Climate Dynamics

, Volume 51, Issue 1–2, pp 371–382 | Cite as

Temporal evolution of the spatial covariability of rainfall in South America

  • Catrin CiemerEmail author
  • Niklas Boers
  • Henrique M. J. Barbosa
  • Jürgen Kurths
  • Anja Rammig


The climate of South America exhibits pronounced differences between rainy and dry seasons, associated with specific synoptic features such as the establishment of the South Atlantic convergence zone. Here, we analyze the spatiotemporal correlation structure and in particular teleconnections of daily rainfall associated with these features by means of evolving complex networks. A modification of Pearson’s correlation coefficient is introduced to handle the intricate statistical properties of daily rainfall. On this basis, spatial correlation networks are constructed, and new appropriate network measures are introduced in order to analyze the temporal evolution of the networks’ characteristics. We particularly focus on the identification of coherent areas of similar rainfall patterns and previously unknown teleconnection structures between remote areas. We show that the monsoon onset is characterized by an abrupt transition from erratic to organized regional connectivity that prevails during the monsoon season, while only the onset times themselves exhibit anomalous large-scale organization of teleconnections. Furthermore, we reveal that the two mega-droughts in the Amazon basin were already announced in the previous year by an anomalous behavior of the connectivity structure.


South American monsoon Complex networks Rainfall Teleconnections Correlation measures 



The authors thank Tim Kittel, Jose A. Marengo and Finn Müller-Hansen for helpful discussions. This paper was developed within the scope of the IRTG 1740/TRP 2011/50151-0, funded by the DFG/FAPESP. NB acknowledges funding by the Alexander von Humboldt Foundation and the German Federal Ministry for Education and Research. H.M.J.B. acknowledges the financial support from FAPESP project 2013/50510-5 and CNPq fellowship 312131/2014-3. The authors gratefully acknowledge the European Regional Development Fund (ERDF), the German Federal Ministry of Education and Research and the Land Brandenburg for supporting this project by providing resources on the high performance computer system at the Potsdam Institute for Climate Impact Research.


  1. Arraut JM, Nobre C, Barbosa HM, Obregon G, Marengo J (2012) Aerial rivers and lakes: looking at large-scale moisture transport and its relation to amazonia and to subtropical rainfall in South America. J Clim 25(2):543–556CrossRefGoogle Scholar
  2. 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):4386–4392CrossRefGoogle Scholar
  3. Boers N, Bookhagen B, Barbosa H, Marwan N, Kurths J, Marengo J (2014) Prediction of extreme floods in the eastern central Andes based on a complex networks approach. Nat Commun 5:5199Google Scholar
  4. Boers N, Donner RV, Bookhagen B, Kurths J (2015a) Complex network analysis helps to identify impacts of the El Niño southern oscillation on moisture divergence in South America. Clim Dyn 45(3–4):619–632Google Scholar
  5. Boers N, Bookhagen B, Marengo J, Marwan N, von Storch JS, Kurths J (2015b) Extreme rainfall of the South American monsoon system: a dataset comparison using complex networks. J Clim 28(3):1031–1056CrossRefGoogle Scholar
  6. Boers N, Bookhagen B, Marwan N, Kurths J (2016) Spatiotemporal characteristics and synchronization of extreme rainfall in South America with focus on the Andes mountain range. Clim Dyn 46(1–2):601–617CrossRefGoogle Scholar
  7. Bookhagen B, Strecker MR (2008) Orographic barriers, high-resolution TRMM rainfall, and relief variations along the eastern Andes. Geophys Res Lett 35(6):L06403Google Scholar
  8. Carvalho LMV, Silva AE, Jones C, Liebmann B, Dias PLS, Rocha HR (2011) Moisture transport and intraseasonal variability in the South America monsoon system. Clim Dyn 36(9–10):1865–1880CrossRefGoogle Scholar
  9. Carvalho LMV (2016) The monsoons and climate change. In: The monsoons and climate change. Springer, Cham, pp 1–6Google Scholar
  10. Donges JF, Zou Y, Marwan N, Kurths J (2009) Complex networks in climate dynamics. Eur Phys J Spec Topic 174(1):157–179CrossRefGoogle Scholar
  11. 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):451–462CrossRefGoogle Scholar
  12. Huffman GJ, Bolvin DT, Nelkin EJ, Wolff DB, Adler RF, Gu G, Hong Y, Bowman KP, Stocker EF (2007) The TRMM multisatellite precipitation analysis (tmpa): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J Hydrometeorol 8(1):38–55CrossRefGoogle Scholar
  13. Lewis SL, Brando PM, Phillips OL, van der Heijden GM, Nepstad D (2011) The 2010 amazon drought. Science 331(6017):554–554CrossRefGoogle Scholar
  14. Liebmann B, Mechoso C (2011) The South America moonson system. In: The global monsoon system: research and forecast, 2nd edn. World Scientific Publishing Co, Singapore, pp 421–454Google Scholar
  15. Liebmann B, Jones C, de Carvalho LM (2001) Interannual variability of daily extreme precipitation events in the state of Sao Paulo, Brazil. J Clim 14(2):208–218CrossRefGoogle Scholar
  16. 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):971–987CrossRefGoogle Scholar
  17. Marengo JA, Liebmann B, Grimm A, Misra V, Silva Dias P, Cavalcanti I, Carvalho L, Berbery E, Ambrizzi T, Vera C et al (2012) Recent developments on the South American monsoon system. Int J Climatol 32(1):1–21CrossRefGoogle Scholar
  18. Marengo JA, Soares WR, Saulo C, Nicolini M (2004) Climatology of the low-level jet east of the Andes as derived from the NCEP–NCAR reanalyses: characteristics and temporal variability. J Clim 17(12):2261–2280CrossRefGoogle Scholar
  19. Marengo JA, Nobre CA, Tomasella J, Oyama MD, Sampaio de Oliveira G, De Oliveira R, Camargo H, Alves LM, Brown IF (2008) The drought of amazonia in 2005. J Clim 21(3):495–516CrossRefGoogle Scholar
  20. Marengo JA, Tomasella J, Alves LM, Soares WR, Rodriguez DA (2011) The drought of 2010 in the context of historical droughts in the Amazon region. Geophys Res Lett 38(12):L12703Google Scholar
  21. Newman ME (2003) The structure and function of complex networks. SIAM Rev 45(2):167–256Google Scholar
  22. Nieto-Ferreira R, Rickenbach TM, Wright EA (2011) The role of cold fronts in the onset of the monsoon season in the South Atlantic convergence zone. Q J R Meteorol Soc 137(657):908–922CrossRefGoogle Scholar
  23. Quiroga RQ, Kraskov A, Kreuz T, Grassberger P (2002) Performance of different synchronization measures in real data: a case study on electroencephalographic signals. Phys Rev E 65(4):041903Google Scholar
  24. Radebach A, Donner RV, Runge J, Donges JF, Kurths J (2013) Disentangling different types of El Niño episodes by evolving climate network analysis. Phys Rev E 88(5):052807Google Scholar
  25. Rheinwalt A, Boers N, Marwan N, Kurths J, Hoffmann P, Gerstengarbe FW, Werner P (2015) Non-linear time series analysis of precipitation events using regional climate networks for Germany. Clim Dyn 46(3–4):1065–1074Google Scholar
  26. Rickenbach TM, Ferreira RN, Halverson JB, Herdies DL, Silva Dias MA (2002) Modulation of convection in the Southwestern Amazon basin by extratropical stationary fronts. J Geophys Res 107:8040Google Scholar
  27. Salio P, Nicolini M, Zipser EJ (2007) Mesoscale convective systems over southeastern South America and their relationship with the South American low-level jet. Mon Weather Rev 135(4):1290–1309CrossRefGoogle Scholar
  28. Siqueira JR, Toledo Machado LA (2004) Influence of the frontal systems on the day-to-day convection variability over South America. J Clim 17(9):1754–1766CrossRefGoogle Scholar
  29. Tsonis AA, Swanson KL, Roebber PJ (2006) What do networks have to do with climate? Bull Am Meteorol Soc 87(5):585CrossRefGoogle Scholar
  30. Yamasaki K, Gozolchiani A, Havlin S (2008) Climate networks around the globe are significantly affected by El Nino. Phys Rev Lett 100(22):228501Google Scholar
  31. Yoon JH, Zeng N (2010) An Atlantic influence on Amazon rainfall. Clim Dyn 34(2–3):249–264CrossRefGoogle Scholar
  32. Zhou D, Gozolchiani A, Ashkenazy Y, Havlin S (2015) Teleconnection paths via climate network direct link detection. Phys Rev Lett 115(26):268501Google Scholar
  33. Zhou J, Lau K (1998) Does a monsoon climate exist over South America. J Clim 11(5):1020–1040CrossRefGoogle Scholar
  34. Zipser EJ, Liu C, Cecil DJ, Nesbitt SW, Yorty DP (2006) Where are the most intense thunderstorms on earth? Bull Am Meteorol Soc 87(8):1057–1071CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Catrin Ciemer
    • 1
    • 2
    Email author
  • Niklas Boers
    • 1
    • 3
  • Henrique M. J. Barbosa
    • 4
  • Jürgen Kurths
    • 1
    • 2
    • 5
    • 6
  • Anja Rammig
    • 7
  1. 1.Potsdam Institute for Climate Impact ResearchPotsdamGermany
  2. 2.Department of PhysicsHumboldt UniversityBerlinGermany
  3. 3.Geosciences DepartmentÉcole Normale SupérieureParisFrance
  4. 4.Department of PhysicsUniversity of São PauloSão PauloBrazil
  5. 5.Department of Control TheoryNizhny Novgorod State UniversityNizhny NovgorodRussia
  6. 6.Institute for Complex Systems and Mathematical BiologyUniversity of AberdeenAberdeenUK
  7. 7.TUM School of Life Sciences WeihenstephanTechnical University of MunichMunichGermany

Personalised recommendations