South Africa vs Thailand

South Africa vs Thailand: Tropical nights

Tropical nights in 2014: 27.1 nights in South Africa and 286.4 nights in Thailand, in the CMIP6 climate models’ median run. In 2014 the value was higher in Thailand, by 259.3 nights, or 11 times that in South Africa.

nights a year · CMIP6

South Africa vs Thailand: Tropical nightsTropical nights in 2014: 27.1 nights in South Africa and 286.4 nights in Thailand, in the CMIP6 climate models’ median run. In 2014 the value was higher in Thailand, by 259.3 nights, or 11 times that in South Africa.
South AfricaThailandCMIP6 model median (SSP2-4.5, middle of the road)models’ 10th–90th percentile

What the numbers say

  • Thailand has been higher in every year both have figures for since 1950.
  • The CMIP6 model median (SSP2-4.5, middle of the road) puts South Africa at 58.7 nights and Thailand at 320.8 nights in 2100.

Every year

nights a year · CMIP6

YearSouth AfricaThailand
201427.1286.4
201328.3289.1
201227.9286.6
201126.1286.4
201026286
200925.8286.9
200824.8284.8
200724.7283.2
200625.5284.3
200525.3281.7
200425.8287.5
200325.1279.3
200225280
200124.5285.5
200023.3283.4
199923282.5
199823.4282.7
199722.5284.8
199620.4279.8
199519.3278.7
199420280.8
199318.1275.6
199218.1268.8
199122.3276.7
199022.7279.2
198922278.4
198821.2279.6
198721.9277.9
198621277.5
198518.9276
198420277.9
198319273.9
198220.3275.7
198120277.8
198021.2277.9
197919.9278
197819.8276
197719.2277.3
197619.2275.6
197518.8274.4
197419.7276
197319.6280.6
197219273.9
197119.1273.2
197018.6276
196918.7274.8
196817.8277
196717.5270.3
196617.8274.4
196517.2275.8
196414.9271.9
196316.5274.7
196219.6275.4
196119.7278.5
196019.8276.2
195918.5274.4
195819.3278.7
195719.4277.4
195618277
195519.5274.6
195418.4275
195318.4275.4
195219.3275.1
195118.9277.5
195018.1276.2
CMIP6 model median (SSP2-4.5, middle of the road) · 2015–2100
YearSouth AfricaThailand
201528.620–39.5290.6272.2–305
201626.817.8–37.9287.1269.8–306.2
201728.919.6–37.8290273.8–302.8
201829.620.6–40.6292.6275.1–308.7
201929.820.8–41.5291.2272.8–306.7
202029.518.9–39.1292.4272.2–307.8
202131.121.4–42.9294.2276.3–308.5
20223223.7–43.1294.8277.2–311.9
202331.922.9–41.8296.1281.9–310.2
20243324–45.6291.7275.5–307.8
202531.920.7–44.5294276.7–306.3
202634.521.9–46.9296278–309.3
202734.423–47.7295.8276.3–311.1
202833.522.9–46297279.3–315.1
202936.226.5–48.3300.3278.7–315
203036.124.9–49297.7280.2–312.5
203136.324.5–50.3300.8282.8–317.1
203236.424.5–49.2297.2280.9–311.4
203336.924.5–48.3300281.9–315.8
203437.626.4–51.8300.8279–318.4
203537.625.2–49.5298.6281.5–317.8
20363826.5–50.5300.4279.9–316.9
203739.928.2–53.4299.8280–315.1
203841.528.4–54.2304.8286.6–319.3
203939.227–52.9300.9281.9–317
204038.125.6–55.6301.6284.5–315.7
204140.227.4–54.3303.1285.1–318.8
20424229–56.4303.8290–318.3
204342.128.2–58.4302285.8–317.9
204441.929.5–57.8304.3286.3–318.8
204542.728.2–59307.1287.7–324.2
204644.130–59.5305.9285.2–321
204744.630.7–61.2306.6289.5–321.5
20484531.9–61.5309.4288.9–325.7
204945.632.3–61.4308.1287.2–324.1
205046.931–63.6307.5291–321.5
205147.532–65312.8294.8–326
205248.332.7–64.3310.9291.2–323.4
205345.733.7–62.1308.3289.5–323.3
20544833.3–64.3308.8292.8–322.3
205546.833–68.4308.6290.6–325.1
205647.933.2–64.8311.3294.9–326.3
205750.232.6–68.9308.5286.7–326.4
205847.934–66.4308291.1–325.1
205951.333.1–66.9312.7295.8–325.3
206051.936–69.9313.1294.6–328.6
206150.534.8–71.3314.4289.5–331.1
206250.532.7–71311.4293.4–330.3
206350.434.3–69.7314.8296.9–327.3
20645134.1–67.7312.5295.4–330
206550.433.6–70.2309.6293.1–329.3
20665335.7–72.4312.1292.6–328
206750.634.8–70.4311.2289.6–329.4
206854.837.8–72.5310.8292.4–330.6
206954.339.1–73.4315.5296.1–329.5
207054.337.2–73.3314.2292.9–330.7
207155.837.9–76.9313.8296.4–330.8
20725639.4–78.6314.8296–329.6
207360.443.4–80.3316.5299.9–332.1
207457.439.1–75.3317.2295–333.8
207556.137.7–76317.2295.3–331.4
207658.538.4–75.3317.4299.8–335
207757.341–80.5317.5297.3–335.9
20785639.5–79315.9297.1–331.6
207957.639.3–78318.5300.5–332.3
208059.540.3–80.9319.8297.4–337.5
20815841.5–79.6317.7299.5–334.9
208258.839.7–77.7317.9294.8–333
208360.839.1–82.9315297.8–332.9
208462.540.4–82.9316295.5–330
208560.741.3–87.6322.6300.8–334.7
208660.542.1–83.3319.4295.2–335.2
208757.941–82.9320.2302.2–334.7
208864.243.4–85.3321.4299.8–334.8
208961.541.3–81.2318.9296.4–338.1
209061.641.5–86.8317.6304–337.4
209163.341.6–86.3320.1299.8–336.1
209263.144.9–84.8316.3298–336.4
209362.443.2–86.1316.1297.7–333.7
209463.145.6–87.4323.4301.8–337.3
209565.342.6–91.7322.9302.4–338.9
20966142.7–90.1319.6302.8–336.7
209758.841.2–84.6319.8299.1–336
209865.143.8–89.8320.5303.9–336
209964.345–87319.9303.1–334.4
210058.742–88.2320.8301.1–336.7

Each country on its own

Good to know

  • Nights in a year when the temperature stays above 20 °C, averaged over the country.
  • Source: World Bank Climate Change Knowledge Portal, CMIP6 multi-model median.
  • The dashed line is the CMIP6 model median (SSP2-4.5, middle of the road), a scenario rather than a forecast; the band is its models’ 10th–90th percentile.
  • Climate model values are the median of the CMIP6 models, run on past emissions to 2014 and on SSP2-4.5, a middle-of-the-road path, after: they are not measurements.

How the layers work, where their data comes from and what they can’t tell you: Countries through time

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