research-article

An exploration of climate data using complex networks

Authors:

Karsten Steinhaeuser,

Nitesh V. Chawla,

Auroop R. GangulyAuthors Info & Claims

ACM SIGKDD Explorations Newsletter, Volume 12, Issue 1

Pages 25 - 32

https://doi.org/10.1145/1882471.1882476

Published: 09 November 2010 Publication History

Abstract

Climate change is a pressing focus of research, social and economic concern, and political attention. According to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC), increased frequency of extreme events will only intensify the occurrence of natural hazards, affecting global population, health, and economies. It is of keen interest to identify "regions" of similar climatological behavior to discover spatial relationships in climate variables, including long-range teleconnections. To that end, we consider a complex networks-based representation of climate data. Cross correlation is used to weight network edges, thus respecting the temporal nature of the data, and a community detection algorithm identifies multivariate clusters. Examining networks for consecutive periods allows us to study structural changes over time. We show that communities have a climatological interpretation and that disturbances in structure can be an indicator of climate events (or lackthereof). Finally, we discuss how this model can be applied for the discovery of more complex concepts such as unknown teleconnections or the development of multivariate climate indices and predictive insights.

References

[1]

S. Asur, D. Ucar, S. Parthasarathy. An ensemble framework for clustering protein-protein interaction graphs. Bioinformatics, 23(13), 29--40, 2007.

Digital Library

[2]

A. Clauset, C. Moore, M. E. J. Newman. Hierarchical structure and the prediction of missing links in networks. Nature, 453, 98--101, 2008.

[3]

F. Congbin, J. Fletcher. Large signals of climatic variation over the ocean in the asian monsoon region. Adv. Atmos. Sci., 5(4): 389--404, 1988.

[4]

L. Ertöz, M. Steinbach, V. Kumar. Finding Clusters of Different Sizes, Shapes, and Densities in Noisy, High- Dimensional Data. SIAM Data Mining, 47--58, 2003.

[5]

R. G. Fovell, M.-Y. C. Fovell. Climate Zones of the Conterminous United States Defined Using Cluster Analysis. J. Climate, 6(11): 2103--2135, 1993.

[6]

S. Gadgil, J. Srinivasan, R. S. Nanjundiah. On Forecasting the Indian Summer Monsoon. Curr. Sci. India, 84(4): 394--403, 2002.

[7]

W. W. Hargrove, F. M. Hoffman. Using Multivariate Clustering to Characterize Ecoregion Borders. Comput. Sci. Eng., 1(4): 18--25, 1999.

Digital Library

[8]

J. Hopcroft, O. Khan, B. Kulis, et al. Tracking evolving communities in large linked networks. PNAS, 101(1): 5249--5253, 2004.

[9]

J. E. Janowiak, A. Gruber, C. Kondragunta, et al. A Comparison of NCEP-NCAR Reanalysis Precipitationand GPCP Rain-Gauge Satellite Combined Dataset. J. Climate, 11(11): 2960--2979, 1998.

[10]

E. Kalnay, M. Kanamitsu, R. Kistler, et al. The NCEP/NCAR 40-Year Reanalysis Project. B. Am. Meteorol. Soc., 77(3): 437--471, 1996.

[11]

B. Karrer, E. Levina, M. E. J. Newman. Robustness of community structure in networks. Phys. Rev. E, 77, 046119, 2008.

[12]

S. Khan, S. Bandyopadhyay, A. R. Ganguly, et al. Relative performance of mutual information estimation methods for quantifying the dependence among short and noisy data. Phys. Rev. E, 76(2), 026209, 2007.

[13]

S. Khan, A. R. Ganguly, et al. Nonliear statistics reveals stronger ties between ENSO and the tropical hydrological cycle. Geophys. Res. Lett., 33, L24402, 2006.

[14]

M. Kottek, M. J. Grieser, C. Beck, et al. World Map of the K oppen-Geiger climate classification updated. Meteorol. Z., 15: 259--263, 2006.

[15]

M. E. J. Newman, M. Girvan. Finding and evaluating community structure in networks. Phys. Rev. E, 69, 026113, 2004.

[16]

P. Pons, M. Latapy. Computing communities in large networks using random walks. J. Graph Alg. App., 10(2): 191--218, 2006.

[17]

M. N. M. Sap, A. M. Awan. Finding Spatio-Temporal Patterns in Climate Data Using Clustering. IEEE Cyberworlds, 164--171, 2005.

Digital Library

[18]

M. Steinbach, P.-N. Tan, V. Kumar, et al. Discovery of Climate Indices Using Clustering. ACM SIGKDD, 446--455, 2003.

Digital Library

[19]

A. A. Tsonis, K. Swanson. Topology and Predictability of El Niño and La Niña Networks. Phys. Rev. Lett., 228502, 2008.

[20]

A. A. Tsonis, K. Swanson, S. Kravtsov. A new dynamical mechanism for major climate shifts. Geophys. Res. Lett., 34, L13705, 2007.

[21]

A.A. Tsonis. Introducing Networks in Climate Studies. Nonlinear Dynamics in Geosciences, Springer, 2007.

[22]

K. Wyrtki. Teleconnections in the Equatorial Pacific Ocean. Science, 6(180): 66--68, 1970.

[23]

K. Yamasaki, A. Gozolchiani, S. Havlin. Climate Networks around the Globe are Significantly Affected by El Niño. Phys. Rev. Lett., 228501, 2008.

[24]

http://cdiac.ornl.gov/climate/indices/indices table.html

[25]

http://www.cgd.ucar.edu/cas/catalog/climind/

[26]

http://www.cdc.noaa.gov/data/gridded/data.ncep.reanalysis.html

Cited By

Zhang Z(2022)Climate NetworksNew Prospects in Environmental Geosciences and Hydrogeosciences10.1007/978-3-030-72543-3_1(3-5)Online publication date: 28-Jan-2022
https://doi.org/10.1007/978-3-030-72543-3_1
Kittel TCiemer CLotfi NPeron TRodrigues FKurths JDonner R(2021)Evolving climate network perspectives on global surface air temperature effects of ENSO and strong volcanic eruptionsThe European Physical Journal Special Topics10.1140/epjs/s11734-021-00269-9230:14-15(3075-3100)Online publication date: 16-Aug-2021
https://doi.org/10.1140/epjs/s11734-021-00269-9
Yuvaraj MDey ALyubchich VGel YPoor H(2021)Topological clustering of multilayer networksProceedings of the National Academy of Sciences10.1073/pnas.2019994118118:21Online publication date: 18-May-2021
https://doi.org/10.1073/pnas.2019994118
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Information & Contributors

Information

Published In

cover image ACM SIGKDD Explorations Newsletter

ACM SIGKDD Explorations Newsletter Volume 12, Issue 1

June 2010

77 pages

ISSN:1931-0145

EISSN:1931-0153

DOI:10.1145/1882471

Issue’s Table of Contents

Copyright © 2010 Authors.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 November 2010

Published in SIGKDD Volume 12, Issue 1

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Cited By

Zhang Z(2022)Climate NetworksNew Prospects in Environmental Geosciences and Hydrogeosciences10.1007/978-3-030-72543-3_1(3-5)Online publication date: 28-Jan-2022
https://doi.org/10.1007/978-3-030-72543-3_1
Kittel TCiemer CLotfi NPeron TRodrigues FKurths JDonner R(2021)Evolving climate network perspectives on global surface air temperature effects of ENSO and strong volcanic eruptionsThe European Physical Journal Special Topics10.1140/epjs/s11734-021-00269-9230:14-15(3075-3100)Online publication date: 16-Aug-2021
https://doi.org/10.1140/epjs/s11734-021-00269-9
Yuvaraj MDey ALyubchich VGel YPoor H(2021)Topological clustering of multilayer networksProceedings of the National Academy of Sciences10.1073/pnas.2019994118118:21Online publication date: 18-May-2021
https://doi.org/10.1073/pnas.2019994118
Li JConvertino M(2021)Inferring ecosystem networks as information flowsScientific Reports10.1038/s41598-021-86476-911:1Online publication date: 29-Mar-2021
https://doi.org/10.1038/s41598-021-86476-9
Fan JMeng JLudescher JChen XAshkenazy YKurths JHavlin SSchellnhuber H(2021)Statistical physics approaches to the complex Earth systemPhysics Reports10.1016/j.physrep.2020.09.005896(1-84)Online publication date: Feb-2021
https://doi.org/10.1016/j.physrep.2020.09.005
Vlachogiannis DXu YJin LGonzález M(2021)Correlation networks of air particulate matter ($$\hbox {PM}_{2.5}$$): a comparative studyApplied Network Science10.1007/s41109-021-00373-86:1Online publication date: 23-Apr-2021
https://doi.org/10.1007/s41109-021-00373-8
Xu YLu FZhu KSong XDai Y(2020)Exploring the Clustering Property and Network Structure of a Large-Scale Basin’s Precipitation Network: A Complex Network ApproachWater10.3390/w1206173912:6(1739)Online publication date: 18-Jun-2020
https://doi.org/10.3390/w12061739
Sakib NHossain EAhamed S(2020)A Qualitative Study on the United States Internet of Energy: A Step Towards Computational SustainabilityIEEE Access10.1109/ACCESS.2020.29863178(69003-69037)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2986317
Sun XYu YYang YDong JBöhm CChen X(2020)Modeling and analysis of the ocean dynamic with Gaussian complex networkChinese Physics B10.1088/1674-1056/aba27d29:10(108901)Online publication date: 28-Sep-2020
https://doi.org/10.1088/1674-1056/aba27d
Mondal SMishra ALeung L(2020)Spatiotemporal Characteristics and Propagation of Summer Extreme Precipitation Events Over United States: A Complex Network AnalysisGeophysical Research Letters10.1029/2020GL08818547:15Online publication date: 3-Aug-2020
https://doi.org/10.1029/2020GL088185
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