Iran vs Pakistan

Iran vs Pakistan: Tropical nights

Tropical nights in 2014: 90.8 nights in Iran and 149.9 nights in Pakistan, in the CMIP6 climate models’ median run. In 2014 the value was higher in Pakistan, by 59.1 nights, or 1.65 times that in Iran.

nights a year · CMIP6

Iran vs Pakistan: Tropical nightsTropical nights in 2014: 90.8 nights in Iran and 149.9 nights in Pakistan, in the CMIP6 climate models’ median run. In 2014 the value was higher in Pakistan, by 59.1 nights, or 1.65 times that in Iran.
IranPakistanCMIP6 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 Iran at 124.4 nights and Pakistan at 177.6 nights in 2100.

Every year

nights a year · CMIP6

YearIranPakistan
201490.8149.9
201390.6149.1
201290.3149.9
201189.8149
201087148.4
200987.2147.1
200888147.4
200787.9147.3
200688.2146.7
200587.1146.5
200486.4147.5
200385.9146
200287.7148.3
200186.6145.4
200086.5144.7
199985.1146.9
199883.8144.8
199784.3143.2
199682.4141.2
199581.6141.8
199480.6142.1
199377.7140.4
199277139.9
199181.9142.5
199083.1142.6
198981.7142.5
198880.4140.3
198779.8140
198680.1140
198579.7140
198478.8138.9
198375.5136.3
198277.9138.5
198180.2141.5
198079.4139.5
197980.2139.1
197878.1139
197777.8138.6
197677.2139.2
197576.6136.5
197476.4138.7
197376.9137.8
197277.4137.4
197175136
197075.3137.7
196975.6135.9
196876.3136.6
196776.1135.2
196675.5136.9
196575.3138.3
196475.3135.1
196376.4135.7
196277.5137
196177.7138.6
196077.6139
195976.8138.6
195878.4137.7
195778.2140
195676.2137.8
195576.5137.5
195477.6137.8
195377.3136.9
195278.2138.1
195176.6136.7
195077.7139.1
CMIP6 model median (SSP2-4.5, middle of the road) · 2015–2100
YearIranPakistan
201594.482.7–106152.4138.7–163.3
201694.183.1–105.3150.6139.4–161.7
201793.981.7–104151.3138.8–162
201894.183–106.3152.6139.8–161.8
201995.183.4–105.8151.8138.1–163.3
202095.985.5–107.5153.1140.3–164.5
202197.286–107.7154.1140.6–165.7
202296.285.2–108.3153.1140.2–165.7
202398.485.7–110.6155.5141.6–165.9
20249885.1–110.5156.8143.3–168.7
202598.787.2–110.5155.7141.5–168.6
202699.186.7–111.7155.3142.2–167.9
2027100.288.7–112.1156.8140.9–168.9
202899.686.2–111.6156.5143.9–167.6
202999.386.7–109.9155.9141.5–167.7
2030100.889.8–111.6156.3143.3–167.6
2031102.990.1–114.8157.3144.4–168.1
2032101.289–113.9157.4142.5–169.6
2033102.889.9–115.1157.7143.6–169.4
2034104.690.7–117.3159.5145.6–173.4
203510592.1–116.2159.3146.7–170.3
2036103.591.4–115.2159.1147.5–171.4
203710693.1–119.4162.2147.5–175.3
2038105.993.2–116.5161.4148.6–171.7
2039104.392.9–116.6160148.2–170.4
2040105.592.3–118.4160.9147.9–171.3
2041104.892.5–116.4160.8146.7–171.7
2042107.793.4–120.7162.3149.9–175.6
204310692.9–118.8161.1150.3–173.8
2044106.893.6–121.2163150.9–173.9
2045108.795.1–121.8163149.5–174.8
2046109.296.1–121.9163.5149.8–177.2
2047110.596.9–123.3165.2151.8–178.5
2048111.498.6–124166.4154.1–179.4
204911095.7–123.4165150.8–178.2
2050112.199.6–125.1166.9153.5–179.3
2051111.797.6–124.1165.6150.8–180
205211298.3–126166.7155.4–179.5
2053111.597.5–124166.3154.2–178.8
2054111.899–126.3167.5153.9–181
2055114.6101.4–128167.8155.8–182.5
2056113.1100.2–126.2166.3153.6–179.7
2057113.899.9–128.9168.3153.9–182.3
2058114.2100–127.7167.5157–179.9
2059113.498.9–126.3170.9155.9–183.8
2060116.3100.3–129.5170.3156.5–181.8
2061115100.5–127.6168.8156.8–179.6
2062115.799.7–131.1171.6153.5–186.5
2063117103.7–130.3171.3159.5–183.8
2064115.1101.7–130.3170.2155.3–184.5
2065117.3102.8–132.3169.7156.4–185
2066117.7103.7–132.1172.2157–186.7
2067119103–133.5173.2160.2–186.3
2068118.1104.3–131.4172.6157.7–186.3
2069118.4103.8–133.2171.8157.3–185.6
2070118.2104.9–132.9171.7158.9–186.7
2071119.3105.1–135.3173.4158.7–186.8
2072119103.2–135.1174161.5–186.9
2073120.6104.9–137.7175.6157.2–187.9
2074119.1104.4–134.5173.5160–186.3
2075119.7105.9–135.5173161.5–187.8
2076118.9102.6–137172.9158.8–186.7
2077120.3104.3–135.3175161.3–187.8
2078119.2105.3–136174.4160.9–189.3
2079120.1103.2–135.6172.5158.3–187.7
2080120.4106.1–139.5175.1160.7–189.2
2081120.5104.3–137.1174.9159.2–190.2
2082122.5107.5–137.7175.4161.8–190.6
2083122.6105.8–140.4176.5161.8–190.8
2084123.7107.6–139.1176.1160.9–190.5
2085121.3106.6–140.6176.3161.9–193.6
2086122.1107.9–139.8173.9160–189.1
2087123.7108–140.7176161–193.1
2088124106.5–142.2175.4160.5–195.2
2089124.1109–139.7177.2164.2–191.7
2090122.5105.4–141.9177.7163.9–192.2
2091124.3108–140.5177.8162.8–192.2
2092125.5110.1–140178.7162.9–192.9
2093124.5109–139.3177164–191
2094125108.7–142.9176.8164.1–190.7
2095125.3108.5–141178.6163.8–193.1
2096125.8109–142.4177.8163.7–192.5
2097123.6106.9–141.8177.3160.4–192.5
2098125.8109.4–143.8178.7162.6–195.7
2099123.1107.8–140.1176159.9–193.2
2100124.4107.9–143.7177.6162.2–193

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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