India vs Mexico

India vs Mexico: Tropical nights

Tropical nights in 2014: 215.5 nights in India and 103.7 nights in Mexico, in the CMIP6 climate models’ median run. In 2014 the value was higher in India, by 111.8 nights, or 2.08 times that in Mexico.

nights a year · CMIP6

India vs Mexico: Tropical nightsTropical nights in 2014: 215.5 nights in India and 103.7 nights in Mexico, in the CMIP6 climate models’ median run. In 2014 the value was higher in India, by 111.8 nights, or 2.08 times that in Mexico.
IndiaMexicoCMIP6 model median (SSP2-4.5, middle of the road)models’ 10th–90th percentile

What the numbers say

  • India has been higher in every year both have figures for since 1950.
  • The CMIP6 model median (SSP2-4.5, middle of the road) puts India at 250.4 nights and Mexico at 142.8 nights in 2100.

Every year

nights a year · CMIP6

YearIndiaMexico
2014215.5103.7
2013216103.9
2012217.4102.8
2011215.5102.9
2010215.2101.5
2009213.7101.8
2008213.7100.1
2007213.399.1
2006212.599.8
2005212.999.5
2004214.999.4
2003212.198.3
200221399.1
2001212.298.7
2000210.897.9
1999211.996.6
1998210.395.8
1997209.595.9
199620793.8
1995208.892.7
1994207.190.7
199320689
1992203.588.5
199120893.4
1990209.994.9
198920893
1988207.392.1
1987207.292
1986207.290.2
1985205.990
1984206.788
1983203.386.7
1982205.788.7
1981208.491.5
1980205.390.1
1979205.590.8
1978204.189
1977205.989
1976204.689.1
1975202.686.8
1974205.387.9
1973205.688.6
1972203.887.7
1971203.686.8
1970202.686.1
1969203.687.5
1968201.787.1
1967201.686.7
1966203.986.4
1965202.484.9
1964201.284.7
1963201.887.1
1962203.888
1961206.389.5
196020689.7
1959206.189.6
195820489.1
1957206.589
195620488.5
1955203.288.2
195420688.4
195320488.6
1952204.487.8
1951204.389.4
1950206.388.1
CMIP6 model median (SSP2-4.5, middle of the road) · 2015–2100
YearIndiaMexico
2015216.9202.1–228.4106.192.9–118.4
2016215202.5–227.6107.695–120
2017216.2202.1–228.5108.696.3–119.7
2018218.3204.5–231.410997.1–120.4
2019217.4202.3–230.4110.396.4–123
2020217.6203.1–232109.696.8–121.8
2021219.7204.5–233.2111.296.9–124.1
2022220.5205.8–233109.796–121.7
2023220.4206.4–233.7111.898.6–125.3
2024222.6208.1–236.3112.298.2–126.2
2025219.7202.7–236.211298.8–125.8
2026220.2204.2–236.8113.999.5–126.4
2027223.6207.1–239112.999.5–128.3
2028222.1205.1–235.7115.2101.9–127.9
2029225.4210–240.3115.6101.9–127.9
2030222.7207.3–235.6115.9101.7–128.4
2031223.8207.1–237.8116.1100.9–129.9
2032225.9210–240.5116.1102.5–129.1
2033223.8209.3–235.4117.5102.5–131.1
2034225.8210–242.7117.2104.1–130.3
2035224.9210.6–240.9116.4103.1–130.3
2036224.2210.4–240.3117.8105.1–131
2037228.3213.6–242.6118.6104.3–131.7
2038228.1212.6–241.4121107.5–133.4
2039227.4211.9–242.8121.3107–135.6
2040227212.2–242121.9108.1–135.1
2041227.1210.7–240.5121.3105.3–134.6
2042229.1214.8–244.2122.7107.7–137.1
2043228.9214.5–244122.4108.1–137.5
2044229.7214.7–244.2121.9108–135.6
2045231.2216.6–246.1123.1108.8–139.2
2046232.6216.2–248.6123.8109.9–139.4
2047232.4215.5–250125.6111.9–139.4
2048235.6218.5–254.3126.3113–140.3
2049235.3218.3–249.9127111.2–140.8
2050233.5216.6–253.4128.1112.6–142.5
2051234.9218.6–249.9127.2113.4–140.8
2052234219.4–249.3126.2112.5–141.1
2053234.9219.5–252.5128.4111.5–142.7
2054237.2219.4–253.1128.1113.1–142.3
2055237.2220.7–256.6129114.4–144.7
2056238.8221.2–254.9129.5114.3–143.8
2057235.7218.5–253.8130.3113.5–145.9
2058238.8222.8–254.1128.8114.1–144.5
2059241.1224.9–256.2129.5114.5–144.5
2060240.9225.6–255.6131.4115.5–146.5
2061237.9220.6–253.7131.3118.4–146.8
2062239.9221.2–259.7130.8115.1–146.3
2063239.9222.3–256.1131.2115.6–147.9
2064240223.8–256.3132.2116.5–147.8
2065240.9222.9–257.3133.1115.8–148.6
2066241.5224.1–258.6133.3117.2–149.4
2067241.6224.8–257.9133.3117–150.3
2068242.3224.6–262.1134.6117.4–150.5
2069242.8225.4–259.1134.4117.8–150.9
2070240.1225.3–257.1134.2116.7–151.8
2071241.8223.7–260.2136.3120.8–152.7
2072242.6225.4–259.2137.2119.9–153.1
2073243.8228–259.3136.1119.1–153.6
2074241.6226.9–258.4136119.7–152.2
2075243.5226.4–258.2135.2118.5–151.8
2076243.2224.6–259.9136.3117.4–154.7
2077243.7226.6–264.6136.8119.6–152.8
2078246.7230.1–261.5137.1118.3–154.2
2079244.9226.4–261.3138.1121.3–157.1
2080246.7227–263.7137.6120.5–157.9
2081247.5230.4–264.8137.8120.7–154.7
2082244.6227.9–262.3138.7121.6–155.2
2083247.5227.8–265.1139.2119.3–158.8
2084247.3228.3–266.8140.3123.4–157.5
2085246.5227.5–267.1139.2120.9–157.2
2086245.6227.7–266.5139.8121–157.8
2087245.5226.7–266.2139.5121.5–157.4
2088247229.4–266.9141121.8–159.9
2089248.1230–266.3140.4121.7–158.9
2090249228.9–266.4141.2123–160.7
2091248.9230–266.5141.8123–161.6
2092249.8232.3–267.8141.3122.5–159.3
2093248.1229–266.5141.4124.3–161
2094249.1230.6–268.4142123.3–159.6
2095251233.2–270143.5124–160.7
2096247.8231.4–269.3142.3125–161.3
2097251.1231.4–268141.2123.2–160
2098252.3230.8–268.3142.2122.5–162
2099248.7230.1–268.3141.8122.4–160.7
2100250.4232.9–266.7142.8124.3–160.8

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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