Bangladesh vs Iran

Bangladesh vs Iran: Tropical nights

Tropical nights in 2014: 239.9 nights in Bangladesh and 90.8 nights in Iran, in the CMIP6 climate models’ median run. In 2014 the value was higher in Bangladesh, by 149.1 nights, or 2.64 times that in Iran.

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nights a year · CMIP6

Bangladesh vs Iran: Tropical nightsTropical nights in 2014: 239.9 nights in Bangladesh and 90.8 nights in Iran, in the CMIP6 climate models’ median run. In 2014 the value was higher in Bangladesh, by 149.1 nights, or 2.64 times that in Iran.
BangladeshIranCMIP6 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 Iran at 124.4 nights in 2100.

Every year

nights a year · CMIP6

YearBangladeshIran
2014239.990.8
201323990.6
2012240.490.3
2011240.589.8
201024187
2009238.687.2
2008236.788
2007236.987.9
2006238.988.2
2005238.287.1
2004238.786.4
2003236.585.9
2002237.687.7
2001237.286.6
2000239.386.5
1999236.685.1
1998235.583.8
1997235.584.3
1996235.582.4
1995234.881.6
1994233.680.6
1993232.277.7
199222877
1991233.481.9
1990234.683.1
1989235.781.7
1988235.180.4
1987236.479.8
1986233.880.1
1985233.679.7
198423678.8
198323075.5
1982232.377.9
1981235.980.2
1980233.479.4
1979234.580.2
1978231.878.1
1977232.377.8
197623277.2
1975231.376.6
1974232.976.4
1973232.176.9
1972234.377.4
1971233.175
1970232.575.3
1969231.175.6
1968229.776.3
1967231.676.1
1966232.675.5
1965232.575.3
1964229.375.3
1963233.876.4
1962231.977.5
1961236.277.7
1960232.577.6
1959234.776.8
1958233.578.4
1957232.578.2
1956234.376.2
1955230.276.5
1954232.977.6
195323477.3
1952230.378.2
1951233.576.6
1950233.977.7
CMIP6 model median (SSP2-4.5, middle of the road) · 2015–2100
YearBangladeshIran
2015241.2227–252.494.482.7–106
2016237.9226.6–249.394.183.1–105.3
2017239.8227.5–249.493.981.7–104
2018242.6227–25594.183–106.3
2019241.2228.5–251.995.183.4–105.8
2020242.6229.3–254.495.985.5–107.5
2021244.3230.4–254.497.286–107.7
2022243.8231.5–254.296.285.2–108.3
2023243.5229.2–254.598.485.7–110.6
2024245.7231.4–257.19885.1–110.5
2025242.5229.4–253.998.787.2–110.5
2026243.9227.9–258.199.186.7–111.7
2027247.1232.8–259.3100.288.7–112.1
2028244.2232.2–254.499.686.2–111.6
2029247.3232.7–261.599.386.7–109.9
2030243.9232.3–255.2100.889.8–111.6
2031245.1232.9–262.3102.990.1–114.8
2032247.6236.6–257.8101.289–113.9
2033245.3231.7–256.6102.889.9–115.1
2034246.3233.2–260.4104.690.7–117.3
2035246.4234.1–260.910592.1–116.2
2036246.2228.6–260.4103.591.4–115.2
2037247.7235.4–260.210693.1–119.4
2038249232.1–261105.993.2–116.5
2039250234.4–262.7104.392.9–116.6
2040248.9235.5–260.5105.592.3–118.4
2041248.3235.6–261.9104.892.5–116.4
2042252.5237.8–265.3107.793.4–120.7
2043250.8234.7–263.410692.9–118.8
2044251.8239.1–265.2106.893.6–121.2
2045250.3239.1–265.6108.795.1–121.8
2046252.7238.9–267.8109.296.1–121.9
2047253.9239.4–267.1110.596.9–123.3
2048254.4242.7–270.5111.498.6–124
2049253.7241.5–266.411095.7–123.4
2050254.6242.5–269.2112.199.6–125.1
2051254.7242.7–270.1111.797.6–124.1
2052252.3240.2–264.611298.3–126
2053256.9244–272.4111.597.5–124
2054255.9243.5–269.4111.899–126.3
2055255.2243.8–273.6114.6101.4–128
2056259.1243.6–274.7113.1100.2–126.2
2057257.5243.8–273.7113.899.9–128.9
2058257.4245.6–274.2114.2100–127.7
2059258.3246.4–271.8113.498.9–126.3
2060259.2246.1–272.6116.3100.3–129.5
2061257.7244.8–270.1115100.5–127.6
2062260.4246.4–274.2115.799.7–131.1
2063259.9241.3–272.7117103.7–130.3
2064260.2245.9–273.1115.1101.7–130.3
2065259.8244.1–274.7117.3102.8–132.3
2066260246.6–274.6117.7103.7–132.1
2067261.4246.4–272.3119103–133.5
2068260.1245.8–274.8118.1104.3–131.4
2069263.3249.8–276.6118.4103.8–133.2
2070259.6245.8–272.6118.2104.9–132.9
2071262.2248.7–283.4119.3105.1–135.3
2072259.8248.6–276119103.2–135.1
2073264.1246.6–277.2120.6104.9–137.7
2074262.6248.3–276.6119.1104.4–134.5
2075263.6248.5–277.7119.7105.9–135.5
2076261.6251–276.9118.9102.6–137
2077264251.1–279.8120.3104.3–135.3
2078265251.6–281.6119.2105.3–136
2079263.8248.5–277.2120.1103.2–135.6
2080266.8251.9–280.3120.4106.1–139.5
2081266.5250.7–279.9120.5104.3–137.1
2082264249.6–277.3122.5107.5–137.7
2083266.6251.8–282.8122.6105.8–140.4
2084264.8253.1–284.2123.7107.6–139.1
2085265.4251.2–283.4121.3106.6–140.6
2086268.2252.8–285.3122.1107.9–139.8
2087265.7251.3–280.8123.7108–140.7
2088267.2252.6–284.4124106.5–142.2
2089267.1252.5–286.7124.1109–139.7
2090267.9254.9–283.4122.5105.4–141.9
2091267.7254.5–282.2124.3108–140.5
2092268.8253.6–281.7125.5110.1–140
2093268.1251.7–287124.5109–139.3
2094267.4251.3–287.4125108.7–142.9
2095270.8253.7–284.9125.3108.5–141
2096267.9250.5–285.8125.8109–142.4
2097269.3254.9–287.3123.6106.9–141.8
2098268.8252.4–284.9125.8109.4–143.8
2099269253–282.3123.1107.8–140.1
2100269.8254.8–284.9124.4107.9–143.7

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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