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# Paper - Ludescher et al - Improved El Niño forecasting by cooperativity detection

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51.
edited September 2014

Abstract

Using measurements of atmospheric temperatures, we create a weighted network in different regions on the globe. The weight of each link is composed of two numbers — the correlations strength between the two places and the time delay between them. A characterization of the different typical links that exist is presented. A surprising outcome of the analysis is a new dynamical quantity of link blinking that seems to be sensitive especially to El Nino even in geographical regimes outside the Pacific Ocean.

Abstract

Although the El Nino Southern Oscillation (ENSO) is the most prominent phenomenon of climate variability and affects weather and climate in large parts of the world, an efficient prediction has only been achieved up to six months ahead. Here we develop a dynamical network approach which allows a prediction of El Nino about one year ahead with a hit rate above 0.5 at a very small false alarm rate (about 0.1). In the considered network, the nodes are grid points in the Pacific and the strengths of the links (teleconnections) between them are characterized by the cross-correlations of the observed atmospheric surface temperatures at the grid points. We focus on the time span between 1950 and 2011 where high quality observational data are available. We find that well before an El-Nino episode, the links between the El Nino basin and the rest of the Pacific tend to strengthen, and show explicitly that this feature can be used for an efficient forecast of the next El Nino episode about one year in advance.

Abstract

The observed relations between temperature fluctuations in different geographical regions yields a very robust climate network pattern that remains highly stable during time. Here, we break up the different elements that contribute to this stability, and quantify them [Y. Berezin, et. al., Nat. Sci. Rep. (2012)]. Due to its high stability, the climate network adjacency matrix can be regarded as a spatial field on its own right, and its typical profiles indeed have been the topic of recent studies. We have demonstrated [K. Yamasaki et. al. PRL (2008),A. Gozolchiani et. al. EPL (2008)] that during El-Nino times large portions of this field have a reduced value, corresponding to a less correlated atmosphere. We are now able to pinpoint a peculiar and rich pattern in this effect - the unique autonomous component in the eastern pacific [A. Gozolchiani et. al. PRL 107, 148501 (2011)]. In contrast to our and others earlier works, the different stages of the ENSO cycle come out as the 2 dominant K-means centroids, without pre-identification based on ENSO indices. Finally, the different feedback mechanisms which contribute to events are shown to be reflected in the network profile.

Abstract

We construct a network from climate records of different geographical sites in the North Atlantic. A link between two sites represents the cross-correlations between the records of each site. We find that within the different phases of the North Atlantic Oscillation (NAO) the correlation values of the links are significantly different. By setting an optimize threshold on the correlation values, we find that the number of strong links in the network is increased during times of positive NAO indices, and decreased during times of negative NAO indices. We find a pronounced sensitivity of the network structure to the oscillations which is significantly higher compared to the observed response of spatial average of the records. Our result suggests a new measure that tracks the NAO pattern.

Abstract

The global temperature anomaly field within an isobar, T(t), can be regarded as a multidimensional vector. Small regional portions of it may be usefully analyzed in isolation, and show typical robustness of pattern, i.e. few preferred directions in hyper-space. However,when viewed in its entirety, T(t) acts almost randomly. The field of cross covariances between each pair of coordinates of T(t) composes a network of links, which show a much more robust behavior. Each of the components of this climate network typically vary by 10 to 30%, and the connectivity structure in its entirety, when represented by a direction in the multi-dimensional vector space, vary by 10 to 15%.

On top of this robust backbone, we follow the dynamics of the most pronounced autonomous cluster, using pattern recognition. This cluster is confined to the equatorial pacific region, which broadens as a function of altitude. The autonomous property is quantified as a function of time, and is found to strongly depend on the magnitude of the event. The two extremes of the spatial reminiscent patterns of an event, the cold tongue and the warm pool patterns, are shown to influence the connectivity of the autonomous cluster in a significantly different manner.

Abstract

Different definitions of links in climate networks may lead to considerably different network topologies. We construct a network from climate records of surface level atmospheric temperature in different geographical sites around the globe using two commonly used definitions of links. Utilizing detrended fluctuation analysis, shuffled surrogates and separation analysis of maritime and continental records, we find that one of the major influences on the structure of climate networks is due to the auto-correlation in the records, that may introduce spurious links. This may explain why different methods could lead to different climate network topologies.

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52.

Abstract

This paper is a synthesis of work spanning the last 25 years. It is largely based on the use of climate networks to identify climate subsystems and to subsequently study how their collective behavior explains decadal variability. The central point is that a network of coupled nonlinear subsystems may at times synchronize. If during synchronization the coupling between the subsystems increases, the synchronous state may be destroyed, shifting climate to a new regime. This climate shift manifests itself as a change in global temperature trend. This mechanism, which is consistent with the theory of synchronized chaos, appears to be a very robust mechanism of the climate system. It is found in the instrumental records and in forced and unforced climate simulations, as well as in proxy records spanning several centuries.

Comment Source:* Anastasios A. Tsonis, [Climate subsystems: pacemakers of decadal climate variability (2011)](http://www.agu.org/books/gm/v196/2011GM001053/2011GM001053.pdf) Abstract > This paper is a synthesis of work spanning the last 25 years. It is largely based on the use of climate networks to identify climate subsystems and to subsequently study how their collective behavior explains decadal variability. The central point is that a network of coupled nonlinear subsystems may at times synchronize. If during synchronization the coupling between the subsystems increases, the synchronous state may be destroyed, shifting climate to a new regime. This climate shift manifests itself as a change in global temperature trend. This mechanism, which is consistent with the theory of synchronized chaos, appears to be a very robust mechanism of the climate system. It is found in the instrumental records and in forced and unforced climate simulations, as well as in proxy records spanning several centuries.
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53.

In #44 John said:

Actually you gave me a fun idea, Graham! In a sandpile when the sand is at the critical angle of repose, as steep as possible, small landslides occur... and at least in theoretical models, these landslides are roughly scale-invariant: there are small ones and big ones and bigger ones, with the frequency of a landslide of size $x$ being $\propto x^{-p}$ for some power $p$. Under some conditions sand naturally organizes itself into dunes that are near the critical angle of repose: this is called self-organized criticality. The idea is that this system naturally has a second-order phase transition as some sort of attractor.

Maybe Pacific warm water that's just about ready to slosh back east is a bit like a sandpile at its critical angle of repose! If so, there might be a second-order phase transition here.

I feel this idea is a overly naive, but it might have some merit, or lead to some better ideas.

I was thinking about this idea and searched the forum to see if it came up before. The discussions of sloshing driving El Nino sound a lot like self organized criticality to me. That suggests one possible approach.

Bialek, Nemenman & co as well as Sejnowski & Saremi have papers on measuring criticality in complex natural signals, particularly images and neural data by treating the pixel/signal intensities as the order parameter. This approach could be applied to the various gridded data sets like the NOAA surface tempreature, pressure, humidity ... data sets. It sounds like the they should be in a near critical state most of the time, and El Nino's should correspond to departures from criticality.

I have seen a paper claiming that epilepsy attacks are departures from criticality I also think one that claims it for stock market crashes, but everything eventually get claimed to cause those.

Link strength sounds like a partial indicator of criticality. Looking for criticality on the full data could be more promising.

Comment Source:In #44 John said: >Actually you gave me a fun idea, Graham! In a sandpile when the sand is at the [critical angle of repose](http://en.wikipedia.org/wiki/Angle_of_repose), as steep as possible, small landslides occur... and at least in theoretical models, these landslides are roughly scale-invariant: there are small ones and big ones and bigger ones, with the frequency of a landslide of size $x$ being $\propto x^{-p}$ for some power $p$. Under some conditions sand naturally organizes itself into dunes that are near the critical angle of repose: this is called [self-organized criticality](http://en.wikipedia.org/wiki/Self-organized_criticality). The idea is that this system naturally has a second-order phase transition as some sort of attractor. >Maybe Pacific warm water that's just about ready to slosh back east is a bit like a sandpile at its critical angle of repose! If so, there might be a second-order phase transition here. >I feel this idea is a overly naive, but it might have some merit, or lead to some better ideas. I was thinking about this idea and searched the forum to see if it came up before. The discussions of sloshing driving El Nino sound a lot like self organized criticality to me. That suggests one possible approach. Bialek, Nemenman & co as well as Sejnowski & Saremi have papers on measuring criticality in complex natural signals, particularly images and neural data by treating the pixel/signal intensities as the order parameter. This approach could be applied to the various gridded data sets like the NOAA surface tempreature, pressure, humidity ... data sets. It sounds like the they should be in a near critical state most of the time, and El Nino's should correspond to departures from criticality. I have seen a paper claiming that epilepsy attacks are departures from criticality I also think one that claims it for stock market crashes, but everything eventually get claimed to cause those. Link strength sounds like a partial indicator of criticality. Looking for criticality on the full data could be more promising.