Bangladesh vs Nepal

Bangladesh vs Nepal: Tropical nights

Tropical nights in 2014: 239.9 nights in Bangladesh and 65.9 nights in Nepal, in the CMIP6 climate models’ median run. In 2014 the value was higher in Bangladesh, by 174.1 nights, or 3.64 times that in Nepal.

nights a year · CMIP6

Bangladesh vs Nepal: Tropical nightsTropical nights in 2014: 239.9 nights in Bangladesh and 65.9 nights in Nepal, in the CMIP6 climate models’ median run. In 2014 the value was higher in Bangladesh, by 174.1 nights, or 3.64 times that in Nepal.
BangladeshNepalCMIP6 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 Nepal at 89.9 nights in 2100.

Every year

nights a year · CMIP6

YearBangladeshNepal
2014239.965.9
201323966.5
2012240.466
2011240.564.7
201024165.7
2009238.663.8
2008236.764.1
2007236.964.3
2006238.964.4
2005238.264.4
2004238.764.2
2003236.564.2
2002237.665
2001237.263.6
2000239.363.4
1999236.663.3
1998235.561.8
1997235.562.3
1996235.560.9
1995234.860.7
1994233.659.8
1993232.259.6
199222857.3
1991233.462
1990234.662.1
1989235.761
1988235.159.8
1987236.460.2
1986233.859.9
1985233.659.2
198423659.4
198323057.4
1982232.360.2
1981235.959.9
1980233.460.3
1979234.559.2
1978231.858.8
1977232.359.3
197623258.8
1975231.358.3
1974232.959.8
1973232.159.2
1972234.357.9
1971233.158.9
1970232.558.1
1969231.157.9
1968229.757.6
1967231.657.3
1966232.658.8
1965232.557.3
1964229.357.3
1963233.858.1
1962231.958.8
1961236.259.1
1960232.559.8
1959234.760.2
1958233.559.1
1957232.559.3
1956234.359.1
1955230.258.2
1954232.959.3
195323458.7
1952230.359
1951233.559.3
1950233.959.1
CMIP6 model median (SSP2-4.5, middle of the road) · 2015–2100
YearBangladeshNepal
2015241.2227–252.466.560–73.8
2016237.9226.6–249.366.358.3–73.6
2017239.8227.5–249.466.758.6–73.6
2018242.6227–25565.658.9–73.3
2019241.2228.5–251.966.358–74.3
2020242.6229.3–254.468.360.6–75.9
2021244.3230.4–254.467.959.6–75.8
2022243.8231.5–254.268.661–75.3
2023243.5229.2–254.569.361.6–76.2
2024245.7231.4–257.17063–78.7
2025242.5229.4–253.968.660.3–78.1
2026243.9227.9–258.169.859.8–78.5
2027247.1232.8–259.369.761–78.1
2028244.2232.2–254.470.562.6–78
2029247.3232.7–261.570.461.7–80.6
2030243.9232.3–255.27061.3–78.5
2031245.1232.9–262.371.262.8–78.6
2032247.6236.6–257.872.163.9–81.1
2033245.3231.7–256.671.264.8–78
2034246.3233.2–260.471.663.8–80.2
2035246.4234.1–260.972.664.3–83
2036246.2228.6–260.471.965–80.3
2037247.7235.4–260.274.165.9–84.2
2038249232.1–26174.167–83.3
2039250234.4–262.773.565.2–82.5
2040248.9235.5–260.573.865.9–83.3
2041248.3235.6–261.975.368.2–83.4
2042252.5237.8–265.375.667.7–84.9
2043250.8234.7–263.475.566.3–85
2044251.8239.1–265.276.467.5–85.4
2045250.3239.1–265.676.567.9–85.2
2046252.7238.9–267.876.966.8–86
2047253.9239.4–267.177.766.9–86.2
2048254.4242.7–270.578.367.8–88
2049253.7241.5–266.478.568.6–87.8
2050254.6242.5–269.279.367.8–90
2051254.7242.7–270.17970.1–88.5
2052252.3240.2–264.679.570.4–88.6
2053256.9244–272.479.170.1–88.4
2054255.9243.5–269.479.168.9–89
2055255.2243.8–273.680.571.8–89.3
2056259.1243.6–274.779.771.5–89.6
2057257.5243.8–273.780.671.9–89.8
2058257.4245.6–274.280.771–91.5
2059258.3246.4–271.880.770.6–93.7
2060259.2246.1–272.682.472.9–91.7
2061257.7244.8–270.180.871.4–90.4
2062260.4246.4–274.282.172–93.2
2063259.9241.3–272.782.271.5–92.1
2064260.2245.9–273.182.872.6–92
2065259.8244.1–274.782.672.2–93.3
2066260246.6–274.683.772.4–94.2
2067261.4246.4–272.382.771.9–93.3
2068260.1245.8–274.884.772.4–95.1
2069263.3249.8–276.68573–94.3
2070259.6245.8–272.683.273.2–92.7
2071262.2248.7–283.483.872.9–93.9
2072259.8248.6–27685.274.6–95.4
2073264.1246.6–277.285.875.3–96.4
2074262.6248.3–276.684.474.5–95.8
2075263.6248.5–277.78574.2–95.4
2076261.6251–276.985.974.8–95
2077264251.1–279.886.775.4–97.4
2078265251.6–281.685.775.7–96.6
2079263.8248.5–277.285.973.7–96
2080266.8251.9–280.387.576.4–97.7
2081266.5250.7–279.987.577.2–98.1
2082264249.6–277.386.877.3–98.8
2083266.6251.8–282.88876.6–99.5
2084264.8253.1–284.288.377.6–99.6
2085265.4251.2–283.487.876.3–100.8
2086268.2252.8–285.387.975.2–100.2
2087265.7251.3–280.887.476.2–98.9
2088267.2252.6–284.487.375.6–99.2
2089267.1252.5–286.78776.2–100.1
2090267.9254.9–283.487.977.5–99
2091267.7254.5–282.288.877.6–101.7
2092268.8253.6–281.788.477.7–101.1
2093268.1251.7–28788.776.8–99.9
2094267.4251.3–287.488.677.6–99.5
2095270.8253.7–284.989.478.8–99.7
2096267.9250.5–285.889.178.6–101.7
2097269.3254.9–287.387.976.6–99.4
2098268.8252.4–284.989.678.1–100.1
2099269253–282.389.778.5–100.7
2100269.8254.8–284.989.980–100

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

Bangladesh vs Nepal

Health

People

Economy

Education

Politics

Conflict

Military

Climate

Technology and environment