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

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.

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References

  1. Attanasio A (2012) Testing for linear Granger causality from natural/anthropogenic forcings to global temperature anomalies. Theor Appl Climatol 110(1–2):281–289

    Article  Google 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):67–72

    Article  Google 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):4844–4888

    Article  Google Scholar 

  4. Bartlett KB, Harriss RC (1993) Review and assessment of methane emissions from wetlands. Chemosphere 26(1–4):261–320

    Article  Google Scholar 

  5. Beer J, Mende W, Stellmacher R (2000) The role of the sun in climate forcing. Quat Sci Rev 19(1):403–415

    Article  Google 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):104

    Article  Google 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):111–116

    Article  Google Scholar 

  8. Cao M, Gregson K, Marshall S (1998) Global methane emission from wetlands and its sensitivity to climate change. Atmos Environ 32(19):3293–3299

    Article  Google Scholar 

  9. Carbone A (2013) Information measure for long-range correlated sequences: the case of the 24 human chromosomes. Scientific reports 3

  10. Carbone A, Stanley HE (2007) Scaling properties and entropy of long-range correlated time series. Phys A: Stat Mech Appl 384(1):21–24

    Article  Google 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):026,105

  12. Carslaw K, Harrison R, Kirkby J (2002) Cosmic rays, clouds, and climate. Science 298(5599):1732–1737

    Article  Google 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):271–276

    Article  Google 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)

  15. DeLand MT, Cebula RP (2008) Creation of a composite solar ultraviolet irradiance data set. J Geophys Res: Space Phys 113(A11)

  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)

  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):959–976

    Article  Google Scholar 

  18. Dickinson RE (1975) Solar variability and the lower atmosphere. Bull Am Meteorol Soc 56(12):1240–1248

    Article  Google Scholar 

  19. Eddy JA (1976) The maunder minimum. Science 192(4245):1189–1202

    Article  Google Scholar 

  20. Fröhlich C (2006) Solar irradiance variability since 1978. Space Sci Rev 125(1–4):53–65

    Google Scholar 

  21. Grassberger P (1988) Finite sample corrections to entropy and dimension estimates. Phys Lett A 128(6):369–373

    Article  Google Scholar 

  22. Haigh JD (1996) The impact of solar variability on climate. Science 272(5264):981–984

    Article  Google 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):9875–9880

    Article  Google 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 265–318

  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):614–619

    Article  Google 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):304–306

    Article  Google Scholar 

  27. Johnson JR, Wing S (2014) External versus internal triggering of substorms: an information-theoretical approach. Geophys Res Lett 41(16):5748–5754

    Article  Google 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:A29

    Article  Google 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):167–171

    Google Scholar 

  30. Kerton AK (2009) Climate change and the earth’s magnetic poles, a possible connection. Energy Environ 20(1):75–83

    Article  Google Scholar 

  31. Kleeman R (2007) Information flow in ensemble weather predictions. J Atmos Sci 64(3):1005–1016

    Article  Google Scholar 

  32. Kleeman R (2011) Information theory and dynamical system predictability. Entropy 13(3):612–649

    Article  Google 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):325–335

    Article  Google 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:A18

    Article  Google Scholar 

  36. Lean J (1989) Contribution of ultraviolet irradiance variations to changes in the sun’s total irradiance. Science 244(4901):197–200

    Article  Google Scholar 

  37. Lean JL (2010) Cycles and trends in solar irradiance and climate. Wiley Interdiscip Rev: Clim Change 1(1):111–122

    Google Scholar 

  38. Lean JL, Rind DH (2009) How will earth’s surface temperature change in future decades? Geophys Res Lett 36(15)

  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):273–286

    Article  Google 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):5549–5564

    Article  Google 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):275–281

    Article  Google Scholar 

  42. Mende W, Stellmacher R (1994) Solar radiative forcing und klimaentwicklung. Potsdam Institute for Climate Impact Research, Potsdam

  43. Montzka S, Dlugokencky E, Butler J (2011) Non-CO\(_2\) greenhouse gases and climate change. Nature 476(7358):43–50

    Article  Google 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)

  45. Nemanill R (1997) Increased plant growth in the northern high latitudes from 1981 to 1991

  46. Robock A (2000) Volcanic eruptions and climate. Rev Geophys 38(2):191–219

  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):061121

    Article  Google 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):720–739

    Article  Google 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):22987–22994

  50. Schreiber T (2000) Measuring information transfer. Phys Rev Lett 85(2):461

    Article  Google Scholar 

  51. Scott DW (1979) On optimal and data-based histograms. Biometrika 66(3):605–610

    Article  Google Scholar 

  52. Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27:623–656

    Article  Google 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):2149–2152

    Article  Google Scholar 

  54. Shindell DT, Walter BP, Faluvegi G (2004) Impacts of climate change on methane emissions from wetlands. Geophys Res Lett 31(21)

  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):18354–18359

    Article  Google Scholar 

  56. Stern DI, Kaufmann RK (2014) Anthropogenic and natural causes of climate change. Clim Change 122(1–2):257–269

    Article  Google 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):77–94

    Article  Google 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):694–697

    Article  Google Scholar 

  59. Tinsley B (2008) The global atmospheric electric circuit and its effects on cloud microphysics. Rep Prog Phys 71(6):066801

    Article  Google Scholar 

  60. Trenberth KE (1997) The definition of el nino. Bull Am Meteorol Soc 78(12):2771

    Article  Google Scholar 

  61. Trenberth KE, Hoar TJ (1997) El niño and climate change. Geophys Res Lett 24(23):3057–3060

    Article  Google 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)

  63. Usoskin IG, Kovaltsov GA (2008) Cosmic rays and climate of the earth: possible connection. Comptes Rendus Geoscience 340(7):441–450

    Article  Google Scholar 

  64. Verdes P (2005) Assessing causality from multivariate time series. Phys Rev E 72(2):026222

    Article  Google Scholar 

  65. Verdes PF (2007) Global warming is driven by anthropogenic emissions: a time series analysis approach. Phys Rev Lett 99(4):048501

    Article  Google 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)

  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):1859–1870

    Article  Google 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 99–106

  69. Watson R, Meira Filho L, Sanhueza E, Janetos A (1992) Greenhouse gases: sources and sinks. Clim change 92:25–46

    Google Scholar 

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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.

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Correspondence to Ankush Bhaskar.

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Bhaskar, A., Ramesh, D.S., Vichare, G. et al. Quantitative assessment of drivers of recent global temperature variability: an information theoretic approach. Clim Dyn 49, 3877–3886 (2017). https://doi.org/10.1007/s00382-017-3549-5

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Keywords

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