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Climate Dynamics

, Volume 49, Issue 11–12, pp 3877–3886 | Cite as

Quantitative assessment of drivers of recent global temperature variability: an information theoretic approach

  • Ankush BhaskarEmail author
  • Durbha Sai Ramesh
  • Geeta Vichare
  • Triven Koganti
  • S. Gurubaran
Article

Abstract

Identification and quantification of possible drivers of recent global temperature variability remains a challenging task. This important issue is addressed adopting a non-parametric information theory technique, the Transfer Entropy and its normalized variant. It distinctly quantifies actual information exchanged along with the directional flow of information between any two variables with no bearing on their common history or inputs, unlike correlation, mutual information etc. Measurements of greenhouse gases: \(\hbox {CO}_{2}\), \(\hbox {CH}_{4}\) and \(\hbox {N}_{2}\hbox {O}\); volcanic aerosols; solar activity: UV radiation, total solar irradiance (TSI) and cosmic ray flux (CR); El Niño Southern Oscillation (ENSO) and Global Mean Temperature Anomaly (GMTA) made during 1984–2005 are utilized to distinguish driving and responding signals of global temperature variability. Estimates of their relative contributions reveal that \(\hbox {CO}_{2}\) (\({\sim } 24 \%\)), \(\hbox {CH}_{4}\) (\({\sim } 19 \%\)) and volcanic aerosols (\({\sim }23 \%\)) are the primary contributors to the observed variations in GMTA. While, UV (\({\sim } 9 \%\)) and ENSO (\({\sim } 12 \%\)) act as secondary drivers of variations in the GMTA, the remaining play a marginal role in the observed recent global temperature variability. Interestingly, ENSO and GMTA mutually drive each other at varied time lags. This study assists future modelling efforts in climate science.

Keywords

Aerosols Global temperature variability ENSO Greenhouse gases Transfer entropy Climate Information theory 

Notes

Acknowledgements

Authors thank World Data Center of Greenhouse Gases (http://ds.data.jma.go.jp/gmd/wdcgg/wdcgg.html), World Radiation Center (http://www.pmodwrc.ch/), Oulu Cosmic Ray Station (http://cosmicrays.oulu.fi/), National Geophysical Data Center, Met office, Hadley Center, UK (http://www.metoffice.gov.uk/) and Goddard Space Flight Center Sciences and Exploration Directorate Earth Sciences Division (http://data.giss.nasa.gov/modelforce/strataer/) for making necessary data available in public domain. Authors gratefully acknowledge Joanna Haigh, Imperial College, London for valuable discussions and constructive comments on the manuscript. Finally, authors thank both the reviewers for their valuable comments and time which helped authors to improve the manuscript.

References

  1. Attanasio A (2012) Testing for linear Granger causality from natural/anthropogenic forcings to global temperature anomalies. Theor Appl Climatol 110(1–2):281–289CrossRefGoogle Scholar
  2. Attanasio A, Pasini A, Triacca U (2012) A contribution to attribution of recent global warming by out-of-sample Granger causality analysis. Atmos Sci Lett 13(1):67–72CrossRefGoogle Scholar
  3. Balasis G, Donner RV, Potirakis SM, Runge J, Papadimitriou C, Daglis IA, Eftaxias K, Kurths J (2013) Statistical mechanics and information-theoretic perspectives on complexity in the earth system. Entropy 15(11):4844–4888CrossRefGoogle Scholar
  4. Bartlett KB, Harriss RC (1993) Review and assessment of methane emissions from wetlands. Chemosphere 26(1–4):261–320CrossRefGoogle Scholar
  5. Beer J, Mende W, Stellmacher R (2000) The role of the sun in climate forcing. Quat Sci Rev 19(1):403–415CrossRefGoogle Scholar
  6. Bhaskar A, Subramanian P, Vichare G (2016) Relative contribution of the magnetic field barrier and solar wind speed in ICME-associated Forbush decreases. Astrophys J 828(2):104CrossRefGoogle Scholar
  7. Cai W, Borlace S, Lengaigne M, Van Rensch P, Collins M, Vecchi G, Timmermann A, Santoso A, McPhaden MJ, Wu L et al (2014) Increasing frequency of extreme el niño events due to greenhouse warming. Nat Clim Change 4(2):111–116CrossRefGoogle Scholar
  8. Cao M, Gregson K, Marshall S (1998) Global methane emission from wetlands and its sensitivity to climate change. Atmos Environ 32(19):3293–3299CrossRefGoogle Scholar
  9. Carbone A (2013) Information measure for long-range correlated sequences: the case of the 24 human chromosomes. Scientific reports 3Google Scholar
  10. Carbone A, Stanley HE (2007) Scaling properties and entropy of long-range correlated time series. Phys A: Stat Mech Appl 384(1):21–24CrossRefGoogle Scholar
  11. Carbone A, Castelli G, Stanley H (2004) Analysis of clusters formed by the moving average of a long-range correlated time series. Phys Rev E 69(2):026,105Google Scholar
  12. Carslaw K, Harrison R, Kirkby J (2002) Cosmic rays, clouds, and climate. Science 298(5599):1732–1737CrossRefGoogle Scholar
  13. Cobb KM, Charles CD, Cheng H, Edwards RL (2003) El nino/southern oscillation and tropical pacific climate during the last millennium. Nature 424(6946):271–276CrossRefGoogle Scholar
  14. Das Sharma S, Ramesh D, Bapanayya C, Raju P (2012) Sea surface temperatures in cooler climate stages bear more similarity with atmospheric CO\(_2\) forcing. J Geophys Res: Atmos (1984–2012) 117(D13)Google Scholar
  15. DeLand MT, Cebula RP (2008) Creation of a composite solar ultraviolet irradiance data set. J Geophys Res: Space Phys 113(A11)Google Scholar
  16. De Michelis P, Consolini G, Materassi M, Tozzi R (2011) An information theory approach to the storm–substorm relationship. J Geophys Res: Space Phys (1978–2012) 116(A8)Google Scholar
  17. Dergachev V, Vasiliev S, Raspopov O, Jungner H (2012) Impact of the geomagnetic field and solar radiation on climate change. Geomagn Aeron 52(8):959–976CrossRefGoogle Scholar
  18. Dickinson RE (1975) Solar variability and the lower atmosphere. Bull Am Meteorol Soc 56(12):1240–1248CrossRefGoogle Scholar
  19. Eddy JA (1976) The maunder minimum. Science 192(4245):1189–1202CrossRefGoogle Scholar
  20. Fröhlich C (2006) Solar irradiance variability since 1978. Space Sci Rev 125(1–4):53–65Google Scholar
  21. Grassberger P (1988) Finite sample corrections to entropy and dimension estimates. Phys Lett A 128(6):369–373CrossRefGoogle Scholar
  22. Haigh JD (1996) The impact of solar variability on climate. Science 272(5264):981–984CrossRefGoogle Scholar
  23. Hansen J, Sato M, Ruedy R, Lacis A, Oinas V (2000) Global warming in the twenty-first century: an alternative scenario. Proc Natl Acad Sci 97(18):9875–9880CrossRefGoogle Scholar
  24. Herschel W (1801) Observations tending to investigate the nature of the sun, in order to find the causes or symptoms of its variable emission of light and heat; with remarks on the use that may possibly be drawn from solar observations. Philos Trans R Soc Lond, pp 265–318Google Scholar
  25. Hofmann D, Butler J, Dlugokencky E, Elkins J, Masarie K, Montzka S, Tans P (2006) The role of carbon dioxide in climate forcing from 1979 to 2004: introduction of the annual greenhouse gas index. Tellus B 58(5):614–619CrossRefGoogle Scholar
  26. Jenkinson DS, Adams D, Wild A (1991) Model estimates of CO\(_{2}\) emissions from soil in response to global warming. Nature 351(6324):304–306CrossRefGoogle Scholar
  27. Johnson JR, Wing S (2014) External versus internal triggering of substorms: an information-theoretical approach. Geophys Res Lett 41(16):5748–5754CrossRefGoogle Scholar
  28. Kakad B, Kakad A, Ramesh DS (2015) A new method for forecasting the solar cycle descent time. J Space Weather Space Clim 5:A29CrossRefGoogle Scholar
  29. Kantz H, Schürmann T (1996) Enlarged scaling ranges for the ks-entropy and the information dimension. Chaos: an interdisciplinary. J Nonlinear Sci 6(2):167–171Google Scholar
  30. Kerton AK (2009) Climate change and the earth’s magnetic poles, a possible connection. Energy Environ 20(1):75–83CrossRefGoogle Scholar
  31. Kleeman R (2007) Information flow in ensemble weather predictions. J Atmos Sci 64(3):1005–1016CrossRefGoogle Scholar
  32. Kleeman R (2011) Information theory and dynamical system predictability. Entropy 13(3):612–649CrossRefGoogle Scholar
  33. Knuth KH, Gotera A, Curry CT, Huyser KA, Wheeler KR, Rossow WB (2013) Revealing relationships among relevant climate variables with information theory. arXiv preprint. arXiv:13114632
  34. Kodra E, Chatterjee S, Ganguly AR (2011) Exploring Granger causality between global average observed time series of carbon dioxide and temperature. Theor Appl Climatol 104(3–4):325–335CrossRefGoogle Scholar
  35. Laken BA, Pallé E, Čalogović J, Dunne EM (2012) A cosmic ray-climate link and cloud observations. J Space Weather Space Clim 2:A18CrossRefGoogle Scholar
  36. Lean J (1989) Contribution of ultraviolet irradiance variations to changes in the sun’s total irradiance. Science 244(4901):197–200CrossRefGoogle Scholar
  37. Lean JL (2010) Cycles and trends in solar irradiance and climate. Wiley Interdiscip Rev: Clim Change 1(1):111–122Google Scholar
  38. Lean JL, Rind DH (2009) How will earth’s surface temperature change in future decades? Geophys Res Lett 36(15)Google Scholar
  39. Le Mouël JL, Kossobokov V, Courtillot V (2005) On long-term variations of simple geomagnetic indices and slow changes in magnetospheric currents: the emergence of anthropogenic global warming after 1990? Earth Planet Sci Lett 232(3):273–286CrossRefGoogle Scholar
  40. Li J, Liang C, Zhu X, Sun X, Wu D (2013) Risk contagion in Chinese banking industry: a transfer entropy-based analysis. Entropy 15(12):5549–5564CrossRefGoogle Scholar
  41. Marschinski R, Kantz H (2002) Analysing the information flow between financial time series. Eur Phys J B-Condens Matter Complex Syst 30(2):275–281CrossRefGoogle Scholar
  42. Mende W, Stellmacher R (1994) Solar radiative forcing und klimaentwicklung. Potsdam Institute for Climate Impact Research, PotsdamGoogle Scholar
  43. Montzka S, Dlugokencky E, Butler J (2011) Non-CO\(_2\) greenhouse gases and climate change. Nature 476(7358):43–50CrossRefGoogle Scholar
  44. Morice CP, Kennedy JJ, Rayner NA, Jones PD (2012) Quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: the hadcrut4 data set. J Geophys Res: Atmos 117(D8)Google Scholar
  45. Nemanill R (1997) Increased plant growth in the northern high latitudes from 1981 to 1991Google Scholar
  46. Robock A (2000) Volcanic eruptions and climate. Rev Geophys 38(2):191–219Google Scholar
  47. Runge J, Heitzig J, Marwan N, Kurths J (2012) Quantifying causal coupling strength: a lag-specific measure for multivariate time series related to transfer entropy. Phys Rev E 86(6):061121CrossRefGoogle Scholar
  48. Runge J, Petoukhov V, Kurths J (2014) Quantifying the strength and delay of climatic interactions: the ambiguities of cross correlation and a novel measure based on graphical models. J Clim 27(2):720–739CrossRefGoogle Scholar
  49. Sato M, Hansen JE, McCormick MP, Pollack JB (1993) Stratospheric aerosol optical depths, 1850–1990. J Geophys Res: Atmos (1984–2012) 98(D12):22987–22994Google Scholar
  50. Schreiber T (2000) Measuring information transfer. Phys Rev Lett 85(2):461CrossRefGoogle Scholar
  51. Scott DW (1979) On optimal and data-based histograms. Biometrika 66(3):605–610CrossRefGoogle Scholar
  52. Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27:623–656CrossRefGoogle Scholar
  53. Shindell DT, Schmidt GA, Mann ME, Rind D, Waple A (2001) Solar forcing of regional climate change during the maunder minimum. Science 294(5549):2149–2152CrossRefGoogle Scholar
  54. Shindell DT, Walter BP, Faluvegi G (2004) Impacts of climate change on methane emissions from wetlands. Geophys Res Lett 31(21)Google Scholar
  55. Solomon S, Daniel JS, Sanford TJ, Murphy DM, Plattner GK, Knutti R, Friedlingstein P (2010) Persistence of climate changes due to a range of greenhouse gases. Proc Natl Acad Sci 107(43):18354–18359CrossRefGoogle Scholar
  56. Stern DI, Kaufmann RK (2014) Anthropogenic and natural causes of climate change. Clim Change 122(1–2):257–269CrossRefGoogle Scholar
  57. Theiler J, Eubank S, Longtin A, Galdrikian B, Doyne Farmer J (1992) Testing for nonlinearity in time series: the method of surrogate data. Phys D: Nonlinear Phenom 58(1):77–94CrossRefGoogle Scholar
  58. Timmermann A, Oberhuber J, Bacher A, Esch M, Latif M, Roeckner E (1999) Increased el niño frequency in a climate model forced by future greenhouse warming. Nature 398(6729):694–697CrossRefGoogle Scholar
  59. Tinsley B (2008) The global atmospheric electric circuit and its effects on cloud microphysics. Rep Prog Phys 71(6):066801CrossRefGoogle Scholar
  60. Trenberth KE (1997) The definition of el nino. Bull Am Meteorol Soc 78(12):2771CrossRefGoogle Scholar
  61. Trenberth KE, Hoar TJ (1997) El niño and climate change. Geophys Res Lett 24(23):3057–3060CrossRefGoogle Scholar
  62. Tsutsumi Y, Mori K, Hirahara T, Ikegami M, Conway TJ (2009) Technical report of global analysis method for major greenhouse gases by the world data center for greenhouse gases. WMO/TD (1473)Google Scholar
  63. Usoskin IG, Kovaltsov GA (2008) Cosmic rays and climate of the earth: possible connection. Comptes Rendus Geoscience 340(7):441–450CrossRefGoogle Scholar
  64. Verdes P (2005) Assessing causality from multivariate time series. Phys Rev E 72(2):026222CrossRefGoogle Scholar
  65. Verdes PF (2007) Global warming is driven by anthropogenic emissions: a time series analysis approach. Phys Rev Lett 99(4):048501CrossRefGoogle Scholar
  66. Vernier JP, Thomason LW, Pommereau JP, Bourassa A, Pelon J, Garnier A, Hauchecorne A, Blanot L, Trepte C, Degenstein D et al (2011) Major influence of tropical volcanic eruptions on the stratospheric aerosol layer during the last decade. Geophys Res Lett 38(12)Google Scholar
  67. Vichare G, Bhaskar A, Ramesh DS (2016) Are the equatorial electrojet and the Sq coupled systems? Transfer entropy approach. Adv Space Res 57(9):1859–1870CrossRefGoogle Scholar
  68. Wang C, Yu H, Grout RW, Ma KL, Chen JH (2011) Analyzing information transfer in time-varying multivariate data. In: Pacific visualization symposium (PacificVis), 2011 IEEE, IEEE, pp 99–106Google Scholar
  69. Watson R, Meira Filho L, Sanhueza E, Janetos A (1992) Greenhouse gases: sources and sinks. Clim change 92:25–46Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.Indian Institute of GeomagnetismNavi MumbaiIndia
  2. 2.Indian Institute of TechnologyKharagpurIndia
  3. 3.The University of New South WalesSydneyAustralia

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