Pakistan vs Tunisia

Pakistan vs Tunisia: Tropical nights

Tropical nights in 2014: 149.9 nights in Pakistan and 110.5 nights in Tunisia, in the CMIP6 climate models’ median run. In 2014 the value was higher in Pakistan, by 39.4 nights, or 1.36 times that in Tunisia.

nights a year · CMIP6

Pakistan vs Tunisia: Tropical nightsTropical nights in 2014: 149.9 nights in Pakistan and 110.5 nights in Tunisia, in the CMIP6 climate models’ median run. In 2014 the value was higher in Pakistan, by 39.4 nights, or 1.36 times that in Tunisia.
PakistanTunisiaCMIP6 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 Tunisia at 143.8 nights in 2100.

Every year

nights a year · CMIP6

YearPakistanTunisia
2014149.9110.5
2013149.1110.7
2012149.9110
2011149108.9
2010148.4109.6
2009147.1106.1
2008147.4108.6
2007147.3106.6
2006146.7107.6
2005146.5105.6
2004147.5105
2003146105.5
2002148.3103.8
2001145.4101.8
2000144.7104.5
1999146.9100.1
1998144.8100.1
1997143.2100.1
1996141.299.1
1995141.895.9
1994142.195
1993140.491.3
1992139.989.9
1991142.598.2
1990142.698.6
1989142.594.7
1988140.394.2
198714092.7
198614093.7
198514090.6
1984138.991.8
1983136.390
1982138.593.9
1981141.593.3
1980139.592.5
1979139.193.8
197813990.2
1977138.688.3
1976139.290.5
1975136.588.6
1974138.788.3
1973137.890.5
1972137.491
197113688.8
1970137.787.3
1969135.990.1
1968136.688.9
1967135.287.6
1966136.987.5
1965138.384.5
1964135.185.7
1963135.788
196213792.7
1961138.688.3
196013990.7
1959138.692.2
1958137.791.8
195714092.3
1956137.890
1955137.592.7
1954137.889.9
1953136.989.4
1952138.191.8
1951136.791.6
1950139.189.6
CMIP6 model median (SSP2-4.5, middle of the road) · 2015–2100
YearPakistanTunisia
2015152.4138.7–163.3110.796.9–126.1
2016150.6139.4–161.7111.596.6–130.1
2017151.3138.8–162116.5102.4–131.8
2018152.6139.8–161.8115.999.4–131.9
2019151.8138.1–163.3116.598.5–129.5
2020153.1140.3–164.5113.899.9–130.1
2021154.1140.6–165.7117.4104.1–129.3
2022153.1140.2–165.7119.7103–137.3
2023155.5141.6–165.9118.7102.2–131.9
2024156.8143.3–168.7121107.5–132.6
2025155.7141.5–168.6120.2100–134
2026155.3142.2–167.9122.1110.1–134.5
2027156.8140.9–168.9119103.6–134.1
2028156.5143.9–167.6121.1104.5–135.4
2029155.9141.5–167.7121.5110.2–135.8
2030156.3143.3–167.6121.6104–133.4
2031157.3144.4–168.1121.8105.7–134.9
2032157.4142.5–169.6125.2111.6–138.9
2033157.7143.6–169.4124.7107.6–137.5
2034159.5145.6–173.4124.6107–141.7
2035159.3146.7–170.3126.3115.5–142.6
2036159.1147.5–171.4126112.5–141.9
2037162.2147.5–175.3126.5110–140
2038161.4148.6–171.7126.2111.1–139.1
2039160148.2–170.4127112.5–142.3
2040160.9147.9–171.3127.5112.8–144.3
2041160.8146.7–171.7125.5109.1–140.4
2042162.3149.9–175.6129.9114.8–145.8
2043161.1150.3–173.8129.1111.6–142
2044163150.9–173.9127.1112.8–141.8
2045163149.5–174.8128.5113.8–140.8
2046163.5149.8–177.2128.3109.9–144
2047165.2151.8–178.5131.2113.6–147.5
2048166.4154.1–179.4129.9114.9–142.3
2049165150.8–178.2130115.4–143.5
2050166.9153.5–179.3132.3117.6–147.6
2051165.6150.8–180130.1112.6–148.6
2052166.7155.4–179.5132.7116.8–146.7
2053166.3154.2–178.8132.5119.5–149.8
2054167.5153.9–181135.2118.4–148.8
2055167.8155.8–182.5135.1119.2–151.6
2056166.3153.6–179.7134.8119.6–149.5
2057168.3153.9–182.3131.9118.5–148.6
2058167.5157–179.9137.8122.5–156.5
2059170.9155.9–183.8134.9117.1–152.5
2060170.3156.5–181.8136.2120.5–150.1
2061168.8156.8–179.6139119.4–154
2062171.6153.5–186.5134.9119.1–155.9
2063171.3159.5–183.8138.6119.2–152.5
2064170.2155.3–184.5134.9121.1–152.7
2065169.7156.4–185137.1123.3–150.5
2066172.2157–186.7136.6119.2–155.1
2067173.2160.2–186.3138.9124.4–154.3
2068172.6157.7–186.3141.7124.8–159.2
2069171.8157.3–185.6140.3123.5–154.1
2070171.7158.9–186.7137123.6–155.3
2071173.4158.7–186.8140.5123.1–156.1
2072174161.5–186.9142.6128–154.3
2073175.6157.2–187.9139.3124.7–154.6
2074173.5160–186.3140.1123–156.5
2075173161.5–187.8139.6124.5–157.1
2076172.9158.8–186.7143.4125.2–158.8
2077175161.3–187.8141.3119.1–158.3
2078174.4160.9–189.3143127.2–155.9
2079172.5158.3–187.7144.7123.9–158.5
2080175.1160.7–189.2143.6123.6–158.7
2081174.9159.2–190.2139.9126.3–158.8
2082175.4161.8–190.6145.5125.9–158.1
2083176.5161.8–190.8143.4124.3–158.7
2084176.1160.9–190.5144.8129.6–159.7
2085176.3161.9–193.6145.3124.7–159.5
2086173.9160–189.1144.7129.5–162.7
2087176161–193.1144125.7–159.2
2088175.4160.5–195.2147.3127.2–165.4
2089177.2164.2–191.7142.7125.3–164.8
2090177.7163.9–192.2145.2126.3–165.1
2091177.8162.8–192.2144.9127.4–164.4
2092178.7162.9–192.9148.2126.3–161.1
2093177164–191146.9127.2–165.6
2094176.8164.1–190.7145.7126.8–160.4
2095178.6163.8–193.1145.5125.4–161.5
2096177.8163.7–192.5146.3130–169.4
2097177.3160.4–192.5144.6125.7–160.6
2098178.7162.6–195.7147129.6–161.9
2099176159.9–193.2145.8130.3–163.7
2100177.6162.2–193143.8124.8–163.8

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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