2002
DOI: 10.1023/a:1022411209257
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Cited by 205 publications
(59 citation statements)
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“…The occurrence of two IMFs associated with the Gleissberg cycle suggests that it could be made by multiple branches at different scales of variability. This is in agreement with Ogurtsov et al (2002), who suggested that this solar cycle oscillation has a double structure consisting of two distinct scales around 50−80 and 90−140 yr. We note that approaches suggesting that the Gleissberg cycle could be made by a single oscillation of varying period in time (e.g., Kolláth & Oláh 2009), mainly based on Fourier and wavelet-based analysis, could be biased by the combination of non-adaptive techniques, poor frequency sample, and shortness of the observed sunspot datasets providing solutions in which the oscillating modes are mixed together, while actually distinct oscillations are present. Indeed, the use of fixed basis functions, which in a nonstationary case are far from being eigenfunctions of the phenomenon at hand, with short (with respect to the oscillation wavelength) datasets can provide a solution where modes are mixed together in such a way that the solution is compatible with the fictitious conditions imposed by the analysis.…”
Section: Results For Wsn and Gsnsupporting
confidence: 93%
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“…The occurrence of two IMFs associated with the Gleissberg cycle suggests that it could be made by multiple branches at different scales of variability. This is in agreement with Ogurtsov et al (2002), who suggested that this solar cycle oscillation has a double structure consisting of two distinct scales around 50−80 and 90−140 yr. We note that approaches suggesting that the Gleissberg cycle could be made by a single oscillation of varying period in time (e.g., Kolláth & Oláh 2009), mainly based on Fourier and wavelet-based analysis, could be biased by the combination of non-adaptive techniques, poor frequency sample, and shortness of the observed sunspot datasets providing solutions in which the oscillating modes are mixed together, while actually distinct oscillations are present. Indeed, the use of fixed basis functions, which in a nonstationary case are far from being eigenfunctions of the phenomenon at hand, with short (with respect to the oscillation wavelength) datasets can provide a solution where modes are mixed together in such a way that the solution is compatible with the fictitious conditions imposed by the analysis.…”
Section: Results For Wsn and Gsnsupporting
confidence: 93%
“…A deeper characterization of the Gleissberg and Suess cycles is possible owing to the long duration of SNR datasets with respect to WSN/GSN ones. Our findings indicate that the Gleissberg cycle, often identified with a single oscillation of varying period in time, and the Suess cycle are actually composed of a multibranch structure at distinct scales of oscillations (Ogurtsov et al 2002;Usoskin 2013). We verified that the Gleissberg and Suess cycles, containing most of the signal energy, are strongly involved in the grand minima generation.…”
Section: Gleissberg and Suess Cyclessupporting
confidence: 65%
“…However, the Gleissberg cycle is not a cycle in the strict periodic sense but rather a modulation of the cycle envelope with a varying timescale of 60-120 years (e.g., Gleissberg 1971;Kuklin 1976;Ogurtsov et al 2002).…”
Section: Centennial Gleissberg Cyclementioning
confidence: 99%
“…A simple period- Sunspot activity (decadal data) throughout the Holocene, reconstructed from 14 C by Usoskin et al (2016a). Blue circles and red stars denote grand minima and maxima, respectively icity analysis yields several peaks in the range of periods between 80 and 150 years (cf., Peristykh and Damon 2003), corresponding to the frequency band of the Gleissberg cycle (Ogurtsov et al 2002). The de Vries/Suess cycle, with a period of about 210 years, forms a prominent in the power spectrum, but it is intermittent and tends to become strong with around 2400 clustering time .…”
Section: Quasi-periodicities and Characteristic Timesmentioning
confidence: 99%
“…It has been successfully applied in analysis of solar and geophysical data (e.g., Ogurtsov et al 2002;Sello 2003a). This approach is very appropriate to search for multiscale nonlinear variability behavior.…”
Section: Approachmentioning
confidence: 99%