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

Wiedermann, Marc; Radebach, Alexander; Donges, Jonathan F.; Kurths, Jüergen; Donner, Reik V.

doi:10.1002/2016gl069119

Cited by 59 publications

(107 citation statements)

References 70 publications

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“…These nodes may correspond to different channels of electroencephalography (EEG) signals in neural networks [7] or records of climatic variables at different locations of the Earth in so-called climate networks [34,35]. Specifically, the latter have been shown to encode valuable information on the large-scale dynamical organization of spatially extended components of the climate system, such as ocean currents [78] or the El Niño Southern Oscillation [79]. As an example for such functional climate networks, we compute the pairwise Pearson correlation between all N = 10, 224 time series of (i) monthly averaged surface air temperature and (ii) monthly averaged sea level pressure from the NCEP/NCAR 40-year reanalysis project [80] that is provided by the National Center of Oceanic and Atmospheric Research.…”

Section: E Threshold-based Networkmentioning

confidence: 99%

Mapping and discrimination of networks in the complexity-entropy plane

et al. 2017

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Complex networks are usually characterized in terms of their topological, spatial, or informationtheoretic properties and combinations of the associated metrics are used to discriminate networks into different classes or categories. However, even with the present variety of characteristics at hand it still remains a subject of current research to appropriately quantify a network's complexity and correspondingly discriminate between different types of complex networks, like infrastructure or social networks, on such a basis. Here, we explore the possibility to classify complex networks by means of a statistical complexity measure that has formerly been successfully applied to distinguish different types of chaotic and stochastic time series. It is composed of a network's averaged pernode entropic measure characterizing the network's information content and the associated JensonShannon divergence as a measure of disequilibrium. We study 29 real world networks and show that networks of the same category tend to cluster in distinct areas of the resulting complexity-entropy plane. We demonstrate that within our framework, connectome networks exhibit among the highest complexity while, e.g, transportation and infrastructure networks display significantly lower values. Furthermore, we demonstrate the utility of our framework by applying it to families of random scale-free and Watts-Strogatz model networks. We then show in a second application that the proposed framework is useful to objectively construct threshold-based networks, such as functional climate networks or recurrence networks, by choosing the threshold such that the statistical network complexity is maximized.

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Section: E Threshold-based Networkmentioning

confidence: 99%

Mapping and discrimination of networks in the complexity-entropy plane

et al. 2017

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“…From its famous first use in climate science by Tsonis and Roebber [2004], it gained much interested in the community and led to some significant advances in our understanding of climate (e.g. Tsonis et al [2006]; Ebert-Uphoff and Deng [2012]; Fountalis et al [2015]; Wiedermann et al [2016]; and much more). In a field of statistical climatology, a large body of approaches has been used in the past, including empirical orthogonal functions (EOF), maximum covariance analysis (MCA), or canonical correlation analysis (CCA) [von Storch and Zwiers, 2002].…”

Section: Complex Network Paradigmmentioning

confidence: 99%

Spatial and temporal scales of atmospheric dynamics

Jajcay¹

2018

Preprint

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Earth climate, in general, varies on many temporal and spatial scales. In particular, climate observables exhibit recurring patterns and quasi-oscillatory phenomena with different periods. Although these oscillations might be weak in amplitude, they might have a non-negligible influence on variability on shorter time-scales due to cross-scale interactions, recently observed by Paluš [2014b]. This thesis supplies an introductory material for inferring the cross-scale information transfer from observational data, where the time series of interest are obtained using wavelet transform, and possible information transfer is studied using the tools from information theory. Finally, crossscale interactions are studied in two climate phenomena: air temperature variability in Europe, in which we study phase-amplitude coupling from a slower oscillatory mode with an 8-year period on faster variability and its effects, and El Niño/ Southern Oscillation where we observe a causal chain of phase-phase and phase-amplitude couplings among distinct oscillatory modes.

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“…An evolution of PC2 regressed SST anomalies conditions from preceding spring to summer is presented in Figures (a) and (b), reflecting that the second mode of summer precipitation over NA is the response to the SST forcing associated with the developing La Niña conditions over tropical East Pacific and the developing cold SST anomalies over the tropical Indian Ocean (Wiedermann et al, ). SST regressions in the BCC_CSM in Figures (c) and (d) exhibit cold SST circumstances evolved from a weak phase to a strong one in the tropical Pacific, implying that the second mode associated atmospheric responses to the SST forcing in the model are in phase with the developing La Niña condition in the reanalysis.…”

Section: Tele‐responses Of Atmospheric Circulation To Sst Anomaliesmentioning

confidence: 99%

Assessment and correction of BCC_CSM's performance in capturing leading modes of summer precipitation over North Asia

Gong

Dogar

Qiao

et al. 2017

Intl Journal of Climatology

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This article examines the ability of Beijing Climate Center Climate System Model (BCC_CSM) in demonstrating the prediction accuracy and the leading modes of the summer precipitation over North Asia (NA). A dynamic‐statistic combined approach for improving the prediction accuracy and the prediction of the leading modes of the summer precipitation over NA is proposed. Our results show that the BCC_CSM can capture part of the spatial anomaly features of the first two leading modes of NA summer precipitation. Moreover, BCC_CSM regains relationships such that the first and second mode of the empirical orthogonal function (EOF1 and EOF2) of NA summer precipitation, respectively, corresponds to the development of the El Niño and La Niña conditions in the tropical East Pacific. Nevertheless, BCC_CSM exhibits limited prediction skill over most part of NA and presents a deficiency in reproducing the EOF1's and EOF2's spatial pattern over central NA and EOF2's interannual variability. This can be attributed as the possible reasons why the model is unable to capture the correct relationships among the basic climate elements over the central NA, lacks in its ability to reproduce a consistent zonal atmospheric pattern over NA, and has bias in predicting the relevant Sea Surface Temperature (SST) modes over the tropical Pacific and Indian Ocean regions. Based on the proposed dynamic‐statistic combined correction approach, compared with the leading modes of BCC_CSM's original prediction, anomaly correlation coefficients of corrected EOF1/EOF2 with the tropical Indian Ocean SST are improved from 0.18/0.36 to 0.51/0.62. Hence, the proposed correction approach suggests that the BCC_CSM's prediction skill for the summer precipitation prediction over NA and its ability to capture the dominant modes could be certainly improved by choosing proper historical analogue information.

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

Cited by 59 publications

References 70 publications

Mapping and discrimination of networks in the complexity-entropy plane

Mapping and discrimination of networks in the complexity-entropy plane

Spatial and temporal scales of atmospheric dynamics

Assessment and correction of BCC_CSM's performance in capturing leading modes of summer precipitation over North Asia

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