Number of Sunspot Groups and Individual Sunspots Recorded by Tevel for the Period 1816–1836 in the Dalton Minimum

Cornelis Tevel made sunspot observations during the period 1816–1836, including the Dalton Minimum. In this work, the first revision of these observations since Wolf incorporated them into his database is presented. On the one hand, the number of individual sunspots from Tevel’s drawings was counted. This is of special interest for the sunspot number reconstruction because this kind of information is not as common in historical sunspot records as the number of groups. Thus, Tevel could be considered for the future reconstruction of the sunspot number index. On the other hand, the number of groups counted according to modern sunspot group classifications finding significant misinterpretations with the number of groups assigned to Tevel in the existing databases. Tevel was a relevant sunspot observer in the Dalton Minimum. In fact, he was the observer with the highest number of groups observed in Solar Cycles 6 and 7 according to the existing sunspot group number databases. According to the raw group number recount in this work, the maximum amplitudes for Solar Cycles 6 and 7 are, respectively, 27% and 7% lower than those previously determined. Moreover, Solar Cycle 6 is the weakest solar cycle since the Maunder Minimum after applying these new counts. Group counts from Tevel’s observations were compared with those from relevant contemporary astronomers, demonstrating that Schwabe and Tevel systematically recorded a higher number of groups than Flaugergues and Derfflinger. In addition, sunspot areas and positions recorded by Tevel should be used with caution for scientific purposes.

Research on Mount Wilson Magnetic Classification Based on Deep Learning

The Mount Wilson magnetic classification of sunspot groups is thought to be meaningful to forecast flares’ eruptions. In this paper, we adopt a deep learning method, CornerNet-Saccade, to perform the Mount Wilson magnetic classification of sunspot groups. It includes three stages, generating object locations, detecting objects, and merging detections. The key technologies consist of the backbone as Hourglass-54, the attention mechanism, and the key points’ mechanism including the top-left corners and the bottom-right corners of the object by corner pooling layers. These technologies improve the efficiency of detecting the objects without sacrificing accuracy. A dataset is built by a total of 2486 composited images which are composited with the continuum images and the corresponding magnetograms from HMI and MDI. After training the network, the sunspot groups in a composited solar full image are detected and classified in 3 seconds on average. The test results show that this method has a good performance, with the accuracy, precision, recall, and mAP as 0.94, 0.93, 0.94, and 0.90, respectively. Moreover, the flare productivities of different types of sunspot groups from 2011 to 2020 are calculated. As I tot ≥ 1, the flare productivities of α , β , β γ , β δ , and β γ δ sunspot groups are 0.14, 0.28, 0.61, 0.71, and 0.87, respectively. As I tot ≥ 10, the flare productivities are 0.02, 0.07, 0.27, 0.45, and 0.65, respectively. It means that the β γ , β δ , and β γ δ types are indeed very closely related to the eruption of solar flares, especially the β γ δ type. Based on the reliability of this method, the sunspot groups of the HMI solar full images from 2011 to 2020 are detected and classified, and the detailed data are shared on the website (https://61.166.157.71/MWMCSG.html).

Evidence for a Causal Relationship between the Solar Cycle and Locust Abundance

Time series of abundance indices for Desert Locusts Schistocerca gregaria (Forskål 1775) and Oriental Migratory Locusts Locusta migratoriamanilensis (Meyen 1835) were analysed independently and in relation to measures of solar activity and ocean oscillation systems. Data were compiled on the numbers of territories infested with swarms of the Desert Locust from 1860–2015 and an inferred series that compensated for poor reporting in the 1860 to 1925 period. In addition, data for 1930 to 2014, when reports are considered to have been consistently reliable were converted to numbers of 1° grid squares infested with swarms and separated according to four different geographical regions. Spectral analysis to test the hypothesis that there are cycles in the locust dynamics revealed periodicities of 7.5 and 13.5 years for the inferred series that were significant according to the Ornstein-Uhlenbeck state-space (OUSS) test. Similar periodicities were evident in the 1° grid square data and in each of the regions but even though these were significantly different from white noise, they were not significant according to the OUSS criterion. There were no significant peaks in the Oriental Migratory Locust results with the OUSS test, but the data were significantly different from white noise. To test hypotheses that long term trends in the locust dynamics are driven by solar activity and/or oceanic oscillation systems (the Southern Oscillation Index (SOI), the North Atlantic Oscillation Index (NAO) and the Indian Ocean Dipole (IOD)), the original locust data series and their Kalman-filtered low frequency (LF) components were tested for causality using both spectral coherence tests and convergent cross mapping. Statistically significant evidence was found that solar activity measured by numbers of sunspot groups drive the dynamics, especially the LF components, of both species. In addition, causal links were inferred between both the SOI and NAO data and Desert Locust dynamics. Spectral coherence was also found between sunspot groups and the NAO, the IOD and LF SOI data. The data were also analysed showing that the LF SOI had causal links with the LF inferred Desert Locust series. In addition, the LF NAO was causally linked to the LF 1° grid square data, with the NAO for December-March being most influential. The results suggest that solar activity plays a role in driving locust abundance, but that the mechanisms by which this happens, and whether they are mediated by fluctuations in oceanic systems, is unclear. Furthermore, they offer hope that information on these phenomena might enable a better early warning forecasting of Desert Locust upsurges.

Tilt angle and lifetime of sunspot groups

We use the Catalog of Solar Activity (CSA) to study the latitudinal variations of tilt of solar active regions. The tilt angles β are computed taking into account changes of the heliographic grid with latitude ϕ. We show that when sunspot groups of different sizes and lifetimes are included, a classical representation of the Joy’s law as a linear function of latitude (β∝ϕ) is only the first approximation valid within a limited range of latitudes (−25○ ≤ ϕ ≤ +25○). Outside this range, the functional dependence β = f(ϕ) becomes non-linear. Separating the dataset on large long-living groups (LLG) and small short-living groups (SSG) reveals two quite different dependencies in β = f(ϕ): non-linearity in tilt is only present in LLGs and the steepness of linear section of β = f(ϕ) fit is higher for LLGs. This suggests a difference in the physical properties of two populations of solar groups, which could be hypothesized as an indication of different localization of subsurface zones of their formation in the framework of a distributed dynamo. However, since CSA contains the coordinates of sunspots averaged over the lifetime (or disk passage) of each group, one cannot ruled out that the difference in tilts of SSG and LLG groups may be affected by the evolution of tilt angles during the lifetime/disk passage of the groups.