The El Niño-Southern Oscillation Phenomenon

Sarachik, E. S.; Cane, Mark A.

doi:10.1017/cbo9780511817496

Cited by 285 publications

(190 citation statements)

References 229 publications

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“…T he so-called El Niño-Southern Oscillation (ENSO) is the most important phenomenon of contemporary natural climate variability (1)(2)(3)(4). It can be perceived as a self-organized dynamical see-saw pattern in the Pacific ocean-atmosphere system, featured by rather irregular warm (El Niño) and cold (La Niña) excursions from the long-term mean state.…”

mentioning

confidence: 99%

Improved El Niño forecasting by cooperativity detection

Ludescher

Gozolchiani

Bogachev

et al. 2013

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

162

141

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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.

162

141

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“…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).…”

mentioning

confidence: 99%

“…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).…”

mentioning

confidence: 99%

Network analysis reveals strongly localized impacts of El Niño

Fan

Meng

Ashkenazy

et al. 2017

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

108

101

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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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“…The height of the color bars indicates the variance contribution of the leading PCs, from the first to the fourth: red, green, blue, and purple, in this order. The full list of the index acronyms on the abscissa is given in Table 1. of intensive study over the last three decades [Philander, 1990;Sarachik and Cane, 2010].…”

Section: Principal Components (Pcs) and Optimization Proceduresmentioning

confidence: 99%

“…All four PCs have a highly significant peak, obtained by both methods, in the range of 2.3-2.8 years, which is most likely associated with the global effects of ENSO's quasi-biennial oscillation [Philander, 1990;Sarachik and Cane, 2010;Ghil et al, 2002]. In addition, modes 2 and 3 also exhibit a longer a Note that singular spectrum analysis (SSA) has been used as a pre-filter for the MEM spectrum.…”

Section: Spectral Contentmentioning

confidence: 99%

Global modes of climate variability

Viron

Dickey

Ghil

2013

Geophysical Research Letters

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International audienceThe atmosphere, hydrosphere, and cryosphere form a fully coupled climate system. This system exhibits a number of large-scale phenomena, such as the El Niño-Southern Oscillation, the Asian Monsoon, the North Atlantic Oscillation, and the Madden-Julian Oscillation. While these modes of variability are not exactly periodic, they are oscillatory in character, and their state is monitored using so-called climate indices. Each of these scalar indices is a combination of several climate variables. Here we use a comprehensive set of 25 climate indices for time intervals that range between 1948 and 2011 and estimate an optimal set of lags between these indices to maximize their correlation. We show that most of the index pairs drawn from this set present a significant correlation on interannual time scales. It is also shown that on average, about two thirds of the total variability in each index can be described by using only the four leading principal components of the entire set of lagged indices. Our index set's leading orthogonal modes exhibit several interannual frequencies and capture separately variability associated with the North Atlantic and the North Pacific. These modes are associated, in turn, with large-scale variations of sea surface temperatures. Key PointsMost of the climate indices are cross-correlated significantlyMost of their variability can be captured with only four principal componentsThose components are associated with global signatures in the SST ©2013. American Geophysical Union. All Rights Reserved

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The El Niño-Southern Oscillation Phenomenon

Abstract: Este es un artículo de acceso abierto distribuido bajo los términos de una licencia de uso y distribución Creative Commons Reconocimiento 4.0 Internacional (CC BY 4.0).

Cited by 285 publications

References 229 publications

Improved El Niño forecasting by cooperativity detection

Improved El Niño forecasting by cooperativity detection

Network analysis reveals strongly localized impacts of El Niño

Global modes of climate variability

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