time scales in a model multifractal process and applied in a study of cross-scale interactions in atmospheric dynamics. Applying a form of the conditional mutual information and a statistical test based on the Fourier transform and multifractal surrogate data to about a century long records of daily mean surface air temperature from various European locations, an information transfer from larger to smaller time scales has been observed as the influence of the phase of slow oscillatory phenomena with the periods around 6-11 years on the amplitudes of the variability characterized by the smaller temporal scales from a few months to 4-5 years. These directed cross-scale interactions have a non-negligible effect on interannual air temperature variability in a large area of Europe.

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Regional and inter-regional effects in evolving climate networks

Cited by 24 publications

References 44 publications

A climate network‐based index to discriminate different types of El Niño and La Niña

A climate network‐based index to discriminate different types of El Niño and La Niña

Imprints of climate forcings in global gridded temperature data

Cross-Scale Interactions and Information Transfer

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