What Do Networks Have to Do with Climate?

Tsonis, Anastasios A.; Swanson, Kyle L.; Roebber, Paul J.

doi:10.1175/bams-87-5-585

Cited by 298 publications

(324 citation statements)

References 47 publications

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“…Instead of considering the time dependence of climate records at single grid points i, we study the time evolution of the interactions (teleconnections) between pairs of grid points i and j, which are represented by the strengths of the cross-correlations between the climate records at these sites. The interactions can be considered as links in a climate network where the nodes are the grid points (31)(32)(33)(34). Recent empirical studies have shown that in the large-scale climate network the links tend to weaken significantly during El Niño episodes, and this phenomenon is most pronounced for those links that connect the "El Niño basin" (solid circles in Fig.…”

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confidence: 99%

Improved El Niño forecasting by cooperativity detection

Ludescher

Gozolchiani

Bogachev

et al. 2013

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

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The authors note that: "Due to a minor technical error in the calculation of the climatological average of the considered atmospheric temperatures for each calendar day, Figs. 2 and 3 appeared incorrectly. The amended figures and their legends are provided below. The main message and the interpretation of our paper remain unaffected by this correction." "The figures in the Supporting Information have been exchanged accordingly. We'd like to add that for the calculation of the climatological average, the leap days have been removed, and in the prediction phase, only the data from the past up to the prediction date have been considered. In addition, we note that for calculating the link strengths S ij (t), not the cross-covariance function C ij (t) (τ) has been considered but the absolute values of the corresponding cross-correlation functions c ij (t) (τ). When averaging over all link strengths, we obtain the time dependent average link strength S(t). In the learning phase, we compare S(t) with decision thresholds above its mean to obtain the optimal threshold used in the prediction phase." Fig. 2. The forecasting algorithm. We compare the average link strength S(t) in the climate network (red curve) with a decision threshold Θ (horizontal line, here Θ = 2.82) (left scale) with the standard NINO3.4 index (right scale), between January 1, 1950 and December 31, 2011. Only thresholds above the average of S(t) in the learning phase are considered. When the link strength crosses the threshold from below outside an El Niño episode, we give an alarm and predict that an El Niño episode will start in the following calendar year. The El Niño episodes (when the NINO3.4 index is above 0.5°C for at least 5 mo) are shown by the filled blue areas. The first half of the record (A) is the learning phase where we optimize the decision threshold. In the second half (B), we use the threshold obtained in (A) to predict El Niño episodes. Correct predictions are marked by green arrows and false alarms by dashed arrows. The index n marks a nonpredicted El Niño episode. To resolve by eye the accurate positions of the alarms, we show in SI Appendix, Fig. S5, magnifications of those parts of Fig. 2 where the crossings or non-crossings are difficult to see clearly without magnification. We also show the alarms for the slightly larger threshold Θ = 2.83 (SI Appendix, Fig. S6), which yields the same performance in the learning phase and one more false alarm in the prediction phase. The lead time between the prediction and the beginning of the El Nino episodes is 1.01 ± 0.28 y, while the lead time to the maximal NINO3.4 value is 1.35 ± 0.47 y.

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mentioning

confidence: 99%

Improved El Niño forecasting by cooperativity detection

Ludescher

Gozolchiani

Bogachev

et al. 2013

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

157

141

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“…To resolve this problem, we recently introduced an alternative forecasting approach (31) based on complex networks analysis (32)(33)(34)(35) that can considerably shift the probabilistic prediction horizon. The approach exploits the remarkable observation that a large-scale cooperative mode linking the "El Niño basin" (i.e., the equatorial Pacific corridor) and the rest of the Pacific ocean ( Fig.…”

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confidence: 99%

Very early warning of next El Niño

Ludescher

Gozolchiani

Bogachev

et al. 2014

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

192

141

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The most important driver of climate variability is the El Niño Southern Oscillation, which can trigger disasters in various parts of the globe. Despite its importance, conventional forecasting is still limited to 6 mo ahead. Recently, we developed an approach based on network analysis, which allows projection of an El Niño event about 1 y ahead. Here we show that our method correctly predicted the absence of El Niño events in 2012 and 2013 and now announce that our approach indicated (in September 2013 already) the return of El Niño in late 2014 with a 3-in-4 likelihood. We also discuss the relevance of the next El Niño to the question of global warming and the present hiatus in the global mean surface temperature.dynamic networks | ENSO | spring barrier

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“…It was recently suggested that climate fields such as temperature and geopotential height at a certain pressure level can be represented as a climate network [1]. In this network, different regions of the world are represented as nodes which communicate by exchanging heat, material, and by direct forces.…”

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Emergence of El Niño as an Autonomous Component in the Climate Network

2011

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We construct and analyze a climate network which represents the interdependent structure of the climate in different geographical zones and find that the network responds in a unique way to El-Niño events. Analyzing the dynamics of the climate network shows that when El-Niño events begin, the El-Niño basin partially loses its influence on its surroundings. After typically three months, this influence is restored while the basin loses almost all dependence on its surroundings and becomes autonomous. The formation of an autonomous basin is the missing link to understand the seemingly contradicting phenomena of the afore-noticed weakening of the interdependencies in the climate network during El-Niño and the known impact of the anomalies inside the El-Niño basin on the global climate system. In this network, different regions of the world are represented as nodes which communicate by exchanging heat, material, and by direct forces. These interactions are represented by the links of the climate network. Interactions between two nodes may also exist due to processes which take place outside the atmospheric pressure level or through interactions with the ocean and the lands. Each link is quantified by a weight based on measures of similarity between the time series (e.g. correlations) of the corresponding individual nodes (see [2] for an experimental evidence for the relations between heat exchange and synchronized fluctuations of the temperature field).Recent studies [3][4][5] show that many links in the climate network break during El-Niño events. The climate network contains several types of links, that have different levels of responsiveness to El-Niño. From the maps of Tsonis and Swanson [4], one can locate the responsive nodes (the nodes attached to the most responsive links) to be in the pacific El-Niño Basin (ENB) [6]. These maps together with the tremendous impact of the El-Niño Southern Oscillations on world climate, suggest that ENB have a unique dynamical role in the dynamics of the climate network. Indeed ENB has unique topological properties. Its connectivity and clustering coefficient fields studied by Donges et. al. [7] can be distinguished from their surrounding by their particularly higher values. Also, the betweenness centrality field [8] in ENB is very low. However, the dynamics related to the interaction of ENB with its surroundings and the origin of its unique features are still not known [9].In this Letter we follow the dynamics of the climate network in time, between the years 1979 and 2009 where eight El-Niño events took place. We identify a cluster around ENB that shows a clear autonomous behavior during El-Niño epochs, and determine the dynamics of its interactions with the surroundings. We also find an epoch of a decreased influence of ENB on the surroundings, typically three months before the emergence of the autonomous behavior. Our findings resolve the seemingly contradictory situation of decreased interactions of ENB with its surroundings on one hand, as explored in previous works [3][4...

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What Do Networks Have to Do with Climate?

Abstract: Advances in understanding coupling in complex networks offer new ways of studying the collective behavior of interactive systems and already have yielded new insights in many areas of science.

Cited by 298 publications

References 47 publications

Improved El Niño forecasting by cooperativity detection

Improved El Niño forecasting by cooperativity detection

Very early warning of next El Niño

Emergence of El Niño as an Autonomous Component in the Climate Network

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