Mexico vs Vietnam

Mexico vs Vietnam: Tropical nights

Tropical nights in 2014: 103.7 nights in Mexico and 242.6 nights in Vietnam, in the CMIP6 climate models’ median run. In 2014 the value was higher in Vietnam, by 138.9 nights, or 2.34 times that in Mexico.

nights a year · CMIP6

Mexico vs Vietnam: Tropical nightsTropical nights in 2014: 103.7 nights in Mexico and 242.6 nights in Vietnam, in the CMIP6 climate models’ median run. In 2014 the value was higher in Vietnam, by 138.9 nights, or 2.34 times that in Mexico.
MexicoVietnamCMIP6 model median (SSP2-4.5, middle of the road)models’ 10th–90th percentile

What the numbers say

  • Vietnam 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 Vietnam at 282.7 nights in 2100.

Every year

nights a year · CMIP6

YearMexicoVietnam
2014103.7242.6
2013103.9243.6
2012102.8242.4
2011102.9241.9
2010101.5240.9
2009101.8240
2008100.1238.7
200799.1238
200699.8239.1
200599.5238.3
200499.4241.8
200398.3235.9
200299.1236.3
200198.7241.3
200097.9237
199996.6236
199895.8236.8
199795.9237.1
199693.8231.4
199592.7229.9
199490.7231.2
199389225.3
199288.5221.9
199193.4229.8
199094.9233.1
198993229.2
198892.1231.7
198792230.3
198690.2230.9
198590228
198488230.2
198386.7224.9
198288.7227.5
198191.5229.7
198090.1228.1
197990.8228.9
197889227.5
197789229
197689.1225.8
197586.8225.8
197487.9225.7
197388.6230.3
197287.7225.6
197186.8225.5
197086.1226.1
196987.5227
196887.1227.1
196786.7222.9
196686.4226.8
196584.9225.4
196484.7222.6
196387.1224.3
196288227.8
196189.5229.4
196089.7228.5
195989.6226.2
195889.1230.9
195789228.4
195688.5228.7
195588.2227.5
195488.4226.3
195388.6228.2
195287.8228.5
195189.4229.2
195088.1228.9
CMIP6 model median (SSP2-4.5, middle of the road) · 2015–2100
YearMexicoVietnam
2015106.192.9–118.4245.8228.2–261.9
2016107.695–120243.7226.1–259.4
2017108.696.3–119.7247229.6–260.7
201810997.1–120.4248.3228.8–263.3
2019110.396.4–123248.1229.2–262.8
2020109.696.8–121.8248.8229.6–262.9
2021111.296.9–124.1251.1233.1–266.7
2022109.796–121.7252.4234.3–266.8
2023111.898.6–125.3253.4235.4–266.6
2024112.298.2–126.2251.3236.7–264.6
202511298.8–125.8252.4234.1–266.2
2026113.999.5–126.4252.8232.9–268.5
2027112.999.5–128.3253.2237–268.8
2028115.2101.9–127.9254.3237.7–272
2029115.6101.9–127.9258.1238.5–273.7
2030115.9101.7–128.4256.4240.9–270.1
2031116.1100.9–129.9255.9241.2–270.9
2032116.1102.5–129.1257.4239.1–269.9
2033117.5102.5–131.1258.3241.7–273.2
2034117.2104.1–130.3258.8240.7–275.8
2035116.4103.1–130.3258.6242.5–275.4
2036117.8105.1–131258.3239.1–273.8
2037118.6104.3–131.7258.7242.4–272.4
2038121107.5–133.4263.4247.6–277.3
2039121.3107–135.6260.2243.7–275.9
2040121.9108.1–135.1260.1242.8–274.8
2041121.3105.3–134.6263.1245.9–279
2042122.7107.7–137.1263.6246.5–279
2043122.4108.1–137.5261.4245.5–279.6
2044121.9108–135.6263.2245.2–277.6
2045123.1108.8–139.2265.5249.6–281.4
2046123.8109.9–139.4264.9246.8–278.1
2047125.6111.9–139.4266.9250.4–282.9
2048126.3113–140.3268250.5–283.8
2049127111.2–140.8267.8251.2–284.8
2050128.1112.6–142.5268251.2–283.5
2051127.2113.4–140.8270.9254.3–285.4
2052126.2112.5–141.1268.3253.3–282.8
2053128.4111.5–142.7268.5250.1–283.2
2054128.1113.1–142.3269.1250.8–283.6
2055129114.4–144.7270249.8–284.2
2056129.5114.3–143.8271.7254.8–290.1
2057130.3113.5–145.9270253–285.7
2058128.8114.1–144.5270.7252.9–286.5
2059129.5114.5–144.5273257.6–286.6
2060131.4115.5–146.5275.7255.6–290.6
2061131.3118.4–146.8273.5254.6–289.5
2062130.8115.1–146.3271.4253.1–290.2
2063131.2115.6–147.9272.9252.7–288.1
2064132.2116.5–147.8273.8254.4–288.1
2065133.1115.8–148.6272255.8–286.3
2066133.3117.2–149.4271.8254.4–287.3
2067133.3117–150.3270.8252.1–287.6
2068134.6117.4–150.5275.6256.9–292.4
2069134.4117.8–150.9276.4258.5–292
2070134.2116.7–151.8274.6257.8–292.8
2071136.3120.8–152.7276.2255.2–294.1
2072137.2119.9–153.1277256.8–291.4
2073136.1119.1–153.6278.5262.7–293.4
2074136119.7–152.2277.6259.2–295.8
2075135.2118.5–151.8276.4258.7–294.4
2076136.3117.4–154.7279.2258.5–296.6
2077136.8119.6–152.8280264.9–294.7
2078137.1118.3–154.2278.4258.6–295.5
2079138.1121.3–157.1279.3263.4–293.6
2080137.6120.5–157.9280261.4–296.5
2081137.8120.7–154.7280.4261.2–297.9
2082138.7121.6–155.2280.9258.9–296
2083139.2119.3–158.8278.4261.6–295
2084140.3123.4–157.5279257.4–293.8
2085139.2120.9–157.2281.3265–297.8
2086139.8121–157.8282.3260.2–297.5
2087139.5121.5–157.4281.9261.3–300.7
2088141121.8–159.9283.1265.7–301.5
2089140.4121.7–158.9280.8261.2–301.6
2090141.2123–160.7280.5264.5–297.6
2091141.8123–161.6283.3264.3–298.9
2092141.3122.5–159.3281.1261.5–299.4
2093141.4124.3–161280.5263.4–296.1
2094142123.3–159.6283264.5–300
2095143.5124–160.7285.5264.5–302.9
2096142.3125–161.3281.8264.8–299.8
2097141.2123.2–160281.5263.3–299
2098142.2122.5–162282.5263.9–301.5
2099141.8122.4–160.7282.7265.1–298
2100142.8124.3–160.8282.7261–298.2

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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