Physics
Scientific paper
May 2001
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2001agusm..sp41c09j&link_type=abstract
American Geophysical Union, Spring Meeting 2001, abstract #SP41C-09
Physics
2162 Solar Cycle Variations (7536), 7522 Helioseismology, 7524 Magnetic Fields
Scientific paper
Velocity shear at the solar tachocline with a period of 16 months has recently been detected. This kind of behavior is predicted even in the simplest 1-D dynamo models when they are operating in a chaotic regime. Sunspot numbers, as a manifestation of solar activity driven by a dynamo, should also reflect such chaotic behavior. We look at daily sunspot number data for the northern and southern solar hemispheres over almost a whole solar cycle using the methods of nonlinear time series analysis in an effort to find a signature of chaotic behaviour on short time scales. Nonlinear time series analysis holds the promise of allowing us to infer information about the dynamics behind the data but it is ineffectual if averaging is used, if the data are integer and too coarsely grained and if the data spans a limited dynamic range. All three are true for the sunspot number record. By separately looking at data for the north and south hemispheres, the averaging problem is somewhat alleviated. Noise may be added to coarsely grained data of limited resolution such as the sunspot number to enhance the ability to access structures at the lowest data values. However, since daily sunspot numbers cannot be smaller than 1, zero being the absence of spots, this limits the observation of solar dynamics using sunspot numbers. This limitation decisively affects any attempt to determine the correlation dimension as the signature of chaotic behaviour. Thus, the data set itself limits, if not precludes, access to information on whether the solar cycle is chaotic on time scales of a solar cycle. Moreover, this mechanism also limits the analysis of long-term data. Furthermore, the nonlinear time series analysis of sunspot data over one cycle from near 22 cycle maximum to the current cycle 23 maximum indicates different behavior at maximum and minimum. The minimum shows no scaling region whereas the maximum sections have a region that bears the marks of self-similarity. Evidence is presented that solar minimum daily sunspot data determine the overall estimate of the correlation dimension as a dynamical measure over one complete cycle and over longer periods may mask the potentially self-similar sections at maximum. In addition, the method of artifactual correlations was used to investigate the noise content of the sunspot data. This method indicated that both the north and south data sets were relatively free of additive noise. We also compare 10.7 cm and SMSMF data that do not suffer from some of the limitations of sunspot data with a 1-D dynamo model.
Cellucci C. J.
Jevtic Nada
Schweitzer J. S.
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