Nonlinear Variability in the Geomagnetic Secular Variation of the Last 150 Years
Language
English
Obiettivo Specifico
3.4. Geomagnetismo
Status
Published
JCR Journal
N/A or not JCR
Peer review journal
Yes
Journal
Issue/vol(year)
3 / 10 (2002)
Publisher
World Scientific Publishing Company
Pages (printed)
297-303
Date Issued
2002
Abstract
A nonlinear forecasting analysis has been applied to the secular variation of the three-component annual means of 14 observatories, unevenly distributed over the Earth's surface (12 in the northern and 2 in the southern hemisphere) and spanning the last 150 years. All results were in agreement, either in terms of possible evidence of chaos (as opposed to the
hypothesis of white or colored noise), or in terms of the Kolmogorov entropy, confirming
previous results obtained with only three European observatories, i.e. it is practically impossible to predict the secular variation of the geomagnetic field more than six years into the future.
hypothesis of white or colored noise), or in terms of the Kolmogorov entropy, confirming
previous results obtained with only three European observatories, i.e. it is practically impossible to predict the secular variation of the geomagnetic field more than six years into the future.
References
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15. Rotanova N. M., Papitashvili N. Ye., Pushkov A. N., Interpretation of 60-year
variations in the geomagnetic field as a quasi-harmonic process. Geomagn. &
Aeronomy., 22 (1982), pp. 733-735.
16. Rotanova N. M., Papitashvili N. Ye., Filippov S. V. and Chernova T. A.,
Identification and analysis of 60-year variations of the geomagnetic field from
the time-series of spherical harmonics. Geomagn. & Aeron., 23 (1983), pp.
673-679 (in English translation); pp. 829-836 (in Russian).
17. Sugihara G. and May R. M., Nonlinear forecasting as a way of distinguishing
chaos from measurement error in time series. Nature, 344 (1990), pp. 734-741.
18. Takens F., Detecting strange attractors in turbulence. In Lecture notes in
mathematics, ed. by D. A. Rand and L. S. Young , Vol. 898 (Springer, Berlin,
1981), pp. 366-381.
19. Theiler J., Eubank S., Longtin A., Galdrikian B. and Farmer J. D., Testing for
nonlinearity in time series: the method of surrogate data. Physica D, 58 (1992),
pp. 77-94.
20. Tsonis A. A. and Elsner J. B., Nonlinear prediction as a way of distinguishing
chaos from random fractal sequences. Nature, 358 (1992), pp. 217-220.
21. Wales D. J., Calculating the rate of loss of information from chaotic time series
by forecasting. Nature, 350 (1991), pp. 485-488.
York, 1996).
2. Baker, L. G., Gollub, J. P., Chaotic dynamics. Cambridge University Press,
Cambridge, pp. 182., 1990.
3. Barraclough D. R. and De Santis, A., Some possible evidence for a chaotic
geomagnetic field from observational data. Phys. Earth Planet. Inter., 99
(1997), pp. 207-220.
4. Cafarella L., De Santis A. and Meloni A., Secular variation in Italy from
historical geomagnetic field measurements. Phys. Earth Planet. Inter., 73
(1992), pp. 206-221.
5. Currie R. G., Geomagnetic time spectrum. Atmos. Space Sc., 21 (1973), pp.
425-438.
6. De Michelis P., Consolini G. and Meloni A., Sign Singularity in the Secular
Acceleration of the Geomagnetic Field. Phys. Rev. Lett., 81, pp. 5023-5026.
7. Farmer, J. D. and Sidorowich, J. J., Predicting chaotic time series. Phys. Rev.
Lett., 59 (1987), pp. 845-848.
8. Fowler A.D., and Roach, D.E., Dimensionality analysis of time-series data:
nonlinear methods. Comp. & Geosc., 19 (1993), pp. 41-52.
9. Grassberger P. and Procaccia I., Measuring the strangeness of strange
attractors. Physica D, 9 (1983), pp. 189-208.
10. Langel, R. A., The Main Field, Chapter 4. In Geomagnetism, Vol. 1., ed. by J.
A. Jacobs (Academic Press, London, 1987), pp. 249-512.
11. Marzocchi W., Mulargia F. and Gonzato G., Detecting low-dimensional chaos
in geophysical time. J. Geoph. Res., 102, NO. B2 (1997), pp. 3195-3209.
12. Osborne A.R., and Provenzale A., Finite correlation dimension for stochastic
systems with power-law spectra. Physica D, 35 (1989), pp. 357-381.
13. Procaccia I., Complex or just complicated? Nature, 339 (1988), pp.498-499.
14. Robbins K.A., A new approach to subcritical instability and turbulent
transitions in a simple dynamo. Math. Proc. Camb. Phil. Soc., 82 (1977), pp.
309-325.
15. Rotanova N. M., Papitashvili N. Ye., Pushkov A. N., Interpretation of 60-year
variations in the geomagnetic field as a quasi-harmonic process. Geomagn. &
Aeronomy., 22 (1982), pp. 733-735.
16. Rotanova N. M., Papitashvili N. Ye., Filippov S. V. and Chernova T. A.,
Identification and analysis of 60-year variations of the geomagnetic field from
the time-series of spherical harmonics. Geomagn. & Aeron., 23 (1983), pp.
673-679 (in English translation); pp. 829-836 (in Russian).
17. Sugihara G. and May R. M., Nonlinear forecasting as a way of distinguishing
chaos from measurement error in time series. Nature, 344 (1990), pp. 734-741.
18. Takens F., Detecting strange attractors in turbulence. In Lecture notes in
mathematics, ed. by D. A. Rand and L. S. Young , Vol. 898 (Springer, Berlin,
1981), pp. 366-381.
19. Theiler J., Eubank S., Longtin A., Galdrikian B. and Farmer J. D., Testing for
nonlinearity in time series: the method of surrogate data. Physica D, 58 (1992),
pp. 77-94.
20. Tsonis A. A. and Elsner J. B., Nonlinear prediction as a way of distinguishing
chaos from random fractal sequences. Nature, 358 (1992), pp. 217-220.
21. Wales D. J., Calculating the rate of loss of information from chaotic time series
by forecasting. Nature, 350 (1991), pp. 485-488.
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