Dominant Imprint of Rossby Waves in the Climate Network

Wang, Yang; Gozolchiani, Avi; Ashkenazy, Yosef; Berezin, Yehiel; Guez, Oded; Havlin, Shlomo

doi:10.1103/physrevlett.111.138501

Cited by 77 publications

(65 citation statements)

References 36 publications

(56 reference statements)

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“…where "mean" and "std" are the mean and SD of the crosscorrelation function, respectively (21,22). We construct networks based on both C y i,j (θ) and W y i,j , and these are consistent with each other.…”

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confidence: 95%

“…A network is composed of nodes and links; in a climate network, the nodes are the geographical locations, and the links are the correlations between them. The "strength" of the links is quantified according to the strength of the correlations between the different nodes (21,26,27).El Niño is probably the strongest climate phenomenon that occurs on interannual time scales (28,29). El Niño refers to the warming of the central and eastern equatorial Pacific Ocean by several degrees ( • C).…”

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Network analysis reveals strongly localized impacts of El Niño

Fan

Meng

Ashkenazy

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

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103

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Climatic conditions influence the culture and economy of societies and the performance of economies. Specifically, El Niño as an extreme climate event is known to have notable effects on health, agriculture, industry, and conflict. Here, we construct directed and weighted climate networks based on near-surface air temperature to investigate the global impacts of El Niño and La Niña. We find that regions that are characterized by higher positive/negative network "in"-weighted links are exhibiting stronger correlations with the El Niño basin and are warmer/cooler during El Niño/La Niña periods. In contrast to non-El Niño periods, these stronger inweighted activities are found to be concentrated in very localized areas, whereas a large fraction of the globe is not influenced by the events. The regions of localized activity vary from one El Niño (La Niña) event to another; still, some El Niño (La Niña) events are more similar to each other. We quantify this similarity using network community structure. The results and methodology reported here may be used to improve the understanding and prediction of El Niño/La Niña events and also may be applied in the investigation of other climate variables.climate | dynamic network | ENSO M ore than a decade ago, networks became the standard framework for studying complex systems (1-5). In recent years, network theory has been implemented in climate sciences to construct "climate networks." These networks have been used successfully to analyze, model, understand, and even predict climate phenomena (6-16). Specific examples of climate network studies include the investigation of the interaction structure of coupled climate subnetworks (17), the multiscale dependence within and among climate variables (18), the temporal evolution and teleconnections of the North Atlantic Oscillation (19,20), the finding of the dominant imprint of Rossby waves (21), the optimal paths of teleconnection (22), the influence of El Niño on remote regions (8,23,24), the distinction of different types of El Niño events (25), and the prediction of these events (15,16). A network is composed of nodes and links; in a climate network, the nodes are the geographical locations, and the links are the correlations between them. The "strength" of the links is quantified according to the strength of the correlations between the different nodes (21,26,27).El Niño is probably the strongest climate phenomenon that occurs on interannual time scales (28,29). El Niño refers to the warming of the central and eastern equatorial Pacific Ocean by several degrees ( • C). La Niña is the cooling of sea surface temperatures (SSTs) in the eastern tropical Pacific Ocean. La Niña usually follows an El Niño event, but not always; the overall phenomenon is referred to as El Niño-Southern Oscillation (ENSO). This cycle occurs every 3-5 y with different magnitudes. There are several indices that quantify the El Niño activity, including the Niño 3.4 Index, the Southern Oscillation Index (SOI) (see, e.g., ref. 30), and the Oceanic Niño ...

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Network analysis reveals strongly localized impacts of El Niño

Fan

Meng

Ashkenazy

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

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“…NCEP/NCAR: reanalysis data (Kalnay et al, 1996 2010; Gozolchiani et al, 2008Gozolchiani et al, , 2011Yamasaki et al, 2009;Paluš et al, 2011;Barreiro et al, 2011;Deza et al, 2013Deza et al, , 2014Martin et al, 2013;Tirabassi and Masoller, 2013). While most of these studies are focused on global climate networks of temperature fields and precipitation (Donges et al, 2009a, b;Tsonis and Roebber, 2004;Tsonis et al, 2006;Gozolchiani et al, 2011;Yamasaki et al, 2008Yamasaki et al, , 2009Scarsoglio et al, 2013), others consider smaller, regional networks that focus on a specific climate phenomenon of interest, such as El Niño Gozolchiani et al, 2008), Rossby waves (Wang et al, 2013), continental rainfall in Germany (Rheinwalt et al, 2012), the South American Monsoon System (SAMS) (Boers et al, 2013), and the Indian Summer Monsoon (Malik et al, 2010(Malik et al, , 2011Rehfeld et al, 2012). In the work by Rehfeld et al (2012) on Indian Summer Monsoon (ISM) dynamics, a paleoclimate network approach has revealed a strong influence of the ISM on the East Asian Summer monsoon during the late Holocene period, but with varying strength according to the warm vs. cold epochs.…”

Section: Climate Network As a Tool For Ism Analysismentioning

confidence: 99%

Topology and seasonal evolution of the network of extreme precipitation over the Indian subcontinent and Sri Lanka

Stolbova

Martin

Bookhagen

et al. 2014

Nonlin. Processes Geophys.

109

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Abstract. This paper employs a complex network approach to determine the topology and evolution of the network of extreme precipitation that governs the organization of extreme rainfall before, during, and after the Indian Summer Monsoon (ISM) season. We construct networks of extreme rainfall events during the ISM (June-September), post-monsoon (October-December), and pre-monsoon (March-May) periods from satellite-derived (Tropical Rainfall Measurement Mission, TRMM) and rain-gauge interpolated (Asian Precipitation Highly Resolved Observational Data Integration Towards the Evaluation of Water Resources, APHRODITE) data sets. The structure of the networks is determined by the level of synchronization of extreme rainfall events between different grid cells throughout the Indian subcontinent. Through the analysis of various complex-network metrics, we describe typical repetitive patterns in North Pakistan (NP), the Eastern Ghats (EG), and the Tibetan Plateau (TP). These patterns appear during the pre-monsoon season, evolve during the ISM, and disappear during the post-monsoon season. These are important meteorological features that need further attention and that may be useful in ISM timing and strength prediction.

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“…Applications to climate science have received growing attention since 2004, 25 when graph theory was applied to the investigation of global geopotential height. Network analysis has been since applied to studies of numerous climate modes, [26][27][28][29][30][31] of atmospheric and oceanic circulation drivers, [32][33][34][35] of precipitation in different time periods, [36][37][38] and of Rossby wave dynamics. 39 Generally networks are constructed as undirected, binary graphs.…”

Section: Complex Network Analysis and Climate Sciencementioning

confidence: 99%

Advancing climate science with knowledge-discovery through data mining

et al. 2018

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Global climate change represents one of the greatest challenges facing society and ecosystems today. It impacts key aspects of everyday life and disrupts ecosystem integrity and function. The exponential growth of climate data combined with KnowledgeDiscovery through Data-mining (KDD) promises an unparalleled level of understanding of how the climate system responds to anthropogenic forcing. To date, however, this potential has not been fully realized, in stark contrast to the seminal impacts of KDD in other fields such as health informatics, marketing, business intelligence, and smart city, where big data science contributed to several of the most recent breakthroughs. This disparity stems from the complexity and variety of climate data, as well as the scientific questions climate science brings forth. This perspective introduces the audience to benefits and challenges in mining large climate datasets, with an emphasis on the opportunity of using a KDD process to identify patterns of climatic relevance. The focus is on a particular method, δ-MAPS, stemming from complex network analysis. δ-MAPS is especially suited for investigating local and non-local statistical interrelationships in climate data and here is used is to elucidate both the techniques, as well as the results-interpretation process that allows extracting new insight. This is achieved through an investigation of similarities and differences in the representation of known teleconnections between climate reanalyzes and climate model outputs.

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Dominant Imprint of Rossby Waves in the Climate Network

Cited by 77 publications

References 36 publications

Network analysis reveals strongly localized impacts of El Niño

Network analysis reveals strongly localized impacts of El Niño

Topology and seasonal evolution of the network of extreme precipitation over the Indian subcontinent and Sri Lanka

Advancing climate science with knowledge-discovery through data mining

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