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SUNSPOT ACTIVITY


We will take a closer look at the the surface of the Sun, specifically at sunspots, as their presence or lack thereof can have significant impact of Earth and near-Earth weather. We will use Python and Matplotlib to plot trends in solar activity and the near-Earth space environment.

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 Sunspot Activity

Sunspots - courtesy of NASA

When astronomers began photographing the sun, they realized that the visible surface, known as the photosphere, is covered in dark regions. Dark cool regions, known as sunspots, vary in size and follow an 11-year cycle, where they increase and decrease in frequency. The solar cycle is divided into four phases: solar minimum, rising phase, solar maximum and declining phase.

Sunspots are about 4000 K (compared to the normal 6000 K temperatures of the sun’s surface) which causes sunspots to appear dimmer than the surrounding photosphere. Sunspots show where the sun’s magnetic field is strongest. For example, the average magnetic field on the sun’s surface is 1 gauss, but in a sunspot, the magnetic field can be over 3,000 gauss. The higher magnetic fields within these areas keep the sunspots cool and therefore dark. While most sunspots disappear with a day or two, some sunspots can be identified and tracked for weeks or even months at a time. The apparent movement of sunspots across the Sun’s surface indicates that the solar surface is rotating anticlockwise.

Solar radio emissions originate within the upper portions of the Sun’s chromosphere and lower portions of the corona. Solar radio flux (10.7cm) is used to indicate solar activity. The use of this wavelength to study the incident solar flux dates back to the beginning of Canadian radio astronomy. Following WWII, Aurther Covington and his colleagues at the National Research Council in Ottawa used pieces of surplus military radar equipment to build a radio telescope. This telescope operated at 2800 MHz, which translates to a wavelength of 10.7 cm. Covington showed that the 10.7 cm wavelength solar flux correlates with indices of solar activity, such as sunspot numbers and number of solar flares.

In this tutorial we will plot the mean number of sunspots, incident radio flux, planetary magnetic field and number of tropical storms, which occured from 1991 to 2017. The number of sunspots, radio flux and geomagnetic field data can be obtained from NOAA's Space Weather Prediction Center (SWPC) website.

This dataset can also be downloaded via ftp at: ftp://ftp.swpc.noaa.gov/pub/weekly/RecentIndices.txt

For our calculations, we will use the observed Space Weather Operations (SWO) sunspot numbers issued by NOAA SWPC, in Boulder, CO instead of the official international sunspot number (RI) issued by the Sunspot Index Data Center (SIDC) in Brussels, Belgium. Tropical storm data is obtained from NOAA's National Hurricane Center (NHC) website.

10.7 cm Radio Flux is the preliminary observed value measured in Penticton, B.C. Canada. The values are displayed in solar flux units (1 sfu = 10-22 W/m2 / Hz). Ap Geomagnetic Index is the preliminary estimate calculated by the GeoForschungsZentrum in Postdam, Germany. According to the SWPC, the Ap index is derived from the Kp index by converting each individual, three-hourly Kp index to an equivalent amplitude three-hourly akp index. The average of eight, three-hourly akp indices is then calculated to produce the 24 hour Ap index.

We will simply obtain the Ap index from the Recent Solar Indices of Observed Monthly Means dataset linked above.

The next bit of Python code will plot a set of time series data, inlcuding the mean total number of sunspots, 10.7 cm wavelength incident solar radiation, the Earth's magnetic field strength and the number of tropical storms. We will then use Matplotlib to to chart this information for analysis.

import matplotlib.pyplot as plt

# Create a list of years
year = [
    1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24,
    25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48,
    49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72,
    73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96,
    97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120,
    121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144,
    145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168,
    169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192,
    193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216,
    217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240,
    241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264,
    265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289,
    290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313,
    314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 323, 324
]

# Create a list of anual mean number of sunspots
sunspots = [
    220.5, 221.5, 220.7, 220.7, 219.6, 218.9, 219.5, 218.3, 214.2, 208.4, 202.2, 193.7,
    183.3, 171.8, 161.6, 154.3, 148.9, 143.3, 134.3, 124.4, 117.5, 113.4, 110.4, 107.7,
    104.5, 101.2, 97.0, 91.9, 86.2, 81.0, 78.7, 75.7, 70.7, 65.5, 60.8, 57.9,
    55.6, 53.5, 52.9, 53.0, 51.9, 49.6, 46.1, 43.8, 43.4, 43.2, 42.5, 41.4,
    39.6, 37.8, 36.3, 33.8, 31.6, 29.9, 28.1, 25.4, 22.0, 19.7, 18.5, 17.6,
    16.8, 16.2, 15.4, 13.6, 12.9, 13.5, 13.4, 13.1, 13.3, 14.0, 15.4, 16.2,
    16.5, 17.4, 20.4, 24.0, 26.4, 29.0, 32.4, 35.9, 40.5, 45.4, 49.3, 54.2,
    60.6, 67.4, 73.3, 77.7, 81.4, 85.9, 90.3, 93.7, 96.1, 97.7, 101.3, 108.8,
    116.5, 120.2, 120.5, 123.8, 131.7, 136.0, 138.0, 142.8, 150.0, 158.5, 164.7, 165.9,
    168.0, 172.1, 175.4, 176.3, 173.1, 172.0, 173.0, 171.8, 169.0, 166.2, 162.7, 160.8,
    156.3, 151.4, 154.0, 159.4, 163.1, 167.2, 172.1, 176.7, 178.8, 179.5, 183.7, 184.5,
    184.8, 188.6, 188.9, 186.2, 183.6, 179.9, 175.4, 169.2, 163.4, 158.8, 150.9, 145.0,
    141.7, 136.4, 128.1, 121.5, 118.3, 113.6, 106.9, 102.8, 100.7, 96.6, 93.6, 91.4,
    87.9, 84.2, 80.9, 77.9, 74.1, 70.4, 68.3, 66.6, 63.7, 61.3, 60.0, 58.8,
    57.3, 56.4, 55.8, 52.6, 48.3, 47.9, 48.1, 45.4, 42.9, 42.6, 42.1, 40.1,
    37.2, 33.4, 31.0, 30.6, 30.7, 28.9, 27.2, 27.6, 27.7, 25.2, 22.3, 20.7,
    19.7, 18.9, 17.5, 16.0, 14.2, 12.8, 11.6, 10.2, 9.9, 10.0, 9.4, 8.1,
    6.9, 5.9, 5.3, 5.3, 5.7, 5.2, 4.5, 4.4, 3.7, 2.9, 2.7, 2.7,
    3.0, 3.1, 3.4, 3.7, 3.8, 4.4, 5.8, 7.7, 9.9, 11.3, 12.4, 13.6,
    14.8, 16.7, 19.1, 21.4, 23.8, 25.2, 25.9, 27.3, 30.6, 35.9, 40.5, 43.8,
    47.2, 50.6, 55.2, 61.5, 69.0, 76.5, 82.5, 84.9, 84.6, 84.6, 86.3, 89.2,
    92.0, 94.2, 94.1, 91.3, 87.7, 83.9, 82.4, 83.1, 83.7, 85.0, 87.3, 88.0,
    87.1, 86.7, 85.7, 86.7, 90.5, 94.4, 97.9, 103.7, 111.0, 114.3, 114.6, 115.4,
    117.7, 119.5, 123.2, 124.8, 122.3, 121.4, 120.4, 115.1, 107.4, 101.7, 97.9, 95.2,
    92.1, 88.3, 84.2, 80.5, 77.5, 73.1, 68.2, 65.5, 64.0, 61.8, 59.0, 55.1,
    51.4, 49.6, 47.7, 45.0, 42.1, 39.0, 36.5, 34.2, 32.1, 31.1, 29.4, 28.1,
    27.3, 25.5, 24.6, 24.3, 23.1, 22.0, 20.8, 19.7, 18.6, 16.8, 15.7, 14.6
]

# Create a list of incident solar flux
radio_flux = [
   205.5, 206.3, 205.9, 206.8, 207.1, 207.4, 207.7, 206.8, 203.9, 199.7, 195.4, 188.9,
   181.3, 174.8, 168.5, 162.9, 158.9, 154.3, 146.7, 138.9, 133.8, 130.5, 128.2, 127.4,
   125.7, 123.1, 120.7, 118.1, 114.8, 111.3, 109.6, 107.6, 103.9, 100.4, 97.5, 94.8,
   92.7, 91.2, 90.2, 89.3, 88.1, 86.4, 83.9, 82.0, 81.2, 80.9, 80.6, 80.4,
   80.1, 79.7, 79.3, 78.6, 77.9, 77.4, 76.9, 76.0, 74.8, 73.8, 73.2, 72.8,
   72.4, 72.2, 72.1, 71.6, 71.4, 71.8, 72.0, 72.1, 72.3, 72.6, 73.0, 73.3,
   73.4, 73.7, 75.1, 76.8, 78.4, 80.1, 81.8, 83.4, 85.7, 88.6, 91.3, 94.2,
   97.5, 101.7, 105.8, 108.9, 112.0, 115.8, 120.0, 124.1, 126.8, 127.9, 130.0, 134.3,
   139.0, 142.6, 144.0, 145.8, 149.9, 152.9, 154.4, 156.3, 161.0, 167.2, 171.5, 173.4,
   175.5, 176.8, 178.4, 180.5, 180.0, 179.7, 180.2, 179.4, 177.1, 175.5, 173.8, 172.0,
   168.7, 165.6, 167.8, 171.6, 174.7, 178.7, 183.8, 188.8, 191.3, 191.9, 193.7, 193.9,
   194.6, 197.2, 195.7, 191.5, 188.0, 182.9, 176.2, 169.3, 164.0, 159.3, 154.1, 150.7,
   148.0, 143.5, 138.3, 135.0, 133.1, 130.2, 127.2, 125.2, 123.7, 121.8, 120.1, 118.0,
   116.3, 115.5, 114.6, 112.4, 109.3, 107.4, 106.1, 105.2, 103.8, 102.3, 101.6, 101.5,
   100.4, 98.6, 97.3, 95.5, 93.2, 91.9, 90.9, 89.2, 87.8, 87.3, 86.7, 85.2,
   83.6, 82.3, 81.2, 80.6, 80.5, 80.2, 80.0, 80.1, 80.0, 79.1, 78.2, 77.8,
   77.5, 76.9, 76.0, 75.3, 74.3, 73.3, 72.7, 72.1, 71.8, 71.8, 71.4, 70.8,
   70.3, 69.9, 69.8, 69.8, 69.8, 69.4, 68.8, 68.6, 68.4, 68.2,68.3, 68.5,
   68.7, 68.8, 69.0, 69.3, 69.7, 70.2, 71.0, 72.1, 73.3, 74.1, 74.5, 74.9,
   75.5, 76.5, 77.5, 78.3, 79.0, 79.7, 80.1, 80.7, 82.4, 85.3, 87.7, 89.6,
   91.2, 92.7, 95.8, 100.4, 105.6, 110.9, 115.4, 117.9, 118.4, 118.4, 119.5, 121.6,
   124.4, 126.7, 126.8, 125.8, 123.8, 121.1, 119.5, 119.2, 118.9, 119.2, 120.1, 120.1,
   118.9, 118.0, 117.1, 116.6, 118.1, 120.9, 123.9, 127.9, 132.3, 134.7, 135.4, 135.9,
   137.3, 138.6, 140.8, 143.5, 144.7, 145.5, 145.2, 142.8, 140.1, 138.4, 137.4, 137.0,
   135.8, 133.8, 131.2, 127.3, 123.5, 119.5, 116.0, 113.3, 110.8, 107.9, 105.3, 102.5,
   99.9, 98.1, 96.6, 95.3, 93.2, 90.4, 87.7, 85.5, 83.7, 82.5, 81.1, 80.0,
   79.4, 78.7, 78.6, 78.4, 77.7, 77.3, 76.8, 76.3, 75.9, 75.1, 74.6, 74.0
]

# Create a list of Earth's Geomagnetic Field (AP)
geomagnetic_field = [
   17.4, 18.4, 19.1, 20.0, 21.7, 23.0, 23.6, 24.7, 25.0, 24.3, 24.1, 23.0, 21.1, 19.8, 19.4, 18.9, 17.5, 16.6, 16.6, 16.1, 15.9, 16.7, 16.6, 16.1,
   16.0, 15.9, 15.3, 14.9, 14.9, 15.0, 14.9, 15.4, 16.0, 16.4, 17.4, 18.1, 18.2, 18.1, 17.8, 18.0, 18.3, 18.2, 18.1, 17.5, 16.5, 15.5, 14.7, 14.2,
   14.0, 13.9, 14.0, 13.8, 13.3, 12.9, 12.5, 12.1, 11.8, 11.4, 10.7, 10.0, 9.7, 9.7, 9.8, 9.7, 9.5, 9.4, 9.3, 9.4, 9.3,9.1, 9.1, 9.3,
   9.3, 9.2, 8.9, 8.5, 8.5, 8.5, 8.4, 8.2, 8.3, 8.5, 8.9, 9.5, 9.8, 10.5, 11.0, 11.3, 11.6, 12.0, 12.3, 12.5, 12.7, 12.8, 12.5, 12.0,
   11.8, 11.6, 11.8, 12.3, 12.4, 12.4, 12.6, 12.9, 12.9, 12.8, 13.2, 13.8, 14.6, 15.1, 15.1, 15.0, 15.1, 15.1, 14.8, 14.2, 14.3, 15.0, 15.1, 14.7,
   14.0, 13.3, 12.9, 12.8, 12.8, 12.8, 12.9, 13.0, 12.8, 12.1, 12.0, 12.0, 11.9, 12.1, 12.3, 12.5, 12.7, 12.9, 13.3, 13.8, 14.5, 15.1, 15.8, 17.1,
   18.2, 18.9, 19.5, 20.1, 21.0, 21.5, 22.0, 22.3, 21.8, 21.1, 20.0, 18.6, 18.1, 17.7, 16.7, 15.2, 14.0, 13.6, 13.5, 13.5, 13.3, 13.3, 13.7, 14.3,
   14.1, 14.0, 14.6, 15.1, 14.4, 13.6, 12.8, 11.8, 11.4, 11.3, 10.8, 10.0, 9.5, 9.0, 8.3, 7.8, 7.9, 8.2, 8.6, 8.8, 8.8, 8.8, 8.7, 8.7,
   8.7, 8.6, 8.5, 8.5, 8.3, 7.9, 7.4, 7.5, 7.8, 7.9, 7.8, 7.8, 7.8, 7.6, 7.5, 7.3, 7.2, 7.0, 6.8, 6.3, 5.8, 5.4, 5.1, 4.9,
   4.7, 4.7, 4.6, 4.3, 4.1, 4.0, 3.9, 3.8, 3.8, 4.1, 4.5, 4.8, 5.0, 5.1, 5.3, 5.5, 5.7, 5.8, 6.0, 6.2, 6.3, 6.4, 6.4, 6.5,
   6.7, 6.8, 7.2, 7.5, 7.5, 7.4, 7.3, 7.4, 7.7, 8.0, 8.0, 8.0, 8.3, 8.4, 8.1, 8.0, 8.2, 8.3, 8.3, 8.1, 7.8, 7.4, 7.3, 7.5,
   7.5, 7.4, 7.4, 7.2, 7.0, 7.1, 7.3, 7.6, 7.8, 7.8, 7.9, 7.5, 7.1, 6.9, 7.2, 7.5, 7.9, 8.4, 8.8, 8.9, 9.3, 9.9, 10.1, 10.5,
   11.0, 11.5, 12.0, 12.4, 12.7, 13.0, 13.1, 13.1, 12.8, 12.5, 12.5, 12.5, 12.3, 12.0, 11.8, 11.8, 11.7, 11.4, 11.2, 11.2, 11.3, 11.6, 11.6, 11.4,
   11.3, 11.3, 11.5, 11.5, 11.3, 11.3, 11.0, 10.7, 10.3, 9.8, 9.5, 8.9
]

# Create a list of the number of tropical storms
tropical_storms = [
   0, 0, 0, 0, 1, 3, 2, 2, 5, 5, 1, 0, 0, 0, 0, 1, 0, 3, 7, 5, 10, 6, 0, 0, 0, 0, 0, 0, 0, 3, 3, 9, 6, 1, 0, 0, 0, 0, 0, 0, 0, 4, 4, 6, 7, 1, 2, 0,
   0, 0, 0, 0, 0, 2, 7, 10, 6, 4, 0, 0, 0, 0, 0, 0, 1, 3, 4, 5, 5, 3, 1, 0, 0, 0, 0, 0, 0, 5, 7, 3, 6, 3, 1, 0, 0, 0, 0, 0, 0, 2, 4, 7, 7, 5, 1, 0,
   0, 0, 0, 0, 0, 2, 2, 7, 5, 4, 1, 0, 0, 0, 0, 0, 1, 2, 1, 10, 10, 6, 1, 0, 0, 0, 0, 0, 1, 2, 3, 4, 9, 9, 2, 0, 0, 0, 0, 0, 1, 1, 4, 6, 10, 2, 0, 0,
   0, 0, 0, 1, 1, 3, 5, 7, 7, 6, 0, 2, 0, 0, 0, 0, 1, 0, 4, 11, 6, 4, 1, 0, 0, 0, 0, 0, 1, 4, 7, 9, 7, 0, 0, 0, 0, 0, 0, 0, 1, 1, 7, 8, 6, 3, 2, 0,
   0, 0, 0, 0, 3, 1, 4, 6, 9, 2, 0, 1, 0, 0, 0, 0, 2, 2, 7, 7, 6, 6, 2, 0, 0, 0, 0, 0, 0, 1, 4, 11, 5, 5, 1, 0, 0, 0, 0, 0, 1, 4, 1, 6, 9, 5, 0, 0,
   0, 0, 0, 0, 0, 3, 6, 9, 7, 3, 2, 0, 0, 0, 0, 0, 4, 3, 3, 11, 7, 8, 0, 0, 0, 0, 0, 0, 2, 4, 5, 6, 6, 6, 2, 1, 0, 0, 0, 0, 1, 4, 5, 6, 6, 6, 0, 0,
   0, 0, 0, 0, 3, 2, 6, 7, 6, 3, 3, 0, 1, 0, 0, 0, 1, 2, 8, 9, 10, 2, 3, 0, 0, 0, 0, 1, 2, 4, 7, 7, 8, 5, 1, 0
]

plt.title('Solar Indices of Observed Monthly Mean Values')
plt.figure(1)

plt.subplot(211)
plt.plot(year, sunspots)
plt.plot(year, radio_flux)
plt.legend(['Number of Sunspots', 'Radio Flux (10.7 cm)'])
plt.ylabel('Solar Indices')
plt.xlabel('Year (1991 - 2017)')
plt.grid(True)

plt.subplot(212)
plt.plot(year, geomagnetic_field, color='orange')
plt.bar(year, tropical_storms)
plt.legend(['Geomagnetic Field', 'Tropical Storms'])
plt.ylabel('Planetary Indices')
plt.xlabel('Year (1991 - 2017)')
plt.grid(True)
plt.show()

Analysis of the figure below, which plots the number of sunspots from 1991 to 2017, shows a corelation between the number of sunspots and 10.7 cm radio flux to the relative strength of the Earth's magnetic field and the number of tropical storms during that time. Sunspot numbers, solar flares and solar radio flux follows an 11-year cycle and a larger 22-year cycle, however, appear to fluctuate on an annual basis. Earth’s planetary magnetic field (Ap) appears to fluctuate according to a 11/12-year cycle, although it lags behind, with smaller scale fluctuations.

Astronomers have been monitoring the number of sunspots since the age of modern astronomy. Sunspot numbers, dating back to the early 1700's, allows us to study variations in the sun and it's relation to the incident solar radio flux.

import matplotlib.pyplot as plt

# Create a list of years
year = [
   1700, 1701, 1702, 1703, 1704, 1705, 1706, 1707, 1708, 1709, 1710, 1711, 1712, 1713, 1714, 1715, 1716, 1717, 1718, 1719,
   1720, 1721, 1722, 1723, 1724, 1725, 1726, 1727, 1728, 1729, 1730, 1731, 1732, 1733, 1734, 1735, 1736, 1737, 1738, 1739,
   1740, 1741, 1742, 1743, 1744, 1745, 1746, 1747, 1748, 1749, 1750, 1751, 1752, 1753, 1754, 1755, 1756, 1757, 1758, 1759,
   1760, 1761, 1762, 1763, 1764, 1765, 1766, 1767, 1768, 1769, 1770, 1771, 1772, 1773, 1774, 1775, 1776, 1777, 1778, 1779,
   1780, 1781, 1782, 1783, 1784, 1785, 1786, 1787, 1788, 1789, 1790, 1791, 1792, 1793, 1794, 1795, 1796, 1797, 1798, 1799,
   1800, 1801, 1802, 1803, 1804, 1805, 1806, 1807, 1808, 1809, 1810, 1811, 1812, 1813, 1814, 1815, 1816, 1817, 1818, 1819,
   1820, 1821, 1822, 1823, 1824, 1825, 1826, 1827, 1828, 1829, 1830, 1831, 1832, 1833, 1834, 1835, 1836, 1837, 1838, 1839,
   1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1848, 1849, 1850, 1851, 1852, 1853, 1854, 1855, 1856, 1857, 1858, 1859,
   1860, 1861, 1862, 1863, 1864, 1865, 1866, 1867, 1868, 1869, 1870, 1871, 1872, 1873, 1874, 1875, 1876, 1877, 1878, 1879,
   1880, 1881, 1882, 1883, 1884, 1885, 1886, 1887, 1888, 1889, 1890, 1891, 1892, 1893, 1894, 1895, 1896, 1897, 1898, 1899,
   1900, 1901, 1902, 1903, 1904, 1905, 1906, 1907, 1908, 1909, 1910, 1911, 1912, 1913, 1914, 1915, 1916, 1917, 1918, 1919,
   1920, 1921, 1922, 1923, 1924, 1925, 1926, 1927, 1928, 1929, 1930, 1931, 1932, 1933, 1934, 1935, 1936, 1937, 1938, 1939,
   1940, 1941, 1942, 1943, 1944, 1945, 1946, 1947, 1948, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959,
   1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979,
   1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999,
   2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017
]

# Create a list of sunspots
sunspots = [
   5, 11, 16, 23, 36, 58, 29, 20, 10, 8, 3, 0, 0, 2, 11, 27, 47, 63, 60, 39,
   28, 26, 22, 11, 21, 40, 78, 122, 103, 73, 47, 35, 11, 5, 16, 34, 70, 81, 111, 101,
   73, 40, 20, 16, 5, 11, 22, 40, 60, 80.9, 83.4, 47.7, 47.8, 30.7, 12.2, 9.6, 10.2, 32.4, 47.6, 54,
   62.9, 85.9, 61.2, 45.1, 36.4, 20.9, 11.4, 37.8, 69.8, 106.1, 100.8, 81.6, 66.5, 34.8, 30.6, 7, 19.8, 92.5, 154.4, 125.9,
   84.8, 68.1, 38.5, 22.8, 10.2, 24.1, 82.9, 132, 130.9, 118.1, 89.9, 66.6, 60, 46.9, 41, 21.3, 16, 6.4, 4.1, 6.8,
   14.5, 34, 45, 43.1, 47.5, 42.2, 28.1, 10.1, 8.1, 2.5, 0, 1.4, 5, 12.2, 13.9, 35.4, 45.8, 41.1, 30.1, 23.9,
   32.3, 54.3, 59.7, 63.7, 63.5, 52.2, 25.4, 13.1, 6.8, 6.3, 70.9, 47.8, 27.5, 8.5, 13.2, 56.9, 121.5, 138.3, 103.2, 85.7,
   64.6, 36.7, 24.2, 10.7, 15, 40.1, 61.5, 98.5, 124.7, 96.3, 66.6, 64.5, 54.1, 39, 20.6, 6.7, 4.3, 22.7, 54.8, 93.8,
   95.8, 77.2, 59.1, 44, 47, 30.5, 16.3, 7.3, 37.6, 74, 139, 111.2, 101.6, 66.2, 44.7, 17, 11.3, 12.4, 3.4, 6,
   32.3, 54.3, 59.7, 63.7, 63.5, 52.2, 25.4, 13.1, 6.8, 6.3, 7.1, 35.6, 73, 85.1, 78, 64, 41.8, 26.2, 26.7, 12.1,
   9.5, 2.7, 5, 24.4, 42, 63.5, 53.8, 62, 48.5, 43.9, 18.6, 5.7, 3.6, 1.4, 9.6, 47.4, 57.1, 103.9, 80.6, 63.6,
   37.6, 26.1, 14.2, 5.8, 16.7, 44.3, 63.9, 69, 77.8, 64.9, 35.7, 21.2, 11.1, 5.7, 8.7, 36.1, 79.7, 114.4, 109.6, 88.8,
   67.8, 47.5, 30.6, 16.3, 9.6, 33.2, 92.6, 151.6, 136.3, 134.7, 83.9, 69.4, 31.5, 13.9, 4.4, 38, 141.7, 190.2, 184.8, 159,
   112.3, 53.9, 37.6, 27.9, 10.2, 15.1, 47, 93.8, 105.9, 105.5, 104.5, 66.6, 68.9, 38, 34.5, 15.5, 12.6, 27.5, 92.5, 155.4,
   154.6, 140.4, 115.9, 66.6, 45.9, 17.9, 13.4, 29.4, 100.2, 157.6, 142.6, 145.7, 94.3, 54.6, 29.9, 17.5, 8.6, 21.5, 64.3, 93.3,
   119.6, 110.9, 104.1, 63.6, 40.4, 29.8, 15.2, 7.6, 2.9, 3.1, 16.5, 55.7, 57.6, 64.7, 79.3, 41.9, 23.9, 13.1
]

plt.plot(year, sunspots)
plt.title('Yearly Mean Sunspot Numbers (1700 - 2017)')
plt.ylabel('Number of Sunspots')
plt.xlabel('Year')
plt.grid(True)
plt.show()


James Elsner, a climatologist at Florida State University, has found a 10- to 12- year hurricane cycle that corresponds to the 11-year solar cycle. Elsner’s study suggests that more sunspots imply less intense hurricanes on Earth, with a 10% decrease in hurricane intensity for every 100 sunspots recorded.



Johnathan Nicolosi - 13 Jun 2018