Pakistan vs Syria

Pakistan vs Syria: Tropical nights

Tropical nights in 2014: 149.9 nights in Pakistan and 89.6 nights in Syria, in the CMIP6 climate models’ median run. In 2014 the value was higher in Pakistan, by 60.3 nights, or 1.67 times that in Syria.

nights a year · CMIP6

Pakistan vs Syria: Tropical nightsTropical nights in 2014: 149.9 nights in Pakistan and 89.6 nights in Syria, in the CMIP6 climate models’ median run. In 2014 the value was higher in Pakistan, by 60.3 nights, or 1.67 times that in Syria.
PakistanSyriaCMIP6 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 Syria at 133.8 nights in 2100.

Every year

nights a year · CMIP6

YearPakistanSyria
2014149.989.6
2013149.190.8
2012149.991.2
201114989.6
2010148.488.1
2009147.186.2
2008147.487.9
2007147.385
2006146.787.3
2005146.585.3
2004147.586.7
200314686.7
2002148.384.3
2001145.485.9
2000144.783.2
1999146.982.1
1998144.880.5
1997143.280.5
1996141.278.1
1995141.875.8
1994142.176.7
1993140.471.5
1992139.971.3
1991142.575.5
1990142.679.3
1989142.575.7
1988140.376.2
198714073
198614076
198514072.6
1984138.969.6
1983136.367.9
1982138.572
1981141.572
1980139.575
1979139.167.7
197813970.2
1977138.670.1
1976139.273.4
1975136.569
1974138.770
1973137.870.7
1972137.470.1
197113670.7
1970137.770.9
1969135.971.3
1968136.668.7
1967135.268.8
1966136.968.8
1965138.364.3
1964135.166.4
1963135.769.1
196213771.5
1961138.670.8
196013971.7
1959138.672.7
1958137.772.8
195714072.6
1956137.872.2
1955137.570.3
1954137.868.9
1953136.972.2
1952138.170.8
1951136.772.4
1950139.168.9
CMIP6 model median (SSP2-4.5, middle of the road) · 2015–2100
YearPakistanSyria
2015152.4138.7–163.396.981.9–110.9
2016150.6139.4–161.793.581.2–108.2
2017151.3138.8–1629782.1–109.5
2018152.6139.8–161.897.785.9–111
2019151.8138.1–163.398.983.7–111.2
2020153.1140.3–164.598.683.7–112.6
2021154.1140.6–165.710087.1–110.7
2022153.1140.2–165.7101.684.9–113.2
2023155.5141.6–165.9101.988.3–115.3
2024156.8143.3–168.7102.790.7–113.8
2025155.7141.5–168.6102.988.6–114.2
2026155.3142.2–167.9103.389.5–116.1
2027156.8140.9–168.9103.886.7–116.5
2028156.5143.9–167.6103.589.2–116.4
2029155.9141.5–167.7105.694–116.3
2030156.3143.3–167.6105.991.3–119.2
2031157.3144.4–168.1105.190.7–116
2032157.4142.5–169.6107.893.2–119.4
2033157.7143.6–169.4108.796.6–119.1
2034159.5145.6–173.4107.593.9–119
2035159.3146.7–170.3108.596.3–118.3
2036159.1147.5–171.4105.793.2–118.7
2037162.2147.5–175.3109.294.8–121.9
2038161.4148.6–171.7111.897.8–125.5
2039160148.2–170.4112.398.9–128
2040160.9147.9–171.3111.898.5–127.5
2041160.8146.7–171.7110.799.1–124.5
2042162.3149.9–175.6112.996.4–126.5
2043161.1150.3–173.8113.199.7–127.3
2044163150.9–173.9112.599.8–128.8
2045163149.5–174.8118.4105.5–131.3
2046163.5149.8–177.2117.2104.7–129
2047165.2151.8–178.5114.9100.6–129.1
2048166.4154.1–179.4114.3101.2–130.8
2049165150.8–178.211499.3–128.9
2050166.9153.5–179.3116.8104.6–132.7
2051165.6150.8–180117.7105.5–129
2052166.7155.4–179.5116.2103.9–128.2
2053166.3154.2–178.8119.1105.2–131.3
2054167.5153.9–181119.7102.7–135.5
2055167.8155.8–182.5120.8104.3–134.2
2056166.3153.6–179.7119.799.9–132.9
2057168.3153.9–182.3120.6102.3–137.1
2058167.5157–179.9120.1103.1–136.5
2059170.9155.9–183.8123.5107.4–137.2
2060170.3156.5–181.8119.6106.4–140.3
2061168.8156.8–179.6122.8103.9–136.1
2062171.6153.5–186.5121.8104.3–136.5
2063171.3159.5–183.8122.1106.2–136.7
2064170.2155.3–184.5124.3108.4–136.6
2065169.7156.4–185123.7108.9–138.7
2066172.2157–186.7122.1104.4–136.2
2067173.2160.2–186.3126111.1–142.7
2068172.6157.7–186.3126.8112.1–140.3
2069171.8157.3–185.6123107.9–137.3
2070171.7158.9–186.7126.5110.3–139.4
2071173.4158.7–186.8126109.5–142.8
2072174161.5–186.9128111.8–141.9
2073175.6157.2–187.9127.6108–143.7
2074173.5160–186.3124.2107–139.7
2075173161.5–187.8124.7109.2–140.6
2076172.9158.8–186.7126.6109.8–140.8
2077175161.3–187.8127.5109–143.3
2078174.4160.9–189.3127.2113.3–141.8
2079172.5158.3–187.7132.9116–146.9
2080175.1160.7–189.2129111.8–144.3
2081174.9159.2–190.2127.6112.5–145.6
2082175.4161.8–190.6129.1111.3–143.8
2083176.5161.8–190.8133.2110.3–144.1
2084176.1160.9–190.5132.2111–145.1
2085176.3161.9–193.6130.9112.7–143.3
2086173.9160–189.1130.9112.6–146.5
2087176161–193.1129.6109.8–148.7
2088175.4160.5–195.2132.5116.9–146.5
2089177.2164.2–191.7130.4115.3–148.4
2090177.7163.9–192.2128.9115–145.8
2091177.8162.8–192.2133.4116–150.3
2092178.7162.9–192.9131.6115.3–149
2093177164–191135119.8–151
2094176.8164.1–190.7134118–148.5
2095178.6163.8–193.1134.3117.3–148.9
2096177.8163.7–192.5131.5113.7–148.7
2097177.3160.4–192.5136.2112.7–154.1
2098178.7162.6–195.7131.5115.2–152.2
2099176159.9–193.2132.8116–145.1
2100177.6162.2–193133.8113.2–152.2

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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