South Korea vs Tanzania

South Korea vs Tanzania: Tropical nights

Tropical nights in 2014: 68.9 nights in South Korea and 106.3 nights in Tanzania, in the CMIP6 climate models’ median run. In 2014 the value was higher in Tanzania, by 37.4 nights, or 1.54 times that in South Korea.

nights a year · CMIP6

South Korea vs Tanzania: Tropical nightsTropical nights in 2014: 68.9 nights in South Korea and 106.3 nights in Tanzania, in the CMIP6 climate models’ median run. In 2014 the value was higher in Tanzania, by 37.4 nights, or 1.54 times that in South Korea.
South KoreaTanzaniaCMIP6 model median (SSP2-4.5, middle of the road)models’ 10th–90th percentile

What the numbers say

  • Tanzania 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 Korea at 96.3 nights and Tanzania at 208 nights in 2100.

Every year

nights a year · CMIP6

YearSouth KoreaTanzania
201468.9106.3
201369.7105.5
201267.7108.4
201165.3109
201068.3103.9
200965.8101.1
200863.7100.3
200764.299.5
200664.8100.4
200565.198.7
200466.8100.4
200363.299.6
200263.398.4
200163.4101.5
200061.794.9
199964.294.2
199860.796.8
199760.493.5
199661.289.6
199555.288.5
199454.885.4
199349.281.2
199250.780.1
199156.989.6
199058.891.1
198958.789.1
198855.687.6
198752.688.5
198651.489.4
198553.183
198450.483.1
198346.882.1
19825484.8
198153.988
198051.685
197952.685.5
197856.482.9
197751.884.1
197652.881
197554.680.8
197452.782.2
197350.483.6
197251.881.4
197149.780.7
197050.481.8
196955.279.5
196851.779.1
196757.377.5
196652.978.1
196552.878
196452.273.8
196357.973.1
196253.381.1
196158.282.6
196055.283.9
195954.881.4
19585682.1
195755.880.9
195659.180
195555.781.6
19545481.4
195355.480.6
19525781.4
195154.782.1
195059.880.7
CMIP6 model median (SSP2-4.5, middle of the road) · 2015–2100
YearSouth KoreaTanzania
201568.656.8–82114.895.2–137.4
201670.353.5–81.8108.391.6–125.6
201770.559.5–82.9114.893.6–137.8
201869.156.9–81.2116.497.4–137.1
201974.663.1–84.4113.495.2–133.2
20207157.6–83119.396.2–143.5
202170.252.7–84.1119.5100.9–147.1
202272.563–81.2124.4105.3–147.4
202374.563.2–84.2122.1102.1–147.8
202473.558.6–87.1126.2100–150.1
202575.762.3–87.1124.6100.9–154
20267564–88.4126.999.1–155.6
202774.163.4–88.9128102.9–147.7
202877.361.5–87.9129.9106.4–152.1
202975.463.1–86.7134.1111–156.5
203075.364.5–87.6129.4106.4–151.1
203176.763.6–87.3136.4115.9–158.4
203276.761–89.2135.1114–166.8
203378.860–92.7135.2110.9–161.9
203477.264.7–87.2134.7112.5–166.3
20358168.9–92.1140.2114.6–165
203681.964.1–95137.8113.3–165.5
203778.465.8–89.3142.9121.5–168.9
203878.866.9–89.4147.9124.3–169.3
203977.564.7–89.2144.3117.6–171.3
204080.567.4–92.7140.5117–178
204180.966.4–97.7149.1121.7–175.6
204282.469.1–95146.3120.5–185.3
204383.268.5–97.9144.6117–173
204483.169.3–91.6147.7121.2–175.8
20458170.4–92.2150.1125.2–183.4
20468668.3–95.2161.5128.6–192.2
204783.469.7–96.7160.2129.7–194.4
204882.769.2–95.2158.8132.7–193.1
204981.669–96.8157.6131.1–196
205081.569.8–93.8159.7127.8–192
205184.572.7–98.8163.2135.5–197.8
205283.167.8–98.5161.9136.2–199.1
205386.969.8–98.1160.6127.5–197.7
205484.773.3–97.5163.2131.5–200.2
205586.570.8–101.3167.4137–208.3
205685.874.5–99.8166.4130.8–209.7
205787.976.1–100.7168134.6–208.3
205886.275.2–99.4168.7134.9–210.3
205986.766.9–100.4174.7144.9–219.5
206086.371.9–101.9181.7138.1–219.8
20618974.8–99.5171.8135.5–218.1
206287.772.3–102.2177.5139.9–224
206386.774.6–101.2181.4140.1–213.8
206486.673.6–100.3185.3140.1–215.2
206591.575.3–102.2183.5141.1–221.8
206689.272.6–100.5181.9146.3–219.3
206790.372.7–102.6178.8138.8–229.2
206890.473.8–106.4186144.2–228.4
20699174.5–102.5183.7141.7–225.3
207089.774.6–107180.7140.4–228.2
207192.674.9–107.9184.9146.9–235.2
207290.976.6–103.9191.3155–226.1
207391.376.2–104191.6152.4–235.6
207491.974.6–107183.3149.7–231.7
207594.576.4–103.7183.8144.8–233
207693.477.5–105.6191.1150.9–233.2
207795.776.3–107.2191.3152.6–238.2
207893.576.9–110.5189156.6–233.8
207991.179.2–105.7190.5147–237.3
208092.481.7–107.1197.3150.7–241.2
208193.878–108.8195.6153.4–237.4
208296.279.9–110.1194.5157.3–237.3
208394.481.1–108193.2151–249.8
208491.574.4–107.3201.1152.2–246.7
20859481.4–107.4204.2162.3–252.3
208693.678.5–111.5195156–248.7
208794.480.9–113.5196.7160.8–244.5
208893.779.8–108.5208.4154.7–246.9
208994.478.9–107201.3158.6–248.2
209097.979.5–109197.9160.7–253.6
209195.280.6–107.7210161.6–256
20929580–106.3205.8158.3–253.6
209395.379.5–109.9207.2161.2–251.9
209492.279.8–111207.9161.3–250.6
209594.884–112.2214.2166.4–258.5
2096100.481–113.2205.3159.8–257.4
209797.278.6–108.8206.6158.4–256.8
209897.479.9–110.4209.1161–258.3
209995.879.9–109.3208.5160.1–256
210096.381.6–113.8208158.6–258.6

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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