Prediction of solar cycle 25: maximum in the N- and S-hemispheres

Heading: 
1Pishkalo, MI
1Astronomical Observatory of Taras Shevchenko National University of Kyiv, Kyiv, Ukraine
Kinemat. fiz. nebesnyh tel (Online) 2021, 37(1):48-56
https://doi.org/10.15407/kfnt2021.01.048
Start Page: Solar Physics
Language: Ukrainian
Abstract: 

Solar activity changes with about 11-year periodicity, two 11-year cycles form a complete 22-year magnetic cycle of the Sun. Solar cycle 25 has recently begun, and it is important to know in advance what and when it will be at its maximum. The paper predicts the maximal Wolf number in solar cycle 25 separately in the northern and southern hemispheres. The absolute value of the polar magnetic field near the cycle minimum was used as a precursor of the cycle maximum. The values of solar polar magnetic field measured at the John Wilcox Solar Observatory of Stanford University since 1976 and Wolf numbers in the N- and S-hemispheres in 1975—2020 (in solar cycles 21—24) are analyzed. For the time interval of 1992–2020, Wolf numbers in the N- and S-hemispheres were used according to SILSO (http://sidc.oma.be/SISLO, Version 2.0), and for the time interval of 1975—1992, Wolf numbers were taken from the paper (Temmer et al., 2006, Astron. Astrophys. 2006, 447, 735) and reduced to the modern SILSO scale. Wolf numbers in minima and maxima and corresponding times in 21—24 cycles in the N- and S-hemispheres were found. The correlation coefficient at different time lags between the smoothed monthly Wolf number and the modulus of the Sun’s polar magnetic field in the northern and southern hemispheres has been studied. It was found that maximal correlation coefficients between these parameters are 0.587 at a time lag of 4.76 years in the N-hemisphere and 0.680 at a time lag of 5.45 years in the S-hemisphere. A qualitative forecast of maximal Wolf numbers in solar cycle 25 in the N- and S-hemispheres was obtained when the graphs of the polar fields were shifted forward in time relative to the Wolf numbers graphs by 4.76 and 5.45 years, respectively. This indicates that solar cycle 25 will be slightly stronger than the previous cycle. Using the absolute values of the average polar magnetic fields during the 2 years interval just before the cycle minimum in the N- and S-hemispheres as precursors we found that predicted maximal Wolf numbers in the N- and S-hemisphere are 66 ± 17 and 83 ± 21, respectively. This quantitatively confirms that solar cycle 25 will be slightly (4—10%) more active than the previous one.

Keywords: prediction of solar activity, solar activity, solar cycle, Sun
References: 

1. Babcock H. W. (1961) The topology of the Sun’s magnetic field and the 22-year cycle. Astrophys. J. 133. 572—587.
https://doi.org/10.1086/147060

2. Bhowmik P., Nandy D. (2018) Prediction of the strength and timing of sunspot cycle 25 reveal decadal-scale space environmental conditions. Nature Comm. 9. id. 5209.
https://doi.org/10.1038/s41467-018-07690-0

3. Cameron R. H., Jiang J., Schhssler M. (2016) Solar cycle 25: Another moderate cycle? Astrophys. J. Lett. 823(2). L22.
https://doi.org/10.3847/2041-8205/823/2/L22

4. Charbonneau P. (2010) Dynamo models of the solar cycle. Living Rev. Solar Phys. 7. id. 3.
https://doi.org/10.12942/lrsp-2010-3

5. Clette F., Svalgaard L., Vaquero J. M., Cliver E. W. (2014) Revisiting the sunspot number. A 400-year perspective on the solar cycle. Space Sci. Rev. 186. 35—103.
https://doi.org/10.1007/s11214-014-0074-2

6. Hale G. E., Ellerman F., Nicholson S. B., Joy A. H. (1919) The magnetic polarity of sun-spots. Astrophys. J. 49. 153—178.
https://doi.org/10.1086/142452

7. Hathaway D. H. (2009) Solar cycle forecasting. Space Sci. Rev. 144(1-4). 401—412.
https://doi.org/10.1007/s11214-008-9430-4

8. Jiang J., Wang J.-X., Jiao Q.-R., Cao J.-B. (2018) Predictability of the solar cycle over one cycle. Astrophys. J. 863. id. 159. 15 p.
https://doi.org/10.3847/1538-4357/aad197

9. Leighton R. B. (1969) A magneto-kinematic model of the solar cycle. Astrophys. J. 156. 1—26.
https://doi.org/10.1086/149943

10. Okoh D. I., Seemala G. K., Rabiu A. B., Uwamahoro J. B., Habarulema J. B., Aggarwal M. (2018) A hybrid regression-neural network (HR-NN) method for forecasting the solar activity. Space Weather. 16(9). 1424—1436.
https://doi.org/10.1029/2018SW001907

11. Ossendrijver M. (2003) The solar dynamo. Astron. and Astrophys. Rev. 11. 287—367.
https://doi.org/10.1007/s00159-003-0019-3

12. Pesnell W. D., Schatten K. H. (2018) An early prediction of amplitude of solar cycle 25. Solar Phys. 293. id. 112.
https://doi.org/10.1007/s11207-018-1330-5

13. Petrovay K. (2020) Solar cycle prediction. Living Rev. Solar Phys. 17. id. 2.
https://doi.org/10.1007/s41116-020-0022-z

14. Pishkalo M. I. (2010) Prediction of amplitude of solar cycle 24 based on polar magnetic field of the sun at cycle minimum. Sun and Geosphere. 5(2). 47—51.

15. Pishkalo M. I. (2019) On polar magnetic field reversal in solar cycles 21, 22, 23, and 24. Solar Phys. 294. id. 137.
https://doi.org/10.1007/s11207-019-1520-9

16. Schatten K. H., Scherrer P. H., Svalgaard L., Wilcox J. M. (1978) Using dynamo theory to predict the sunspot number during solar cycle 21. Geophys. Res. Lett. 5(5). 411—414.
https://doi.org/10.1029/GL005i005p00411

17. Temmer M., Ryb<k J., BendRk P., Veronig A., Vogler F., Otruba W., P`tzi W., Hanslmeier A. (2006) Hemispheric sunspot numbers Rn and Rs from 1945—2004: catalogue and N-S asymmetry analysis for solar cycles 18—23. Astron. and Astrophys. 447(2). 735—743.
https://doi.org/10.1051/0004-6361:20054060