Long-term Variability in the Length of the Solar Cycle

• Published in: Publications of the Astronomical Society of the Pacific Vol. 121; no. 881; pp. 797 - 809
• Main Authors: Richards, Mercedes T., Rogers, Michael L., Richards, Donald St. P.
• Format: Journal Article
• Language: English
• Published: Chicago, IL University of Chicago Press 01-07-2009
• Subjects: Astronomy; Data lines; Datasets; Earth, ocean, space; Exact sciences and technology; Mathematical maxima; Mathematical minima; Periodicity; Solar activity cycles; Statistical median; Sunspot cycle; Sunspot number; Sunspots; Solar cycle; Long term variation
• ISSN: 0004-6280; 1538-3873
• DOI: 10.1086/604667

The recent paucity of sunspots and the delay in the expected start of Solar Cycle 24 have drawn attention to the challenges involved in predicting solar activity. Traditional models of the solar cycle usually require information about the starting time and rise time as well as the shape and amplitude of the cycle. With this tutorial, we investigate the variations in the length of the sunspot number cycle and examine whether the variability can be explained in terms of a secular pattern. We identified long-term cycles in archival data from 1610–2000 using median trace analyses of the cycle length and power spectrum analyses of the (
O−C
O
−
C
) residuals of the dates of sunspot minima and maxima. Median trace analyses of data spanning 385 years indicate a cycle length with a period of 183–243 yr, and a power spectrum analysis identifies a period of188 ± 38 yr
188
±
38

yr
. We also find a correspondence between the times of historic minima and the length of the sunspot cycle, such that the cycle length increases during the time when the number of spots is at a minimum. In particular, the cycle length was growing during the Maunder minimum when almost no sunspots were visible on the Sun. Our study suggests that the length of the sunspot number cycle should increase gradually, on average, over the next∼75 yr
∼
75

yr
, accompanied by a gradual decrease in the number of sunspots. This information should be considered in cycle prediction models to provide better estimates of the starting time of each cycle.