Low Dimensional Chaos from the Group Sunspot Numbers

We examine the nonlinear dynamical properties of the monthly smoothed group sunspot number Rg and find that the solar activity underlying the time series of Rg is globally governed by a low-dimensional chaotic attractor. This finding is consistent with the nonlinear study results of the monthly Wolf sunspot numbers. We estimate the maximal Lyaponuv exponent （MLE） for the Rg series to be positive and to equal approximately 0.0187 ± 0.0023 （month^- 1）. Thus, the Lyaponuv time or predictability time of the chaotic motion is obtained to be about 4.46 ± 0.5 years, which is slightly different with the predictability time obtained from Rz. However, they both indicate that solar activity forecast should be done only for a short to medium term due to the intrinsic complexity of the time behavior concerned.

THE FRACTAL RESEARCH AND PREDICATING ON THE TIME SERIES OF SUNSPOT RELATIVE NUMBER

In this paper, with the theory of nonlinear dynamic systems, It is analyzed that the dynamic behavior and the predictability for the monthly mean variations of the sunspot relative number recorded from January 1891 to December 1996. In the progress, the fractal dimension (D = 3.3 +/- 0.2) for the variation process rt as computed. This helped us to determine the embedded dimension [2 x D + 1] = 7. By computing the Lyapunov index (lambda(1) = 0.863), it was indicated that the variation process is a chaotic system. The Kolmogorov entropy (K = 0.0260) was also computed, which provides, theoretically, the predicable time scale. And at the end, according to the result of the analysis above, an experimental predication is made, whose data was a part cut from the sample data.

Predictions of El Nino,La Nina and Record Low Chicago Temperature by Sunspot Number

The El Nino Index, defined as 4 intensities (very strong, strong, moderate, weak) in Oceanic Niño Index (ONI), was positively correlated with the average sunspot number at each intensity. The La Niña Index, defined as 3 intensities (strong, moderate, weak) in ONI, was negatively correlated with the average sunspot number from 1954 to 2017. It appears that very strong El Niño events occur frequently during the maximal sunspot number while strong La Niña events more often occur during the minimal sunspot number. Since greenhouse-gas is continuously increased, it is therefore proposed that the maximal sunspot number is a major parameter for prediction of El Niño while the minimal sunspot number applies in the same way for La Niña. El Nino/La Nina events can be classified as four typical cases depending upon the submarine volcanic activities at seamounts in Antarctica and South America. The Sea Surface Temperature (SST) of the South and Central Americas are warmer than SST of East Australian Current (EAC), due to the strong volcanic eruptions in the Seamounts and the Ridges in South and Central Americas. This results in the Central Pacific Current (CPC) flowing from east to west due to the second law of thermodynamics for thermal flow from hot source to cold sink. In contrast the opposite direction is made if SST in EAC is warmer than SST in the Central/South American Seamounts and Ridges, due to the strong volcanic eruptions in the Antarctic Seamounts and Ridges. Chicago was selected as a case study for the relationship between extreme cold weather conditions and minimal sunspot number. Previous attempts at predicting weather patterns in Chicago have largely failed. The years of the record low temperatures in Chicago were significantly correlated with the years of the minimal sunspot number from 1873 to 2019. It is forecast that there may occur a weak La Niña in 2019 and another record low temperature in Chicago in January of 2020 due to the phase of the minimal sunspot number in 2019. It may be possible to predict very strong El Nino events with the year of maximal sunspot number as El Niño Index (R2 = 0.7363) and the years of strong volcanic eruption in the Galapagos Hot Spot (GHS) (R2 = 0.9939), respectively. An El Niño event is thus expected during the year of strong volcanic eruption in the GHS. Strong La Niña events can be expected during the year of minimal sunspot number with La Niña Index (R2 = 0.9922). Record low temperatures in Chicago can be also predicted (R2 = 0.9995) during the year of the minimal sunspot number, as was recently the case in January, 2019.

Including Planet 9 in the Solar System Increases the Coherence between the Sunspot Number Record and Solar Inertial Motion

The Sun would be subject to a significant variation of orbital motion about the solar system barycentre if a small planet is orbiting at a very large distance. This paper assesses if the Planet 9 hypothesis, the existence of a ninth planet, is consistent with the planetary hypothesis: the synchronisation of sunspot emergence to solar inertial motion (SIM) induced by the planets. We show that SIM would be profoundly affected if Planet 9 exists and that the hypothesised effect of SIM on sunspot emergence would be radically different from the effect of SIM due to the existing eight planets. We compare the frequency and time variation of Sun to barycentre distance, RB, calculated for both the eight and nine planet systems, with the frequency and time variation of sunspot number (SSN). We show that including Planet 9 improves the coherence between RB and SSN in the decadal, centennial and millennial time range. Additionally, as the variation of RB is sensitive to the longitude and period of Planet 9, it is possible to adjust both parameters to fit the variation of RB to the SSN record and obtain new estimates of the period and present longitude of Planet 9. Finally, we develop the hypothesis that planetary induced solar acceleration reduces meridional flow and consequently sunspot emergence thereby providing an explanation for the observed coincidence of grand solar minima with intervals of extreme solar acceleration.

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

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

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

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

Study of Surface Ozone over an American Station for a Period of 3.5 Decade

In this research paper we have evaluated the relation between surface Ozone (O3), Sun Spot Number (SSN) and Carbon Monoxide (CO) over an American station “Tutuila” for the long period of 35 years (1980-2015). It was analyzed that CO and O3 show an increasing trend over the maximum months of the year, whereas SSN shows decreasing trend throughout the year. We have concluded that, for O3 the increasing trend is found to be maximum in the month of December, whereas surprisingly just a month before it i.e., in November, the value was negative. We also analyze here the CO data for the same period. It is observed that the CO increases from January to June. Its increment is found to be minimum in January month and maximum in the month of April. After it, the CO shows the decay trend from July to September, and then again increases from October to December months. NO2 data of 11 years is also studied here and concluded that, the variation observed in March month is very small and is positive. In the same way, a positive trend is observed for NO2 data in June month, but in rest all the months the value is negative.

An application of artificial intelligence for investigating the effect of COVID-19 lockdown on three-dimensional temperature variation in equatorial Africa

We present interesting application of artificial intelligence for investigating effect of the COVID-19 lockdown on 3-dimensional temperature variation across Nigeria(2°-15°E,4°-14°N),in equatorial Africa.Artificial neural networks were trained to learn time-series temperature variation patterns using radio occultation measurements of atmospheric temperature from the Constellation Observing System for Meteorology,Ionosphere,and Climate(COSMIC).Data used for training,validation and testing of the neural networks covered period prior to the lockdown.There was also an investigation into the viability of solar activity indicator(represented by the sunspot number)as an input for the process.The results indicated that including the sunspot number as an input for the training did not improve the network prediction accuracy.The trained network was then used to predict values for the lockdown period.Since the network was trained using pre-lockdown dataset,predictions from the network are regarded as expected temperatures,should there have been no lockdown.By comparing with the actual COSMIC measurements during the lockdown period,effects of the lockdown on atmospheric temperatures were deduced.In overall,the mean altitudinal temperatures rose by about 1.1℃ above expected values during the lockdown.An altitudinal breakdown,at 1 km resolution,reveals that the values were typically below0.5℃ at most of the altitudes,but exceeded 1℃ at 28 and 29 km altitudes.The temperatures were also observed to drop below expected values at altitudes of 0-2 km,and 17-20 km.

Statistical study on great geomagnetic storms during solar cycle 23

Characteristics of great geomagnetic storms during solar cycle 23 were statistically investigated. Firstly, we focused on the uniqueness of solar cycle 23 by analyzing both the great storm number and sunspot number from 1957 to 2008. It was found that the relationship between the sunspot number and great storm number weakened as the activity of the storms strengthened. There was no obvious relationship between the annual sunspot number and great storm number with Dst≤-300 nT. Secondly, we studied the relationship between the peak Dst and peak Bz in detail. It was found that the condition Bz〈-10 nT is not necessary for storms with Dst≤-100 nT, but seems necessary for storms with Dst≤-150 nT. The duration for Bz≤-10 nT has no direct relationship with the giant storm. The correlation coefficient between the Dst peak and Bz peak for the 89 storms studied is 0.81. After removing the effect of solar wind dynamic pressure on the Dst peak, we obtained a better correlation coefficient of 0.86. We also found the difference between the Dst peak and the corrected Dst peak was proportional to the Dst peak.

Sun's total irradiance reconstruction based on multiple solar indices

In order to uncover a possible influence on the Earth's climate, we need a much longer time series, i.e., the total solar irradiance (TSI) which is also an interesting issue in its own right in solar physics. By comparing different solar indices associated with TSI during the period 1979 to 2009, several empirical models in the TSI are presented. We verify that the reconstruction model based on the three variables: sunspot number, sunspot area, and solar 10.7 cm radio flux, is the best one. As demonstrated by model calculations, the history of TSI was reconstructed back to 1947 based on 3-indices and to 1874 based on 2-indices, respectively. The reason that the reduced irradiance on the trough during 2006 to 2009 lasts long may be due to the about 85-year cycle of solar activity, which modulates the intensity of the 11-year cycle (Schwabe cycles), possesses a considerable potential to produce an effective reducing, and holds on a steadily lower level of irradiance.