Advances in Astrophysics

Download PDF (1271.8 KB) PP. 47 - 60 Pub. Date: May 31, 2019

DOI: 10.22606/adap.2019.42001

• Amy L. Potrzeba-Macrina^*
  Syracuse University, Department of Mathematics, 215 Carnegie Building, Syracuse, NY 13244, USA
• Igor G. Zurbenko
  University at Albany, Department of Epidemiology & Biostatistics, 1 University Place, Rensselaer, NY 12144, USA

Solar activity has a well-known periodicity of approximately 10-11 years, an oscillation that was first observed in China several thousand years ago. The purpose of this paper is to explain the driving force behind this periodicity and to explain other periodicities inherent to solar activities. In science, spectral analysis is an essential tool used for the identification of periodicities that are natural to a given dataset. In this paper the authors use spectral analysis to investigate planetary gravitational periods to explain periodicities of sunspot numbers and to make conclusions about the driving force of the sunspot numbers and solar activity. Precise analysis of inherent periodicities provides the capability to predict future fluctuations in solar activities. The authors show clear evidence of long periodicities within sunspot numbers. The combination of several periodic components, while complex, remains perfectly predictable. The authors show that the long-term component of sunspot fluctuations is perfectly proportional to the total solar irradiation near Earth measured by satellites. While satellite measurements of the total solar irradiance cover a short time interval, sunspot numbers have been recorded for a long time and essentially have more value on the prediction of solar influence on Earth’s climate. This allows for the numerical evaluation of solar energy delivered to Earth. Numerical evaluations of fluctuations in solar energies delivered to Earth are an essential achievement for any climate change analysis. The removal of solar influences from long-term temperature data provides the opportunity to numerically identify the human impact on Earth’s climate. A better understanding and prediction of the Sun’s long oscillations may influence important predictions of climatic events and impact emergency preparedness.

difference frequency, spectrum of sunspots, planet periods, extreme weather events, solar radiation

[1] Hathaway, D (2015). “The Solar Cycle.” Living Reviews in Solar Physics. 12 (4). DOI: 10.1007/lrsp-2015-4

[2] Ahluwalia, HS (2017). “Heuristic study of evolution of sunspot number timeline for next several cycles.” J. Atmos. Solar-Terr. Phys. DOI:10.1016/j.jastp.2017.05.007. https://www.sciencedirect.com/science/article/pii/ S1364682616302449

[3] Pesnell, W.D. (2014). “Predicting Solar Cycle 24 Using a Geomagnetic Precursor Pair.” Solar Physics (ISSN 0038-0938). 289 (6), p. 2317-2331. DOI 10.1007/s11207-013-0470-x

[4] Ng, K. Kwee (2016). “Prediction Methods in Solar Sunspots Cycles.” Scientific Reports (www.nature.com/ scientificreports/) DOI: 10.1038/srep21028

[5] Marra S, & Morabito, F (2005). “A New Technique for Solar Activity Forecasting using Recurrent Elman Networks.” International Journal of Computational Intelligence. 3 (1), p 8-13. http://proceedings.spiiras.nw.ru/ ojs/index.php/sp/article/view/1134

[6] Chernykh, YV (2003). “Methods of the analysis of time series connected with solar activity.” SPIRAS Proceedings. 3 (1), p. 126-136.

[7] Zurbenko, I. G & Potrzeba, A.L (2013) “Tides in the Atmosphere.” Air Quality, Atmos, & Health, 6 (1): 39-46. Springer, DOI 10.1007/s11869-011-0143-6. http://www.springerlink.com/content/e124604331626295

[8] Zurbenko, I.G & Potrzeba, A.L (2009) “Tidal Waves in Atmosphere and Their Effects.” Acta Geophysica, Springer, Vol 58 (2): 356-373. DOI 10.2478/s11600-009-0049-y

[9] Strugarek A, Beaudoin P, Charbonneau P, Brun, A.S, do Nascimento Jr, J.D. (2017). “Reconciling solar and stellar magnetic cycles with nonlinear dynamo simulations.” Science Vol. 357 (6347) p. 185-187. DOI: 10.1126/science.aal3999

[10] Valachovic, E. & Zurbenko I (2014). “Skin Cancer, Irradiation, and Sunspots: The Solar Cycle Effect.” Biomedical Research International. Vol 2014. http://dx.doi.org/10.1155/2014/538574

[11] WDC-SILSO, Royal Observatory of Belgium, Brussels. Sunspot Numbers: http://www.sidc.be/silso/datafiles

[12] Frohlich C, Crommelynck D, Wehrli C, Anklin M, Dewitte S, Fichot A, Finsterle W, Jiménez, A. Chevalier A, and Roth, HJ. (1997). “In-flight performances of VIRGO solar irradiance instruments on SOHO.” Sol. Phys., 175:267a€“286. doi:10.1023/A:1004929108864

[13] Research Data Archive/Computational and Information Systems Laboratory/National Center for Atmospheric Research/University Corporation for Atmospheric Research, et al. 1984, updated monthly. International Comprehensive Ocean-Atmosphere Data Set (ICOADS) Release 2.5, Monthly Summaries. Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory. https://doi.org/10.5065/D6CF9N3F. Accessed 01 Mar 2018.

[14] Stephenson, FR & Clark, DH (1978). Applications of Early Astronomical Records. Monographs on Astronomical Subjects: 4. Oxford University Press, New York.

[15] Yang, W. & Zurbenko I (2012), KZFT package version 0.17, R-software first published version (19-Sept-2007) and latest published version (2012), Package Sources https://cran.r-project.org/web/packages/kzft/kzft.pdf

[16] DiRienzo G, Zurbenko I. (1999). “Semi-adaptive nonparametric spectral estimation.” Journal of Computational Graphical Statistics. 8:41–59.

[17] Zurbenko, I.G (1986). The Spectral Analysis of Time Series. North-Holland Series in Statistics in Probability.

[18] Close B & Zurbenko I (2013), KZFT in KZA package, R-software first published version kza_0.4 (27 Apr 2004) and latest published version (2013), Package Sources. http://cran.r-project.org/web/packages/kza/index.html

[19] Yang, W & Zurbenko, I.G (2010). “Kolmogorov-Zurbenko filters.” Wiley interdisciplinary revives in computational statistics. WIREs in Comput Stat 2: 340-351. http://wires.wiley.com/WileyCDA/WiresArticle/ wisID-WICS71.html

[20] Zurbenko, I.G. & Smith, D. (2017). “Kolmogorov-Zurbenko filters in spatiotemporal analysis.” WIREs Comput Stat, e1419. DOI: 10.1002/wics.1419

[21] Valachovic, E. & Zurbenko I (2017). “Separation of Spatio-Temporal Frequencies.” Quest Journals: Journal of Research in Applied Mathematics. Vol 3 (7), p. 29-37.

[22] Zurbenko I.G. & Cyr, D.D (2011). “Climate fluctuations in time and space.” Climate Research. Vol (46) p. 67- 76. DOI 10.3354/cr00956.

[23] Tsakiri K, Zurbenko I (2010). “Prediction of Ozone Concentrations using Atmospheric Variables.” Journal Air Quality, Atmosphere & Health. Vol 4 (2):111-120. DOI: 10.1007/s11869-010-0084-5

[24] Zurbenko I, Sowizral, M (1999). “Resolution of the destructive effect on noise on linear regression of two time series.” Far East Journal of Theoretical Statistics. Vol 3 (1): 139-157.

[25] Zurbenko, I.G. & Sun, M (2014). “Periods of High Risk in Tornado Outbreaks in Central USA.” Advances in Research, 2 (8) http://www.sciencedomain.org/issue.php?iid=512&id=31

[26] Zurbenko, I.G. & Sun, M (2015). “Associations of Jet Streams with Tornado Outbreaks in the North America.” Atmospheric and Climate Sciences, Vol. 5 (3), p. 336-344. http://dx.doi.org/10.4236/acs.2015.53026

[27] Zurbenko, I.G. & Sun, M (2016). “Jet Stream as a major factor of tornados in USA.” Atmospheric and Climate Sciences, Vol. 6 (2), p. 236 – 253. DOI: 10.4236/acs.2016.62020 http://www.scirp.org/journal/ACS/.

[28] Zurbenko, I. G & Potrzeba, AL (2013a). “Periods of Excess Energy in Extreme Weather Events.” Journal of Climatology, Vol. 2013. Article ID 410898. http://dx.doi.org/10.1155/2013/410898