On fast and slow Earth’s magnetospheric dynamics during geomagnetic storms: a stochastic Langevin approach | Journal of Space Weather and Space Climate

System Science: Application to Space Weather Analysis, Modelling, and Forecasting

Open Access

Issue		J. Space Weather Space Clim. Volume 8, 2018 System Science: Application to Space Weather Analysis, Modelling, and Forecasting


Article Number		A56
Number of page(s)		14
DOI		https://doi.org/10.1051/swsc/2018039
Published online		07 December 2018

Ahn BH, Akasofu SI, Kamide Y. 1983. The Joule heat-production rate and the particle energy injection rate as a function of the geomagnetic indices AE and AL. J Geophys Res, 88, 6275. [CrossRef] [Google Scholar]
Alberti T. 2018. Multivariate Empirical Mode Decomposition analysis of Swarm data. Il Nuovo Cimento 41 C: 113. [Google Scholar]
Alberti T, Lepreti F, Vecchio A, Bevacqua E, Capparelli V, Carbone V. 2014. Natural periodicities and Northern Hemisphere-Southern Hemisphere connection of fast temperature changes during the last glacial period: EPICA and NGRIP revisited. Clim Past, 10, 1751–1762. [NASA ADS] [CrossRef] [Google Scholar]
Alberti T, Piersanti M, Vecchio A, De Michelis P, Lepreti F, Carbone V, Primavera L. 2016. Identification of the different magnetic field contributions during a geomagnetic storm in magnetospheric and ground observations. Ann Geophys, 34, 1069–1084. [CrossRef] [Google Scholar]
Alberti T, Consolini G, Lepreti F, Laurenza M, Vecchio A, Carbone V. 2017a. Timescale separation in the solar wind-magnetosphere coupling during St. Patrick’s Day storms in 2013 and 2015. J Geophys Res, 122, 4266. [CrossRef] [Google Scholar]
Alberti T, Laurenza M, Cliver EW, Storini M, Consolini G, Lepreti F. 2017b. Solar activity from 2006 to 2014 and short-term forecasts of solar proton events using the ESPERTA Model. Astrophys J, 838, 59. [CrossRef] [Google Scholar]
Baker DN, Klimas AJ, Vassiliadis DV. 1995. Energy transfer between the solar wind and the magnetosphere-ionosphere system. J Geomag Geoelectr, 47, 1171–1182. [CrossRef] [Google Scholar]
Balasis G, Egbert GD. 2006. Empirical orthogonal function analysis of magnetic observatory data: Further evidence for non-axisymmetric magnetospheric sources for satellite induction studies. Geophys Res Lett, 33, L11311, DOI: 10.1029/2006GL025721 [CrossRef] [Google Scholar]
Balasis G, Daglis IA, Kapiris P, Mandea M, Vassiliadis D, Eftaxias K. 2006. From pre-storm activity to magnetic storms: a transition described in terms of fractal dynamics. Ann Geophys, 24, 3557–3567. [CrossRef] [Google Scholar]
Bargatze LF, Baker DN, McPherron RL, Hones EW. 1985. Magnetospheric impulse response for many levels of geomagnetic activity. J Geophys Res, 90, 6387–6399. [CrossRef] [Google Scholar]
Benzi R, Sutera A, Vulpiani A. 1981. The mechanism of stochastic resonance. J Phys, A14, L453–L457. [Google Scholar]
Benzi R, Parisi G, Sutera A, Vulpiani A. 1982. Stochastic resonance in climate change. Tellus, 34, 10–16. [CrossRef] [Google Scholar]
Chang T, Vvedensky DD, Nicoll JF. 1992. Differential renormalization-group generators for static and dynamical critical phenomena. Phys Rep, 217, 279. [CrossRef] [Google Scholar]
Chang T, Tam SWY, Wu C-C, Consolini G. 2003. Complexity, forced and/or self-organized criticality and topological phase transitions in space plasmas. Space Sci Rev, 107, 425–445. [CrossRef] [Google Scholar]
Consolini G. 1997. Sandpile cellular automata and the magnetospheric dynamics, in: Aiello S, et al., Editor. Cosmic physics in the year 2000, Proceedings of VIII GIFCO Conference, SIF: Bologna, 123 p. [Google Scholar]
Consolini G. 2002. Self-organized criticality: a new paradigm for the magnetotail dynamics. Fractals, 10, 275. [CrossRef] [Google Scholar]
Consolini G. 2018. Emergence of dynamical complexity in the Earth’s magnetosphere. In: Machine learning techniques for space weather, Camporeale, et al. (Eds.), Elsevier, Amsterdam, Netherlands, pp. 177–202. [CrossRef] [Google Scholar]
Consolini G, De Michelis P. 2005. Local intermittency measure analysis of AE index: The directly driven and unloading component. Geophys Res Lett, 32, L05101, DOI: 10.1029/2004GL022063. [CrossRef] [Google Scholar]
Consolini G, Marcucci MF, Candidi M. 1996. Multifractal structure of auroral electrojet index data. Phys Rev Lett, 76, 4082–4085. [CrossRef] [Google Scholar]
Consolini G, Alberti T, Yordanova E, Marcucci MF, Echim M. 2017. A Hilbert-Huang transform approach to space plasma turbulence at kinetic scales. J Phys Conf Ser, 900, 012003. [CrossRef] [Google Scholar]
Daglis LA, Livi S, Sarris ET, Wilken B. 1994. Energy density of ionospheric and solar wind origin ions in the near-Earth magnetotail during substorms. J Geophys Res, 99, 5691–5703. [CrossRef] [Google Scholar]
Davies TN, Sugiura M. 1966. Auroral electroject activity index AE and its universal time variations. J Geophys Res, 71, 785. [CrossRef] [Google Scholar]
De Michelis P, Consolini G, Tozzi R. 2012. On the multi-scale nature of large geomagnetic storms: An empirical mode decomposition analysis. Nonlinear Process Geophys, 19, 667–673. [NASA ADS] [CrossRef] [Google Scholar]
De Michelis P, Consolini G. 2015. On the local Hurst exponent of geomagnetic field fluctuations: Spatial distribution for different geomagnetic activity levels. J Geophys Res, 120, 2691–2701. [Google Scholar]
De Michelis P, Consolini G, Tozzi R. 2015. Latitudinal dependence of short timescale fluctuations during intense geomagnetic storms: A permutation entropy approach. J Geophys Res, 120, 5633–5644. [CrossRef] [Google Scholar]
Ditlevsen PD. 1999. Observation of α-stable noise induced millennial climate changes from an ice-core record. Geophys Res Lett, 26, 1441–1444. [CrossRef] [Google Scholar]
Flandrin P, Gonçalvès P, Rilling G. 2004. Detrending and de-noising with empirical mode decompositions. Proceedings of Eusipco, Wien, Austria. [Google Scholar]
Hamilton DC, Gloeckler G, Ipavich FM, Stüdemann W, Wilken B, Kremser G. 1988. Ring current development during the great geomagnetic storm of February 1986. J Geophys Res (Space Phys), 93, 14343–14355. [CrossRef] [Google Scholar]
Huang NE, Wu Z. 2008. A review on Hilbert-Huang transform: Method and its applications to geophysical studies. Rev Geophys, 46, DOI: 10.1029/2007RG000228. [Google Scholar]
Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, Yen NC, Tung CCHH, Liu HH. 1998. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc Royal Soc London A, 454, 903–995. [CrossRef] [Google Scholar]
Huang NE, Shen Z, Long RS. 1999. A new view of nonlinear water waves – the Hilbert spectrum. Ann Rev Fluid Mech, 31, 417–457. [Google Scholar]
Ichimaru S. 1973. Basic principles of plasma physics, 1st edn. Benjamin, USA, 231 p. ISBN:0805387536. [Google Scholar]
Iyemori T. 1990. Storm-time magnetospheric currents inferred from midlatitude geomagnetic field variations. J Geomagn Geoelectr, 42, 1249. [CrossRef] [Google Scholar]
Joselyn JA, Tsurutani BT. 1990. Geomagnetic Sudden impulses and storm sudden commencements: A note on terminology. Eos Trans AGU, 71(47), 1808–1809. [CrossRef] [Google Scholar]
Kamide Y, Kokubun S. 1996. Two-component auroral electrojet: Importance for substorm studies. J Geophys Res, 101, 089. [Google Scholar]
Klimas AJ, Vassiliadis D, Baker D, Roberts D. 1996. The organized nonlinear dynamics of the magnetosphere. J Geophys Res, 101, 13089, DOI: 10.1029/96JA00563. [CrossRef] [Google Scholar]
Kozyra JU, Liemohn MW, Clauer CR, Ridley AJ, Thomsen MF, Borovsky JE, Roeder JL, Jordanova VK, Gonzalez WD. 2002. Multistep Dst development and ring current composition changes during the magnetic storm 4–6 June 1991. J Geophys Res (Space Phys), 107(A8), 1224, DOI: 10.1029/2001JA000023. [Google Scholar]
Kwasniok F, Lohmann G. 2009. Deriving dynamical models from paleoclimatic records: Application to glacial millennial-scale climate variability. Phys Rev E, 80, 066104, DOI: 10.1103/PhysRevE.80.066104. [CrossRef] [Google Scholar]
Langevin P. 1908. Sur la théorie du mouvement brownien [On the Theory of Brownian Motion]. C R Acad Sci Paris, 146, 530–533. [Google Scholar]
Laurenza M, Cliver EW, Hewitt J, Storini M, Ling A, Balch CC, Kaiser ML. 2009. A technique for shortterm warning of solar energetic particle events based on flare location, flare size, and evidence of particle escape. Space Weather, 7, S04008. [NASA ADS] [CrossRef] [Google Scholar]
Laurenza M, Vecchio A, Storini M, Carbone V. 2014. Drift effects on the galactic cosmic ray modulation. Astrophys J, 781, 71. [NASA ADS] [CrossRef] [Google Scholar]
Laurenza M, Alberti T, Cliver EW. 2018. A short-term ESPERTA-based forecast tool for moderate-to-extreme solar proton events. Astrophys J, 857, 107. [CrossRef] [Google Scholar]
Lemons DS, Gythiel A. 1997. Paul Langevin’s 1908 paper on the theory of Brownian motion [Sur la théorie du mouvement brownien, C. R. Acad. Sci. (Paris) 146, 530533 (1908)]. Am J Phys, 65, 1079–1081, DOI: 10.1119/1.18725. [NASA ADS] [CrossRef] [Google Scholar]
Livina VN, Kwasniok F, Lenton TM. 2010. Potential analysis reveals changing number of climate states during the last 60 kyr. Clim Past, 6, 77–82. [CrossRef] [Google Scholar]
Pallocchia G, Amata E, Consolini G, Marcucci MF, Bertello I. 2007. AE index forecast at different time scales through an ANN algorithm based on L1 IMF and plasma measurements. J Atmos Sol Terr Phys, 70, 663–668. [CrossRef] [Google Scholar]
Piersanti M, Alberti T, Bemporad A, Berrilli F, Bruno R. 2017. Comprehensive analysis of the geoeffective solar event of 21 June 2015: Effects on the magnetosphere, plasmasphere, and ionosphere systems. Sol Phys, 292, 169. [CrossRef] [Google Scholar]
Silverman SM, Shapiro R. 1983. Power spectral analysis of auroral occurrence frequency. J Geophys Res, 88, 6310–6316, DOI: 10.1029/JA088iA08p06310 [NASA ADS] [CrossRef] [Google Scholar]
Sitnov MI, Sharma AS, Papadopoulos K, Vassiliadis D, Valdivia JA, Klimas AJ, Baker DN. 2000. Phase transition-like behavior of the magnetosphere. J Geophys Res (Space Phys), 105, 12955. [CrossRef] [Google Scholar]
Sitnov MI, Sharma AS, Papadopoulos K, Vassiliadis D. 2001. Modeling substorm dynamics of the magnetosphere: from self-organization and self-organized criticality to non-equilibrium phase transitions. Phys Rev E, 65, 16116. [CrossRef] [Google Scholar]
Stangalini M, Consolini G, Berrilli F, De Michelis P, Tozzi R. 2014. Observational evidence for buffeting-induced kink waves in solar magnetic elements. A&A, 569, A102. [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
Tsurutani B, Sugiura M, Iyemori T, Goldstein BE, Gonzalez WD, Akasofu SI, Smith EJ. 1990. The nonlinear response of AE to the IMF Bs driver: A spectral break at 5 hours. Geophys Res Lett, 17, 279–282. [CrossRef] [Google Scholar]
Uritsky VM, Pudovkin MI. 1998. Low frequency 1/f-like fluctuations of the AE-index as a possible manifestation of self-organized criticality in the magnetosphere. Ann Geophys, 16, 1580. [NASA ADS] [CrossRef] [Google Scholar]
Uritsky VM, Klimas AJ, Vassiliadis D, Chua D, Parks G. 2002. Scale-free statistics of spatiotemporal auroral emissions as depicted by POLAR UVI images: The dynamic magnetosphere is an avalanching system. J Geophys Res, 107, 1426. [CrossRef] [Google Scholar]
Vassiliadis DV, Sharma AS, Eastman TE, Papadopoulos K. 1990. Low-dimensional chaos in magnetospheric activity from AE time series. Geophys Res Lett, 17, 1841–1844. [CrossRef] [Google Scholar]
Vecchio A, Laurenza M, Storini M, Carbone V. 2012. New insights on cosmic ray modulation through a joint use of nonstationary data-processing methods. Adv Astron, 2012, 834247. [NASA ADS] [CrossRef] [Google Scholar]
Vecchio A, Lepreti F, Laurenza M, Alberti T, Carbone V. 2017. Connection between solar activity cycles and grand minima generation. A&A, 599, A58. [CrossRef] [EDP Sciences] [Google Scholar]
Wanliss JA. 2005. Fractal properties of SYM-H during quiet and active times. J Geophys Res (Space Phys), 110, A03202, DOI: 10.1029/2004JA010544. [Google Scholar]
Wanliss JA, Showalter KM. 2006. High-resolution global storm index: Dst versus SYM-H. J Geophys Res, 111, A02202, DOI: 10.1029/2005JA011034. [Google Scholar]
Wanliss JA, Dobias P. 2007. Space storms as a phase transition. J Atmos Sol Terr Phys, 69, 675–684. [CrossRef] [Google Scholar]
Weygand JM, McPherron RL. 2006. Dependence of ring curent asymmetry on storm phase. J Geophys Res, 111, A11221. [CrossRef] [Google Scholar]
Wiener N. 1923. Differential space. J Math Phys, 2, 131–174, DOI: 10.1002/sapm192321131 [CrossRef] [Google Scholar]
Wu Z, Huang NE. 2004. A study of the characteristics of white noise using the Empirical mode decomposition method. Proc Royal Soc London A, 460, 1597. [NASA ADS] [CrossRef] [Google Scholar]
Wu Z, Huang NE. 2009. Ensemble empirical mode decomposition: A noise-assisted data analysis method. Adv Adapt Data Anal, 1, 1–41. [CrossRef] [Google Scholar]
Wu Z, Schneider EK, Kirtman BP, Sarachik ES, Huang NE, Tucker CJ. 2008. The modulated annual cycle: An alternative reference frame for climate anomalies. Clim Dyn, 31, 823. [CrossRef] [Google Scholar]

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