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The accurate knowledge of cosmic ion fluxes is essential for fundamental physics, deep space missions, and exploration activities in the solar system. In the HelMod-4 model the Parker transport equation is solved using a Monte Carlo approach to evaluate the solar modulation effect on local interstellar spectra of Galactic Cosmic Rays (GCRs). This work presents the latest updates to the HelMod-4 model parameters, focusing on the descending phase of solar cycle 24. The updates are motivated by the latest high-precision measurements from the AMS-02 detector which revealed for the first time with high accuracy the features of GCR fluxes' evolution during a period of positive interplanetary magnetic field polarity. Furthermore, we present HelMod-4/CUDA , a GPU-accelerated approach for solving the Parker equation in the heliosphere using a stochastic differential equation method. The code is an evolution of the HelMod-4 code, porting the algorithm to GPU architecture using the CUDA programming language. This approach achieves significant speedup compared to a CPU implementation. The HelMod-4/CUDA code has been validated by comparing its results with the most precise and updated experimental GCR spectra observed during high and low solar activity periods, both in the inner and outer heliosphere, at the Earth location, and outside the ecliptic plane. The comparison shows that HelMod-4 and HelMod-4/CUDA can be equivalently used to provide solar-modulated spectra with a similar degree of accuracy in reproducing observed data. [ABSTRACT FROM AUTHOR]

We propose a new method for predicting the solar cycle in terms of the sunspot number (S N) 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 S N are presented for Solar Cycles 25 (the current one) and 26 (using the 13-month smoothed S N , 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. [ABSTRACT FROM AUTHOR]

Runoff prediction is of great importance to water utilization and water-project regulation. Although sun activity has been considered an important factor in runoff, little modeling has been constructed. This study put forward a forecast heuristic combining back propagation neural network (BPNN) and particle swarm optimization (PSO) for annual runoff based on sunspot number and applied it to the Yellow River of China for the period 1956–2016 and assessed the contribution of the sunspot number by placing sole BPNN modeling on the time series as a contrast. First, the heuristic is made up of BPNN calibration and PSO optimization: (1) we use historical data to calibrate BPNN models and obtain a prediction of the sunspot number for training and testing stages; (2) we use the PSO to minimize the difference between the predicted runoff of both BPNN and a linear equation for forecasting stage. Second, the application offers interesting findings: (1) while BPNN calibration obtains first-class forecasting with the ratio >85% with <20% absolute error in training and testing stages, the PSO can achieve similar performance in the forecasting stage; (2) the heuristic can achieve better prediction in years with a lower sunspot number; (3) besides the influence of the sun activity, atmospheric circulation, water usage, and water-project regulation do play important roles on the measured or natural runoff to some extent. This study could provide useful insights into further forecasting of measured and natural runoff under this forecast heuristic in the world. [ABSTRACT FROM AUTHOR]

This study introduces a novel method for predicting the sunspot number ( S N ) of Solar Cycles 25 (the current cycle) and 26 using multivariate machine-learning techniques, the Sun's polar flux as a precursor parameter, and the fast Fourier transform to conduct a spectral analysis of the considered time series. Using the 13-month running average of the version 2 of the S N provided by the World Data Center—SILSO, we are thus able to present predictive results for the S N until January 2038, giving maximum peak values of 131.4 (in July 2024) and 121.2 (in September 2034) for Solar Cycles 25 and 26, respectively, with a root mean square error of 10.0. These predicted dates are similar to those estimated for the next two polar flux polarity reversals (April 2024 and August 2034). Furthermore, the values for the S N maxima of Solar Cycles 25 and 26 have also been forecasted based on the known correlation between the absolute value of the difference between the polar fluxes of both hemispheres at an S N minimum and the maximum S N of the subsequent cycle, obtaining similar values to those achieved with the previous method: 142.3 ± 34.2 and 126.9 ± 34.2 for Cycles 25 and 26, respectively. Our results suggest that Cycle 25 will have a maximum amplitude that lies below the average and Cycle 26 will reach an even lower peak. This suggests that Solar Cycles 24 (with a peak of 116.4), 25, and 26 would belong to a minimum of the centennial Gleissberg cycle, as was the case in the final years of the 19th and the early 20th centuries (Solar Cycles 12, 13, and 14). [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The current study explores the relationship between solar variability and tropical cyclone (TC) activity using sunspot number (SSN) and TC best‐track data as respective proxies. We have considered six regions of the globe, for example, EP: Eastern Pacific, NA: North Atlantic, NI: North Indian, SI: South Indian, SP: South Pacific, and WP: Western Pacific. The results show strong anti‐correlation between yearly TC activity and yearly SSN while considering their 11‐year moving averages. This behavior is consistent for TC counts as well as accumulated cyclone energy. However, this is true only for the North Atlantic region. Overall, when we consider all regions together, more TCs (in terms of counts) are observed during lower solar activity periods (SSN < 50) as compared to higher solar activity conditions (SSN > 100). However, the yearly rates remain more or less similar. On the other hand, extreme TC events with a maximum wind speed of 137 knots and higher (category 5) are most likely to occur during the declining phase of a solar cycle and least likely to occur during the ascending phase or the maximum phase. Although solar activity levels are similar during the declining and ascending phases, the yearly occurrence rate is nearly double in the declining phase (1.123) as compared to that in the ascending phase (0.625). Key Points: Tropical cyclones (TCs) occurrences and solar activity show strong anti‐correlation in the North Atlantic sectorExtreme TCs are most likely to occur during the declining phase of a solar cycle and least likely to occur during the maximum phaseThe yearly occurrence rate of extreme TCs is nearly double in the declining phase of a solar cycle as compared to that in the ascending phase [ABSTRACT FROM AUTHOR]

To improve the predictive accuracy of sunspot numbers, a hybrid model was built to forecast future sunspot numbers. In this paper, we present a prediction model based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), gated recurrent unit (GRU), and error compensation for predicting sunspot numbers. CEEMAND is applied to decompose the original sunspot number data into several components, which are then used to train and test the GRU for the optimal parameters of the corresponding sub-models. Error compensation is utilized to solve the delay phenomenon between the original sunspot number and the predictive result. We compare our method with the informer, extreme gradient boosting combined with deep learning (XGboost-DL), and empirical mode decomposition combined long short-term memory neutral network and attention mechanism (EMD-LSTM-AM) methods, and evaluation metrics, such as RMSE and MAE, are used to measure their performance. Our method decreases more than 2.2813 and 3.5827 relative to RMSE and MAE, respectively. Thus, the experiment can demonstrate that our method has an obvious advantage compared to others. [ABSTRACT FROM AUTHOR]

To shed light on potential differences in the magnetosphere-solar wind coupling processes between both cycles, this study examines the link between solar wind characteristics and the Dst index during Solar Cycles (SCs) 24 and 25. In this study, we look into the correlation between geomagnetic storms and several solar wind parameters (solar wind speed, solar wind plasma temperature, solar wind proton density, and flow pressure), sunspot number, Ey, cosmic ray intensity (CRI) and geomagnetic index Dst for SC 24 (start Dec 2008 to end Dec 2019) and SC 25 (start Dec 2019 to 10th August 2023). With the use of the superposed epoch method (Chree analysis), comparative research has been conducted. Solar wind plasma temperature is a geoeffective parameter for SC 24, according to the findings of the current investigation. Compared to other solar wind parameters, the temperature (0.7) of the solar wind has a stronger link with geomagnetic storms. For SCs 24 and 25, the correlation between Dst and CRI is 0.2 and 0.9, respectively. SC 24 and 25 for Dst and Ey have correlations of -0.5 and -0.9, respectively. These variables are highly correlated. There is a modest link between sunspot number and Dst. Which solar wind parameter increases or causes these severe geomagnetic storms was the primary motivation for this investigation. Such understandings are essential for enhancing our capacity to forecast and mitigate the effects of geomagnetic storms on technological systems and societal infrastructure, as well as our comprehension of the dynamics of space weather. [ABSTRACT FROM AUTHOR]

In 1876, Alfred Wolfer started observing the Sun and recording the properties of sunspots alongside Rudolf Wolf. Their observations became the basis for the construction of the sunspot-number series. After Wolf's death in 1893, Wolfer became the primary observer for the sunspot-number series. Even though the observations of Wolf and Wolfer had an overlap of almost 17 years (1876–1893), this shift of primary observer from Wolf to Wolfer seems to have led to inconsistencies in the sunspot-number series, primarily due to inhomogeneities in Wolf's observations. To address this issue we digitise Mittheilungen (Wolf's journals) and analyse their tabulated datasets. These journals include the raw sunspot data from various observers that the Zürich Observatory used to compile the sunspot number series (SNV1). These datasets have been used as source data for the construction of the first version of the sunspot number (SNV1) series, but they were not digitally accessible for a recalibration of the sunspot-number series until recently. We have also acquired external datasets from recent archival investigations for contemporaneous sunspot observations. In this study, we use the Mittheilungen dataset to produce a new recalibration of the sunspot-number series covering 1816–1944, using four major observers (Tevel, Schwabe, Weber and Wolfer) as backbones. The availability of the raw data allows us to identify issues in the determination of the scaling factors or k -factors, between the records of different observers, but also the use of modern techniques for cross-calibrations. Our reconstruction for the years 1816–1944 is carried out with a novel method inspired by Chatzistergos et al. (Astron. Astrophys. 602, A69, 2017) allowing us to eliminate inconsistencies that resulted from the application of erroneous k -factors. [ABSTRACT FROM AUTHOR]

Abstract The current study explores the relationship between solar variability and tropical cyclone (TC) activity using sunspot number (SSN) and TC best‐track data as respective proxies. We have considered six regions of the globe, for example, EP: Eastern Pacific, NA: North Atlantic, NI: North Indian, SI: South Indian, SP: South Pacific, and WP: Western Pacific. The results show strong anti‐correlation between yearly TC activity and yearly SSN while considering their 11‐year moving averages. This behavior is consistent for TC counts as well as accumulated cyclone energy. However, this is true only for the North Atlantic region. Overall, when we consider all regions together, more TCs (in terms of counts) are observed during lower solar activity periods (SSN 100). However, the yearly rates remain more or less similar. On the other hand, extreme TC events with a maximum wind speed of 137 knots and higher (category 5) are most likely to occur during the declining phase of a solar cycle and least likely to occur during the ascending phase or the maximum phase. Although solar activity levels are similar during the declining and ascending phases, the yearly occurrence rate is nearly double in the declining phase (1.123) as compared to that in the ascending phase (0.625).

Runoff prediction is of great importance to water utilization and water-project regulation. Although sun activity has been considered an important factor in runoff, little modeling has been constructed. This study put forward a forecast heuristic combining back propagation neural network (BPNN) and particle swarm optimization (PSO) for annual runoff based on sunspot number and applied it to the Yellow River of China for the period 1956–2016 and assessed the contribution of the sunspot number by placing sole BPNN modeling on the time series as a contrast. First, the heuristic is made up of BPNN calibration and PSO optimization: (1) we use historical data to calibrate BPNN models and obtain a prediction of the sunspot number for training and testing stages; (2) we use the PSO to minimize the difference between the predicted runoff of both BPNN and a linear equation for forecasting stage. Second, the application offers interesting findings: (1) while BPNN calibration obtains first-class forecasting with the ratio >85% with

• Hemispheric prediction of solar cycle 25 has been done using sunspot number data. • Deep Learning algorithm has been used for the purpose of prediction. • Method of Expanding widow has been used for cross-validation. • Validation of model performance has been done by predicting past solar cycles 20–24. • Developed model achieves a high degree of accuracy in hemispheric prediction of cycle 25. In the current work, single-and double-layered Long Short-Term Memory (LSTM) model have been developed and compared to find the most suitable one for hemispheric prediction of solar cycle 25. The smoothed Hemispheric Sunspot Number (HSN) reconstructed by Veronig et al. (2021) has been used to achieve the objective. The deep learning models are evaluated over different pairs of train and test set for both the hemispheres using the 5-fold cross-validation scheme, to determine the best effective model for hemispheric prediction of cycle 25. Our analysis reveals that the single-layered LSTM model achieves lower RMSE when predicting the test dataset. To prove the reliability of our suggested LSTM models, we have predicted the hemispheric profiles of solar cycles 21–24. From our analysis, we found that the single-layered LSTM model performed better in comparison to the double-layered LSTM model in predicting past solar cycles 21–24. Hence, hemispheric prediction of solar cycle 25 is carried out utilizing the single-layered LSTM model due to better performance capability during experimental analysis. This work founds that the northern hemisphere will peak with an amplitude of 56 ± 5 during July 2023 + 3 months while the southern hemisphere will have a maximum amplitude of 54 ± 4 during June 2023 + 2 months. So, it can be said that the southern HSN will peak before the northern HSN and that the peak of northern HSN will be slightly higher than the southern one. [ABSTRACT FROM AUTHOR]

There is a close correlation between sunspot number and sunspot area. Through the distribution of sunspot number and area size, we could note significantly disagreements for the two hemispheres. The paper concentrates on the relationship for the northern, the southern hemispheres and the global, and we propose a new method from segmentation regression for description the relationship between the sunspot number and sunspot area of different hemispheres and the global. By the sunspot number, we divide up the data into three parts, the low, the medium, and the high. The results indicate that the segmentation regression method could better describe the relation between sunspot area and number, in particular at the lower solar activity part and the higher part. We could obtain a better regression result for these parts, data more or less static, and data more discrete. Moreover, sunspot area and number behave very differently relationship for the north, the south and the global. In addition, the sunspot number being equal, the sunspot area of the south is usually greater than the north. [ABSTRACT FROM AUTHOR]

A multi-layer, deep-learning (DL) architecture consisting of stacked Convolutional Long Short Term Memory (sConvLSTM1D) layers is proposed to forecast the sunspot number (SSN) more effectively. The proposed model with optimized hyper-parameters performs efficiently on four kinds of sunspot data with different frequencies of time that are yearly, monthly, daily, and 13-month smoothed provided by the World Data Center-Sunspot Index and Long Term Solar Observation (WDC-SILSO), the Royal Observatory of Belgium (SILSO World Data Center). The model was contrasted with other traditional DL models on different performance metrics, namely root-mean-square error (RMSE), mean-absolute error (MAE), mean-absolute-percentage error (MAPE), and mean-absolute-scaled error (MASE). A non-parametric statistical test has also been carried out to confirm the model's effectiveness. The prediction of the highest yearly mean of total sunspot number (SSN) in Solar Cycle 25 (SC25) has also been performed. The proposed sConvLSTM1D model suggests that the solar cycle exhibits the characteristics of a weak cycle. However, it is anticipated to be stronger than the preceding Solar Cycle 24 (SC24). The year of peak sunspot number will be 2024, as per the prediction, with the peak value of yearly mean sunspot number as 140.84, which is 24.3% higher than the peak value of the yearly mean of total sunspot number, which was 113.3 in the Solar Cycle 24 in the year 2014. [ABSTRACT FROM AUTHOR]

Sunspot Cycle 25 over 3 years past the cycle minimum of December 2019. At this point, curve‐fitting becomes reliable and consistently indicates a maximum sunspot number of 135 ± 10—slightly larger than Cycle 24's maximum of 116.4, but well below the Cycles 1–24 average of 179 (ranging from 81 for Cycle 6 to 285 for Cycle 19). A geomagnetic precursor, the minimum in the aa‐index, and the Sun's magnetic precursors, the polar field strength and axial dipole moment at the time of minimum, are often used to predict the amplitude of the cycle at (or before) the onset of the cycle. We examine Cycle 25 predictions produced by these precursors. The geomagnetic precursor indicated a Cycle 25 slightly stronger than Cycle 24, with a maximum of 132 ± 8. The Sun's magnetic precursors indicated that Cycle 25 would be similar to Cycle 24, with a maximum sunspot number of 120 ± 10 or 114 ± 15. Combining the curve‐fitting results with the precursor predictions, we conclude that Cycle 25 will have a maximum smoothed sunspot number of 134 ± 8 with maximum occurring late in the fall of 2024. Models for predicting the Sun's magnetic field ahead of minimum, were generally successful at predicting the polar precursors years in advance. The fact that Sun's magnetic precursors at cycle minimum were successfully predicted years before minimum and that the precursors are consistent with the size of Cycle 25 suggests that we can now more reliably predict the solar cycle. Plain Language Summary: Now that over 3 years have passed since the start of Cycle 25, we can determine the size of the cycle and look back at previous predictions. For the last eleven months we have consistently found that Cycle 25 is following the behavior of a smaller than average sunspot cycle, just slightly larger than the last cycle. The strength of the Sun's magnetic field at the start of a sunspot cycle has become recognized as the best predictor for the ultimate strength of that cycle. This follows from solar magnetic dynamo models in which the magnetic field at minimum gets stretched and strengthened to produce the magnetic sunspots and explosive magnetic activity of cycle maximum. Three different measurements of the strength of the Sun's magnetic field in late 2019 and early 2020 (at the start of the current sunspot cycle) indicated that this cycle would be slightly stronger than the previous cycle, but still weaker than average. Models can be used to estimate two of these measurements well before cycle minimum, thus providing a reliable prediction years before the start of a sunspot cycle. Key Points: Solar Cycle 25 has revealed itself as a small cycle with an expected sunspot number maximum of about 134Three magnetic precursors from the cycle minimum in 2019 predicted a similar Cycle 25 sunspot number maximumTwo of these magnetic precursors were accurately predicted years before cycle minimum using surface flux transport [ABSTRACT FROM AUTHOR]

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.

In this work, we predict the maximum amplitude (using the 13-month smoothed Solar Influences Data Analysis Center (SILSO) Sunspot Number, version 2) of Solar Cycle 25 using as a predictor the slope of the inflection point during the ascending part of the cycle. After a description of the data and methodology employed in this work, we obtain a value of 131 ± 32 for the maximum amplitude of Solar Cycle 25. Finally, we discuss this result in the context of the current debate on the prediction of solar activity and compare it with other predictions of Solar Cycle 25 obtained by other methods. [ABSTRACT FROM AUTHOR]

We report on the annual variation of quiet-time suprathermal ion composition for C through Fe using Advanced Composition Explorer (ACE)/Ultra-Low Energy Isotope Spectrometer data over the energy range 0.3–1.28 MeV nuc−1 from 1998 through 2019, covering solar cycle 23's rising phase through Solar Cycle 24's declining phase. Our findings are: (1) quiet-time suprathermal abundances resemble CIR-associated particles during solar minima; (2) quiet-time suprathermals are M/Q fractionated in a manner that is consistent with M/Q fractionation in large gradual solar energetic particle events (GSEP) during solar maxima; and (3) variability within the quiet-time suprathermal pool increases as a function of M/Q and is consistent with the analogous variability in GSEP events. From these observations, we infer that quiet-time suprathermal ions are remnants of CIRs in solar minima and GSEP events in solar maxima. Coincident with these results, we also unexpectedly show that S behaves like a low FIP ion in the suprathermal regime, and is therefore drawn from low FIP solar sources. [ABSTRACT FROM AUTHOR]

Fire is a principal disturbance agent in Northeastern Asia forests with the highest fire radiative power globally. The limitation of the wide network of sites with long reconstructed fire histories restricts our understanding of factors forcing regional fire dynamics. We aggregated 30 fire scars-based fire chronologies in Northeastern Asia to create a composite region chronology from 1701 to 2010. We assessed the correlation between solar activity, drought (as represented by the Self-Calibrated Palmer Drought Severity Index, scPDSI), Arctic Oscillation (AO), and forest fire. Large fires were associated with drought, positive AO, and lower sunspot numbers, as revealed by Superposed epoch analysis. The pathways of solar activity force drought and AO, hence, driving fire activity indicated by structural equation modeling. As Solar Cycle 25 (starting in December 2019 and lasting until about 2030) is projected to be as weak as the Dalton minimum (1795–1820 AD), we expect fire activity to remain high over this period. Our findings provide insights into the extraterrestrial forcing of forest fire activity and its prediction in Northeastern Asia.

Neutron monitor counting rates show periodicities in the ≈ 1.6 – 2.2-year range. These periodicities have been associated with a solar origin affecting the cosmic ray propagation conditions through the heliosphere. Our hypothesis is that the periodicities in the ≈ 1.6 – 2.2-years range correspond to a single periodicity that changes its duration over time. López-Comazzi and Blanco (Astrophys. J.927(2), 155, 2022) found that the duration of the ≈ 1.6 – 2.2-year period (τ ) is linearly related to the average sunspot number (S S N a ) in each solar cycle. The relationship shows that shorter ≈ 1.6 – 2.2-year periods occur during stronger cycles when S S N a is higher. Therefore, the duration of this period varies from one solar cycle to another. This study focuses on this relation. For obtaining this relation, the values of the duration of the ≈ 1.6 – 2.2-year period in global neutron monitor counting rates (a virtual station determined by averaging of the different neutron monitor counting rates along the world) along the Solar Cycles 20 – 24 have been used. We extend the sample by adding the duration of the ≈ 1.6 – 2.2-year period in Huancayo neutron monitor counting rates along Solar Cycle 19 to this linear relationship. Once the linear relationship is extended, τ for the current Solar Cycle 25 is computed giving ≈ 2.24 years. Drawing on this more accurate relationship given by S S N a = (− 120 ± 10) τ + (320 ± 20) , we computed τ for the cycles previous to the existence of neutron monitors (Solar Cycles 7 – 18). These ≈ 1.6 – 2.2-year periodicities in neutron monitor counting rates could be produce by variations in the solar magnetic field due to an internal mechanism of the solar dynamo called Rossby waves. Concretely, the harmonic of fast Rossby waves with m = 1 and n = 8 fit with the detected ≈ 1.6 – 2.2-year periodicity. In addition, the variation of the solar magnetic-field strength from weaker to stronger solar cycles could explain the different periods detected in each cycle. Based on the detected periodicities using the dispersion relation for fast Rossby waves, a solar tachocline magnetic-field strength of ≈ 7 – 25 kG has been estimated. [ABSTRACT FROM AUTHOR]

The sunspot number (SSN) is an important – albeit nuanced – parameter that can be used as an indirect measure of solar activity. Predictions of upcoming active intervals, including the peak and timing of solar maximum, can have important implications for space-weather planning. Forecasts for the strength of Solar Cycle 25 have varied considerably, from being very weak to one of the strongest cycles in recorded history. In this study, we develop a novel quantile-based superposed-epoch analysis that currently predicts that Solar Cycle 25 will be very modest (within the lowest 25th percentile of all numbered cycles), with a monthly averaged (13-month average) peak of ≈ 130 (110) likely occurring in December 2024. We validate the model by performing retrospective forecasts (hindcasts) for the previous 24 cycles, finding that it outperforms the baseline (reference) model (the "average cycle") 75% of the time. [ABSTRACT FROM AUTHOR]

In this work, we present hemispheric sunspot number counts made in the Astronomical Observatory of the University of Valencia for the period 1940 – 1956. We found these data in a secondary source (the bulletins of the Madrid Astronomical Observatory). We digitized the hemispheric sunspot number and group number recorded by the observers. Unfortunately, the sunspot positions or areas were not published in this secondary source. A machine-readable version of the dataset is available as supplementary material. We compare the Valencia hemispheric series with others from other observatories, such as the Madrid Astronomical Observatory, finding similar hemispheric behaviors. We also computed the asymmetry index from the hemispheric data. We found that the northern hemisphere was predominant in our study period (declining phase of Solar Cycle 17, Solar Cycle 18, and rising phase of Solar Cycle 19), and the southern hemisphere was leading. These results agree with those obtained in previous studies. Therefore, the Valencia hemispheric records are one more piece of information to know the behavior of the northern and southern hemispheres during the 20th century. [ABSTRACT FROM AUTHOR]

The sunspot number is the most used solar-activity index to study the behavior of solar activity. In this work, we reproduce the methodology of Coban, Raheem, and Cavus (Solar Phys. 296, 156, 2021) using a long short-term memory model with daily data from the American Association of Variable Star Observers (AAVSO) to predict the maximum amplitude of Solar Cycle 25. We have also used that same methodology with daily values from the official sunspot number (Version 2) of the Sunspot Index and Long-term Solar Observations (SILSO). The objective of this work is to analyze if the predictions obtained from that methodology agree with the observed values available for the current Solar Cycle 25. Thus, we conclude that the predictions are not reproducing well the behavior of the Solar Cycle 25 in its rising phase. Moreover, contrary to the previous prediction, no minor peak occurred in February 2022, and we also conclude that it seems unlikely that the combination of the solar-activity level of Solar Cycle 24 and 25 constitutes a new Dalton-type Minimum, such as Coban, Raheem, and Cavus (2021) proposed. [ABSTRACT FROM AUTHOR]

A deep-learning Vanilla, or single layer, Long Short-Term Memory model is proposed for improving the prediction of Solar Cycle 25. WDC-SILSO the Royal Observatory of Belgium, Brussels provides the 13-month smoothed sunspot-number data that were used to make this prediction. The root mean square error (RMSE) obtained by the proposed model, which is improved in comparison to the existing stacked LSTM model, lies within the range of 1.65 – 4.92, according to analysis on a number of temporal intervals taken into consideration in this study. The model performance has been validated by forecasting the peak amplitude of Solar Cycles 21 – 24. It is shown that for Cycles 21 and 22, the prediction error in estimating the peak is 1.159% and 0.423%, while the RMSE is estimated to be 4.149 and 3.274, respectively. For Cycle 23, the relative error and RMSE are 1.054% and 2.985, respectively, whereas for Cycle 24 they are 1.117% and 3.406, respectively. The current proposed model has exactly predicted the timing when the SSN reached its maximum for Cycle 23. While for Cycle 21, the prediction has a 1-month delay from the actual timing. For Cycles 22 and 24, the year during which the SSN reached maximum coincides with the observed year, although their month of peak occurrence showed a difference of three months and one month, respectively. The current proposed model suggests that the Cycle 25 will peak in April 2023 with an amplitude value of 136.9, which will be approximately 17.68% stronger compared with Cycle 24. [ABSTRACT FROM AUTHOR]

In addition to the Gnevyshev–Ohl rule (GOR), the relationship of an odd solar cycle with the subsequent even one in a 22-year Hale solar cycle was found. It is shown that three years before the 11-year minimum, the value of the relative sunspot number [SN] in an odd cycle is closely related to the value of the maximum in the next even cycle (the correlation coefficient ρ = 0.94 ), and the same relation of an odd cycle with the previous even one is weaker. As in the GOR, cycles are linked in pairs, but when viewed in this way, the odd cycle is leading in the pair, opposite to the GOR. Based on this result, we propose to use the index of the third year before the minimum [ S N m − 3 ] on the descending phase of the previous odd cycle as a precursor of the subsequent even cycle. This method gives less reliable results for the prediction of an odd cycle or a prediction without consideration of parity (Brajša et al., Astron. Nachr.343, e13960, 2022). To predict the amplitude of an odd cycle, we propose to use the seventh year before its maximum [ S N M − 7 ] on the descending phase of the previous even cycle. It turned out that, in this case, we can also predict years near the maximum with a high correlation coefficient (ρ = 0.90 – 0.94 ). Thus, the proposed approaches allow us to predict cycles of different parity. According to our prediction, the current Solar Cycle 25 will reach a maximum at the turn of 2023 and 2024; the yearly mean SN in 2023 will be 132 ± 27 and in 2024 SN = 136 ± 25 . The maximum 13-month smoothed monthly mean SN will be 149 ± 28 . [ABSTRACT FROM AUTHOR]

This study examines the occurrences rate of geomagnetic storms during the solar cycles (SCs) 20–24. It also investigates the solar sources at SCs 23 and 24. The Disturbed storm time (Dst) and Sunspot Number (SSN) data were used in the study. The study establishes that the magnitude of the rate of occurrences of geomagnetic storms is higher (lower) at the descending phases (minimum phases) of solar cycle. It as well reveals that severe and extreme geomagnetic storms (Dst < -250 nT) seldom occur at low solar activity but at very high solar activity and are mostly associated with coronal mass ejections (CMEs) when occurred. Storms caused by CME + CH-HSSW are more prominent during the descending phase than any other phase of the solar cycle. Solar minimum features more CH-HSSW- associated storms than any other phase. It was also revealed that all high intensity geomagnetic storms (strong, severe and extreme) are mostly associated with CMEs. However, CH-HSSW can occasionally generate strong storms during solar minimum. The results have proven that CMEs are the leading cause of geomagnetic storms at the ascending, maximum and the descending phases of the cycles 23 and 24 followed by CME + CH-HSSW. The results from this study indicate that the rate of occurrence of geomagnetic storms could be predicted in SC phases. [ABSTRACT FROM AUTHOR]

China has six observing stations, providing over 52,000 handwritten sunspot drawings from 1947–2016. The observing stations are the Purple Mountain Astronomical Observatory (PMO), Yunnan Astronomical Observatory (YNAO), Qingdao Observatory Station (QDOS), Sheshan Observatory Station (SSOS), Beijing Planetarium (BJP), and Nanjing University (NJU). In this paper, we propose a new cotraining semisupervised learning method combining a semantic segmentation method named dynamic mutual training (DMT) boundary-guided semantic segmentation (BGSeg), i.e., DMT_BGSeg, which makes full use of the labeled data from PMO and the unlabeled data from the other five stations to detect and segment sunspot components in all sunspot drawings of the six Chinese stations. The sunspot is detected and segmented. Additionally, each sunspot is split into four types of components: pore, spot, umbra, and hole. The testing results show the mIoU values of PMO, YNAO, BJP, NJU, QDOS and SSOS are 85.29, 72.65, 73.82, 64.28, 62.26, and 60.07, respectively. The results of the comparison also show that DMT_BGSeg is effective in detecting and segmenting sunspots in Chinese sunspot drawings. The numbers and areas of sunspot components are measured separately. All of the detailed data are publicly shared on China-VO, which will advance the comprehensive augmentation of the global historical sunspot database and further the understanding of the long-term solar activity cycle and solar dynamo.

The relative sunspot number is one of the major parameters for the study of long-term solar activity. The automatic calculation of the relative sunspot number is more stable and accurate as compared to manual methods. In this paper, we propose an algorithm that can detect sunspots, and divide them into groups to automatically calculate the relative sunspot number. Mathematical morphology was adopted to detect sunspots then group them. The data set used were the continuum images from SDO/HMI. The process was carried out on the overall HMI data available on the timespan from 2022 January to 2023 May with a time cadence of one day. The experimental results indicated that the method achieved high accuracy of 85.3%. It was well fitted with the international relative sunspot number provided by Solar Influences Data Analysis Center (CC = 0.91). We calculated the conversion factor K value of SDO/HMI for calculating the relative sunspots number ( K = 1.03).

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.

The focus of this study is to reveal the reason behind a scale problem detected around 1849 in the historical version of the International Sunspot Number Series, i.e. version 1 (Leussu et al. in Astron. Astrophys.559, A28, 2013; Friedli in Solar Phys.291, 2505, 2016). From 1826 to 1848 Heinrich Schwabe's observations were considered primary by Rudolf Wolf, and a shift of primary observer from Schwabe to Wolf in 1849 seems to have led to an inconsistency in the Sunspot Number series. In this study we benefited from various datasets, the most important being Schwabe's raw counts from the Mittheilungen (Prof. Wolf's Journals) that have been digitised at the Royal Observatory of Belgium between 2017 and 2019. We provide a robust quantification of the detected problem by using classic algebraic calculations, but also different methods such as a method inspired by Lockwood et al. (J. Geophys. Res.119(7), 5172, 2014), hence assigning a modern k-factor to Schwabe's observations before 1849. We also assess the implications of this 1849 inconsistency on the International Sunspot Number series (Versions 1 and 2) before and after 1849. [ABSTRACT FROM AUTHOR]

Sunspot drawings around the globe provide long historical records for understanding the long-term trends in the solar activity cycle. Yunnan Astronomical Observatory (YNAO) in China contributes to the relatively continuous sunspot drawings from 1957 to 2015. This paper proposes a new deep learning method named SPR-mask to extract pores, spots, umbrae and penumbrae in the YNAO sunspot drawings. SPR-mask consists of three parts: backbone, shared head and mask branch. It especially adopts a scale-aware attention network (SAAN) and a PointRend module in the mask branch to improve the accuracy of target edge segmentation. Besides that, each sunspot belonging to the northern or southern (N-S) hemisphere is determined by transforming its cartesian coordinates to spherical coordinates after extracting P , B 0 and L 0 handwritten in sunspot drawings using a revised Lenet-5 deep learning method. The precision, recall and A P of SPR-mask are 0.92, 0.93, and 0.92, respectively. The test results show the SPR-mask method has a good performance. The numbers and areas of pores, spots, umbrae and penumbrae for the N-S hemisphere are presented and analyzed separately. The YNAO data are also compared with Royal Greenwich Observatory (RGO), Kanzelhöhe Observatory (KSO) and Purple Mountain Astronomical Observatory (PMO) data. The results show similar trends, high correlations, and N-S asymmetries. All data of YNAO are publicly shared at https://github.com/yzs64/YNAO%5fsd/, which are abundant and complementary to the other sunspot catalogs in the world. [ABSTRACT FROM AUTHOR]

On a centennial timescale, solar activity oscillates quasi-periodically and also tends to occasionally get into a low-activity period. The Dalton Minimum (circa 1790s–1820s) was one of such low-activity periods that had been captured in telescopic sunspot observations. However, it has been challenging to analyse the Dalton Minimum, as contemporary source records remained mostly unpublished and almost inaccessible to the scientific community. Recent studies have established reliable datasets for sunspot group number, sunspot number, and sunspot positions. This study further analyzes independent Silesian sunspot observations from 1800 to 1827 in a manuscript from the Library of Wrocław University (Ms AKC.1985/15) and complements it with the metadata for the observer: Karl Christian Reinhold von Lindener. We identified 547 days of sunspot observations in these records and derived the sunspot group number, individual sunspot number, and sunspot positions between 1800 and 1827. The results of this study have significantly revised von Lindener's sunspot group number, which was previously known for only 517 days in scientific databases, and removed contamination from general descriptions. Using our results, we extended investigations into individual sunspot counts and derived their positions. In our analysis, we locate von Lindener's sunspot positions in both solar hemispheres and contrast the Dalton Minimum with the Maunder Minimum, adding further independent credits to the previous results for Derfflinger's and Prantner's datasets. Sunspot positions are also slightly biased towards the northern solar hemisphere in early Solar Cycle 6 (1812–1813). The high-latitude sunspot positions indicate the onset of Solar Cycle 7 as early as June 1822. [ABSTRACT FROM AUTHOR]

This paper focuses on the impact of solar activity changes on rainfall in Nigeria. Rainfall data were got from Gridded Time Series (CRU TS v4) of the University of East Anglia, United Kingdom. Various statistical tools like Pearson rank correlation coefficient, regression analysis, covariance, skewness, and kurtosis are employed in the study. The results show that there are generally weak correlations between rainfall and solar activity in most seasons. However, low correlation coefficients (0.0424 and 0.1040, respectively for sunspot numbers and F10.7 mean a very weak positive relationship between early rainfall, main rainfall and annual rainfall and negative for dry season rainfall and Harmattan rainfall. Also, there is a negative time-series relationship between the rainfall amount in dry and Harmattan seasons, with the highest rainfall amount during low solar activity and vice versa. Therefore, it can be concluded that solar activity has little effect on Nigeria's annual and main season rainfall. However, during the dry season and Harmattan, heavy rainfall appears to be associated with low solar activity, while high solar activity appears to be associated with low rainfall. This study will be beneficial to farmers in Nigeria, majority of whom are peasant farmers that practice rain-fed farming. • Work is on relationship between solar activity and Nigerian rainfall. • Area of coverage is Nigeria and period is 1902–2021. • Modeled rainfall data are used. • Bias correction using SCL employed. • Statistical approach was used in the analysis. [ABSTRACT FROM AUTHOR]

HelMod model allows one to describe how solar modulation affects the propagation of galactic cosmic rays (GCR) through the heliosphere with an accuracy of the level of actual experimental uncertainties. The GCRs mainly constitute the high energy population of the so-called space radiation environment. The model treats the physical processes involved in solar modulation, like diffusion, particle drift, convection and adiabatic energy losses, and it embeds a description of both the inner and outer heliosphere. To obtain the modulated intensities, the model requires the knowledge of a few time-dependent heliospheric quantities, i.e., sunspot number, tilt angle of the neutral current sheet, solar wind speed and density, and interplanetary magnetic field. Using historical records, we present a template-based procedure that allows one to predict the heliospheric parameters for coming years, and, in turn, the forecasted modulated spectra. The forecasting templates reconstruct the typical time variation along with solar cycles and may be tuned to the current solar cycle. We estimate that the uncertainty of the forecasted cosmic rays intensity is below 5% (± 10 % at 68% C.L.) on average for short time predictions (up to 4 years), and below 15% (± (20 - 25) % at 68% C.L.) for long time predictions (up to 11 years). [ABSTRACT FROM AUTHOR]

For magnitude MS≥7.0 earthquakes that occurred in mainland China from 1900 to 2017，we investigated the correlation between the 11-year-cycle variation of sunspot number and the occurrence of these earthquakes via the phase angle with a statistical test. The obtained results show that approximately 66 percent of them often occur within the period from two years before the valley point to three years after. This result is significant at the significance level （α=0.001）. At the end of 2019，the sunspot number dropped to the valley point. Therefore，earthquakes of MS7.0 could be more prone to occur in mainland China from 2018 to 2022.

We report on the annual variation of quiet-time suprathermal ion composition for C through Fe using Advanced Composition Explorer (ACE)/Ultra-Low Energy Isotope Spectrometer data over the energy range 0.3–1.28 MeV nuc ^−1 from 1998 through 2019, covering solar cycle 23's rising phase through Solar Cycle 24's declining phase. Our findings are: (1) quiet-time suprathermal abundances resemble CIR-associated particles during solar minima; (2) quiet-time suprathermals are M/Q fractionated in a manner that is consistent with M/Q fractionation in large gradual solar energetic particle events (GSEP) during solar maxima; and (3) variability within the quiet-time suprathermal pool increases as a function of M/Q and is consistent with the analogous variability in GSEP events. From these observations, we infer that quiet-time suprathermal ions are remnants of CIRs in solar minima and GSEP events in solar maxima. Coincident with these results, we also unexpectedly show that S behaves like a low FIP ion in the suprathermal regime, and is therefore drawn from low FIP solar sources.

In this paper, an optimized long short-term memory model is proposed to deal with the smoothed monthly F _10.7 and nonsmoothed monthly sunspot area (SSA) data, aiming to forecast the peak amplitude of both solar activities and the occurring time for Solar Cycle 25 (SC-25), as well as to obtain the maximum amplitude of sunspot number (SSN) and the reaching time according to the relationships between them. The “reforecast” process in the model uses the latest forecast results obtained from the previous forecast as the input for the next forecasting calculation. The forecasting errors between the forecast and observed peak amplitude of F _10.7 for SC-23 and SC-24 are 2.87% and 1.09%, respectively. The results of this evaluation indicator of SSA for SC-21 to SC-24 were 8.85%, 4.49%, 2.88%, and 4.57%, respectively, and the errors for the occurring time were all within 6 months. The forecast peak amplitude of F _10.7 and SSA for SC-25 is 156.3 and 2562.5 respectively, and the maximum values of SSN are calculated as 147.9 and 213 based on F _10.7 and SSA respectively, which implies that SC-25 will be stronger than SC-24, and that SC-25 will reach its peak at the beginning of 2025.

The aim of this study is to predict solar activity for the next 10 years (Solar Cycle 25) using a deep learning technique known as a stateful Long Short-Term Memory (LSTM) network. To achieve this goal the number of daily sunspots observed by the American Association of Variable Star Observers (AAVSO) organization from 1945 to 2020 is used as training data for a stateful LSTM model. Time slices are produced by dividing the data between 1945–2020; then data are predicted and examined for the test years of the network trained on these time slices. The mean and smoothed values are calculated from the estimated daily data, compared with the actual mean, and smoothed values, including standard deviations, and the prediction accuracy of the model is examined. Finally, the number of daily sunspots for 10 years of Cycle 25 is estimated. The results are discussed by calculating the mean and smoothed values. The predicted Solar Cycle 25 shows features of a new Dalton Minimum together with Cycle 24. We conclude that Cycle 25 will be the marker of a new Dalton Minimum period. [ABSTRACT FROM AUTHOR]

As an important indicator of solar activity, the long‐term total solar irradiance (TSI) observations are needed to uncover the impact of solar activity on Earth's climate. In this paper, the periodic variation of TSI and its relationships with sunspot number and Ca II K index are analyzed by using the satellite observation data from 1979 to 2015 and the wavelet method. The results of continuous wavelet analysis show that TSI, sunspot number and Ca II K index all have significant and stable oscillation periods of 9∼13 years and intermittent oscillation periods of 2∼6 months only during the time of intense solar activity. Moreover, the results of cross wavelet analysis indicate that the effect of sunspot number and Ca II K index on TSI is mainly reflected in the period of 9∼13 years with one month phase lag, and sunspot number and Ca II K index cannot explain the TSI variation in the period of 3∼6 months. Under the condition of considering the phase relationship and the model order, the TSI reconstruction model is established and the monthly TSI time series from 1907 to 1978 is restored. Key Points: Sunspot number and Ca II K index all have significant oscillation periods of 9∼13 years and intermittent oscillation periods of 2∼6 monthsThe effect of sunspot number and Ca II K index on total solar irradiance (TSI) is mainly reflected in the period of 9∼13 years with one month phase lagThe TSI reconstruction model is established with considering the phase relationship [ABSTRACT FROM AUTHOR]

Abstract As an important indicator of solar activity, the long‐term total solar irradiance (TSI) observations are needed to uncover the impact of solar activity on Earth's climate. In this paper, the periodic variation of TSI and its relationships with sunspot number and Ca II K index are analyzed by using the satellite observation data from 1979 to 2015 and the wavelet method. The results of continuous wavelet analysis show that TSI, sunspot number and Ca II K index all have significant and stable oscillation periods of 9∼13 years and intermittent oscillation periods of 2∼6 months only during the time of intense solar activity. Moreover, the results of cross wavelet analysis indicate that the effect of sunspot number and Ca II K index on TSI is mainly reflected in the period of 9∼13 years with one month phase lag, and sunspot number and Ca II K index cannot explain the TSI variation in the period of 3∼6 months. Under the condition of considering the phase relationship and the model order, the TSI reconstruction model is established and the monthly TSI time series from 1907 to 1978 is restored.

The focus of this article is a re-count of Richard Carrington's original sunspot observations from his book drawings (Carrington in Observations of the Spots on the Sun from November 9, 1853, to March 24, 1861 Made at Redhill, Williams and Norgate, London, 1863) by an observer from the World Data Center-SILSO (WDC-SILSO, http://www.sidc.be/silso/home) network, Thomas H. Teague (UK). This modern re-count will enable the recomputation of the entire Sunspot Number series in a way Carrington's original counts (Casas and Vaquero in Solar Phys. 289(1), 79, 2014) did not. Here we present comparison studies of the new re-counted series with contemporary observations, new data extracted from the Journals of the Zürich Observatory and other sources of Carrington's own observations and conclude that Carrington's group counting is very close to the modern way of counting while his method for counting individual spots lags significantly behind modern counts. We also test the quality and robustness of the new recount with methods developed in Mathieu et al. (Astrophys. J. 886(1), 7, 2019). [ABSTRACT FROM AUTHOR]

The number of sunspots shows the solar activity level. During the high solar activity, emissions of matter and electromagnetic fields from the Sun make it difficult for cosmic rays to penetrate the Earth. When solar energy is high, cosmic ray intensity is lower, so that the solar magnetic field and solar winds affect the Earth externally and originate new viruses. In this paper, we assess the possible effects of sunspot numbers on the world virus appearance. The literature has no sufficient results about these phenomena. Therefore, we try to relate solar ray extremum to virus generation and the history of pandemics. First, wavelet decomposition is used for smoothing the sunspot cycle to predict past pandemics and forecast the future time of possible virus generation. Finally, we investigate the geographical appearance of the virus in the world to show vulnerable places in the world. The result of the analysis of pandemics that occurred from 1750 to 2020 shows that world's great viral pandemics like COVID-19 coincide with the relative extrema of sunspot number. Based on our result, 27 pandemic (from 36) incidences are on sunspot extrema. Then, we forecast future pandemics in the world for about 110 years or 10 cycles using presented multi-step autoregression (MSAR). To confirm these phenomena and the generation of new viruses because of solar activity, researchers should carry out experimental studies. [ABSTRACT FROM AUTHOR]

The sunspot cycle is quite variable in duration and amplitude, yet in the long term, it seems to return to solar minimum on schedule, as if guided by a clock with an average period of close to 11.05 years for the sunspot number cycle and 22.1 years for the magnetic cycle. This paper provides a brief review of the sunspot number cycle since 1750, discusses some of the processes controlling the solar dynamo, and provides clues that may add to our understanding of what controls the cadence of the solar clock. [ABSTRACT FROM AUTHOR]

This work aims to investigate the effect of solar-geomagnetic activities on atmospheric circulation and to examine their influences on global surface temperature (GST). Used data are the sunspot number (Rz) as a solar activity index, geomagnetic activity index (aa), the North Atlantic Oscillation index (NAO), and GST. We have performed an extensive analysis by using the wavelet power spectra (WPS) and global wavelet spectra (GWS). Results from WPS showed that both NAO and GST exhibited periodicities of ~ 22 years and 11 years, indicating the imprint of solar activity on both parameters. Additionally, the WPS of NAO exhibited three dominant modes; firstly, periodicity of 2 to 4 years appeared in the time intervals: (1954–1967), (1987–1997), and (2007–2015), possibly related to El-Nino Southern Oscillation. Secondly, sporadic oscillation of 1 to 2.5 years observed during the whole period, which corresponding to quasi-biennial oscillation. Finally, oscillatory modes with high power on the band 7 to 8.5 years are observed on NAO spectrum: 7.6 years for the period (1872–1892) and 8.3 years for 1996–2005. This oscillatory mode with enhanced power in the 8.3 years has been detected significantly on the GWS of GST, indicating the influence of climatic pattern on GST. Our results showed that the solar-geomagnetic activities influence partially and indirectly on GST through atmospheric circulation pattern. [ABSTRACT FROM AUTHOR]

The aim of this paper is to broaden the scope of a recent adaptive model in order to obtain predictions of total column ozone (TCO) trends over the Amazon Inter-Tropical Confluence Zone (ITCZ). The adaptive model makes daily TCO predictions over the tropical equator-Andes-Region, relying on seasonal patterns and the solar cycle. This study uses daily observations of the sunspot number cycle, given by the World Data Center for the production, preservation and dissemination of the international sunspot number (Royal Observatory of Belgium), and satellite total-column ozone data, collected by NASA (January 1979 to April 2018), for two Colombian locations: one in and one adjacent to the ITCZ. The agreement between daily total-column predictions by the adaptive model and satellite observations is excellent. Daily averaged relative errors around of 3.7 % and 2.8 % for both locations are reported herein.