Periodicities in Solar Activity, Solar Radiation and Their Links with Terrestrial Environment

Solar magnetic activity is expressed via variations of sunspots and active regions varying on different timescales. The most accepted is an 11-year period supposedly induced by the electromagnetic solar dynamo mechanism. There are also some shorter or longer timescales detected: the biennial cycle (2 - 2.7 years), Gleisberg cycle (80 - 100 years), and Hallstatt’s cycle (2100 - 2300 years). Recently, using Principal Component Analysis (PCA) of the observed solar background magnetic field (SBMF), another period of 330 - 380 years, or Grand Solar Cycle (GSC), was derived from the summary curve of two eigenvectors of SBMF. In this paper, a spectral analysis of the averaged sunspot numbers, solar irradiance, and the summary curve of eigenvectors of SBMF was carried out using Morlet wavelet and Fourier transforms. We detect a 10.7-year cycle from the sunspots and modulus summary curve of eigenvectors as well a 22-year-cycle and the grand solar cycle of 342 - 350-years from the summary curve of eigenvectors. The Gleissberg centennial cycle is only detected on the full set of averaged sunspot numbers for 400 years or by adding a quadruple component to the summary curve of eigenvectors. Another period of 2200 - 2300 years is detected in the Holocene data of solar irradiance measured from the abundance of 14C isotope. This period was also confirmed with the period of about 2000 - 2100 years derived from a baseline of the solar background magnetic field, supposedly, caused by the solar inertial motion (SIM) induced by the gravitation of large planets. The implication of these findings for different deposition of solar radiation into the northern and southern hemispheres of the Earth caused by the combined effects of the solar activity and solar inertial motion on the terrestrial atmosphere is also discussed.

Investigation of a Possible Link between Solar Activity and Climate Change in Saudi Arabia: Rainfall Patterns

In this study, annual, quarterly, and monthly mean precipitation data in Saudi Arabia were correlated with sunspot number (SSN) and galactic cosmic ray (CR) flux over 35 years (1985-2019). The results show that the strength, magnitude, proportion and statistical significance of the relationship between precipitation and the two variables varied by season and month. We find that mean annual precipitation in Saudi Arabia, from May to November, and summer and autumn are correlated with cosmic rays and inversely correlated with SSN. Correlations of varying intensities and scales were found during the remaining months and during winter and spring. The relationships between the rainfall and SSN and CR for each solar cycle were investigated and showed that for all three cycles, the annual rainfall over Saudi Arabia has a positive correlation with CR. Different results were obtained when the seasonal rainfall data correlated with the SSNs and CRs during each cycle. The results obtained, in terms of their strength and magnitude, are affected by terrestrial and extra-terrestrial factors. These factors have been briefly presented and discussed. These findings represent a step towards understanding the possible role of solar activity in climate change for future meteorological phenomenon forecasting, even if the physical mechanism is still poorly quantified.

Application of the Cross Wavelet Transform to Solar Activity and Major Earthquakes Occurred in Chile

Historical earthquakes registered in Chile (from 1900 up to 2015) with epicenters located between 17?30'S and 56?0'S latitude and yearly mean total sunspot number have been considered in order to evaluate a significant linkage between them. The occurrence of strong earthquakes along Chile and the sunspots activity are analyzed to inspect possible influence of solar cycles on earthquakes. The cross wavelet transform and wavelet coherence analysis were applied for sequences of sunspots and earthquakes activity. An 8 - 12 years modulation of earthquakes activity has been identified.

The Effect of Solar Activity on the Annual Precipitation in the Beijing Area

Using continuous wavelet transform, we examine the relationship between solar activity and the annual precipitation in the Beijing area. The results indicate that the annual precipitation is closely related to the variation of sunspot numbers, and that solar activity probably plays an important role in influencing the precipitation on land.

Solar Activity during the Rising Phase of Solar Cycle 24

Solar activity refers to any natural phenomenon occurring on the sun such as sunspots, solar flare and coronal mass ejection etc. Such phenomena have their roots deep inside the sun, where the dynamo mechanism operates and fluid motions occur in a turbulent way. It is mainly driven by the variability of the sun’s magnetic field. The present paper studies the relation between various solar features during January 2009 to December 2011. A good correlation between various parameters indicates similar origin.

The Dependence between Solar Flare Emergence and the Average Background Solar X-Ray Flux Emission

Solar flares, sudden bursts of intense electromagnetic radiation from the Sun, can significantly disrupt technological infrastructure, including communication and navigation satellites. To mitigate these risks, accurate forecasting of solar activity is crucial. This study investigates the potential of the Sun’s background X-ray flux as a tool for predicting solar flares. We analyzed data collected by solar telescopes and satellites between the years 2013 and 2023, focusing on the duration, frequency, and intensity of solar flares. We compared these characteristics with the background X-ray flux at the time of each flare event. Our analysis employed statistical methods to identify potential correlations between these solar phenomena. The key finding of this study reveals a significant positive correlation between solar flare activity and the Sun’s background X-ray flux. This suggests that these phenomena are interconnected within the framework of overall solar activity. We observed a clear trend: periods with increased occurrences of solar flares coincided with elevated background flux levels. This finding has the potential to improve solar activity forecasting. By monitoring background flux variations, we may be able to develop a more effective early warning system for potentially disruptive solar flares. This research contributes to a deeper understanding of the complex relationship between solar flares and the Sun’s overall radiative output. These findings indicate that lower-resolution X-ray sensors can be a valuable tool for identifying periods of increased solar activity by allowing us to monitor background flux variations. A more affordable approach to solar activity monitoring is advised.

Long-term variations of solar activity

Using the Lomb-Scargle periodogram we analyzed two sunspot series: the one over the past 11000 years at the 10-year interval based upon the survey data of 14C concentration in tree-rings, recon- structed by Solanki et al.; and the sunspot number over the past 7000 years, derived from geomagnetic variations by Usoskin et al. We found the periods and quasi-periods in solar activity, such as about 225, 352, 441, 522 and 561 a, and near 1000 and 2000 a. An approach of wavelet transform was applied to check the two sunspot time series, with emphasis on investigating time-varying characteristics in the long-term fluctuations of solar activity. The results show that the lengths and amplitudes of the periods have changed with time, and large variations have taken place during some periods.

Predicting the 25th solar cycle using deep learning methods based on sunspot area data

It is a significant task to predict the solar activity for space weather and solar physics. All kinds of approaches have been used to forecast solar activities, and they have been applied to many areas such as the solar dynamo of simulation and space mission planning. In this paper, we employ the long-shortterm memory(LSTM) and neural network autoregression(NNAR) deep learning methods to predict the upcoming 25 th solar cycle using the sunspot area(SSA) data during the period of May 1874 to December2020. Our results show that the 25 th solar cycle will be 55% stronger than Solar Cycle 24 with a maximum sunspot area of 3115±401 and the cycle reaching its peak in October 2022 by using the LSTM method. It also shows that deep learning algorithms perform better than the other commonly used methods and have high application value.

Signature of the 27-day variation in hemispheric sunspot activity and asymmetry during 2010-2015

In the present work,we study the time evolution,significance of the N-S asymmetry excesses presented as a function of the solar cycle and prominent rotational periods(~27 d)separately for the northern and southern hemispheres.We have investigated short-term variations of the hemispheric solar activity(sunspot numbers and sunspot areas)during the time period 2010-2015,which covers the ascending and the maximum phase of solar cycle 24.We have implemented the Lomb-Scargle periodogram and continuous wavelet transform power spectrum techniques to study the time evolution and dominant rotational periods separately for the northern and southern hemispheres,and whole solar disk.Our results showed that the northern hemisphere exhibited longer solar synodic periods than the southern hemisphere,indicating that the northern hemisphere has a lower rotation rate.Moreover,the northern hemisphere was found to be dominant before transferring to the southern hemisphere during mid-2013.Also,the sunspot areas clearly demonstrated a two-peak structure of solar activity in the northern and southern hemispheres respectively during 2012 and 2014.The statistical significance of the southern hemisphere affirmed enhanced excess during the maximum phase of solar cycle 24.

Solar cycle prediction using a long short-term memory deep learning model

In this paper,we propose a long short-term memory(LSTM)deep learning model to deal with the smoothed monthly sunspot number(SSN),aiming to address the problem whereby the prediction results of the existing sunspot prediction methods are not uniform and have large deviations.Our method optimizes the number of hidden nodes and batch sizes of the LSTM network structures to 19 and 20,respectively.The best length of time series and the value of the timesteps were then determined for the network training,and one-step and multi-step predictions for Cycle 22 to Cycle 24 were made using the well-established network.The results showed that the maximum root-mean-square error(RMSE)of the one-step prediction model was6.12 and the minimum was only 2.45.The maximum amplitude prediction error of the multi-step prediction was 17.2%and the minimum was only 3.0%.Finally,the next solar cycles(Cycle 25)peak amplitude was predicted to occur around 2023,with a peak value of about 114.3.The accuracy of this prediction method is better than that of the other commonly used methods,and the method has high applicability.

Solar cycle distribution of major geomagnetic storms

We examine the solar cycle distribution of major geomagnetic storms （Dst ≤ -100 nT）, including intense storms at the level of -200 nT〈 Dst ≤ -100 nT, great storms at -300 nT〈 Dst ≤-200 nT, and super storms at Dst ≤ -300 nT, which occurred during the period of 1957-2006, based on Dst indices and smoothed monthly sunspot numbers. Statistics show that the majority （82%） of the geomagnetic storms at the level of Dst≤ -100 nT that occurred in the study pe- riod were intense geomagnetic storms, with 12.4% ranked as great storms and 5.6% as super storms. It is interesting to note that about 27% of the geomagnetic storms that occurred at all three intensity levels appeared in the ascending phase of a solar cycle, and about 73% in the descending one. Statistics also show that 76.9% of the intense storms, 79.6% of the great storms and 90.9% of the super storms occurred during the two years before a solar cycle reached its peak, or in the three years after it. The correlation between the size of a solar cycle and the percentage of major storms that occurred, during the period from two years prior to maximum to three years af- ter it, is investigated. Finally, the properties of the multi-peak distribution for major geomagnetic storms in each solar cycle is investigated.

Solar Influences on the North Atlantic Oscillation by Wavelet-Based Multifractal Analysis

There is increasing interest in the relation between the solar activity and climate change. Regarding the solar activity, the fractal property of the sunspot number (SSN) has been studied by many previous works. In general, fractal properties have been observed in the time series of the dynamics of complex systems. The purpose of this research is to investigate the relationship between the solar activity, total ozone, and the North Atlantic Oscillation (NAO) from a viewpoint of multi-fractality. To detect the changes of multifractality, we performed the wavelets analysis, and plotted the τ-function derived from the wavelets of these time series. We showed that the solar activity relate to the NAO, by observing the matching in monofractality or multifractality of these indices. When the SSN increased and the solar activity was stable, the NAO also became stable. When the SSN became maximum, the fractality of the SSN, F10.7 flux, geomagnetic aa, and NAO indices changed from multifractality to monofractality and those states became stable for most of the solar cycles. When the SSN became maximum, the fluctuations became large and multifractality became strong, and a change from multifractal to monofractal behavior was observed in the SSN, F10.7 flux, geomagnetic aa, and NAO indices. The strong interactions of the solar flux, geomagnetic activity, total ozone, and NAO occur in the SSN maximum. The strong interactions were inferred from the similarity of fractality changes and the wavelet coherence. The influence of the solar activity on the NAO was shown from a viewpoint of multi-fractality. These findings will contribute to the research on the effects of the solar activity on climate change.

Analyzing Dominant 13.5 and 27 day Periods of Solar Terrestrial Interaction:A New Insight into Solar Cycle Activities

Our analysis presents an explanation of the Sun–Earth coupling mechanism during declining phase of a solar cycle,and how the dominant 13.5 and 27 day periods play roles in the coupling mechanism which led to intense terrestrial magnetic storms during this declining phase compared to the rising phase of a solar cycle.Moreover,it is observed that while the 27 day period gets strongly modulated in the rising phase,the 13.5 day period modulation is more prominent during the declining phase.It is suggested that out of the 27 and 13.5 day periods of Sun–Earth interaction,the preferred period of modulation happens to be the one which is more dominant for the less random or quieter system participating in the coupling.It is reported for the first time that the 13.5 day period is more prominent in the Sun–Earth interaction during the declining phase of a solar cycle,as it is the most dominant period of Earth's magnetic system,which happens to be more persistent as a dynamical system and hence quieter or more receptive than the Sun.

The Additional Criterion for the Determination of the Time of Minimum of a Solar Cycle

The sunspot number is becoming an increasingly insufficiently reliable parameter for the determination of the time of minimum of a solar cycle during the prolonged and deep minimum of the 23rd solar cycle. Moreover, the sunspot number does not quantitatively reflect physical processes and is a practically conventional qualitative “noisy” parameter. Introduction of an additional criterion for the determination of the time of minimum of a solar cycle is becoming particularly topical due to the upcoming common descent of the level of the 2-secular cycle, when the amplitude of sunspot activity variation will sequentially decrease during several subsequent cycles (after the 23rd cycle). We propose the adoption of the smoothed minimal level of the total solar irradiance (TSI) as an additional physically justified criterion for the determination of the time of minimum of a solar cycle during the minimum of sunspot activity. The minimal level of the monthly average values of the TSI smoothed for 13 months when the last two of its values exceed the preceding value at the point of minimum will additionally indicate the time of minimum of a cycle. The additional criterion has been successfully used for the determination of the time of minima of the preceding 21st and 22nd cycles.

Cosmic Ray Associated with Coronal Index and Solar Flare Index during Solar Cycle 22 - 23

We study the relation between monthly average counting rates of the cosmic ray intensity (CRI) observed at Moscow Neutron Monitoring Station, solar flare index (SFI) and coronal index during the solar cycles 22 and 23, for the period 1986-2008. The long-term behaviour of various solar activity parameters: sunspot numbers (SSN), solar flare index (Hα flare index), coronal index (CI) in relation to the duration of solar cycles 22 and 23 is examined. We find that the correlation coefficient of CRI with the coronal index as well as Hα flare index is relatively large anti-correlation during solar cycle 22. However, the monthly mean values of sunspot number, Hα flare index, and coronal index are well positively correlated with each other. We have analyzed the statistical analysis of the above parameters using of linear model and second order polynomial fits model.

亮桥与冕环关系的研究进展

亮桥是太阳黑子内部一种常见的结构,通常在亮桥区域会产生一系列的爆发活动,并对黑子演化产生影响。利用太阳动力学天文台(Solar Dynamics Observatory,SDO)搭载的大气成像阵列(Atmospheric Imaging Assembly,AIA)极紫外成像数据和日震学与磁场成像仪(Helioseismic and Magnetic Imager,HMI)视向磁图和矢量磁图,通过统计大数据样本,表明亮桥与冕环之间的抑制特征具有一定的普遍性。冕环缺失区域主要位于亮桥锚定点附近的本影-半影边界上,在该区域,随着亮桥的演化和形态的改变,冕环也表现出显现和消失现象。通过分析HMI磁图数据,亮桥锚定区通常伴随着极性相反的小尺度磁场结构,可以推断亮桥锚定点区域的磁力线与周围相反极性的小尺度磁场结构相连,并形成短程闭合磁环,由于磁环的长度较短,无法延伸到日冕层,因此在日冕层无法观测到冕环。根据研究结果,提出了亮桥与冕环关系模型,用来解释亮桥与冕环之间的磁连接性,这个模型可以解释许多与亮桥相关的物理过程。

Periodicity of sunspot activity in the modern solar cycles

Sunspot number, sunspot area and sunspot unit area are usually used to show sunspot activity. In this paper, periodicity of sunspot activity of modern solar cycles has been investigated through analyzing the monthly mean val- ues of the three indices in the time interval of May 1874 to May 2004 by use of the wavelet transform. Their global power spectra and local power spectra are given while the statistical tests of these spectra are taken into account. The main results are (1) the local wavelet power spectrum of the sunspot number seems like that of the sunspot area, indicat- ing that the periodicity of the both indices is similar. The local power spectrum of the sunspot unit area resembles the local power spectra of the previous two indices, but looks more complicated. (2) the possible periods in sunspot activity are about 10.6 (or 10.9 years for the sunspot unit area), 31, and 42 years, and the period of about 10.6 years is statisti- cally significant in the considered time. For the periods of about 31 and 42 years, their power peaks are under the 95% confidence level line but over the mean red-noise spectral line, and for the other rest periods, their power peaks are even under the mean red-noise spectral line, which are sta- tistically insignificant. (3) the local power of the three periods is higher in the late stage than in the early stage of the con- sidered time. (4) the period characteristics of the three indi- ces, shown in the global power spectra and the local power spectra, are similar but there is difference in detail.

用径向基函数神经网络方法预报太阳黑子数平滑月均值

简单介绍了径向基函数神经网络方法的原理和应用,发展了用径向基函数(RBF)对平滑月平均黑子数进行预报的方法.用不同的数据序列对网络进行训练,对未来8个月的平滑月平均黑子数进行预报.用该方法对第23周开始后的平滑月平均黑子数进行逐月预报,并与实测值进行比较,结果表明随着预报实效的延长预报误差被逐渐放大,该方法可以较准确地做出未来4个月的预报,绝对误差可以控制在20以内,标准差为4.8,相对误差控制在38%以内,大部分相对误差不超过15%(占总预报数的89%),具有较好的应用价值.用于网络训练的样本数量对预报结果会产生一定的影响.

基于神经网络的太阳黑子面积平滑月均值预测

黑子面积数是表征太阳活动的重要物理量,准确预测黑子面积能为太阳活动研究、空间天气业务等提供重要参考依据。本文提出一种基于BP神经网络的黑子面积平滑月均值预测方法,利用第20个太阳周之前的数据对网络进行训练,建立预测模型。对第21个太阳周至今的数据进行预测试验,并考虑不同训练步长、预测步长对模型精度的影响。结果表明,该模型能准确逐月预测黑子面积,采用不同训练步长时相对误差均不超过5%,进行更长时间的预测,相对误差会逐渐增大。

太阳黑子相对数最强周期的小波分析

利用小波变换 ,分析了 174 9年以来每个太阳活动周太阳黑子相对数的最强周期以及第 1～ 2 2太阳活动周的最强周期 .分析结果表明 ,在第 5和第 6个太阳活动周 ,太阳黑子相对数最强的周期分别为 6 4 .6 7年和 6 9.31年 ;在第 13～ 15太阳活动周 ,太阳黑子相对数的最强周期分别为 98.0 2年 ,10 5 .0 6年和 10 5 .0 6年 .在第 1～ 2 2太阳活动周中 ,太阳黑子相对数最强的周期是 12 8个月 ,约 10 .6 7年 ,其他太阳活动周的最强周期介于 9.2 9～ 11.4 3年之间 .本文最后给出了 12 8个月周期的幅度随时间的变化 .