Mexico vs Tanzania

Mexico vs Tanzania: Tropical nights

Tropical nights in 2014: 103.7 nights in Mexico and 106.3 nights in Tanzania, in the CMIP6 climate models’ median run. In 2014 the value was higher in Tanzania, by 2.6 nights, or 1.03 times that in Mexico.

nights a year · CMIP6

Mexico vs Tanzania: Tropical nightsTropical nights in 2014: 103.7 nights in Mexico and 106.3 nights in Tanzania, in the CMIP6 climate models’ median run. In 2014 the value was higher in Tanzania, by 2.6 nights, or 1.03 times that in Mexico.
MexicoTanzaniaCMIP6 model median (SSP2-4.5, middle of the road)models’ 10th–90th percentile

What the numbers say

  • Tanzania has been higher since 2010, when it overtook Mexico.
  • The CMIP6 model median (SSP2-4.5, middle of the road) puts Mexico at 142.8 nights and Tanzania at 208 nights in 2100.

Every year

nights a year · CMIP6

YearMexicoTanzania
2014103.7106.3
2013103.9105.5
2012102.8108.4
2011102.9109
2010101.5103.9
2009101.8101.1
2008100.1100.3
200799.199.5
200699.8100.4
200599.598.7
200499.4100.4
200398.399.6
200299.198.4
200198.7101.5
200097.994.9
199996.694.2
199895.896.8
199795.993.5
199693.889.6
199592.788.5
199490.785.4
19938981.2
199288.580.1
199193.489.6
199094.991.1
19899389.1
198892.187.6
19879288.5
198690.289.4
19859083
19848883.1
198386.782.1
198288.784.8
198191.588
198090.185
197990.885.5
19788982.9
19778984.1
197689.181
197586.880.8
197487.982.2
197388.683.6
197287.781.4
197186.880.7
197086.181.8
196987.579.5
196887.179.1
196786.777.5
196686.478.1
196584.978
196484.773.8
196387.173.1
19628881.1
196189.582.6
196089.783.9
195989.681.4
195889.182.1
19578980.9
195688.580
195588.281.6
195488.481.4
195388.680.6
195287.881.4
195189.482.1
195088.180.7
CMIP6 model median (SSP2-4.5, middle of the road) · 2015–2100
YearMexicoTanzania
2015106.192.9–118.4114.895.2–137.4
2016107.695–120108.391.6–125.6
2017108.696.3–119.7114.893.6–137.8
201810997.1–120.4116.497.4–137.1
2019110.396.4–123113.495.2–133.2
2020109.696.8–121.8119.396.2–143.5
2021111.296.9–124.1119.5100.9–147.1
2022109.796–121.7124.4105.3–147.4
2023111.898.6–125.3122.1102.1–147.8
2024112.298.2–126.2126.2100–150.1
202511298.8–125.8124.6100.9–154
2026113.999.5–126.4126.999.1–155.6
2027112.999.5–128.3128102.9–147.7
2028115.2101.9–127.9129.9106.4–152.1
2029115.6101.9–127.9134.1111–156.5
2030115.9101.7–128.4129.4106.4–151.1
2031116.1100.9–129.9136.4115.9–158.4
2032116.1102.5–129.1135.1114–166.8
2033117.5102.5–131.1135.2110.9–161.9
2034117.2104.1–130.3134.7112.5–166.3
2035116.4103.1–130.3140.2114.6–165
2036117.8105.1–131137.8113.3–165.5
2037118.6104.3–131.7142.9121.5–168.9
2038121107.5–133.4147.9124.3–169.3
2039121.3107–135.6144.3117.6–171.3
2040121.9108.1–135.1140.5117–178
2041121.3105.3–134.6149.1121.7–175.6
2042122.7107.7–137.1146.3120.5–185.3
2043122.4108.1–137.5144.6117–173
2044121.9108–135.6147.7121.2–175.8
2045123.1108.8–139.2150.1125.2–183.4
2046123.8109.9–139.4161.5128.6–192.2
2047125.6111.9–139.4160.2129.7–194.4
2048126.3113–140.3158.8132.7–193.1
2049127111.2–140.8157.6131.1–196
2050128.1112.6–142.5159.7127.8–192
2051127.2113.4–140.8163.2135.5–197.8
2052126.2112.5–141.1161.9136.2–199.1
2053128.4111.5–142.7160.6127.5–197.7
2054128.1113.1–142.3163.2131.5–200.2
2055129114.4–144.7167.4137–208.3
2056129.5114.3–143.8166.4130.8–209.7
2057130.3113.5–145.9168134.6–208.3
2058128.8114.1–144.5168.7134.9–210.3
2059129.5114.5–144.5174.7144.9–219.5
2060131.4115.5–146.5181.7138.1–219.8
2061131.3118.4–146.8171.8135.5–218.1
2062130.8115.1–146.3177.5139.9–224
2063131.2115.6–147.9181.4140.1–213.8
2064132.2116.5–147.8185.3140.1–215.2
2065133.1115.8–148.6183.5141.1–221.8
2066133.3117.2–149.4181.9146.3–219.3
2067133.3117–150.3178.8138.8–229.2
2068134.6117.4–150.5186144.2–228.4
2069134.4117.8–150.9183.7141.7–225.3
2070134.2116.7–151.8180.7140.4–228.2
2071136.3120.8–152.7184.9146.9–235.2
2072137.2119.9–153.1191.3155–226.1
2073136.1119.1–153.6191.6152.4–235.6
2074136119.7–152.2183.3149.7–231.7
2075135.2118.5–151.8183.8144.8–233
2076136.3117.4–154.7191.1150.9–233.2
2077136.8119.6–152.8191.3152.6–238.2
2078137.1118.3–154.2189156.6–233.8
2079138.1121.3–157.1190.5147–237.3
2080137.6120.5–157.9197.3150.7–241.2
2081137.8120.7–154.7195.6153.4–237.4
2082138.7121.6–155.2194.5157.3–237.3
2083139.2119.3–158.8193.2151–249.8
2084140.3123.4–157.5201.1152.2–246.7
2085139.2120.9–157.2204.2162.3–252.3
2086139.8121–157.8195156–248.7
2087139.5121.5–157.4196.7160.8–244.5
2088141121.8–159.9208.4154.7–246.9
2089140.4121.7–158.9201.3158.6–248.2
2090141.2123–160.7197.9160.7–253.6
2091141.8123–161.6210161.6–256
2092141.3122.5–159.3205.8158.3–253.6
2093141.4124.3–161207.2161.2–251.9
2094142123.3–159.6207.9161.3–250.6
2095143.5124–160.7214.2166.4–258.5
2096142.3125–161.3205.3159.8–257.4
2097141.2123.2–160206.6158.4–256.8
2098142.2122.5–162209.1161–258.3
2099141.8122.4–160.7208.5160.1–256
2100142.8124.3–160.8208158.6–258.6

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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