Cuba vs Peru

Cuba vs Peru: Tropical nights

Tropical nights in 2014: 309.7 nights in Cuba and 164.5 nights in Peru, in the CMIP6 climate models’ median run. In 2014 the value was higher in Cuba, by 145.2 nights, or 1.88 times that in Peru.

nights a year · CMIP6

Cuba vs Peru: Tropical nightsTropical nights in 2014: 309.7 nights in Cuba and 164.5 nights in Peru, in the CMIP6 climate models’ median run. In 2014 the value was higher in Cuba, by 145.2 nights, or 1.88 times that in Peru.
CubaPeruCMIP6 model median (SSP2-4.5, middle of the road)models’ 10th–90th percentile

What the numbers say

  • Cuba has been higher in every year both have figures for since 1950.
  • The CMIP6 model median (SSP2-4.5, middle of the road) puts Cuba at 346 nights and Peru at 192.3 nights in 2100.

Every year

nights a year · CMIP6

YearCubaPeru
2014309.7164.5
2013315.2163.1
2012312164.8
2011312.1164.3
2010313163.3
2009308.8162.3
2008309.3162
2007308.5159.8
2006309.9159.1
2005309.1159.3
2004309.3161.8
2003304.6160.5
2002310.4159.9
2001304.8160.7
2000308.1158
1999304.4155.7
1998304.9157.4
1997299.8156.1
1996298152.2
1995295.1152.1
1994297.3150.8
1993294.5148.5
1992295.7144
1991299.1156
1990302158.3
1989298.1153.2
1988298.3151.2
1987294152
1986293.6153.4
1985297.4149
1984289.4148.3
1983292.2145.4
1982294151.4
1981293.6153.1
1980295.8150.9
1979295.1150.5
1978290.9149.1
1977289.3150.3
1976293.4147.5
1975289142.1
1974291.6148.6
1973291.2151.5
1972289.4147.6
1971286.5145.7
1970283.3145.6
1969287.2147
1968290.6146
1967289.9142.3
1966290.7145.7
1965284.8143.3
1964287.7141.6
1963288.9135.9
1962292.9148.9
1961292.2146.5
1960293.3149.8
1959292.7146.1
1958291.6149.1
1957292148.7
1956294.6144
1955293.4148.3
1954289.5148.7
1953289.2148.2
1952290.9148.1
1951292.1147.2
1950292.3147.8
CMIP6 model median (SSP2-4.5, middle of the road) · 2015–2100
YearCubaPeru
2015315.1292.1–332.6164.3148.5–174.9
2016317.9302.7–334.8163.4149.8–171.5
2017317304–329.5165.5155–175.2
2018319.5301–334.2165152.9–176.6
2019317.6297.3–336.1163.7151–176.8
2020319.5296.6–334.3165.3151.6–176.2
2021320.6301.8–335.1167.6154.7–177.3
2022318.2303.4–331.8169.5156.6–179.2
2023322.4302.9–336.9167.1156.3–178.4
2024320.1297.1–341.5170.6157.3–180.2
2025319.6297.1–334.8168.6155–179.9
2026327.3300.6–343.1167.8153.3–179.7
2027328.7307.3–344.9168.3155.2–178.8
2028327.5306.8–343.5169.8158.3–179.9
2029326.5309–340.3174162.8–181.7
2030328.8305–342.3168.1153.2–180.6
2031328.6308.6–343.5172.5156.2–183.7
2032332.1308.4–344.7173.2162.7–184.7
2033328.3305.5–342.9172156.5–181
2034329.4308.9–346.2171.6158.7–185.6
2035325.7307.3–342.4172159.3–184.1
2036329307–341.7172.5160.7–183.2
2037332.6311.4–346.9175.6166.3–185.3
2038331.4313–345.1175.8164.6–184.9
2039328305.6–345.9174.1162.7–185
2040328.8309–347.6175.4163.9–187
2041331.4307.4–344.3176.5165.3–186.1
2042331.6309.6–345.1176.3161.9–189.7
2043332.1311–348.9174.8163.7–185.3
2044335.1315.6–348.6175.3161.9–188.2
2045336.3317.6–348.6177.3165.5–186.9
2046328.7309–349.2180.3167.4–189.9
2047334.4316.3–350.1178.6167.5–189
2048335.2316.3–349.1181.2168.2–192.2
2049339.5317–350.8180.4168.1–189.8
2050338.7316.7–350.6178.6166.4–190.8
2051337.4313.8–349.8180.3168.5–190.6
2052331311.8–349.4181.5170.8–191.7
2053339.5314.6–351.2179.5168.3–190.8
2054338.4322.5–348.7180.1167.3–193.9
2055336.9318.7–350.1181.7171–194.8
2056338.1320.4–351.5182.2170–193
2057340.6316.7–351.1181.6168.7–193
2058340.8322.3–351.7183.1169.4–195.5
2059334.6319.6–349.6184.6169.7–195.3
2060340.7320.6–352.9185.6171.1–197.1
2061337.9324.8–351.5182.7169.9–195.8
2062340.1323.4–351184.3168.7–196.9
2063340.9316.3–354.4184.6170.8–196.9
2064341315.6–353.1184.4170.1–194.8
2065340.3319.5–352186.3173.3–196.9
2066339.5319.3–352.1185.6172.3–197.4
2067342.1324.2–351.9184.3169.4–198.2
2068344.3320.4–354.8186.1172.1–198.3
2069342.8323.8–355.6184.9169–198.3
2070344.4322.1–356.2185.4171.8–198.4
2071345326.4–354.8187.3173.3–198.7
2072345318.5–356.3188.1174.9–199.8
2073343.1321.4–355.1187.6175.9–202.1
2074342.9325.4–354.5186.4169.5–198.9
2075341.8319.7–353.7186.4170.6–198.3
2076344.9323.6–356.2188.8174.2–201
2077342320.4–351.9189.1174.7–201.8
2078345324.3–355.7188.4175.6–202.3
2079343.3323.8–356188172–199.7
2080344.9328.1–358.4189.1172.8–201.7
2081342.7326–353.6188.4173.6–201.8
2082344.9329.8–355.4188.8174.8–199.7
2083343.8321.8–354.5189.9175.7–204.7
2084344328.8–356.5190.7176.3–204.4
2085346.4322.1–356.6192176.2–204.6
2086342.6316.5–352.9189.1175.4–204
2087345.5325.6–356.7189.1175.9–203.6
2088346.5325.1–358.5191.1174.7–204.9
2089347.8327.2–357.7190.1174.3–203
2090346.9334.4–356.8190.4177–206.2
2091346.9330.1–357.1192.6174.4–205.9
2092346.6327–356192.6176.6–204.7
2093345.4326.9–356.3190.6176–206
2094347326.3–357.7192.3178.2–204.1
2095343.9329.1–355.1193.4176.5–207.3
2096344326.6–356.1191.4176.2–207.6
2097346.7329.2–355.1190.7174.9–202.2
2098347.2326.3–356.6192.9177.8–208.9
2099345.5327.5–357.7191.5176.8–205
2100346329.4–356.1192.3176.6–205.6

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

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