Tingting Xu

Coronal bright points (CBPs) are ubiquitous in the Sun and represent a key class of solar magnetic activities. They offer valuable insights into unresolved problems in solar cycle studies, but their hemispheric distribution in different cycles and for different types has not been understood yet. Based on the observations of the Extreme-ultraviolet Imaging Telescope on board the Solar and Heliospheric Observatory mission, a total of more than 1.8 million CBPs are identified in full-disk images spanning the period 1996–2023, and the hemispheric distribution of bright and dim CBPs is studied in detail. Our main results are as follows: (1) Bright CBPs are significantly correlated with sunspot activity, whereas dim CBPs are in antiphase with the solar activity cycle, suggesting that bright and dim CBPs have different characteristics and sources. (2) Two types of CBPs both exhibit significant hemispherical asymmetry, implying that the north–south asymmetry of CBPs is a real observational phenomenon and not a random fluctuation. (3) Before and after the solar cycle maximum, the slope of the normalized and absolute asymmetry indices of both bright and dim CBPs changes the sign or value, which suggests that the hemispheric distribution of CBPs might be influenced by the reversal of magnetic field polarity. (4) The cumulative trend and difference of bright and dim CBPs both display an intercycle- and intracycle-dependent pattern. Our analysis results are beneficial for elucidating the underlying mechanism of hemispheric variation of small-scale magnetic structures in solar corona, and could offer a new perspective on the fundamental physical processes responsible for the global and local dynamo theories.

The solar chromosphere is a key interface layer for understanding magnetic energy transport and multiscale magnetic structures, yet its multi-scale periodic variations from days to a century remain poorly understood. The longest available composite time series of Ca II K plage areas from 1907 February 1 to 2023 December 31 is used to investigate these characteristics by applying the methods of ensemble empirical mode decomposition (EEMD) and continuous wavelet transform. The data are decomposed by EEMD into 14 intrinsic mode functions, which represent the inherent periodicities of the plages. Periodicity analysis reveals a wide range of statistically significant periods. The periods for short-term components at 2.64, 8.63, 26.62, 27.80, and 77.50 days are associated with magnetic flux emergence and solar rotation. The midterm periods of 154.99 days (Rieger period), 358.15 days (annual variation), and 2.26 yr (quasi-biennial oscillations) are modulated by different harmonics of magnetic Rossby waves at the base of the convection zone. The long-term periods of 10.32 and 11.25 yr correspond to the Schwabe cycle; the 21.48 yr period is associated with the magnetic polarity reversal cycle; and the 49.76 yr period is interpreted as the first subharmonic of the Gleissberg cycle. Overall, chromospheric variations on timescales from days to several decades are tightly modulated by solar magnetic activity, and the century-long ground-based Ca II K observations provide a valuable resource for understanding the long-term behavior of the Sun.

Predicting the probability of extreme solar events is critical to understanding how to mitigate risks associated with space weather disasters, as their occurrence can pose a threat to life and infrastructure on Earth or in near‐Earth orbit. In the present study, extreme value theory is applied to the daily modified coronal index (MCI) for the period 1 January 1939–30 April 2025 to assess the characteristics of the extreme solar activity events over a long time scale and the likelihood of their occurrence in the future. The shape parameters of the generalized extreme value distribution estimated using the block maxima approach and those of the generalized Pareto distribution estimated using the peaks‐over‐threshold approach are both negative within the 95% confidence interval, indicating that the extreme values of the daily MCI have an upper limit. The return level obtained from the peaks‐over‐threshold approach suggests that the 100‐year return level (200‐year and 500‐year return level) is below (close to or above) the observed maximum value of the daily MCI (9.165), implying that it is shown to be a 1‐in‐200 year event. These results indicate that solar green‐line corona has reached its upper limit during the past eight solar cycles and the likelihood of observing values significantly higher than this maximum value in the near future is low. These findings provide clues for avoiding or mitigating the problems that extreme solar activity may pose to human society.

Rotation is one of the fundamental characteristics of the Sun, and it plays a crucial role in understanding the magnetic activities and dynamo processes. To reveal the variability patterns of the global rotation of the solar chromosphere, the Bremen composite Mg II index covering more than four solar cycles during the time interval from 1979 to 2024 is utilized in the present work. Our main findings are as follows: (1) The sidereal rotation period of the solar chromosphere varies between 22.63 and 27.10 days, with an average value of 25.13 days during the studied time interval. (2) From a global point of view, the period lengths of chromospheric rotation exhibit an obvious decreasing trend, which may be associated with the structural changes in magnetic fields and solar atmosphere in the declining phase of the most recent Gleissberg cycle. (3) The time-varying period lengths of chromospheric rotation display significant quasiperiodicities of 8.38 and 19.84 yr, indicating that chromospheric rotation might be related to the Schwabe cycle and Hale cycle. (4) There exists a moderate positive correlation between chromospheric rotation period and sunspot number. Chromospheric rotation is modulated by the solar activity cycle, further supporting its association with the Schwabe cycle. The possible physical mechanisms underlying the analysis results are discussed.

Coronal mass ejections (CMEs) are intense solar eruptions that, once reaching Earth, pose catastrophic risks to the near-Earth space environment. Thus, improving CME transit time forecast accuracy to mitigate damage to ground-based facilities is critical for space weather forecasting. In this study, we constructed a 3D convolutional neural network (3D CNN) framework that learns from Solar and Heliospheric Observatory Large Angle and Spectrometric Coronagraph white-light image sequences to predict CME transit times. Our main contributions are threefold. (1) Unlike prior work, we first integrate the temporal dimension and use 3D CNN for joint spatiotemporal modeling of CME sequences to predict transit times. (2) To address the uneven sequence-length variations across events and to optimize data set usage, we propose a method that samples multiple fixed-length subsequences for each event and applies mean pooling to their features, which enhances model performance and generalization. (3) To enhance interpretability, we apply gradient-weighted class activation mapping across all convolutional layers to reveal each layer’s attention focus. The results show that shallow layers focus on the CME body and evolve over time, while deeper layers show stronger responses in the background and weaker responses to the CME body. On an independent test set, the model achieves a mean absolute error (MAE) of 11.04 hr; in the transit time range with the most samples, MAE is 6.37 hr, comparable to prior studies. These results confirm 3D CNN’s effectiveness in modeling CME spatiotemporal evolution and predicting transit times, offering a promising path for physically interpretable, data-driven space weather forecasting.

The angular width of coronal mass ejections (CMEs) reflects their eruption scale, dynamics, and potential geoeffectiveness. To deepen our understanding of the hemispheric distribution characteristics of CME activities, we systematically investigate the north–south asymmetry of CMEs with different angular widths. Based on the white-light CME catalog from the Coordinated Data Analysis Workshop during solar cycles 23–25, our main results are as follows: (1) the temporal evolution of CME activity across solar cycles is primarily governed by normal and partial-halo CMEs, while narrow CMEs contribute relatively little to the overall variation; (2) CME activity in cycles 23 and 25 exhibits similar hemispheric patterns, with both cycles showing southern hemisphere dominance, in contrast to the northern dominance observed in cycle 24 (this difference may be attributed to the weaker heliospheric pressure during cycle 24); (3) the phase lag between CME activities in the two hemispheres is generally within five months. These findings reveal the solar activity cycle evolution of CME angular width distributions and hemispheric asymmetries, providing new physical constraints for understanding interhemispheric coupling in solar dynamo processes and improving space weather forecasting models.

The long-term variations of quasiperiodic solar flares are of great importance for a better understanding and accurate prediction of solar flare activity. To explore the quasiperiodic characteristics of different classes of solar flares and the hemispheric difference, we comprehensively analyzed the quasiperiodic variations of different classes of solar flares, including the whole solar disk and hemispheres during solar cycles (SC) 21–25. The main results show that: (1) Certain periods are observed only in one hemisphere or some specific solar cycles. For example, over the entire time interval, the period of 3.49 yr was detected exclusively in C-class, M-class, and X-class flares in the northern hemisphere. (2) The quasi-biennial oscillation periodicities were observed for B-class, C-class, M-class, and X-class flares during SC22, SC24, and SC25. Moreover, the quasi-biennial oscillation periodicities were more pronounced in C-class and X-class flares in SC21 and in B-class and M-class flares in SC23. (3) The occurrence of different classes of solar flares displayed statistically significant short- and medium-range oscillations in both hemispheres, with distinct periodic variations and asymmetric evolutionary features. Identifying features such as the 3.49 yr period and quasi-biennial oscillation periodicities may help predict the activity patterns of different classes of solar flares across hemispheres and solar cycles, potentially improving forecasts of solar flare impacts on Earth’s technological infrastructure and space weather systems.

Solar active regions are believed to provide significant information on the mutual conversion of the poloidal and toroidal components of the global magnetic field. However, the multiscale periodic variations, in particular the quasi-biennial oscillations (QBOs), of solar active regions are not fully understood. In the present study, the flux, area, and number of solar active regions, as well as the sunspot number data in the period from 1996 May to 2023 November, are studied in detail. The multiscale periodic components in the above four data sets are investigated by the techniques of ensemble empirical mode decomposition and cross-correlation analysis. The main results are as follows. (1) The four data sets exhibit similar periodic components, including the 11 yr Schwabe cycle, the QBOs, and a Rieger-type period. (2) The multiscale periodicity of solar active regions shows different physical characteristics. Under different periodic scales, the highest correlation is between active region flux and area, indicating that active region flux and area better reflect the evolution of active regions. (3) By superimposing the QBOs on the 11 yr Schwabe cycle, the Gnevyshev gap phenomenon was clearly observed, implying that the Gnevyshev gap may be caused by the modulation of the 11 yr Schwabe cycle. (4) The active region flux in both hemispheres shows similar periodic components to the full disk, but the periodic variations are uneven between the northern and southern hemispheres. The results of our analysis could be beneficial for the understanding of the spatiotemporal distribution of solar active regions, and could also provide statistical constraints on solar dynamo theories.