Climate as complex networks

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The field of complex networks has emerged as an important area of science to generate novel insights into nature of complex systems [1] The application of network theory to climate science is a young and emerging field. [2] [3] [4] To identify and analyze patterns in global climate, scientists model climate data as complex networks.

Contents

Unlike most real-world networks where nodes and edges are well defined, in climate networks, nodes are identified as the sites in a spatial grid of the underlying global climate data set, which can be represented at various resolutions. Two nodes are connected by an edge depending on the degree of statistical similarity (that may be related to dependence) between the corresponding pairs of time-series taken from climate records. [3] [5] The climate network approach enables novel insights into the dynamics of the climate system over different spatial and temporal scales. [3]

Construction of climate networks

Depending upon the choice of nodes and/or edges, climate networks may take many different forms, shapes, sizes and complexities. Tsonis et al. introduced the field of complex networks to climate. In their model, the nodes for the network were constituted by a single variable (500 hPa) from NCEP/NCAR Reanalysis datasets. In order to estimate the edges between nodes, correlation coefficient at zero time lag between all possible pairs of nodes were estimated. A pair of nodes was considered to be connected, if their correlation coefficient is above a threshold of 0.5. [1]

Steinhaeuser and team introduced the novel technique of multivariate networks in climate by constructing networks from several climate variables separately and capture their interaction in multivariate predictive model. It was demonstrated in their studies that in context of climate, extracting predictors based on cluster attributes yield informative precursors to improve predictive skills. [5]

Kawale et al. presented a graph based approach to find dipoles in pressure data. Given the importance of teleconnection, this methodology has potential to provide significant insights. [6]

Imme et al. introduced a new type of network construction in climate based on temporal probabilistic graphical model, which provides an alternative viewpoint by focusing on information flow within network over time. [7]

Agarwal et al. proposed advanced linear [8] and nonlinear [9] methods to construct and investigate climate networks at different timescales. Climate networks constructed using SST datasets at different timescale averred that multi-scale analysis of climatic processes holds the promise of better understanding the system dynamics that may be missed when processes are analyzed at one timescale only [10]

Applications of climate networks

Climate networks enable insights into the dynamics of climate system over many spatial scales. The local degree centrality and related measures have been used to identify super-nodes and to associate them to known dynamical interrelations in the atmosphere, called teleconnection patterns. It was observed that climate networks possess “small world” properties owing to the long-range spatial connections. [2]

Steinhaeuser et al. applied complex networks to explore the multivariate and multi-scale dependence in climate data. Findings of the group suggested a close similarity of observed dependence patterns in multiple variables over multiple time and spatial scales. [4]

Tsonis and Roeber investigated the coupling architecture of the climate network. It was found that the overall network emerges from intertwined subnetworks. One subnetwork is operating at higher altitudes and other is operating in the tropics, while the equatorial subnetwork acts as an agent linking the 2 hemispheres . Though, both networks possess Small World Property, the 2 subnetworks are significantly different from each other in terms of network properties like degree distribution. [11]

Donges et al. applied climate networks for physics and nonlinear dynamical interpretations in climate. The team used measure of node centrality, betweenness centrality (BC) to demonstrate the wave-like structures in the BC fields of climate networks constructed from monthly averaged reanalysis and atmosphere-ocean coupled general circulation model (AOGCM) surface air temperature (SAT) data. [12]

Teleconnection path

Teleconnections are spatial patterns in the atmosphere that link weather and climate anomalies over large distances across the globe. Teleconnections have the characteristics that they are persistent, lasting for 1 to 2 weeks, and often much longer, and they are recurrent, as similar patterns tend to occur repeatedly. The presence of teleconnections is associated with changes in temperature, wind, precipitation, atmospheric variables of greatest societal interest. [13]

Computational issues and challenges

There are numerous computational challenges that arise at various stages of the network construction and analysis process in field of climate networks: [14]

  1. Calculating the pair-wise correlations between all grid points is a non-trivial task.
  2. Computational demands of network construction, which depends upon the resolution of spatial grid.
  3. Generation of predictive models from the data poses additional challenges.
  4. Inclusion of lag and lead effects over space and time is a non-trivial task.

See also

Related Research Articles

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<span class="mw-page-title-main">Wavelet</span> Function for integral Fourier-like transform

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<span class="mw-page-title-main">Small-world network</span> Graph where most nodes are reachable in a small number of steps

A small-world network is a graph characterized by a high clustering coefficient and low distances. On an example of social network, high clustering implies the high probability that two friends of one person are friends themselves. The low distances, on the other hand, mean that there is a short chain of social connections between any two people. Specifically, a small-world network is defined to be a network where the typical distance L between two randomly chosen nodes grows proportionally to the logarithm of the number of nodes N in the network, that is:

<span class="mw-page-title-main">Complex network</span> Network with non-trivial topological features

In the context of network theory, a complex network is a graph (network) with non-trivial topological features—features that do not occur in simple networks such as lattices or random graphs but often occur in networks representing real systems. The study of complex networks is a young and active area of scientific research inspired largely by empirical findings of real-world networks such as computer networks, biological networks, technological networks, brain networks, climate networks and social networks.

<span class="mw-page-title-main">Community structure</span> Concept in graph theory

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<span class="mw-page-title-main">Boolean network</span> Discrete set of boolean variables

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<span class="mw-page-title-main">Jürgen Kurths</span> German physicist

Jürgen Kurths is a German physicist and mathematician. He is senior advisor in the research department Complexity Sciences of the Potsdam Institute for Climate Impact Research, a Professor of Nonlinear Dynamics at the Institute of Physics at the Humboldt University, Berlin, and a 6th-century chair for Complex Systems Biology at the Institute for Complex Systems and Mathematical Biology at Kings College, Aberdeen University (UK). His research is mainly concerned with nonlinear physics and complex systems sciences and their applications to challenging problems in Earth system, physiology, systems biology and engineering.

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<span class="mw-page-title-main">Bianconi–Barabási model</span>

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Michael Ghil is an American and European mathematician and physicist, focusing on the climate sciences and their interdisciplinary aspects. He is a founder of theoretical climate dynamics, as well as of advanced data assimilation methodology. He has systematically applied dynamical systems theory to planetary-scale flows, both atmospheric and oceanic. Ghil has used these methods to proceed from simple flows with high temporal regularity and spatial symmetry to the observed flows, with their complex behavior in space and time. His studies of climate variability on many time scales have used a full hierarchy of models, from the simplest ‘toy’ models all the way to atmospheric, oceanic and coupled general circulation models. Recently, Ghil has also worked on modeling and data analysis in population dynamics, macroeconomics, and the climate–economy–biosphere system.

<span class="mw-page-title-main">Auroop Ratan Ganguly</span> American scientist

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References

  1. 1 2 Albert, Réka; Barabási, Albert-László (2002). "Statistical mechanics of complex networks". Reviews of Modern Physics. 74 (1): 47–97. arXiv: cond-mat/0106096 . Bibcode:2002RvMP...74...47A. doi:10.1103/RevModPhys.74.47. ISSN   0034-6861. S2CID   60545.
  2. 1 2 Tsonis, Anastasios A.; Swanson, Kyle L.; Roebber, Paul J. (2006). "What Do Networks Have to Do with Climate?". Bulletin of the American Meteorological Society. 87 (5): 585–595. Bibcode:2006BAMS...87..585T. doi: 10.1175/BAMS-87-5-585 . ISSN   0003-0007.
  3. 1 2 3 Donges, J. F.; Zou, Y.; Marwan, N.; Kurths, J. (2009). "Complex Networks in Climate Dynamics". The European Physical Journal Special Topics. 174 (1). Springer-Verlag: 157–179. arXiv: 0907.4359 . Bibcode:2009EPJST.174..157D. doi:10.1140/epjst/e2009-01098-2. S2CID   2375970.
  4. 1 2 Steinhaeuser, Karsten; Ganguly, Auroop R.; Chawla, Nitesh V. (2011). "Multivariate and multiscale dependence in the global climate system revealed through complex networks". Climate Dynamics. 39 (3–4): 889–895. Bibcode:2012ClDy...39..889S. doi:10.1007/s00382-011-1135-9. ISSN   0930-7575. S2CID   12086088.
  5. 1 2 Steinhaeuser, K.; Chawla, N.V.; Ganguly, A.R. (2010). "Complex Networks as a Unified Framework for Descriptive Analysis and Predictive Modeling in climate science". Statistical Analysis and Data Mining. 4 (5). John Wiley & Sons, Inc.: 497–511. doi:10.1002/sam.10100. S2CID   6035317.
  6. ↑ Kawale J.; Liess S.; Kumar A.; Steinbach M.; Ganguly A.R.; Samatova F.; Semazzi F.; Snyder K.; Kumar V. (2011). "Data Guided Discovery of Dynamic Climate Dipoles" (PDF). Proceedings of the 2011 Conference on Intelligent Data Understanding, CIDU 2011, October 19–21, 2011, Mountain View, California: 30–44.
  7. ↑ Imme, Ebert-Uphoff; Deng, Yi (2012). "A new type of climate network based on probabilistic graphical models: Results of boreal winter versus summer". Geophysical Research Letters. 39 (19). Springer-Verlag: 157–179. Bibcode:2012GeoRL..3919701E. doi: 10.1029/2012GL053269 .
  8. ↑ Agarwal, Ankit; Maheswaran, Rathinasamy; Marwan, Norbert; Caesar, Levke; Kurths, Jürgen (November 2018). "Wavelet-based multiscale similarity measure for complex networks" (PDF). The European Physical Journal B. 91 (11). doi:10.1140/epjb/e2018-90460-6. eISSN   1434-6036. ISSN   1434-6028. S2CID   254116434.
  9. ↑ Agarwal, Ankit; Marwan, Norbert; Rathinasamy, Maheswaran; Merz, Bruno; Kurths, Jürgen (13 October 2017). "Multi-scale event synchronization analysis for unravelling climate processes: a wavelet-based approach". Nonlinear Processes in Geophysics. 24 (4): 599–611. doi: 10.5194/npg-24-599-2017 . eISSN   1607-7946. S2CID   28114574.
  10. ↑ Agarwal, Ankit; Caesar, Levke; Marwan, Norbert; Maheswaran, Rathinasamy; Merz, Bruno; Kurths, Jürgen (19 June 2019). "Network-based identification and characterization of teleconnections on different scales". Scientific Reports. 9 (1): 8808. doi:10.1038/s41598-019-45423-5. eISSN   2045-2322. PMC   6584743 . PMID   31217490.
  11. ↑ Tsonis, A.A.; Roebber, P.J. (2004). "The architecture of the climate network". Physica A: Statistical Mechanics and Its Applications. 333: 497–504. Bibcode:2004PhyA..333..497T. doi:10.1016/j.physa.2003.10.045. ISSN   0378-4371.
  12. ↑ Donges, J. F.; Zou, Y.; Marwan, N.; Kurths, J. (2009). "The backbone of the climate network". EPL. 87 (4): 48007. arXiv: 1002.2100 . Bibcode:2009EL.....8748007D. doi:10.1209/0295-5075/87/48007. ISSN   0295-5075. S2CID   11225385.
  13. ↑ Feldstein, Steven B.; Franzke, Christian L. E. (January 2017). "Atmospheric Teleconnection Patterns". In Franzke, Christian L. E.; Okane, Terence J. (eds.). Nonlinear and Stochastic Climate Dynamics. pp. 54–104. doi:10.1017/9781316339251.004. ISBN   9781316339251 . Retrieved 2019-12-07.
  14. ↑ Steinhaeuser K.; Chawla N.V.; Ganguly A.R. (2010). "Complex Network in Climate Science". Conference on Intelligent Data Understanding: 16–26.
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