Solar Activity Proxies Research Articles

Are we crossing a minimum of the Gleissberg centennial cycle? Multivariate machine learning-based prediction of the sunspot number using different proxies of solar activity and spectral analysis

We propose a new method for predicting the solar cycle in terms of the sunspot number (SN) based on multivariate machine learning algorithms, various proxies of solar activity, and the spectral analysis of all considered time series via the fast Fourier transform (through the latter we identify periodicities with which to lag these series and thus generate new attributes –predictors– for incorporation in the prediction model). This combination of three different techniques in a single method is expected to enhance the accuracy and reliability of the solar activity prediction models developed to date. Thus, predictive results for SN are presented for Solar Cycles 25 (the current one) and 26 (using the 13-month smoothed SN, version 2) up until January 2038, yielding maximum values of 134.2 (in June 2024) and 115.4 (in May 2034), respectively, with a root mean squared error (RMSE) of 9.8. These results imply, on the one hand, a maximum of Cycle 25 below the average and, on the other hand, a lower peak than the preceding ones for Cycle 26, suggesting that Solar Cycles 24, 25, and 26 are part of a minimum of the centennial Gleissberg cycle, as occurred with Cycles 12, 13, and 14 in the final years of the 19th century and the early 20th century.

Understanding Solar Activity after the Maunder Minimum: Sunspot Records by Rost and Alischer

The Maunder Minimum was a period with significantly reduced solar activity between 1645 and 1715, approximately. The transition between the low solar activity in the Maunder Minimum and the subsequent “normal” regime of solar activity was gradual. However, there are discrepancies in the solar activity level from sunspot number indices and solar activity proxies in that period. Among the contemporaneous observers, Johann L. Rost and Sebastian Alischer were two key sunspot observers to understand the solar activity in this transition just after the Maunder Minimum. We have revised all their sunspot records, counting the number of groups and individual sunspots to derive reliable data for the solar activity level for the period 1716–1726. We found significant misinterpretations of the sunspot group counting assigned to these astronomers in the existing group number databases. Our new group sunspot counting significantly reduces the number of groups for Rost and Alischer’s observations compared to entries in existing databases. Furthermore, our sunspot number estimates (obtained from the active day fraction methodology) of the maximum amplitude of Solar Cycles −3 and −4 are significantly lower than the amplitudes according to the official sunspot number, but they are compatible with sunspot number values obtained from solar activity proxies such as radioisotopes. Our result would imply that solar activity after the Maunder Minimum recovered more gradually and with a lower intensity than previously considered.

Trends in the F2-layer parameters to 2023

Diurnal variations in the long-term trends in the F2-layer critical frequency, foF2, are studied in detail based on the observations to 2022 and use of several solar activity (SA) indices to remove the SA effects in the foF2 variations. The new results confirm the conclusions derived earlier based on the data only to 2014. In the winter months, two well pronounced periods are observed: the nighttime hours when the trends are small by their magnitude and the daytime hours when the negative trends are maximal. In March, the picture is close to that in winter months, however the daytime negative trends are weaker than in winter. No pronounced diurnal variation in the foF2 trends is found in the summer month June. A search for long-term trends in foF2 is performed based on the observations at three stations in the Northern Hemisphere (Juliusruh, Boulder, and Moscow) and three stations in the Southern Hemisphere (Townsville, Hobart, and Canberra). The data for two winter months in each hemisphere for five near-noon LT moments are analyzed. Three solar activity proxies (F30, Ly-α, and MgII) are used to get rid of the SA effects. Negative trends are obtained for all considered situations (station, month, LT moment, SA proxy). The trends for different stations agree well with each other. A tendency in intensification in the negative trends during the recent years is seen in all months.

Connection between Subsurface Layers and Surface Magnetic Activity over Multiple Solar Cycles Using GONG Observations

We investigate the spatiotemporal evolution of high-degree acoustic-mode frequencies of the Sun and surface magnetic activity over the course of multiple solar cycles, to improve our understanding of the connection between the solar interior and atmosphere. We focus on high-degree p-modes due to their ability to characterize conditions in the shear layer just below the solar surface, and analyze 22 yr of oscillation frequencies obtained from the Global Oscillation Network Group. Considering 10.7 cm radio flux measurements, the sunspot number, and the local magnetic activity index as solar-activity proxies, we find strong correlation between the mode frequencies and each activity index. We further investigate the hemispheric asymmetry associated with oscillation frequencies and magnetic activity proxies, and find that both were dominant in the southern hemisphere during the descending phase of cycle 23, while in cycle 24 these quantities fluctuated between northern and southern hemispheres. Analyzing the frequencies at different latitudes with the progression of solar cycles, we observe that the variations at midlatitudes were dominant in the southern hemisphere during the maximum-activity period of cycle 24, but the values overlap as the cycle advances toward the minimum phase. The mode frequencies at the beginning of cycle 25 are found to be dominant in the southern hemisphere following the pattern of magnetic activity. The analysis provides added evidence that the variability in oscillation frequencies is caused by both strong and weak magnetic fields.

Statistical models of the variability of plasma in the topside ionosphere: 1. Development and optimisation

This work presents statistical models of the variability of plasma in the topside ionosphere based on observations made by the European Space Agency’s (ESA) Swarm satellites. The models were developed in the “Swarm Variability of Ionospheric Plasma” (Swarm-VIP) project within the European Space Agency’s Swarm+4D-Ionosphere framework. The configuration of the Swarm satellites, their near-polar orbits and the data products developed, enable studies of the spatial variability of the ionosphere at multiple scale sizes. The statistical modelling technique of Generalised Linear Modelling (GLM) was used to create models of both the electron density and measures of the variability of the plasma structures at horizontal spatial scales between 20 km and 100 km. Despite being developed using the Swarm data, the models provide predictions that are independent of these data. Separate models were created for low, middle, auroral and polar latitudes. The models make predictions based on heliogeophysical variables, which act as proxies for the solar and geomagnetic processes. The first and most significant term in the majority of the models was a proxy for solar activity. The most common second term varied with the latitudinal region. This was the Solar Zenith Angle (SZA) in the polar region, a measure of latitude in the auroral region, solar time in the mid-latitude region and a measure of latitude in the equatorial region. Other, less significant terms in the models covered a range of proxies for the solar wind, geomagnetic activity and location. In this paper, the formulation, optimisation and evaluation of these models are discussed. The models show very little bias, with a mean error of zero to two decimal places in 14 out of 20 cases. The models capture some, but not all, of the trends present in the data, with Pearson correlation coefficients of up to 0.75 between the observations and the model predictions. The models also capture some, but not all, of the variability of the ionospheric plasma, as indicated by the precision, which ranged between 0.20 and 0.83. The addition of the thermospheric density as an explanatory variable in the models improved the precision in the polar and auroral regions. It is suggested that, if the thermosphere could be observed at a higher spatial resolution, then even more of the variability of the plasma structures could be captured by statistical models. The formulation and optimisation of the models are presented in this paper. The capability of the model in reproducing the expected climatological features of the topside ionosphere, in supporting GNSS-based ionospheric observations and the performance of the model against the Thermosphere-Ionosphere-Electrodynamics General Circulation Model (TIE-GCM), are provided in a companion paper (Spogli L et al. 2024. J Space Weather Space Clim https://doi.org/10.1051/swsc/2024003).

Deep Learning LSTM-based approaches for 10.7 cm solar radio flux forecasting up to 45-days

Accurate forecasting of F10.7 index is important on short, medium and long-term timescales since F10.7 is an excellent proxy of solar activity and it plays an important role within the Space Weather framework. The analysis of the signatures of transient solar radio emission and its prediction are a challenging task as the underpinning physical processes are typically nonlinear, non-stationary and chaotic. In this paper we want to present three Deep Learning approaches for the daily forecasting of the adjusted F10.7 solar radio flux up to 45 days, using a family of Long Short Term Memory (LSTM) based models. We investigated two novel hybrid architectures: the LSTM model used in combination with Fast Iterative Filtering as decomposition algorithm (FIF-LSTM) and a method based on Multi-Head-Attention architecture (FIF-LSTM-MHA). FIF is a robust decomposition signal method very suitable for analyzing non-linear and non-stationary time series and it is used to separate the original time series into different oscillation components according to frequency, derived without leaving the time domain before to be fed into the neural network. The Attention mechanism is able to keep track of long-term dependencies in data sequences and improve the computational efficiency of the prediction model by reducing the effect of irrelevant information, mimicking human attention and selecting the most critical input. Our comparative analysis evaluated the models’ performance for different time lags and solar activity levels. The results indicated that the hybrid models achieve better performance than the LSTM model for mid-range F10.7 predictions while the LSTM achieves better performance within the first few time lags. FIF-LSTM-MHA gives more promising output for longer forecasts since it tends to smooth the prediction curve due to the peculiarity of the Attention module to discard less relevant features of the time series and highlight the global trend.

On the external forcing of global eruptive activity in the past 300 years

The decryption of the temporal sequence of volcanic eruptions is a key step in better anticipating future events. Volcanic activity results from a complex interaction between internal and external processes, with time scales spanning multiple orders of magnitude. We review periodicities that have been detected or correlated with volcanic eruptions/phenomena and interpreted as resulting from external forces. Taking a global perspective and longer time scales than a few years, we approach this interaction by analyzing three time-series using singular spectral analysis: the global number of volcanic eruptions (NVE) between 1700 and 2022, the number of sunspots (ISSN), a proxy for solar activity, and polar motion (PM) and length of day (lod), two proxies for gravitational force. Several pseudo-periodicities are common to NVE and ISSN, in addition to the 11-year Schwabe cycle that has been reported in previous work, but NVE shares even more periodicities with PM. These quasi-periodic components range from −5 to −130 years. We interpret our analytical results in light of the Laplace’s paradigm and propose that, similarly to the movement of Earth’s rotation axis, global eruptive activity is modulated by commensurable orbital moments of the Jovian planets together with Pluto, whose influence is also detected in solar activity.

Long-term changes in solar activity and irradiance

The Sun is the main energy source to Earth, and understanding its variability is of direct relevance to climate studies. Measurements of total solar irradiance (TSI) exist since 1978, but this is too short compared to climate-relevant time scales. Coming from a number of different instruments, these measurements require a cross-calibration, which is not straightforward, and thus several composite records have been created. All of them suggest a marginally decreasing secular trend in solar minima levels since 1996. Most composites actually feature a weak decrease over the entire period of observations, which is also seen in observations of the solar surface magnetic field and is further supported by Caii Kdata. Some inconsistencies, however, remain and overall the magnitude and even the presence of the secular change over the satellite era remain uncertain. Different models have been developed, which are used to understand the irradiance variability over the satellite period and to extend the records of solar irradiance back in time. Differing in their methodologies, all models require proxies of solar magnetic activity as input. The most widely used proxies are sunspot records and cosmogenic isotope data on centennial and millennial time scale, respectively. None of this, however, offers a sufficiently good, independent description of the long-term evolution of faculae and network responsible for solar brightening. This leads to significant uncertainties in the amplitude of the changes in solar irradiance on time scales longer than the solar cycle. Here we review recent efforts and advances aiming at improving long-term irradiance reconstructions and to reduce the existing uncertainty in the magnitude of the long-term variability. In particular, by employing state-of-the-art 3D magnetohydrodynamical simulations, an upper limit of 2±0.7 Wm−2 was set on the possible increase of TSI since the end of the Maunder minimum as compared to the 2019 minimum level. Besides, significant progress has been made in collecting and processing historical solar observation in Caii Kspectral line, which provide direct information on bright magnetic features on the Sun and can be used to improve the accuracy of past irradiance reconstructions, pending some remaining issues with the data.

Dependence of long-term trends in foF2 at middle latitudes on different solar activity proxies

The most studied ionospheric parameter for long-term trends is foF2. The dominant factor of foF2 variability is the solar cycle, which is much stronger than the long-term trends. Therefore its effect in data must be removed. However, several decade long homogeneous measurements of the solar EUV fluxes are not available, so various solar activity proxies (solar activity indices) must be used. The aim of this paper is to study the impact of selection of different solar activity proxies on foF2 long-term trends and to find the best solar activity proxy for foF2 trends at middle latitudes. The results based on yearly average data of six midlatitude stations from four continents (1976–2014) and of six solar activity proxies show that the long-term trends in foF2 depend substantially on the solar activity proxy used, and the only solar proxy, which provides trends of the same sign for all stations and both sub-periods, is F30. Based on results of this paper and that of Laštovička and Burešová (2023), I can recommend F30 as the best solar proxy for studying long-term trends of foF2 at middle latitudes (at least for yearly average values).

A Bayesian Model for Inferring Total Solar Irradiance From Proxies and Direct Observations: Application to the ACRIM Gap

AbstractDifferences among total solar irradiance (TSI) estimates are most pronounced during the so‐called “ACRIM Gap” of 1989–1991, when available satellite‐based observations disagree in trend and no observations exist from satellites with on‐board calibration. Different approaches to bias‐correcting noisy satellite‐based observations lead to discrepancies of up to 0.7 W/m2 in the change in TSI during the Gap. Using a Bayesian hierarchical model for TSI (BTSI), we jointly infer TSI during the ACRIM Gap from satellite‐based observations and proxies of solar activity. In addition, BTSI yields estimates of noise and drift in satellite‐based observations and calibration for proxy records. We find that TSI across the ACRIM Gap changes by only 0.01 W/m2, with a 95% confidence interval of [−0.07, 0.09] W/m2. Our results are consistent with the PMOD CPMDF and Community Consensus TSI reconstructions and inconsistent with the 0.7 W/m2 trend reported in the ACRIM composite reconstruction. Constraints on the trend across the ACRIM Gap are primarily obtained through constraints on the drift in the Nimbus‐7 satellite that are afforded by overlapping satellite and proxy observations.

Extending the analysis on the best solar activity proxy for long-term ionospheric investigations

Based on a previous study to select the best solar activity proxy for foF2 modelling, we expand the analysis to include 24 h time span, increase the spatial coverage by considering additional ionospheric stations, and update the analysis to 2021. Annual means of foF2 are analysed for 12 selected stations from Europe, Asia and Oceania, with high-quality data covering the period 1978–2021 in most of the cases, including two of the three European stations of the previous study. The same four solar proxies were used: F10.7, F30, MgII and HeII, which, based on the high linear correlation between each of them and foF2, serve to model this ionospheric parameter through a linear regression. The results of our comparative analysis, extended to more stations, all the hours and updated time series, agree with previous works, with MgII and F30 being the best solar proxies for foF2 modelling, while HeII is found to be the least effective for this purpose. The importance of the solar proxy selection to model foF2 for filtering purposes to later estimate long-term trends is highlighted considering that different solar proxies applied might result in somewhat different foF2 long-term trend values.

Quiet solar corona: daily images at 8.8–10.7 cm wavelengths

We discuss results of test observations of the 3–6 GHz range array of the Siberian Radio Heliograph (SRH). A method for calibrating brightness temperatures of images was verified using measurements of the brightness temperature of the quiet Sun at a minimum between solar activity cycles 20 and 21 known in the literature. The obtained time dependences of the integral solar flux at 2.8 GHz are similar to those measured at the Dominion Radio Astrophysical Observatory (DRAO), but the absolute values of SRH fluxes are lower relative to the DRAO fluxes by 10–15 %.
 The spectral density of the solar microwave flux at a frequency of 2.8 GHz, the so-called F10.7 index, is one of the main solar activity indices used as input parameters in models of Earth’s ionosphere. The paper considers the relationship between total radio fluxes and changes in the structure of sources on the solar disk during an interval of 50 days. During the period of daily observations from September 1 to October 20, 2021, the number of active regions on the disk changed several times, and the integral flux density at 2.8 GHz changed up to 1.5 times. We determine the relative contributions to the integral flux of bremsstrahlung of near-limb brightenings and plage regions, as well as bremsstrahlung in magnetic fields of active regions. The measured brightness temperatures of SRH radio maps are compared to the model temperatures calculated from observations of extreme ultraviolet emission (EUV) with the AIA/SDO telescope. The results of the analysis can be used to organize regular measurements of the corrected solar activity proxy index F10.7 at SRH, in which the contribution of gyroresonance emission is excluded.

Спокойная корона Солнца: ежедневные изображения на длинах волн 8.8–10.7 см

We discuss results of test observations of the 3–6 GHz range array of the Siberian Radio Heliograph (SRH). A method for calibrating brightness temperatures of images was verified using measurements of the brightness temperature of the quiet Sun at a minimum between solar activity cycles 20 and 21 known in the literature. The obtained time dependences of the integral solar flux at 2.8 GHz are similar to those measured at the Dominion Radio Astrophysical Observatory (DRAO), but the absolute values of SRH fluxes are lower relative to the DRAO fluxes by 10–15 %.
 The spectral density of the solar microwave flux at a frequency of 2.8 GHz, the so-called F10.7 index, is one of the main solar activity indices used as input parameters in models of Earth’s ionosphere. The paper considers the relationship between total radio fluxes and changes in the structure of sources on the solar disk during an interval of 50 days. During the period of daily observations from September 1 to October 20, 2021, the number of active regions on the disk changed several times, and the integral flux density at 2.8 GHz changed up to 1.5 times. We determine the relative contributions to the integral flux of bremsstrahlung of near-limb brightenings and plage regions, as well as bremsstrahlung in magnetic fields of active regions. The measured brightness temperatures of SRH radio maps are compared to the model temperatures calculated from observations of extreme ultraviolet emission (EUV) with the AIA/SDO telescope. The results of the analysis can be used to organize regular measurements of the corrected solar activity proxy index F10.7 at SRH, in which the contribution of gyroresonance emission is excluded.

Performance of the IRI-2016 and IRI-Plas 2020 considering Mg II as EUV solar proxy

To assess upper atmosphere parameters, current models use different variables to account for solar activity levels. Studies that evaluate the most recent solar cycles have shown that when dealing with the ionospheric F2 layer, and in particular with foF2, the Mg II index is a better choice than other solar activity proxies to account for solar EUV variation. The International Reference Ionosphere, version 2016 (IRI-2016), a model widely used by the scientific community, uses the Ionosonde Global (IG) index to assess foF2. A most recent and expanded version, IRI-Plas, allows selecting a solar activity proxy among 7 options including IG and Mg II. In the present work, the performance of IRI considering Mg II to assess foF2 is analyzed, comparing three modeling cases: IRI-2016 with its default solar activity index (IG), IRI-2016 with Mg II instead of IG, and IRI-Plas choosing Mg II as the solar proxy option. This model comparison was evaluated in terms of their agreement with measured foF2 data from four stations located in Japan (Northern Hemisphere), and four in Australia (Southern Hemisphere). Monthly medians for the 24 h, along two periods of three years were considered: 2000–2002 and 2008–2010, which correspond to epochs of maximum and minimum solar activity levels, respectively. Based on goodness-of-fit statistics, our results show that in the case of IRI-2016, the Mg II implementation does not reveal a significant improvement for foF2 assessment, and the IRI-Plas performance with the Mg II option is only slightly better than the other two options. Dispersion diagrams between measured and modeled foF2 also evince a similar performance between the three models. All models show the same over- and underestimation patterns, except in the nighttime case during the minimum activity level period for both Australian and Japanese stations where IRI-Plas outperforms the other two model options. The comparable performances of the three options might be attributed to a stronger variation in the evaluated periods which could be related to seasonal changes rather than to solar EUV proxies. Future studies considering time series over several years, covering at least a complete solar activity cycle, are required to determine stronger model performance differences.

Thermosphere and satellite drag

Accurate forecasts of thermosphere densities, realistic calculation of aerodynamic drag, and propagation of the uncertainty on the predicted orbit positions are required for conjunction analysis and collision avoidance decision making. The main focus of the Committee on Space Research (COSPAR) International Space Weather Action Teams (ISWAT) involved in atmosphere variability studies is satellite drag, and this paper reviews our current capabilities and lists recommendations.The uncertainty in the density of thermosphere models is due to the combined effect of employing simplified or incomplete algorithms, inconsistent and sparse density data, incomplete drivers for upper atmosphere heating processes (proxies for solar and geomagnetic activity), and forecast error of said drivers. When calculating drag, the uncertainty is amplified due to the satellite shape and aerodynamic model. The sources of uncertainty are reviewed in this paper, and possible and promising ways forward are proposed. Data assimilation models/approaches have demonstrated superior skill in reproducing the thermosphere’s state and are the most promising way forward. However, data to drive the models is generally lacking, and they require significant computational resources.Substantial progress can only be made by means of setting up a full-blown observing system, including not only density and composition measurements, but equally the necessary model drivers.

Relationships Between foF2 and Various Solar Activity Proxies

AbstractTo study ionospheric climate, to model the ionosphere (e.g., the International Reference Ionosphere—IRI) and to investigate its long‐term changes and trends, solar activity proxies/indices have been used, because long and homogeneous data series of solar ionizing flux are not available. To identify the optimum solar activity proxies, we use yearly average foF2 data of 11 ionospheric stations from middle and low/equatorial latitudes of four continents over 1976–2014 and six solar activity proxies, F10.7, sunspot numbers, F30, Mg II, He II, and solar H Lyman‐α flux. Mg II and F30 are found to be the best solar proxies for variability of foF2 at middle latitudes, not the usually used F10.7 or sunspot numbers. At equatorial latitudes the situation seems to be different with likely He II as the optimum solar proxy but all low/equatorial results are very preliminary. Solar activity describes 99% of the total variance of yearly foF2 and the foF2 dependence on solar proxies is highly linear at middle latitudes. The dependence of foF2 on F10.7 and sunspot numbers is significantly steeper in 1996–2014 than in 1976–1995, whereas for F30 both intervals provide the same dependence. We recommend for investigating the midlatitude yearly values of foF2 the solar proxy F30 followed by Mg II as the second one, not traditional F10.7 or sunspot numbers.

Comparison of solar activity proxies: eigenvectors versus averaged sunspot numbers

ABSTRACTWe explore the links between the averaged sunspot numbers (SSN) and a modulus summary curve (MSC) of two largest eigenvectors of the solar background magnetic field (SBMF) derived from principal component analysis. MSC has rather close correspondence with the whole set of SSN revealing close cycle timings, duration, and maxima times for the cycles 12–24, 6, 7, and −4, −3, while for a few cycles in the mid-18th and mid-19th centuries there are discrepancies in the maximum amplitudes, durations, and times of the maxima. Possible reasons of these discrepancies related to uncertainties in the SSN observations in the 18th–19th centuries, in MSC definition and the different solar activity entities they represent: toroidal (SSN) and poloidal (MSC) magnetic fields, are discussed. Wavelet and Fourier spectral analysis of SSN and MSC series reveal within 95 per cent confidence levels the same prominent period of 10.7 yr, whereas SSN series show a period of 101 yr and MSC of 342 yr close to or above 95 per cent red-noise level. The correlation coefficients between SSN and MSC series vary from 0.25 for the whole SSN data set (from 1700), to 0.56 for the data sets from 1860, to 0.67 for the data sets from 1900 when all SSN restorations agree. These SSN and MSC data sets are confirmed to be closely but not identically related representing the solar activity in different entities of solar dynamo. Use of the summary curve and MSC of eigenvectors of SBMF can provide additional information to SSN for better understanding of solar activity.

Multi-Year Variations in Temperature in Mesopause Region and F2-Region Peak Electron Density over Eastern Siberia

We performed an analysis of year-to-year variations in the characteristics of the upper neutral atmosphere and the ionosphere over Eastern Siberia. The mesopause temperature (Tm) obtained from the spectrometric observations of the OH(6-2) emission and the peak electron density (NmF2) from the ionosonde measurements were used as atmospheric and ionospheric characteristics. We considered the annual mean Tm and yearly average values of NmF2, as well as yearly average values of day-to-day and intradiurnal variability in Tm and NmF2. To interpret the year-to-year variations, we use multiple regressions of the ionospheric and atmospheric characteristics on the F10.7-index (as a proxy of solar activity) and Ap-index (as a proxy of geomagnetic activity). For the atmospheric characteristics, we also used regressions on the SOI index (as a proxy of circulation in the lower atmosphere). The yearly average values of NmF2 are dominantly controlled by changes in the solar flux. The year-to-year variations in the NmF2 variability are mainly driven by changes in both solar and geomagnetic activity. The year-to-year variations in the mesopause temperature weakly correlate with changes in the indices of solar and geomagnetic activity. The yearly average values of Tm variability correlate with changes in the SOI-index: the day-to-day variability demonstrates a positive correlation with the SOI-index, while the intradiurnal variability shows a negative correlation with the SOI-index. The study did not reveal a significant relationship between the year-to-year variations in the NmF2 variability and Tm variability.

Correlation analysis between the occurrence of epidemic in ancient China and solar activity.

As the globe has witnessed the pandemic, epidemic diseases exert a strong impact on human beings and ecosystems. Since the Sun is the primary energy source of the Earth, some scientific pioneers attempted to search for the discernible relation between solar activity and the incidence of epidemics. In this study, the periodic changes and trends of ancient Chinese epidemic data were analyzed in comparison with those of sunspot numbers, a solar activity proxy. The results show that the epidemic and solar activity changes are in good agreement to a certain extent, especially during the Gleissberg and the de Vries cycles. The wavelet coherence shows that the frequency of the epidemic data and sunspot numbers are highly associated. In addition, results from the ensemble empirical mode decomposition illustrate consistent variations in low-frequency decompositions. This study has important implications for further understanding of the potential impact of solar activity on Earth's biosphere, the underlying mechanism of which needs further exploration.

Universal Scaling Laws for Solar and Stellar Atmospheric Heating: Catalog of Power-law Index between Solar Activity Proxies and Various Spectral Irradiances

The formation of extremely hot outer atmospheres is one of the most prominent manifestations of magnetic activity common to late-type dwarf stars, including the Sun. It is widely believed that these atmospheric layers, the corona, transition region, and chromosphere, are heated by the dissipation of energy transported upwards from the stellar surface by the magnetic field. This is signified by the spectral line fluxes at various wavelengths, scaled with power-law relationships against the surface magnetic flux over a wide range of formation temperatures, which are universal to the Sun and Sunlike stars of different ages and activity levels. This study describes a catalog of power-law indices between solar activity proxies and various spectral line fluxes. Compared to previous studies, we expanded the number of proxies, which now includes the total magnetic flux, total sunspot number, total sunspot area, and the F10.7 cm radio flux, and further enhanced the number of spectral lines by a factor of 2. This provides the data to study in detail the flux–flux scaling laws from the regions specified by the temperatures of the corona (log(T/K) = 6–7) to those of the chromosphere (log(T/K) ∼ 4), as well as the reconstruction of various spectral line fluxes of the Sun in the past, F-, G-, and K-type dwarfs, and the modeled stars.