Pakistan vs Tanzania

Pakistan vs Tanzania: Tropical nights

Tropical nights in 2014: 149.9 nights in Pakistan and 106.3 nights in Tanzania, in the CMIP6 climate models’ median run. In 2014 the value was higher in Pakistan, by 43.6 nights, or 1.41 times that in Tanzania.

nights a year · CMIP6

Pakistan vs Tanzania: Tropical nightsTropical nights in 2014: 149.9 nights in Pakistan and 106.3 nights in Tanzania, in the CMIP6 climate models’ median run. In 2014 the value was higher in Pakistan, by 43.6 nights, or 1.41 times that in Tanzania.
PakistanTanzaniaCMIP6 model median (SSP2-4.5, middle of the road)models’ 10th–90th percentile

What the numbers say

  • Pakistan has been higher in every year both have figures for since 1950.
  • The CMIP6 model median (SSP2-4.5, middle of the road) puts Pakistan at 177.6 nights and Tanzania at 208 nights in 2100.

Every year

nights a year · CMIP6

YearPakistanTanzania
2014149.9106.3
2013149.1105.5
2012149.9108.4
2011149109
2010148.4103.9
2009147.1101.1
2008147.4100.3
2007147.399.5
2006146.7100.4
2005146.598.7
2004147.5100.4
200314699.6
2002148.398.4
2001145.4101.5
2000144.794.9
1999146.994.2
1998144.896.8
1997143.293.5
1996141.289.6
1995141.888.5
1994142.185.4
1993140.481.2
1992139.980.1
1991142.589.6
1990142.691.1
1989142.589.1
1988140.387.6
198714088.5
198614089.4
198514083
1984138.983.1
1983136.382.1
1982138.584.8
1981141.588
1980139.585
1979139.185.5
197813982.9
1977138.684.1
1976139.281
1975136.580.8
1974138.782.2
1973137.883.6
1972137.481.4
197113680.7
1970137.781.8
1969135.979.5
1968136.679.1
1967135.277.5
1966136.978.1
1965138.378
1964135.173.8
1963135.773.1
196213781.1
1961138.682.6
196013983.9
1959138.681.4
1958137.782.1
195714080.9
1956137.880
1955137.581.6
1954137.881.4
1953136.980.6
1952138.181.4
1951136.782.1
1950139.180.7
CMIP6 model median (SSP2-4.5, middle of the road) · 2015–2100
YearPakistanTanzania
2015152.4138.7–163.3114.895.2–137.4
2016150.6139.4–161.7108.391.6–125.6
2017151.3138.8–162114.893.6–137.8
2018152.6139.8–161.8116.497.4–137.1
2019151.8138.1–163.3113.495.2–133.2
2020153.1140.3–164.5119.396.2–143.5
2021154.1140.6–165.7119.5100.9–147.1
2022153.1140.2–165.7124.4105.3–147.4
2023155.5141.6–165.9122.1102.1–147.8
2024156.8143.3–168.7126.2100–150.1
2025155.7141.5–168.6124.6100.9–154
2026155.3142.2–167.9126.999.1–155.6
2027156.8140.9–168.9128102.9–147.7
2028156.5143.9–167.6129.9106.4–152.1
2029155.9141.5–167.7134.1111–156.5
2030156.3143.3–167.6129.4106.4–151.1
2031157.3144.4–168.1136.4115.9–158.4
2032157.4142.5–169.6135.1114–166.8
2033157.7143.6–169.4135.2110.9–161.9
2034159.5145.6–173.4134.7112.5–166.3
2035159.3146.7–170.3140.2114.6–165
2036159.1147.5–171.4137.8113.3–165.5
2037162.2147.5–175.3142.9121.5–168.9
2038161.4148.6–171.7147.9124.3–169.3
2039160148.2–170.4144.3117.6–171.3
2040160.9147.9–171.3140.5117–178
2041160.8146.7–171.7149.1121.7–175.6
2042162.3149.9–175.6146.3120.5–185.3
2043161.1150.3–173.8144.6117–173
2044163150.9–173.9147.7121.2–175.8
2045163149.5–174.8150.1125.2–183.4
2046163.5149.8–177.2161.5128.6–192.2
2047165.2151.8–178.5160.2129.7–194.4
2048166.4154.1–179.4158.8132.7–193.1
2049165150.8–178.2157.6131.1–196
2050166.9153.5–179.3159.7127.8–192
2051165.6150.8–180163.2135.5–197.8
2052166.7155.4–179.5161.9136.2–199.1
2053166.3154.2–178.8160.6127.5–197.7
2054167.5153.9–181163.2131.5–200.2
2055167.8155.8–182.5167.4137–208.3
2056166.3153.6–179.7166.4130.8–209.7
2057168.3153.9–182.3168134.6–208.3
2058167.5157–179.9168.7134.9–210.3
2059170.9155.9–183.8174.7144.9–219.5
2060170.3156.5–181.8181.7138.1–219.8
2061168.8156.8–179.6171.8135.5–218.1
2062171.6153.5–186.5177.5139.9–224
2063171.3159.5–183.8181.4140.1–213.8
2064170.2155.3–184.5185.3140.1–215.2
2065169.7156.4–185183.5141.1–221.8
2066172.2157–186.7181.9146.3–219.3
2067173.2160.2–186.3178.8138.8–229.2
2068172.6157.7–186.3186144.2–228.4
2069171.8157.3–185.6183.7141.7–225.3
2070171.7158.9–186.7180.7140.4–228.2
2071173.4158.7–186.8184.9146.9–235.2
2072174161.5–186.9191.3155–226.1
2073175.6157.2–187.9191.6152.4–235.6
2074173.5160–186.3183.3149.7–231.7
2075173161.5–187.8183.8144.8–233
2076172.9158.8–186.7191.1150.9–233.2
2077175161.3–187.8191.3152.6–238.2
2078174.4160.9–189.3189156.6–233.8
2079172.5158.3–187.7190.5147–237.3
2080175.1160.7–189.2197.3150.7–241.2
2081174.9159.2–190.2195.6153.4–237.4
2082175.4161.8–190.6194.5157.3–237.3
2083176.5161.8–190.8193.2151–249.8
2084176.1160.9–190.5201.1152.2–246.7
2085176.3161.9–193.6204.2162.3–252.3
2086173.9160–189.1195156–248.7
2087176161–193.1196.7160.8–244.5
2088175.4160.5–195.2208.4154.7–246.9
2089177.2164.2–191.7201.3158.6–248.2
2090177.7163.9–192.2197.9160.7–253.6
2091177.8162.8–192.2210161.6–256
2092178.7162.9–192.9205.8158.3–253.6
2093177164–191207.2161.2–251.9
2094176.8164.1–190.7207.9161.3–250.6
2095178.6163.8–193.1214.2166.4–258.5
2096177.8163.7–192.5205.3159.8–257.4
2097177.3160.4–192.5206.6158.4–256.8
2098178.7162.6–195.7209.1161–258.3
2099176159.9–193.2208.5160.1–256
2100177.6162.2–193208158.6–258.6

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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