The Shape of Sunspot Cycles Described by Monthly Sunspot Areas

In this paper, we used the same four-parameter function as Hathaway, Wilson, and Reichmann (1994) proposed and studied the temporal behavior of sunspot cycles 12–22. We used the monthly averages of sunspot areas and their 13-point smoothed data. Our results show the following. (1) The four-parameter function may reduce to a function of only two parameters. (2) As a cycle progresses, the two-parameter function can be accurately determined after 4–4.5 years from the start of the cycle. A good prediction can be made for the timing and size of the sunspot maximum and for the behavior of the remaining 5–10 years of the cycle. (3) The solar activity in the remaining and forthcoming years of cycle 23 is predicted. (4) The smoothed monthly sunspot areas are more suitable to be employed for prediction at the maximum and the descending period of a cycle, whereas at the early period of a cycle the (un-smoothed) monthly data are more suitable.     

Two Regularities in the Coronal Green-Line Brightness – Magnetic Field Coupling and the Heating of the Corona

To study the quantitative relationship between the brightness of the coronal green line 530.5 nm Fe xiv and the strength of the magnetic field in the corona, we have calculated the cross-correlation of the corresponding synoptic maps for the period 1977 – 2001. The maps of distribution of the green-line brightness I were plotted using every-day monitoring data. The maps of the magnetic field strength B and the tangential B _t and radial B _r field components at the distance 1.1 R _⊙ were calculated under potential approximation from the Wilcox Solar Observatory (WSO) photospheric data. It is shown that the correlation I with the field and its components calculated separately for the sunspot formation zone ±30° and the zone 40 – 70° has a cyclic character, the corresponding correlation coefficients in these zones changing in anti-phase. In the sunspot formation zone, all three coefficients are positive and have the greatest values near the cycle minimum decreasing significantly by the maximum. Above 40°, the coefficients are alternating in sign and reach the greatest positive values at the maximum and the greatest negative values, at the minimum of the cycle. It is inferred that the green-line emission in the zone ±30° is mainly controlled by B _t, probably due to the existence of low arch systems. In the high-latitude zone, particularly at the minimum of the cycle, an essential influence is exerted by B _r, which may be a manifestation of the dominant role of large-scale magnetic fields. Near the activity minimum, when the magnetic field organization is relatively simple, the relation between I and B for the two latitudinal zones under consideration can be represented as a power-law function of the type I ∝ B ^q. In the sunspot formation zone, the power index q is positive and varies from 0.75 to 1.00. In the zone 40 – 70°, it is negative and varies from −0.6 to −0.8. It is found that there is a short time interval approximately at the middle of the ascending branch of the cycle, when the relationship between I and B vanishes. The results obtained are considered in relation to various mechanisms of the corona heating.     

Fitting the sunspot cycles 10–21 by a modified F-distribution density function

It has been found that sunspot cycles 10–21, represented by quarterly mean values of Zürich sunspot number, can be suitably described by the F-distribution density function provided it is modified by introducing five characteristic parameters, in order to achieve an optimal fitting of each cycle. The average cycle calculated from cycles 10–21 has been used as a basis to forecast time and magnitude of the maximum of each cycle, as a function of various numbers of the first quarterly mean values in the beginning N = 8 to 16 quarters. The standard deviations at a 99% significance level calculated from the observed values depend on N, and vary from 1.6 to 1.1 quarters and 65 to 16 units of sunspot number. A rather sufficient forecast is obtained from N = 12 quarters (with inaccuracy of ± 1.5 quarters and ± 24 units); the forecast for cycle 22 yielded, for N = 12, the values t _m = (15.4 ± 1.5) quarters ( 1990.I) and f(t _m ) = (175 ±24 units).     

The Relationship between Prediction Accuracy and Correlation Coefficient

The correlation coefficient (r) between the maximum amplitude (R _m) of a sunspot cycle and the preceding minimum aa geomagnetic index (aa _min), in terms of geomagnetic cycle, can be fitted by a sinusoidal function with a four-cycle periodicity superimposed on a declining trend. The prediction index (χ) of the prediction error relative to its estimated uncertainty based on a geomagnetic precursor method can be fitted by a sinusoidal function with a four-and-half-cycle periodicity. A revised prediction relationship is found between the two quantities: χ<1.2 if r varies in a rising trend, and χ>1.2 if r varies in a declining trend. The prediction accuracy of R _m depends on the long-term variation in the correlation. These results indicate that the prediction for the next cycle inferred from this method, R _m(24)=87±23 regarding the 75% level of confidence (1.2-σ), is likely to fail. When using another predictor of sunspot area instead of the geomagnetic index, similar results can be also obtained. Dynamo models will have better predictive powers when having considered the long-term periodicities.     

On the long-term secular increase in sunspot number

Correlated with the maximum amplitude (R _max) of the sunspot cycle are the sum (R _sum) and the mean (R _mean) of sunspot number over the duration of the cycle, having a correlation coefficient r equal to 0.925 and 0.960, respectively. Runs tests of R _max, R _sum, and R _mean for cycles 0–21 have probabilities of randomness P equal to 6.3, 1.2, and 9.2%, respectively, indicating a tendency for these solar-cycle related parameters to be nonrandomly distributed. The past record of these parameters can be described using a simple two-parameter secular fit, one parameter being an 8-cycle modulation (the so-called Gleissberg cycle or long period) and the other being a long-term general (linear) increase lasting tens of cycles. For each of the solar-cycle related parameters, the secular fit has an r equal to about 0.7–0.8, implying that about 50–60% of the variation in R _max, R _sum, and R _mean can be accounted for by the variation in the secular fit.Extrapolation of the two-parameter secular fit of R _max to cycle 22 suggests that the present cycle will have an R _max = 74.5 ± 49.0, where the error bar equals ± 2 standard errors; hence, the maximum amplitude for cycle 22 should be lower than about 125 when sunspot number is expressed as an annual average or it should be lower than about 130 when sunspot number is expressed as a smoothed (13-month running mean) average. The long-term general increase in sunspot number appears to have begun about the time of the Maunder minimum, implying that the 314-yr periodicity found in ancient varve data may not be a dominant feature of present sunspot cycles.     

A New Method for Prediction of Peak Sunspot Number and Ascent Time of the Solar Cycle

The purpose of the present study is to develop an empirical model based on precursors in the preceding solar cycle that can be used to forecast the peak sunspot number and ascent time of the next solar cycle. Statistical parameters are derived for each solar cycle using “Monthly” and “Monthly smoothed” (SSN) data of international sunspot number (R _i). Primarily the variability in monthly sunspot number during different phases of the solar cycle is considered along with other statistical parameters that are computed using solar cycle characteristics, like ascent time, peak sunspot number and the length of the solar cycle. Using these statistical parameters, two mathematical formulae are developed to compute the quantities [Q _C] _n and [L] _n for each nth solar cycle. It is found that the peak sunspot number and ascent time of the n+1th solar cycle correlates well with the parameters [Q _C] _n and [L] _n /[S _Max] _n+1 and gives a correlation coefficient of 0.97 and 0.92, respectively. Empirical relations are obtained using least square fitting, which relates [S _Max] _n+1 with [Q _C] _n and [T _a] _n+1 with [L] _n /[S _Max] _n+1. These relations predict a peak of 74±10 in monthly smoothed sunspot number and an ascent time of 4.9±0.4 years for Solar Cycle 24, when November 2008 is considered as the start time for this cycle. Three different methods, which are commonly used to define solar cycle characteristics are used and mathematical relations developed for forecasting peak sunspot number and ascent time of the upcoming solar cycle, are examined separately.     

Wavelet Analysis of solar,solar wind and geomagnetic parameters

The sunspot number, solar wind plasma, interplanetary magnetic field, and geomagnetic activity index A _p have been analyzed using a wavelet technique to look for the presence of periods and the temporal evolution of these periods. The global wavelet spectra of these parameters, which provide information about the temporal average strength of quasi periods, exhibit the presence of a variety of prominent quasi periods around 16 years, 10.6 years, 9.6 years, 5.5 years, 1.3 years, 180 days, 154 days, 27 days, and 14 days. The wavelet spectra of sunspot number during 1873–2000, geomagnetic activity index A _p during 1932–2000, and solar wind velocity and interplanetary magnetic field during 1964–2000 indicate that their spectral power evolves with time. In general, the power of the oscillations with a period of less than one year evolves rapidly with the phase of the solar cycle with their peak values changing from one cycle to the next. The temporal evolution of wavelet power in R _z, v _sw, n, B _y, B _z, |B|, and A _p for each of the prominent quasi periods is studied in detail.     

Solar Cycle Predictions based on Solar Activity at Different Solar Latitudes

Many methods of predictions of sunspot maximum number use data before or at the preceding sunspot minimum to correlate with the following sunspot maximum of the same cycle, which occurs a few years later. Kane and Trivedi (Solar Phys. 68, 135, 1980) found that correlations of R _z(max) (the maximum in the 12-month running means of sunspot number R _z) with R _z(min) (the minimum in the 12-month running means of sunspot number R _z) in the solar latitude belt 20° – 40°, particularly in the southern hemisphere, exceeded 0.6 and was still higher (0.86) for the narrower belt > 30° S. Recently, Javaraiah (Mon. Not. Roy. Astron. Soc. 377, L34, 2007) studied the relationship of sunspot areas at different solar latitudes and reported correlations 0.95 – 0.97 between minima and maxima of sunspot areas at low latitudes and sunspot maxima of the next cycle, and predictions could be made with an antecedence of more than 11 years. For the present study, we selected another parameter, namely, SGN, the sunspot group number (irrespective of their areas) and found that SGN(min) during a sunspot minimum year at latitudes > 30° S had a correlation +0.78±0.11 with the sunspot number R _z(max) of the same cycle. Also, the SGN during a sunspot minimum year in the latitude belt (10° – 30° N) had a correlation +0.87±0.07 with the sunspot number R _z(max) of the next cycle. We obtain an appropriate regression equation, from which our prediction for the coming cycle 24 is R _z(max )=129.7±16.3.     

On the average rate of growth in sunspot number and the size of the sunspot cycle

The average rate of growth during the ascending portion of the sunspot cycle, defined here as the difference in smoothed sunspot number values between elapsed time (in months) t and sunspot minimum divided by t, is shown to correlate (r 0.78) with the size of the sunspot cycle, especially for t 18 months. Also, the maximum value of the average rate of growth is shown to highly correlate (r = 0.98) with the size of the cycle. Based on the first 18 months of the cycle, cycle 22 is projected to have an R(M) = 186.0 ± 27.2 (at the ± 1 level), and based on the first 24 months of the cycle, it is projected to have an R(M) = 201.0 ± 20.1 (at the ± 1 level). Presently, the average rate of growth is continuing to rise, having a value of about 4.5 at 24 months into the cycle, a value second only to that of cycle 19 (4.8 at t = 24 and a maximum value of 5.26 at t = 27). Using 4.5 as the maximum value of the average rate of growth for cycle 22, a lower limit can be estimated for R(M); namely R(M) for cycle 22 is estimated to be 164.0 (at the 97.5% level of confidence). Thus, these findings are consistent with the previous single variate predictions that project R(M) for cycle 22 to be one of the greatest on record, probably larger than cycle 21 (164.5) and near that of cycle 19 (201.3).     

A note on the values of the vertical gradient of the magnetic field in the return flux sunspot model

The Return Flux (RF) sunspot model (Osherovich, 1982) imposes a restriction on the value of the vertical gradient of the magnetic field, dB/dz, analogous to a restriction implied by the self-similar sunspot model of Schlüter and Temesvary (ST). The maximum value of the gradient, (dB/dz)_max, is shown to be 10% smaller in the RF model than in the ST model. The dependence of (dB/dz)_max on the sunspot radius is predicted.     

Sunspot Time Series – Relations Inferred from the Location of the Longest Spotless Segments

Spotless days (i.e., days when no sunspots are observed on the Sun) occur during the interval between the declining phase of the old sunspot cycle and the rising phase of the new sunspot cycle, being greatest in number and of longest continuous length near a new cycle minimum. In this paper, we introduce the concept of the longest spotless segment (LSS) and examine its statistical relation to selected characteristic points in the sunspot time series (STS), such as the occurrences of first spotless day and sunspot maximum. The analysis has revealed statistically significant relations that appear to be of predictive value. For example, for Cycle 24 the last spotless day during its rising phase should be about August 2012 (± 9.1 months), the daily maximum sunspot number should be about 227 (± 50; occurring about January 2014±9.5 months), and the maximum Gaussian smoothed sunspot number should be about 87 (± 25; occurring about July 2014). Using the Gaussian-filtered values, slightly earlier dates of August 2011 and March 2013 are indicated for the last spotless day and sunspot maximum for Cycle 24, respectively.     

An Estimate for the Size of Sunspot Cycle 24

For the sunspot cycles in the modern era (cycle?10 to the present), the ratio of R _Z(max)/R _Z(36th month) equals 1.26±0.22, where R _Z(max) is the maximum amplitude of the sunspot cycle?using smoothed monthly mean sunspot number and R _Z(36th month) is the smoothed monthly mean sunspot number 36 months after cycle?minimum. For the current sunspot cycle?24, the 36th month following the cycle?minimum occurred in November 2011, measuring?61.1. Hence, cycle?24 likely will have a maximum amplitude of about 77.0±13.4 (the one-sigma prediction interval), a value well below the average R _Z(max) for the modern era sunspot cycles (about 119.7±39.5).     

A solar cycle variation of the interplanetary magnetic field

Measurements of the north-south (B _z component of the interplanetary field as compiled by King (1975) when organized into yearly histograms of the values of ¦B _z ¦ reveal the following. (1) The histograms decrease exponentially from a maximum occurrence frequency at the value ¦B _z ¦ = 0. (2) The slope of the exponential on a semi-log plot varies systematically roughly in phase with the sunspot number in such a way that the probability of large values of ¦B _z ¦ is much greater in the years near sunspot maximum than in the years near sunspot minimum. (3) There is a sparsely populated high-value tail, for which the data are too meager to discern any solar cycle variation. The high-value tail is perhaps associated with travelling interplanetary disturbances. (4) The solar cycle variations of B _z and the ordinary indicators of solar activity are roughly correlated. (5) The solar cycle variation of B _z is distinctly different than that of the solar wind speed and that of the geomagnetic Ap disturbance index.Now at the Aerospace Corporation, El Segundo, Calif. 90245, U.S.A.     

The shape of the sunspot cycle

The temporal behavior of a sunspot cycle, as described by the International sunspot numbers, can be represented by a simple function with four parameters: starting time, amplitude, rise time, and asymmetry. Of these, the parameter that governs the asymmetry between the rise to maximum and the fall to minimum is found to vary little from cycle to cycle and can be fixed at a single value for all cycles. A close relationship is found between rise time and amplitude which allows for a representation of each cycle by a function containing only two parameters: the starting time and the amplitude. These parameters are determined for the previous 22 sunspot cycles and examined for any predictable behavior. A weak correlation is found between the amplitude of a cycle and the length of the previous cycle. This allows for an estimate of the amplitude accurate to within about 30% right at the start of the cycle. As the cycle progresses, the amplitude can be better determined to within 20% at 30 months and to within 10% at 42 months into the cycle, thereby providing a good prediction both for the timing and size of sunspot maximum and for the behavior of the remaining 7–12 years of the cycle. The U.S. Government right to retain a non-exclusive, royalty free licence in and to any copyright is acknowledged.     

Prediction of Solar Cycle 24 Using Geomagnetic Precursors: Validation and Update

In the previous study (Dabas et al. in Solar Phys. 250, 171, 2008), to predict the maximum sunspot number of the current solar cycle 24 based on the geomagnetic activity of the preceding sunspot minimum, the Ap index was used which is available from the last six to seven solar cycles. Since a longer series of the aa index is available for more than the last 10 – 12 cycles, the present study utilizes aa to validate the earlier prediction. Based on the same methodology, the disturbance index (DI), which is the 12-month moving average of the number of disturbed days (aa≥50), is computed at thirteen selected times (called variate blocks 1,2,…,13; each of them in six-month duration) during the declining portion of the ongoing sunspot cycle. Then its correlation with the maximum sunspot number of the following cycle is evaluated. As in the case of Ap, variate block 9, which occurs exactly 48 months after the current cycle maximum, gives the best correlation (R=0.96) with a minimum standard error of estimation (SEE) of ± 9. As applied to cycle 24, the aa index as precursor yields the maximum sunspot number of about 120±16 (the 90% prediction interval), which is within the 90% prediction interval of the earlier prediction (124±23 using Ap). Furthermore, the same method is applied to an expanded range of cycles 11 – 23, and once again variate block 9 gives the best correlation (R=0.95) with a minimum SEE of ± 13. The relation yields the modified maximum amplitude for cycle 24 of about 131±20, which is also close to our earlier prediction and is likely to occur at about 43±4 months after its minimum (December 2008), probably in July 2012 (± 4 months).     

Bimodality and the Hale cycle

Because of the bimodal distribution of sunspot cycle periods, the Hale cycle (or double sunspot cycle) should show evidence of modulation between 20 and 24 yr, with the Hale cycle having an average length of about 22 yr. Indeed, such a modulation is observed. Comparison of consecutive pairs of cycles strongly suggests that even-numbered cycles are preferentially paired with odd-numbered following cycles. Systematic variations are hinted in both the Hale cycle period and R _sum (the sum of monthly mean sunspot numbers over consecutively paired sunspot cycles). The preferred even-odd cycle pairing suggests that cycles 22 and 23 form a new Hale cycle pair (Hale cycle 12), that cycle 23 will be larger than cycle 22 (in terms of R _M, the maximum smoothed sunspot number, and of the individual cycle value of R _sum), and that the length of Hale cycle 12 will be longer than 22 yr. Because of the strong correlation (r = 0.95) between individual sunspot cycle values of R _sum and R _M, having a good estimate of R _Mfor the present sunspot cycle (22) allows one to predict its R _sum, which further allows an estimation of both R _Mand R _sum for cycle 23 and an estimation of R _sum for Hale cycle 12. Based on Wilson's bivariate fit (r = 0.98), sunspot cycle 22 should have an R _Mequal to 144.4 ± 27.3 (at the 3- level), implying that its R _sum should be about 8600 ± 2200; such values imply that sunspot cycle 23 should have an R _sum of about 10500 ± 2000 and an R _Mof about 175 ± 40, and that Hale cycle 12 should have an R _sum of about 19100 ± 3000.     

Predicting Solar Cycle 24 Using a Geomagnetic Precursor Pair

We describe using Ap and F_10.7 as a geomagnetic-precursor pair to predict the amplitude of Solar Cycle 24. The precursor is created by using F_10.7 to remove the direct solar-activity component of Ap. Four peaks are seen in the precursor function during the decline of Solar Cycle 23. A recurrence index that is generated by a local correlation of Ap is then used to determine which peak is the correct precursor. The earliest peak is the most prominent but coincides with high levels of non-recurrent solar activity associated with the intense solar activity of October and November 2003. The second and third peaks coincide with some recurrent activity on the Sun and show that a weak cycle precursor closely following a period of strong solar activity may be difficult to resolve. A fourth peak, which appears in early 2008 and has recurrent activity similar to precursors of earlier solar cycles, appears to be the “true” precursor peak for Solar Cycle 24 and predicts the smallest amplitude for Solar Cycle 24. To determine the timing of peak activity it is noted that the average time between the precursor peak and the following maximum is ≈?6.4 years. Hence, Solar Cycle 24 would peak during 2014. Several effects contribute to the smaller prediction when compared with other geomagnetic-precursor predictions. During Solar Cycle 23 the correlation between sunspot number and F_10.7 shows that F_10.7 is higher than the equivalent sunspot number over most of the cycle, implying that the sunspot number underestimates the solar-activity component described by F_10.7. During 2003 the correlation between aa and Ap shows that aa is 10 % higher than the value predicted from Ap, leading to an overestimate of the aa precursor for that year. However, the most important difference is the lack of recurrent activity in the first three peaks and the presence of significant recurrent activity in the fourth. While the prediction is for an amplitude of Solar Cycle 24 of 65±20 in smoothed sunspot number, a below-average amplitude for Solar Cycle 24, with maximum at 2014.5±0.5, we conclude that Solar Cycle 24 will be no stronger than average and could be much weaker than average.     

CCD BV and 2MASS photometric study of the open cluster NGC 1513

We present CCD BV and JHK _s 2MASS photometric data for the open cluster NGC 1513. We observed 609 stars in the direction of the cluster up to a limiting magnitude of V∼19 mag. The star-count method showed that the centre of the cluster lies at α ₂₀₀₀=04 ^h 09 ^m 36 ^s , δ ₂₀₀₀=49°28^′43^″ and its angular size is r=10 arcmin. The optical and near-infrared two-colour diagrams revealed the colour excesses in the direction of the cluster as E(B−V)=0.68±0.06, E(J−H)=0.21±0.02 and E(J−K _s )=0.33±0.04 mag. These results are consistent with normal interstellar extinction values. Optical and near-infrared Zero Age Main-Sequences (ZAMS) provided an average distance modulus of (m−M)₀=10.80±0.13 mag, which can be translated into a distance of 1440±80 pc. Finally, using Padova isochrones we determined the metallicity and age of the cluster as Z=0.015±0.004 ([M/H]=−0.10±0.10 dex) and log (t/yr)=8.40±0.04, respectively.     

Which One is the ‘GNEVYSHEV’ GAP?

Gnevyshev [Solar Phys. 1, 107, 1967] showed that in solar cycle 19 (1954 –1965), the coronal line half-yearly average intensity at 5303 Å (green line) had actually two maxima, the first one in 1957 and the second in 1959–1960. In the present communication, the structures at solar maxima were reexamined in detail. It was noted that the two-peak structure of solar indices near sunspot (Rz) maxima was only a crude approximation. On a finer time scale (monthly values), there were generally more than three peaks, with irregular peak separations in a wide range of ～12± 6 months. The sequences were seen simultaneously (within a month or two) at many solar indices (notably the 2800 MHz radio flux) at and above the photosphere, and these can be legitimately termed ‘Gnevyshev peaks’ and ‘Gnevyshev gaps’. The open magnetic flux emanating from the Sun showed this sequence partially, some peaks matching, others not. In interplanetary space, the interplanetary parameters N (number density), V (solar wind speed), B (magnetic field) showed short-time peak structures but mostly not matching with the Rz peaks. Geomagnetic indices (aa, Dst) had peaked structures, which did not match with Rz peaks but were very well related to V and B, particularly to the product VB. The cosmic ray (CR) modulation also showed peaks and troughs near sunspot maximum, but the matching with Rz peaks was poor. Hence, none of these can be termed Gnevyshev peaks and gaps, particularly the gap between aa peaks, one near sunspot maximum and another in the declining phase, as this gap is qualitatively different from the Gnevyshev gap in solar indices.