Cambodia vs North Korea

Cambodia vs North Korea: Tropical nights

Tropical nights in 2014: 337.4 nights in Cambodia and 29.3 nights in North Korea, in the CMIP6 climate models’ median run. In 2014 the value was higher in Cambodia, by 308.1 nights, or 12 times that in North Korea.

nights a year · CMIP6

Cambodia vs North Korea: Tropical nightsTropical nights in 2014: 337.4 nights in Cambodia and 29.3 nights in North Korea, in the CMIP6 climate models’ median run. In 2014 the value was higher in Cambodia, by 308.1 nights, or 12 times that in North Korea.
CambodiaNorth KoreaCMIP6 model median (SSP2-4.5, middle of the road)models’ 10th–90th percentile

What the numbers say

  • Cambodia has been higher in every year both have figures for since 1950.
  • The CMIP6 model median (SSP2-4.5, middle of the road) puts Cambodia at 357.6 nights and North Korea at 54.8 nights in 2100.

Every year

nights a year · CMIP6

YearCambodiaNorth Korea
2014337.429.3
2013342.529
2012340.128
2011338.326.3
2010338.227.3
2009339.226.8
2008334.925.2
2007336.126.1
200633626.7
200533426.6
2004338.126.6
2003332.925.2
2002333.325.4
2001337.125
2000335.924.3
1999335.125.2
1998334.823.3
1997336.822.9
1996330.522.5
1995328.719.6
1994334.320.6
1993329.916.4
1992321.516.5
1991328.121.3
1990333.321.9
1989330.321.3
198833220.8
1987329.718.2
1986330.918.6
1985330.819
1984331.217.2
1983328.715.6
1982329.318.9
1981331.219.3
1980329.918.1
1979330.417.8
1978327.520.3
1977331.617.8
1976329.219.2
1975328.118.7
1974326.818.6
1973331.216.2
1972327.118
1971327.417.1
1970327.917.5
1969327.419.5
1968327.617.2
1967323.921.2
1966326.418.3
1965329.818.3
1964327.117.8
1963328.621.2
1962326.219.2
1961332.320.5
196032919
1959328.519.7
1958332.720.5
1957331.119.5
195633021.9
1955326.819.3
195432919.5
1953329.619.8
1952329.220.6
1951330.918.5
1950328.621.7
CMIP6 model median (SSP2-4.5, middle of the road) · 2015–2100
YearCambodiaNorth Korea
2015340323.1–351.23020.9–39.9
2016339.6325.3–352.528.319.1–38.6
2017341.9327.4–350.731.722.5–41.4
2018343.5320.1–353.129.419.8–40.4
2019340.8327.2–352.132.524.9–43.3
2020343325.7–352.630.622.9–40.9
2021345.2328.4–35330.219–44
2022344.4332.5–353.433.225.3–40
2023344.7333.7–355.135.424.8–44.5
2024342.1327.5–352.933.123.4–44.2
2025343.4332.6–35135.526–46.5
2026343.9324.7–353.434.125.7–46.4
2027345.3330.6–354.234.526–47.8
2028346.5331–356.934.924–44.4
2029346.5330.1–357.334.726.7–45.2
2030346.7332.4–355.435.225.2–45.6
2031347.7332.3–356.733.926.2–47.1
2032347.5332.3–354.736.125.7–49.1
2033346.5331.7–358.137.225.8–49.9
2034348.1335.3–35736.328.6–44.6
2035347.7330.1–357.739.127.3–49.2
2036348.2329.7–355.840.426.7–52.8
2037347.4334.6–355.437.126.8–48.8
2038348.9335.4–357.337.826.9–50.2
2039349.3335.7–358.337.328–48.6
2040348.9339.2–356.640.328–54.9
2041349.9337.3–358.140.128.8–54.2
2042348.1336.5–357.94030.8–51.4
2043348.4336.5–357.340.929.4–53.8
2044348.6337.6–357.842.429.2–53.6
2045349.4335.2–360.241.130.7–52
2046351.3337.9–359.443.627.7–52.7
2047352.1341.4–359.442.230.2–55.6
2048351.1338.7–360.64029–56
2049352.5339.7–36041.629.8–55
2050352.6340.1–359.740.831.5–53.9
2051354.4343.2–360.942.531–57.3
2052353.3343.5–359.542.129.7–55.5
2053351.8340.5–358.944.231.3–57.8
2054352343.5–359.342.332.5–55.4
2055352.8339.5–359.245.433–60.9
2056353.4339.8–360.943.933.5–56.4
2057352.1338.8–359.444.934.5–59
2058353.3343–360.645.334.8–59.4
2059355341.5–359.745.430.9–58.8
2060354344.1–36245.130.4–61.9
2061355338.3–36147.135.9–61.5
2062352.2341.4–36144.934.3–60.2
2063356344–360.644.433.8–60.5
2064354.4341.4–361.744.332.7–60.5
2065352.7343.4–359.847.835.3–62.1
2066353.7341.8–360.247.434–59.6
2067352.9341.2–360.349.333.1–61.2
2068354.6343.6–361.147.234.1–63.4
2069355.6344.7–361.247.432.6–58.7
2070355343.5–361.44933.9–66.5
2071356346.1–361.148.634.2–65.8
2072356.6345.8–361.548.834.5–64.9
2073357.1347.3–36249.633.9–62.9
2074356.1341.9–362.349.635–65.3
2075354.9340.7–361.949.734.6–64
2076357.6347.1–362.95034.9–65.1
2077356.7345.6–362.352.437.4–64.6
2078355345.5–361.748.435.4–66.5
2079356.5347.1–361.549.737–64.5
2080358347.4–363.950.139.7–68.2
2081355.8345.5–362.549.938.1–69.4
2082354.6345.1–360.952.738.5–70.3
2083355.2345.7–361.751.638.9–68.2
2084355.7343.4–361.649.836.9–66.8
2085358.4350–362.550.638.2–69
2086356.6345.5–361.852.737.2–68
2087357.5346.7–362.551.937.3–71.1
2088357347.6–362.452.636.7–67.4
2089357.2346.7–361.852.839–67.7
2090355.7347.6–362.153.738.3–65.3
2091357.8345.5–362.653.740–67.5
2092355.5343.9–362.353.438.6–67.8
2093355.6344.4–361.253.537.2–69.1
2094359.2347.3–362.952.936.2–73
2095358.6347.7–362.852.741.1–68.9
2096357.8348.4–363.258.337.8–73
2097356.8346.5–362.455.239–69
2098356.2346.9–362.65440.5–67.9
2099356.8346.4–362.752.537.3–67.2
2100357.6347.9–363.154.837.2–70.2

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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