China vs Congo - Kinshasa

China vs Congo - Kinshasa: Tropical nights

Tropical nights in 2014: 41.5 nights in China and 248.5 nights in Congo - Kinshasa, in the CMIP6 climate models’ median run. In 2014 the value was higher in Congo - Kinshasa, by 207.1 nights, or 6 times that in China.

nights a year · CMIP6

China vs Congo - Kinshasa: Tropical nightsTropical nights in 2014: 41.5 nights in China and 248.5 nights in Congo - Kinshasa, in the CMIP6 climate models’ median run. In 2014 the value was higher in Congo - Kinshasa, by 207.1 nights, or 6 times that in China.
ChinaCongo - KinshasaCMIP6 model median (SSP2-4.5, middle of the road)models’ 10th–90th percentile

What the numbers say

  • Congo - Kinshasa has been higher in every year both have figures for since 1950.
  • The CMIP6 model median (SSP2-4.5, middle of the road) puts China at 63.3 nights and Congo - Kinshasa at 324.1 nights in 2100.

Every year

nights a year · CMIP6

YearChinaCongo - Kinshasa
201441.5248.5
201341.7249.2
201240.8250.9
201140.2248.7
201040.5243.5
200939.5243
200839.2239.7
200738.7237.7
200639.5239
200539.5237.3
200439.7238.5
200338.5239.3
200238.8236.5
200139.5242.5
200038.6233.5
199938.5226.2
199837.6230.6
199737.1226.6
199636.6218.6
199535.1217.2
199434.9211.9
199333.2201.5
199232.3197.6
199135.9220.1
199036.4225.4
198936.1218.4
198835.1216.7
198734.5216.6
198634216.8
198533.5206.1
198433.4205.9
198331.5204.3
198234.1204.5
198134.2215.2
198034.3211.4
197933.8208.5
197834.8202.6
197733.3206.5
197632.7201.9
197533.1199
197433.3204
197333.1208.4
197232.6202.5
197133196.3
197032.4201.7
196933.4197.3
196832.8199
196733.4188.8
196633.4194.7
196532.5189
196432.1177.3
196334.5178.5
196234203.2
196134.6203.9
196034.1209.8
195934203.3
195835204.3
195734.4204
195634.6197.6
195534.2200.5
195434.2201
195334.3204.6
195234.3201.9
195135.1204.8
195034.9200.8
CMIP6 model median (SSP2-4.5, middle of the road) · 2015–2100
YearChinaCongo - Kinshasa
201542.334.8–49.8256.6217.3–277.7
201642.134.4–50247.7213.3–269.3
201743.135.3–51.6257.4226.6–278.3
201842.335.5–49.5258.8228.1–276.8
201943.836.5–51.1255.8222.6–278.7
202043.736.8–51.1259.4227.4–282.3
202144.136–52.1262.6234.7–283.8
202244.737.5–52.7264.8235.1–288.5
202345.737.8–53.4263.9236.8–287.2
202445.738.1–54270.9237.9–290
202546.337.5–54.8266.1230.6–292.2
202646.237.6–55.5267.4226.8–294.5
202746.138–54.9268.6230.6–293.6
202846.438.4–54.3271.7239.7–295.9
202946.939.1–55.6275.9249.3–296.6
203047.138.8–55.3270.4241.7–296.1
20314738.8–56279.4246.6–297.5
203248.339.3–57.4277.9249.4–302.3
203347.939.7–56.6276.9245.9–301.6
203448.440.5–57.3276.7247.4–305.6
203549.740.8–58.6281243.7–305.1
20364940.1–58.2278.3244.7–304.9
20374941.2–57.6286.9256.1–308.6
203850.542.4–59.3289261.5–307.1
203949.641.2–59.2284.7253.4–307.3
204050.942.4–60.4284.5254.7–311.5
204151.342.5–61.1289.9258.4–310.8
204251.642–61.5286.9254.1–314.3
204350.742.7–61285.9253–313
204451.942.4–61.9288.3253.6–312.5
204551.542.8–61.1289.4260.1–314.1
20465343.7–62.7298.3261.3–317.3
204753.544.5–62.5298.5268.2–317.8
204853.144–62.6299.1268–320.8
204952.843.8–63.3300.3269.6–320.4
205053.644.4–63.8297.8267.2–320.6
205154.345.1–64.8298.4272.3–323.1
205253.444.6–63.2300.2271.5–324
205354.244.2–64.2298.7265.5–322.6
205454.545.3–64.8300.6269.6–322.8
20555545.1–65.8301.9274.9–325.6
205655.245.1–66.6302.1266.2–328.3
205754.844.9–65.7305.1274.4–326.5
205855.145.8–67.7305.3274.1–327.1
205955.345.1–67.2305.4279–329.6
206057.246.9–68.1310.7273.5–330.2
206156.546.6–66.3307266.1–329.8
206256.846.9–68.5308.8269.1–332
206357.247.2–68.6308.4277.6–328.4
20645747.1–67.7310.6278.4–329.6
206557.347.1–67.8311.9282.4–331.2
206657.347.9–68.7310.4284.2–330.3
206757.546.4–68.2308.8275.9–334
206858.748.5–70.5313.9280.8–334.6
206958.548.4–69312.1270.6–332.7
20705847.6–71.7309.6278–334
207159.347.4–71.6312.6285–336.5
207259.248.1–71.5317287.7–334.6
207359.849.1–70.9316.2291.2–337.1
207459.648.7–71.1311.6284.5–334.9
207558.748.3–71.4312.3279.3–335.2
207660.249–70.9315.9288.7–337.1
207760.851.1–72.4318.5292.6–338.2
207859.648.8–72.4315287.3–337.1
207960.750.1–72.6314.8286.3–336
208059.950–72.3318.5286.8–337.8
208160.950–73.2318.4287.8–336.7
20826149.8–73.3318.7289.8–336.5
208361.251.2–73.6317.4290.6–340.9
208461.749.7–74.6321.6293.8–339.8
208561.250.8–75.2324.4294.3–341.4
208661.648.9–74.5319290.7–340.7
208761.650.6–75318.3291.9–339.6
208862.750.5–77.1320.9292.9–340
208961.950–75.2322.1293.9–339.8
209062.551.4–73.7320.4295.5–341.7
209163.852–75.7325.4294.3–343.3
209263.251.2–75.1321.5294.4–342.4
209363.252–75.4323.1297.8–341.2
20946351.1–76.3323.1296.5–341.1
209563.952.8–76.4325.6299.1–343.7
209663.852.2–76.8321.8296.2–344.1
209762.750.7–75.1321.5292.6–342
209863.451.2–76.8324.2295.1–342.8
209963.851.9–76.1322.8293.7–344.6
210063.351.7–76.2324.1292.1–344.1

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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