Egypt vs Mexico

Egypt vs Mexico: Tropical nights

Tropical nights in 2014: 145 nights in Egypt and 103.7 nights in Mexico, in the CMIP6 climate models’ median run. In 2014 the value was higher in Egypt, by 41.3 nights, or 1.4 times that in Mexico.

nights a year · CMIP6

Egypt vs Mexico: Tropical nightsTropical nights in 2014: 145 nights in Egypt and 103.7 nights in Mexico, in the CMIP6 climate models’ median run. In 2014 the value was higher in Egypt, by 41.3 nights, or 1.4 times that in Mexico.
EgyptMexicoCMIP6 model median (SSP2-4.5, middle of the road)models’ 10th–90th percentile

What the numbers say

  • Egypt has been higher in every year both have figures for since 1950.
  • The CMIP6 model median (SSP2-4.5, middle of the road) puts Egypt at 183.4 nights and Mexico at 142.8 nights in 2100.

Every year

nights a year · CMIP6

YearEgyptMexico
2014145103.7
2013145103.9
2012145.1102.8
2011144.7102.9
2010143.5101.5
2009140.2101.8
2008142.6100.1
2007140.599.1
2006139.699.8
200514099.5
2004141.399.4
200314198.3
2002137.799.1
200113998.7
2000134.897.9
1999135.596.6
199813395.8
199713495.9
1996132.193.8
1995130.292.7
1994129.890.7
1993123.689
1992126.188.5
1991129.793.4
1990129.794.9
198912693
1988124.392.1
1987125.892
1986128.990.2
1985125.790
1984119.488
1983119.486.7
1982120.788.7
1981122.691.5
1980124.990.1
1979119.290.8
1978122.689
1977121.389
1976123.289.1
1975121.986.8
1974123.187.9
1973123.588.6
1972120.187.7
1971120.786.8
1970119.786.1
1969118.387.5
1968119.487.1
1967116.286.7
1966116.186.4
1965112.984.9
1964116.484.7
1963120.287.1
1962118.988
1961118.789.5
1960123.689.7
1959123.289.6
1958120.789.1
1957122.489
1956122.788.5
1955119.488.2
1954120.888.4
1953121.388.6
1952120.787.8
1951121.289.4
1950118.888.1
CMIP6 model median (SSP2-4.5, middle of the road) · 2015–2100
YearEgyptMexico
2015149.6131.4–164.2106.192.9–118.4
2016147.7133.5–160.2107.695–120
2017150.8134.8–163.2108.696.3–119.7
2018147.4133.4–165.610997.1–120.4
2019149.6132.7–164110.396.4–123
2020150.8136.4–164.7109.696.8–121.8
2021152.1137.8–168.1111.296.9–124.1
2022154138.1–170.6109.796–121.7
2023154.4139–168.4111.898.6–125.3
2024155.8141.7–168.5112.298.2–126.2
2025154.5139.6–168.111298.8–125.8
2026155.1144.5–165.9113.999.5–126.4
2027154.2139.5–166.3112.999.5–128.3
2028154.5141.8–168.6115.2101.9–127.9
2029156.3139.9–169.5115.6101.9–127.9
2030156.3143.1–169.1115.9101.7–128.4
2031155.5141.5–171.2116.1100.9–129.9
2032158.3144.6–170.4116.1102.5–129.1
2033159.9143.8–172.9117.5102.5–131.1
2034159.8144.9–173.2117.2104.1–130.3
2035163.5146.6–175.8116.4103.1–130.3
2036156.2143.7–170.6117.8105.1–131
2037158.8142.9–173.8118.6104.3–131.7
2038164.9146.1–177.8121107.5–133.4
2039161.8146.1–180.1121.3107–135.6
2040161.6147.9–176.5121.9108.1–135.1
2041163.7149.4–177.3121.3105.3–134.6
2042166.5149.7–180.6122.7107.7–137.1
2043163.6150.3–176.8122.4108.1–137.5
2044162.2147–178121.9108–135.6
2045167.2152.5–181123.1108.8–139.2
2046166149–181.1123.8109.9–139.4
2047165.9151.8–181.5125.6111.9–139.4
2048167.2154.5–180.1126.3113–140.3
2049165.3148.7–177.9127111.2–140.8
2050170152.8–183128.1112.6–142.5
2051165.6152.7–179.5127.2113.4–140.8
2052167.3156–180.2126.2112.5–141.1
2053170.1153.6–183.8128.4111.5–142.7
2054169153.3–183128.1113.1–142.3
2055170.5153.5–183.3129114.4–144.7
2056168.3151.2–181.4129.5114.3–143.8
2057166.8150.7–185.3130.3113.5–145.9
2058171.8154.2–188.2128.8114.1–144.5
2059173.3157.8–188.6129.5114.5–144.5
2060170.5154.8–189.3131.4115.5–146.5
2061170.3155.1–184.6131.3118.4–146.8
2062172.1155.2–185.7130.8115.1–146.3
2063169.7152.8–182.6131.2115.6–147.9
2064172.3155.9–187.2132.2116.5–147.8
2065173.3159.7–189.9133.1115.8–148.6
2066170155–187.7133.3117.2–149.4
2067176.2161.5–190133.3117–150.3
2068174.6157.3–193.1134.6117.4–150.5
2069175.8158.8–191.7134.4117.8–150.9
2070173.3158.3–191.7134.2116.7–151.8
2071176.8158.1–194.7136.3120.8–152.7
2072177.6160.8–192.1137.2119.9–153.1
2073176159.2–189.3136.1119.1–153.6
2074171.9156.5–194.1136119.7–152.2
2075171.8157–191.9135.2118.5–151.8
2076177161–194.6136.3117.4–154.7
2077179.7157–194.1136.8119.6–152.8
2078178.4161–195137.1118.3–154.2
2079178.2160.2–193.7138.1121.3–157.1
2080178.5163–193.3137.6120.5–157.9
2081177.2160–192.8137.8120.7–154.7
2082178.8164.5–193.3138.7121.6–155.2
2083180.6164.4–195.7139.2119.3–158.8
2084181.4162.4–197.8140.3123.4–157.5
2085178.8163–196.7139.2120.9–157.2
2086181.9166.6–198.8139.8121–157.8
2087178156.4–194.3139.5121.5–157.4
2088177.8163.5–196141121.8–159.9
2089180.9163.5–198.9140.4121.7–158.9
2090179.9162.4–197.8141.2123–160.7
2091184.2165.1–198.9141.8123–161.6
2092180.3162.9–196.3141.3122.5–159.3
2093181.1166.1–198.2141.4124.3–161
2094185.4168–198.9142123.3–159.6
2095181.9165.5–197.3143.5124–160.7
2096183.1163.5–200.3142.3125–161.3
2097181.5165.4–198.9141.2123.2–160
2098181.7162.1–198.8142.2122.5–162
2099179.9164.2–197.8141.8122.4–160.7
2100183.4162.9–198.1142.8124.3–160.8

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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