Bangladesh vs Türkiye

Bangladesh vs Türkiye: Tropical nights

Tropical nights in 2014: 239.9 nights in Bangladesh and 19.8 nights in Türkiye, in the CMIP6 climate models’ median run. In 2014 the value was higher in Bangladesh, by 220.1 nights, or 12 times that in Türkiye.

nights a year · CMIP6

Bangladesh vs Türkiye: Tropical nightsTropical nights in 2014: 239.9 nights in Bangladesh and 19.8 nights in Türkiye, in the CMIP6 climate models’ median run. In 2014 the value was higher in Bangladesh, by 220.1 nights, or 12 times that in Türkiye.
BangladeshTürkiyeCMIP6 model median (SSP2-4.5, middle of the road)models’ 10th–90th percentile

What the numbers say

  • Bangladesh has been higher in every year both have figures for since 1950.
  • The CMIP6 model median (SSP2-4.5, middle of the road) puts Bangladesh at 269.8 nights and Türkiye at 45.5 nights in 2100.

Every year

nights a year · CMIP6

YearBangladeshTürkiye
2014239.919.8
201323918.5
2012240.418.7
2011240.518.5
201024118.7
2009238.617.6
2008236.718.2
2007236.916.6
2006238.917.7
2005238.217.2
2004238.717.1
2003236.517.6
2002237.616.8
2001237.217.3
2000239.316.4
1999236.615.9
1998235.514.7
1997235.515
1996235.514.8
1995234.814.1
1994233.613.5
1993232.212.1
199222811.5
1991233.413.4
1990234.614.6
1989235.713.1
1988235.112.9
1987236.412.5
1986233.813.3
1985233.612.4
198423611.9
198323010.7
1982232.312.4
1981235.912.2
1980233.412.4
1979234.512
1978231.811.5
1977232.311.2
197623212
1975231.311.5
1974232.911.8
1973232.111.6
1972234.311.5
1971233.112.1
1970232.511.6
1969231.112
1968229.712
1967231.611.3
1966232.611.5
1965232.510.8
1964229.311.2
1963233.812
1962231.911.8
1961236.212
1960232.512.9
1959234.712.6
1958233.513
1957232.512.2
1956234.312.7
1955230.211.9
1954232.911.9
195323412.9
1952230.312.1
1951233.512.5
1950233.912.3
CMIP6 model median (SSP2-4.5, middle of the road) · 2015–2100
YearBangladeshTürkiye
2015241.2227–252.422.216.2–29.8
2016237.9226.6–249.321.114.7–29.6
2017239.8227.5–249.421.914.3–29.4
2018242.6227–25522.916–29.9
2019241.2228.5–251.921.916.4–29.5
2020242.6229.3–254.423.617–31.6
2021244.3230.4–254.42317.1–30.9
2022243.8231.5–254.223.617.3–31.6
2023243.5229.2–254.524.717.8–33.4
2024245.7231.4–257.125.418.6–34.1
2025242.5229.4–253.92518.1–33
2026243.9227.9–258.125.618.3–34.1
2027247.1232.8–259.324.816.9–34.8
2028244.2232.2–254.425.218.7–32
2029247.3232.7–261.526.219.8–34.4
2030243.9232.3–255.225.919.3–34.5
2031245.1232.9–262.326.419.7–35.1
2032247.6236.6–257.827.219.9–35.6
2033245.3231.7–256.628.721.4–36.9
2034246.3233.2–260.427.620.8–34.7
2035246.4234.1–260.928.721.7–37
2036246.2228.6–260.428.720.9–38.1
2037247.7235.4–260.229.721.3–38.6
2038249232.1–26129.922–41.6
2039250234.4–262.730.923.6–40.3
2040248.9235.5–260.530.923.2–40.9
2041248.3235.6–261.930.122.2–40.5
2042252.5237.8–265.329.920.1–40.7
2043250.8234.7–263.43123.6–40.4
2044251.8239.1–265.230.823.1–43.5
2045250.3239.1–265.632.424–43.1
2046252.7238.9–267.832.923.5–42.8
2047253.9239.4–267.132.324.1–44.9
2048254.4242.7–270.534.123–45.9
2049253.7241.5–266.431.622.1–43.2
2050254.6242.5–269.233.423.8–44.2
2051254.7242.7–270.134.125–43.1
2052252.3240.2–264.633.325.3–42.6
2053256.9244–272.435.326.6–44.2
2054255.9243.5–269.434.624–44.1
2055255.2243.8–273.635.425.9–47.7
2056259.1243.6–274.734.424.1–45.6
2057257.5243.8–273.736.326–47.4
2058257.4245.6–274.236.425.8–51.8
2059258.3246.4–271.837.124.8–48.1
2060259.2246.1–272.635.826.7–48.3
2061257.7244.8–270.138.826.3–49.7
2062260.4246.4–274.237.725.3–49.5
2063259.9241.3–272.738.426.6–51.7
2064260.2245.9–273.138.928.4–49.9
2065259.8244.1–274.739.628.6–49.4
2066260246.6–274.636.325–49.1
2067261.4246.4–272.338.327.7–50.7
2068260.1245.8–274.839.928–50.7
2069263.3249.8–276.639.729.2–52.2
2070259.6245.8–272.639.527.6–51.7
2071262.2248.7–283.439.829.1–51.7
2072259.8248.6–27641.928.9–54.2
2073264.1246.6–277.240.927.2–54.6
2074262.6248.3–276.639.428–51.9
2075263.6248.5–277.739.828.4–53.9
2076261.6251–276.941.529.1–53.7
2077264251.1–279.841.329–53.3
2078265251.6–281.642.231.3–52.9
2079263.8248.5–277.24430.8–56.6
2080266.8251.9–280.343.432.5–54.9
2081266.5250.7–279.942.131.7–54.6
2082264249.6–277.343.630.9–56
2083266.6251.8–282.84629.6–57.6
2084264.8253.1–284.245.131–56.8
2085265.4251.2–283.443.330.3–54.5
2086268.2252.8–285.343.830.2–56.4
2087265.7251.3–280.844.229.2–59
2088267.2252.6–284.444.932.7–58.1
2089267.1252.5–286.744.630.7–59.3
2090267.9254.9–283.446.132.8–59.2
2091267.7254.5–282.245.332.2–61.2
2092268.8253.6–281.745.631.1–59.2
2093268.1251.7–28747.631.5–61.8
2094267.4251.3–287.447.533.7–61.2
2095270.8253.7–284.946.232.3–60.2
2096267.9250.5–285.845.731.1–60.7
2097269.3254.9–287.348.432.1–61
2098268.8252.4–284.946.431.2–59.5
2099269253–282.345.430.8–58
2100269.8254.8–284.945.530.4–60.1

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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