Abstract
We analyze large-scale interdependencies between sea surface temperature (SST) and rainfall variability. We propose a novel climate network construction scheme which we call teleconnection climate networks (TCN). On account of this analysis, gridded SST and rainfall data sets are coarse grained by merging grid points that are dynamically similar to each other. The resulting clusters of time series are taken as the nodes of the TCN. The SST and rainfall systems are investigated as two separate climate networks, and teleconnections within the individual climate networks are studied with special focus on dipolar patterns. Our analysis reveals a pronounced rainfall dipole between Southeast Asia and the Afghanistan-Pakistan region, and we discuss the influences of Pacific SST anomalies on this dipole.
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Notes
- 1.
Due to the short length of time series we obtain the twin surrogates without embedding.
References
Barlow M, Wheeler M, Lyon B, Cullen H (2005) Modulation of daily precipitation over southwest Asia by the Madden-Julian oscillation. Mon Weather Rev 133(12):3579–3594
Boers N, Bookhagen B, Marwan N, Kurths J, Marengo J (2013) Complex networks identify spatial patterns of extreme rainfall events of the South American Monsoon System. Geophys Res Lett 40(16):4386–4392. Wiley Online Library
Boers N, Bookhagen B, Barbosa HMJ, Marwan N, Kurths J, Marengo JA (2014) Prediction of extreme floods in the eastern Central Andes based on a complex networks approach. Nat Commun 5:5199. Nature Publishing Group
Defays D (1977) An efficient algorithm for a complete link method. Comput J 20(4):364–366. Br Computer Soc
Dommenget D, Latif M (2002) A cautionary note on the interpretation of EOFs. J Clim 15(2):216–225
Donges JF, Zou Y, Marwan, N, Kurths J (2009a) The backbone of the climate network. EPL (Europhys Lett) 87(4):48007. IOP Publishing
Donges JF, Zou Y, Marwan, N, Kurths J (2009b) Complex networks in climate dynamics. Eur Phys J Spec Top 174(1):157–179. Springer
Ebert-Uphoff I, Deng Y (2012) Causal discovery for climate research using graphical models. J Clim 25(17):5648–5665
Everitt BS, Landau S, Leese M (2001) Cluster analysis. Arnold, London
Gill A (1980) Some simple solutions for heat-induced tropical circulation. Q J R Meteorol Soc 106(449):447–462. Wiley Online Library
Ghil M, Allen MR, Dettinger MD, Ide K, Kondrashov D, Mann ME, Robertson AW, Saunders A, Tian Y, Varadi Fet al (2002) Advanced spectral methods for climatic time series. Rev Geophys 40(1):3–1
Heitzig J, Donges JF, Zou Y, Marwan N, Kurths J (2012) Node-weighted measures for complex networks with spatially embedded, sampled, or differently sized nodes. Eur Phys J B-Condens Matter Complex Syst 85(1):1–22. Springer
Hlinka J, Hartman D, Jajcay N, Vejmelka M, Donner R, Marwan N, Kurths J, Paluš M (2014) Regional and inter-regional effects in evolving climate networks. Nonlinear Process Geophys 21(2):451–462. Copernicus GmbH
Malik N, Bookhagen B, Marwan N, Kurths J (2012) Analysis of spatial and temporal extreme monsoonal rainfall over South Asia using complex networks. Clim Dyn 39(3–4):971–987. Springer
MacQueen J et al (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, Berkeley, vol 1, no 14, pp 281–297
Marwan N, Romano MC, Thiel M, Kurths J (2007) Recurrence plots for the analysis of complex systems. Phys Rep 438(5–6):237–329. doi = 10.1016/j. physrep.2006.11.001, ISSN = 03701573
Monahan AH, Fyfe JC, Ambaum MHP, Stephenson DB, North GR (2009) Empirical orthogonal functions: The medium is the message. J Clim 22(24):6501–6514
Reynolds RW, Rayner NA, Smith TM, Stokes DC, Wang W (2002) An improved in situ and satellite SST analysis for climate. J Clim 15(13):1609–1625
Rheinwalt A, Marwan N, Kurths J, Werner P, Gerstengarbe F-W (2012) Boundary effects in network measures of spatially embedded networks. EPL (Europhys Lett) 100(2):28002. IOP Publishing
Romano MC, Thiel M, Kurths J, Mergenthaler K, Engbert R (2009) Hypothesis test for synchronization: twin surrogates revisited. Chaos (Woodbury, N.Y.) 19(1):015108. doi = 10.1063/1.3072784
Thiel M, Romano MC, Kurths J, Rolfs M, Kliegl R (2006) Twin surrogates to test for complex synchronisation. Europhys Lett (EPL) 75(4):535–541. doi = 10.1209/epl/i2006-10147-0, ISSN = 0295-5075
Thiel M, Romano MC, Kurths J, Rolfs M, Kliegl R (2008) Generating surrogates from recurrences. Philos Trans Ser A Math Phys Eng Sci 366(1865):545–557. doi = 10.1098/rsta.2007.2109, ISSN = 1364-503X
Tsonis AA, Roebber PJ (2004) The architecture of the climate network. Phys A Stat Mech Appl 333:497–504. Elsevier
Tsonis AA, Swanson KL, Roebber PJ (2006) What do networks have to do with climate? Bull Am Meteorol Soc 87(5):585–595
Trenberth KE, Stepaniak DP, Caron JM (2000) The global monsoon as seen through the divergent atmospheric circulation. J Clim 13(22):3969–3993
Webster PJ, Magana VO, Palmer TN, Shukla J, Tomas RA, Yanai M, Yasunari T (1998) Monsoons: processes, predictability, and the prospects for prediction. J Geophys Res Oceans (1978–2012) 103(C7):1445114510. Wiley Online Library
Webster PJ, Moore AM, Loschnigg JP, Leben R (1999) Coupled ocean-atmosphere dynamics in the Indian Ocean during 1997–98. Nature 401(6751):356–360. Nature Publishing Group
Wiedermann M, Donges JF, Heitzig J Kurths J (2013) Node-weighted interacting network measures improve the representation of real-world complex systems. EPL (Europhys Lett) 102(2):28007. IOP Publishing
Yamasaki K, Gozolchiani A, Havlin S (2008) Climate networks around the globe are significantly affected by El Nino. Phys Rev Lett 100(22):228501. APS
Yatagai A, Kamiguchi K, Arakawa O, Hamada, A, Yasutomi N, Kitoh A (2012) APHRODITE: constructing a long-term daily gridded precipitation dataset for Asia based on a dense network of rain gauges. Bull Am Meteorolog Soc 93(9):1401–1415. American Meteorological Society
Acknowledgements
Funded by DFG, project Investigation of past and present climate dynamics and its stability by means of a spatio-temporal analysis of climate data using complex networks (MA 4759/4-1). Further support by DFG/FAPESP IRTG 1740/TRP 2011/50151-0.
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Rheinwalt, A. et al. (2015). Teleconnections in Climate Networks: A Network-of-Networks Approach to Investigate the Influence of Sea Surface Temperature Variability on Monsoon Systems. In: Lakshmanan, V., Gilleland, E., McGovern, A., Tingley, M. (eds) Machine Learning and Data Mining Approaches to Climate Science. Springer, Cham. https://doi.org/10.1007/978-3-319-17220-0_3
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DOI: https://doi.org/10.1007/978-3-319-17220-0_3
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