Mexico vs Pakistan

Mexico vs Pakistan: Tropical nights

Tropical nights in 2014: 103.7 nights in Mexico and 149.9 nights in Pakistan, in the CMIP6 climate models’ median run. In 2014 the value was higher in Pakistan, by 46.2 nights, or 1.45 times that in Mexico.

nights a year · CMIP6

Mexico vs Pakistan: Tropical nightsTropical nights in 2014: 103.7 nights in Mexico and 149.9 nights in Pakistan, in the CMIP6 climate models’ median run. In 2014 the value was higher in Pakistan, by 46.2 nights, or 1.45 times that in Mexico.
MexicoPakistanCMIP6 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 Mexico at 142.8 nights and Pakistan at 177.6 nights in 2100.

Every year

nights a year · CMIP6

YearMexicoPakistan
2014103.7149.9
2013103.9149.1
2012102.8149.9
2011102.9149
2010101.5148.4
2009101.8147.1
2008100.1147.4
200799.1147.3
200699.8146.7
200599.5146.5
200499.4147.5
200398.3146
200299.1148.3
200198.7145.4
200097.9144.7
199996.6146.9
199895.8144.8
199795.9143.2
199693.8141.2
199592.7141.8
199490.7142.1
199389140.4
199288.5139.9
199193.4142.5
199094.9142.6
198993142.5
198892.1140.3
198792140
198690.2140
198590140
198488138.9
198386.7136.3
198288.7138.5
198191.5141.5
198090.1139.5
197990.8139.1
197889139
197789138.6
197689.1139.2
197586.8136.5
197487.9138.7
197388.6137.8
197287.7137.4
197186.8136
197086.1137.7
196987.5135.9
196887.1136.6
196786.7135.2
196686.4136.9
196584.9138.3
196484.7135.1
196387.1135.7
196288137
196189.5138.6
196089.7139
195989.6138.6
195889.1137.7
195789140
195688.5137.8
195588.2137.5
195488.4137.8
195388.6136.9
195287.8138.1
195189.4136.7
195088.1139.1
CMIP6 model median (SSP2-4.5, middle of the road) · 2015–2100
YearMexicoPakistan
2015106.192.9–118.4152.4138.7–163.3
2016107.695–120150.6139.4–161.7
2017108.696.3–119.7151.3138.8–162
201810997.1–120.4152.6139.8–161.8
2019110.396.4–123151.8138.1–163.3
2020109.696.8–121.8153.1140.3–164.5
2021111.296.9–124.1154.1140.6–165.7
2022109.796–121.7153.1140.2–165.7
2023111.898.6–125.3155.5141.6–165.9
2024112.298.2–126.2156.8143.3–168.7
202511298.8–125.8155.7141.5–168.6
2026113.999.5–126.4155.3142.2–167.9
2027112.999.5–128.3156.8140.9–168.9
2028115.2101.9–127.9156.5143.9–167.6
2029115.6101.9–127.9155.9141.5–167.7
2030115.9101.7–128.4156.3143.3–167.6
2031116.1100.9–129.9157.3144.4–168.1
2032116.1102.5–129.1157.4142.5–169.6
2033117.5102.5–131.1157.7143.6–169.4
2034117.2104.1–130.3159.5145.6–173.4
2035116.4103.1–130.3159.3146.7–170.3
2036117.8105.1–131159.1147.5–171.4
2037118.6104.3–131.7162.2147.5–175.3
2038121107.5–133.4161.4148.6–171.7
2039121.3107–135.6160148.2–170.4
2040121.9108.1–135.1160.9147.9–171.3
2041121.3105.3–134.6160.8146.7–171.7
2042122.7107.7–137.1162.3149.9–175.6
2043122.4108.1–137.5161.1150.3–173.8
2044121.9108–135.6163150.9–173.9
2045123.1108.8–139.2163149.5–174.8
2046123.8109.9–139.4163.5149.8–177.2
2047125.6111.9–139.4165.2151.8–178.5
2048126.3113–140.3166.4154.1–179.4
2049127111.2–140.8165150.8–178.2
2050128.1112.6–142.5166.9153.5–179.3
2051127.2113.4–140.8165.6150.8–180
2052126.2112.5–141.1166.7155.4–179.5
2053128.4111.5–142.7166.3154.2–178.8
2054128.1113.1–142.3167.5153.9–181
2055129114.4–144.7167.8155.8–182.5
2056129.5114.3–143.8166.3153.6–179.7
2057130.3113.5–145.9168.3153.9–182.3
2058128.8114.1–144.5167.5157–179.9
2059129.5114.5–144.5170.9155.9–183.8
2060131.4115.5–146.5170.3156.5–181.8
2061131.3118.4–146.8168.8156.8–179.6
2062130.8115.1–146.3171.6153.5–186.5
2063131.2115.6–147.9171.3159.5–183.8
2064132.2116.5–147.8170.2155.3–184.5
2065133.1115.8–148.6169.7156.4–185
2066133.3117.2–149.4172.2157–186.7
2067133.3117–150.3173.2160.2–186.3
2068134.6117.4–150.5172.6157.7–186.3
2069134.4117.8–150.9171.8157.3–185.6
2070134.2116.7–151.8171.7158.9–186.7
2071136.3120.8–152.7173.4158.7–186.8
2072137.2119.9–153.1174161.5–186.9
2073136.1119.1–153.6175.6157.2–187.9
2074136119.7–152.2173.5160–186.3
2075135.2118.5–151.8173161.5–187.8
2076136.3117.4–154.7172.9158.8–186.7
2077136.8119.6–152.8175161.3–187.8
2078137.1118.3–154.2174.4160.9–189.3
2079138.1121.3–157.1172.5158.3–187.7
2080137.6120.5–157.9175.1160.7–189.2
2081137.8120.7–154.7174.9159.2–190.2
2082138.7121.6–155.2175.4161.8–190.6
2083139.2119.3–158.8176.5161.8–190.8
2084140.3123.4–157.5176.1160.9–190.5
2085139.2120.9–157.2176.3161.9–193.6
2086139.8121–157.8173.9160–189.1
2087139.5121.5–157.4176161–193.1
2088141121.8–159.9175.4160.5–195.2
2089140.4121.7–158.9177.2164.2–191.7
2090141.2123–160.7177.7163.9–192.2
2091141.8123–161.6177.8162.8–192.2
2092141.3122.5–159.3178.7162.9–192.9
2093141.4124.3–161177164–191
2094142123.3–159.6176.8164.1–190.7
2095143.5124–160.7178.6163.8–193.1
2096142.3125–161.3177.8163.7–192.5
2097141.2123.2–160177.3160.4–192.5
2098142.2122.5–162178.7162.6–195.7
2099141.8122.4–160.7176159.9–193.2
2100142.8124.3–160.8177.6162.2–193

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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