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Components of multifractality in the central England temperature anomaly series
1. B. B. Mandelbrot, The Fractal Geometry of Nature (W. H. Freeman and Co., New York, 1982).
2. B. B. Mandelbrot, Multifractals and 1/f Noise (Springer, New York, 1999).
3. Scaling, Fractals and nonlinear Variability in Geophysics, edited by D. Schertzer and S. Lovejoy (Kluwer, Boston, 1991).
4. K. Falconer, Fractal Geometry: Mathematical Foundations and Applications (Wiley, New York, 1990).
5. M. L. Kurnaz, “ Detrended fluctuation analysis as a statistical tool to monitor the climate,” J. Stat. Mech. P07009 (2004).
6. J. Alvarez-Ramirez, J. Alvarez, L. Dagdug, E. Rodriguez, and J. Carlos Echeverria, “ Long-term memory dynamics of continental and oceanic monthly temperatures in the recent 125 years,” Physica A 387(14), 3629–3640 (2008).
7. Y. Ashkenazy, D. R. Baker, H. Gildor, and S. Havlin, “ Nonlinearity and multifractality of climate change in the past 420,000 years,” Geophys. Res. Lett. 30, 2146, doi:10.1029/2003GL018099 (2003).
9. L. Karimova, Y. Kuandykov, N. Makarenko, M. M. Novak, and S. Helama “ Fractal and topological dynamics for the analysis of paleoclimatic records,” Physica A 373, 737 (2007).
10. J. J. Oñate Rubalcaba, “ Fractal analysis of climatic data: Annual precipitation records in Spain,” Theor. Appl. Climatol. 56, 83 (1997).
11. J. L. Valencia, A. Saa Requejo, J. M. Gascó, and A. M. Tarquis, “ A universal multifractal description applied to precipitation patterns of the Ebro River Basin,” Spain. Clim. Res. 44, 17 (2010).
14. S. Lovejoy and D. Schertzer, “ Scale invariance in climatological temperatures and the spectral plateau,” Ann. Geophys. 4B, 401–410 (1986).
18. A. Hannachi, “ Quantifying changes and their uncertainties in probability distribution of climate variables using robust statistics,” Clim. Dyn. 27(2–3), 301–317 (2006).
19. H.-H. Kwon, U. Lall, and A. F. Khalil, “ Stochastic simulation model for nonstationary time series using an autoregressive wavelet decomposition: Applications to rainfall and temperature,” Water Resour. Res. 43, W05407, doi:10.1029/2006WR005258 (2007).
21. X. Zhang and X. Shao “ Testing the structural stability of temporally dependent functional observations and application to climate projections,” Electron. J. Stat. 5, 1765–1796 (2011).
26. D. E. Parker and E. B. Horton, “ Uncertainties in central England temperature 1878–2003 and some improvements to the maximum and minimum series,” Int. J. Climatol. 25, 1173–1188 (2005).
29. E. Zorita, A. Moberg, L. Leijonhufvud, R. Wilson, R. Brázdil, P. Dobrovolný, J. Luterbacher, R. Böhm, C. Pfister, D. Riemann, R. Glaser, J. Söderberg, and F. González-Rouco, “ European temperature records of the past five centuries based on documentary/instrumental information compared to climate simulations,” Clim. Change 101, 143–168 (2010).
31. J. W. Kantelhardt, S. A. Zschiegner, E. Koscielny-Bunde, S. Havlin, A. Bunde, and H. E. Stanley, “ Multifractal detrended fluctuation analysis of nonstationary times series,” Physica A 316, 87–114 (2002).
32. P. Ch. Ivanov, L. A. N. Amaral, A. L. Goldberger, S. Havlin, M. G. Rosenblum, H. G. Stanley, and Z. R. Struzik, “ From 1/f noise to multifractal cascades in heartbeat dynamics,” Chaos 11, 641–652 (2001).
35. S. M. Duarte Queirós, L. G. Moyano, S. de Souza, and C. Tsallis, “ A nonextensive approach to the dynamics of financial observables,” Eur. Phys. J. B 55, 161–167 (2007).
36. M.-R. Niu, W.-X. Zhou, Z.-Y. Yan, Q.-H. Guo, Q.-F. Liang, F.-C. Wang, and Z.-H. Yu, “ Multifractal detrended fluctuation analysis of combustion flames in four-burner impinging entrained-flow gasifier,” Chem. Eng. J. 143, 230 (2008).
38. M. Ausloos, “ Generalized Hurst exponent and multifractal function of original and translated texts mapped into frequency and length time series,” Phys. Rev. E 86, 031108 (2012).
41. J. Feder, Fractals (Plenum, New York, 1988).
42. C.-K. Peng, S. V. Buldyrev, S. Havlin, M. Simons, H. E. Stanley, and A. L. Goldberger, “ Mosaic organization of DNA nucleotides,” Phys. Rev. E 49, 1685–1689 (1994).
43. H. E. Hurst, “ Long-term storage capacity of reservoirs,” Trans. Am. Soc. Civ. Eng. 116, 770–808 (1951).
44.Throughout the text we employ the term non-Gaussian in the broad sense, i.e., we use it to refer to a distribution that does not have a defined scale as the Gaussian and which is a established terminology for the absence of scale.
45. C. Gourieroux and A. Montfort, Statistics and Econometric Models (Cambridge University Press, Cambridge, 1996).
46. S. de Souza and S. M. Duarte Queirós, “ Effective multifractal features of high-frequency price fluctuations time series and l-variability diagrams,” Chaos, Solitons Fractals 42, 2512–2521 (2009).
47. J. E. Gentle, Random Number Generation and Monte Carlo Methods, 2nd ed. (Wiley, New York, 1995).
48. L. D. Paarmann, Design and Analysis of Analog Filters—A Signal Processing Perspective (Kluwer, Dordrecht, 2001).
49. S. L. Marple, Digital Spectral Analysis (Prentice-Hall, Englewood Cliffs, NJ, 1987).
50. P. Stoica and R. L. Moses, Introduction to Spectral Analysis (Prentice-Hall, Englewood Cliffs, NJ, 1997).
for IPCC 2007 Climate Change 2007—The Physical Science Basis Contribution of Working Group I to the Fourth Assessment Report of the IPCC.
53. J. de Souza, S. M. Duarte Queirós, and A. M. Grimm, “ Time dependence of the multifractal spectrum and other statistical properties of the Central England Temperature time series,” XXXI Enconto Nacional de Física da Matéria Condensada. Águas de Lindóia, Brazil (2008).
57. A. Arneodo, J. F. Muzy, and S. G. Roux, “ Experimental analysis of self-similarity and random cascade processes: Application to fully developed turbulence data,” J. Phys. II France 7, 363 (1997).
58. S. M. D. Queirós and C. Tsallis “ On the connection between financial processes with stochatic volatility and nonextensive statistical mechanics,” Eur. Phys. J. B 48, 139 (2005).
59. T. G. Andersen, T. Bollerslev, and F. X. Diebold “ Parametric and nonparametric volatility measurement,” in Handbook of Financial Econometrics, edited by Y. Aït-Sahalia, Tools and Techniques, Vol. 1 (Elsevier, Amsterdam, 2010).
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We study the multifractal nature of the Central England Temperature (CET) anomaly, a time series that spans more than 200 years. The data are analyzed in two ways: as a single set and by using a sliding window of 11 years. In both cases, we quantify the width of the multifractal spectrum as well as its components, which are defined by the deviations from the Gaussian distribution and the dependence between measurements. The results of the first approach show that the key contribution to the multifractal structure comes from the dynamical dependencies, mainly weak ones, followed by a residual contribution of the deviations from the Gaussian. The sliding window approach indicates that the peaks in the evolution of the non-Gaussian contribution occur almost at the same dates associated with climate changes that were determined in previous works using component analysis methods. Moreover, the strong non-Gaussian contribution from the 1960 s onwards is in agreement with global results recently presented.
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