Mexico vs Myanmar (Burma)

Mexico vs Myanmar (Burma): Tropical nights

Tropical nights in 2014: 103.7 nights in Mexico and 201.9 nights in Myanmar (Burma), in the CMIP6 climate models’ median run. In 2014 the value was higher in Myanmar (Burma), by 98.2 nights, or 1.95 times that in Mexico.

nights a year · CMIP6

Mexico vs Myanmar (Burma): Tropical nightsTropical nights in 2014: 103.7 nights in Mexico and 201.9 nights in Myanmar (Burma), in the CMIP6 climate models’ median run. In 2014 the value was higher in Myanmar (Burma), by 98.2 nights, or 1.95 times that in Mexico.
MexicoMyanmar (Burma)CMIP6 model median (SSP2-4.5, middle of the road)models’ 10th–90th percentile

What the numbers say

  • Myanmar (Burma) 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 Myanmar (Burma) at 244.5 nights in 2100.

Every year

nights a year · CMIP6

YearMexicoMyanmar (Burma)
2014103.7201.9
2013103.9203.1
2012102.8201.1
2011102.9200.9
2010101.5202.1
2009101.8199
2008100.1198.1
200799.1196.8
200699.8198.2
200599.5196.7
200499.4200.3
200398.3195.3
200299.1196.6
200198.7199
200097.9197.2
199996.6196.4
199895.8194.7
199795.9194.4
199693.8191.4
199592.7189.9
199490.7188.8
199389185
199288.5180.5
199193.4191.7
199094.9191.5
198993190.3
198892.1190.7
198792190.8
198690.2189.7
198590187.4
198488189.1
198386.7182.3
198288.7186.7
198191.5188.4
198090.1189
197990.8189.8
197889186.8
197789187.4
197689.1185.8
197586.8183.6
197487.9185.5
197388.6187.5
197287.7184.8
197186.8185.2
197086.1184.9
196987.5184.3
196887.1183.7
196786.7182.3
196686.4186.2
196584.9182.9
196484.7180.6
196387.1183.1
196288187.3
196189.5188.7
196089.7187.1
195989.6186.5
195889.1187.1
195789186.2
195688.5187.9
195588.2185.9
195488.4185.7
195388.6185
195287.8186.1
195189.4187.1
195088.1186.9
CMIP6 model median (SSP2-4.5, middle of the road) · 2015–2100
YearMexicoMyanmar (Burma)
2015106.192.9–118.4205.2186.4–218.9
2016107.695–120202.8186.2–218.8
2017108.696.3–119.7203.4187.3–218.3
201810997.1–120.4205.5190.9–219.9
2019110.396.4–123205.2186.7–222.2
2020109.696.8–121.8208.2188.5–223.5
2021111.296.9–124.1208.9191.2–224.1
2022109.796–121.7209.9192.7–224.9
2023111.898.6–125.3210.6195.4–226.3
2024112.298.2–126.2210.4193.8–226.6
202511298.8–125.8209.9190.3–225.2
2026113.999.5–126.4212.2194.3–229.2
2027112.999.5–128.3212195.6–228.6
2028115.2101.9–127.9213195.7–227.2
2029115.6101.9–127.9215.3193.9–231.9
2030115.9101.7–128.4212.2194.9–228
2031116.1100.9–129.9214.3197.2–230.8
2032116.1102.5–129.1215199.3–229.6
2033117.5102.5–131.1215.5198.5–230.1
2034117.2104.1–130.3214.9198.4–233.5
2035116.4103.1–130.3214.9199.2–236.8
2036117.8105.1–131216198.7–234.5
2037118.6104.3–131.7217.2199.6–233.5
2038121107.5–133.4220.7204.8–235.1
2039121.3107–135.6219.7202.4–234.3
2040121.9108.1–135.1219.2202.9–235
2041121.3105.3–134.6220.7203.9–237.7
2042122.7107.7–137.1221.8206.1–237.2
2043122.4108.1–137.5219.5201.2–237.1
2044121.9108–135.6221.4201.6–239.6
2045123.1108.8–139.2223.7204.8–241.5
2046123.8109.9–139.4223.2203.2–240.2
2047125.6111.9–139.4224.3204.8–241.1
2048126.3113–140.3226.4207.7–243.8
2049127111.2–140.8226.5205.6–242.8
2050128.1112.6–142.5227209.3–243.5
2051127.2113.4–140.8229.6212.4–246.4
2052126.2112.5–141.1227.2208.6–244.2
2053128.4111.5–142.7227.9208.7–247.7
2054128.1113.1–142.3227.9209.6–244.8
2055129114.4–144.7229.1209.8–247.5
2056129.5114.3–143.8231.8210.4–252.1
2057130.3113.5–145.9228.6208–249
2058128.8114.1–144.5228.9208.8–249
2059129.5114.5–144.5230.7212.1–247.5
2060131.4115.5–146.5234.7211.1–251.5
2061131.3118.4–146.8231.9213–247.7
2062130.8115.1–146.3233.6209.9–252.8
2063131.2115.6–147.9233215.4–250.5
2064132.2116.5–147.8233.2214.4–251.3
2065133.1115.8–148.6230.7210.4–250.7
2066133.3117.2–149.4233.5214.3–251.2
2067133.3117–150.3233211.5–253.3
2068134.6117.4–150.5233.1212.4–253.7
2069134.4117.8–150.9236.1214.6–253.4
2070134.2116.7–151.8235.9218.1–253.4
2071136.3120.8–152.7235.1213.5–259.6
2072137.2119.9–153.1234.6216.1–252.6
2073136.1119.1–153.6237.2219.3–257.2
2074136119.7–152.2236.4215.7–254.5
2075135.2118.5–151.8236.7214.4–255.9
2076136.3117.4–154.7237.7216–257
2077136.8119.6–152.8237.5219–258.7
2078137.1118.3–154.2238.5217.3–257.8
2079138.1121.3–157.1238.5219.2–257.2
2080137.6120.5–157.9240.5218.8–258.3
2081137.8120.7–154.7240219.7–260.9
2082138.7121.6–155.2239.4217.1–258.8
2083139.2119.3–158.8239.1220.5–259.2
2084140.3123.4–157.5241.3218.9–260.6
2085139.2120.9–157.2241.7218.8–260.7
2086139.8121–157.8241.7217.4–262.7
2087139.5121.5–157.4241.4220.5–260.9
2088141121.8–159.9241.8222.3–261.5
2089140.4121.7–158.9240.7221.2–262.6
2090141.2123–160.7241.3224.4–260.9
2091141.8123–161.6242.6220.3–262.5
2092141.3122.5–159.3241.9221.6–263.3
2093141.4124.3–161242.1220.2–260
2094142123.3–159.6242.5222.5–261.9
2095143.5124–160.7244.9224–264.4
2096142.3125–161.3243.9221.1–266.5
2097141.2123.2–160243.6219.6–264.2
2098142.2122.5–162243.6222.5–263.9
2099141.8122.4–160.7244.7221.8–263.5
2100142.8124.3–160.8244.5223.2–261.8

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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