Mexico vs Peru

Mexico vs Peru: Tropical nights

Tropical nights in 2014: 103.7 nights in Mexico and 164.5 nights in Peru, in the CMIP6 climate models’ median run. In 2014 the value was higher in Peru, by 60.8 nights, or 1.59 times that in Mexico.

nights a year · CMIP6

Mexico vs Peru: Tropical nightsTropical nights in 2014: 103.7 nights in Mexico and 164.5 nights in Peru, in the CMIP6 climate models’ median run. In 2014 the value was higher in Peru, by 60.8 nights, or 1.59 times that in Mexico.
MexicoPeruCMIP6 model median (SSP2-4.5, middle of the road)models’ 10th–90th percentile

What the numbers say

  • Peru 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 Peru at 192.3 nights in 2100.

Every year

nights a year · CMIP6

YearMexicoPeru
2014103.7164.5
2013103.9163.1
2012102.8164.8
2011102.9164.3
2010101.5163.3
2009101.8162.3
2008100.1162
200799.1159.8
200699.8159.1
200599.5159.3
200499.4161.8
200398.3160.5
200299.1159.9
200198.7160.7
200097.9158
199996.6155.7
199895.8157.4
199795.9156.1
199693.8152.2
199592.7152.1
199490.7150.8
199389148.5
199288.5144
199193.4156
199094.9158.3
198993153.2
198892.1151.2
198792152
198690.2153.4
198590149
198488148.3
198386.7145.4
198288.7151.4
198191.5153.1
198090.1150.9
197990.8150.5
197889149.1
197789150.3
197689.1147.5
197586.8142.1
197487.9148.6
197388.6151.5
197287.7147.6
197186.8145.7
197086.1145.6
196987.5147
196887.1146
196786.7142.3
196686.4145.7
196584.9143.3
196484.7141.6
196387.1135.9
196288148.9
196189.5146.5
196089.7149.8
195989.6146.1
195889.1149.1
195789148.7
195688.5144
195588.2148.3
195488.4148.7
195388.6148.2
195287.8148.1
195189.4147.2
195088.1147.8
CMIP6 model median (SSP2-4.5, middle of the road) · 2015–2100
YearMexicoPeru
2015106.192.9–118.4164.3148.5–174.9
2016107.695–120163.4149.8–171.5
2017108.696.3–119.7165.5155–175.2
201810997.1–120.4165152.9–176.6
2019110.396.4–123163.7151–176.8
2020109.696.8–121.8165.3151.6–176.2
2021111.296.9–124.1167.6154.7–177.3
2022109.796–121.7169.5156.6–179.2
2023111.898.6–125.3167.1156.3–178.4
2024112.298.2–126.2170.6157.3–180.2
202511298.8–125.8168.6155–179.9
2026113.999.5–126.4167.8153.3–179.7
2027112.999.5–128.3168.3155.2–178.8
2028115.2101.9–127.9169.8158.3–179.9
2029115.6101.9–127.9174162.8–181.7
2030115.9101.7–128.4168.1153.2–180.6
2031116.1100.9–129.9172.5156.2–183.7
2032116.1102.5–129.1173.2162.7–184.7
2033117.5102.5–131.1172156.5–181
2034117.2104.1–130.3171.6158.7–185.6
2035116.4103.1–130.3172159.3–184.1
2036117.8105.1–131172.5160.7–183.2
2037118.6104.3–131.7175.6166.3–185.3
2038121107.5–133.4175.8164.6–184.9
2039121.3107–135.6174.1162.7–185
2040121.9108.1–135.1175.4163.9–187
2041121.3105.3–134.6176.5165.3–186.1
2042122.7107.7–137.1176.3161.9–189.7
2043122.4108.1–137.5174.8163.7–185.3
2044121.9108–135.6175.3161.9–188.2
2045123.1108.8–139.2177.3165.5–186.9
2046123.8109.9–139.4180.3167.4–189.9
2047125.6111.9–139.4178.6167.5–189
2048126.3113–140.3181.2168.2–192.2
2049127111.2–140.8180.4168.1–189.8
2050128.1112.6–142.5178.6166.4–190.8
2051127.2113.4–140.8180.3168.5–190.6
2052126.2112.5–141.1181.5170.8–191.7
2053128.4111.5–142.7179.5168.3–190.8
2054128.1113.1–142.3180.1167.3–193.9
2055129114.4–144.7181.7171–194.8
2056129.5114.3–143.8182.2170–193
2057130.3113.5–145.9181.6168.7–193
2058128.8114.1–144.5183.1169.4–195.5
2059129.5114.5–144.5184.6169.7–195.3
2060131.4115.5–146.5185.6171.1–197.1
2061131.3118.4–146.8182.7169.9–195.8
2062130.8115.1–146.3184.3168.7–196.9
2063131.2115.6–147.9184.6170.8–196.9
2064132.2116.5–147.8184.4170.1–194.8
2065133.1115.8–148.6186.3173.3–196.9
2066133.3117.2–149.4185.6172.3–197.4
2067133.3117–150.3184.3169.4–198.2
2068134.6117.4–150.5186.1172.1–198.3
2069134.4117.8–150.9184.9169–198.3
2070134.2116.7–151.8185.4171.8–198.4
2071136.3120.8–152.7187.3173.3–198.7
2072137.2119.9–153.1188.1174.9–199.8
2073136.1119.1–153.6187.6175.9–202.1
2074136119.7–152.2186.4169.5–198.9
2075135.2118.5–151.8186.4170.6–198.3
2076136.3117.4–154.7188.8174.2–201
2077136.8119.6–152.8189.1174.7–201.8
2078137.1118.3–154.2188.4175.6–202.3
2079138.1121.3–157.1188172–199.7
2080137.6120.5–157.9189.1172.8–201.7
2081137.8120.7–154.7188.4173.6–201.8
2082138.7121.6–155.2188.8174.8–199.7
2083139.2119.3–158.8189.9175.7–204.7
2084140.3123.4–157.5190.7176.3–204.4
2085139.2120.9–157.2192176.2–204.6
2086139.8121–157.8189.1175.4–204
2087139.5121.5–157.4189.1175.9–203.6
2088141121.8–159.9191.1174.7–204.9
2089140.4121.7–158.9190.1174.3–203
2090141.2123–160.7190.4177–206.2
2091141.8123–161.6192.6174.4–205.9
2092141.3122.5–159.3192.6176.6–204.7
2093141.4124.3–161190.6176–206
2094142123.3–159.6192.3178.2–204.1
2095143.5124–160.7193.4176.5–207.3
2096142.3125–161.3191.4176.2–207.6
2097141.2123.2–160190.7174.9–202.2
2098142.2122.5–162192.9177.8–208.9
2099141.8122.4–160.7191.5176.8–205
2100142.8124.3–160.8192.3176.6–205.6

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

Mexico vs Peru

Health

People

Economy

Education

Politics

Conflict

Military

Climate

Technology and environment