Brazil vs Thailand

Brazil vs Thailand: Tropical nights

Tropical nights in 2014: 282 nights in Brazil and 286.4 nights in Thailand, in the CMIP6 climate models’ median run. In 2014 the value was higher in Thailand, by 4.4 nights, or 1.02 times that in Brazil.

nights a year · CMIP6

Brazil vs Thailand: Tropical nightsTropical nights in 2014: 282 nights in Brazil and 286.4 nights in Thailand, in the CMIP6 climate models’ median run. In 2014 the value was higher in Thailand, by 4.4 nights, or 1.02 times that in Brazil.
BrazilThailandCMIP6 model median (SSP2-4.5, middle of the road)models’ 10th–90th percentile

What the numbers say

  • Thailand has been higher in every year both have figures for since 1950.
  • The CMIP6 model median (SSP2-4.5, middle of the road) puts Brazil at 317.3 nights and Thailand at 320.8 nights in 2100.

Every year

nights a year · CMIP6

YearBrazilThailand
2014282286.4
2013282.5289.1
2012283.6286.6
2011281.8286.4
2010280.2286
2009279.2286.9
2008277.8284.8
2007275.9283.2
2006275.4284.3
2005275.8281.7
2004278.5287.5
2003276.7279.3
2002276.2280
2001277.7285.5
2000273.3283.4
1999271282.5
1998273.6282.7
1997270.9284.8
1996264.7279.8
1995263.8278.7
1994263.6280.8
1993258.3275.6
1992252.8268.8
1991269.7276.7
1990272.5279.2
1989267.1278.4
1988264.7279.6
1987265.9277.9
1986265.7277.5
1985260.6276
1984261.1277.9
1983255.9273.9
1982264.8275.7
1981265.3277.8
1980262277.9
1979261.2278
1978259.7276
1977261.8277.3
1976258.4275.6
1975252.4274.4
1974258.7276
1973261280.6
1972258.1273.9
1971256.5273.2
1970254.5276
1969255.1274.8
1968254.9277
1967250.3270.3
1966254.5274.4
1965250.8275.8
1964246.2271.9
1963241.9274.7
1962257.9275.4
1961258.2278.5
1960260.3276.2
1959255.7274.4
1958258.1278.7
1957256.6277.4
1956251.2277
1955257.2274.6
1954258275
1953255.2275.4
1952255.8275.1
1951256.3277.5
1950254.4276.2
CMIP6 model median (SSP2-4.5, middle of the road) · 2015–2100
YearBrazilThailand
2015284257.6–299.5290.6272.2–305
2016281.5261.4–296.3287.1269.8–306.2
2017284.1263.2–297.8290273.8–302.8
2018283.4263.1–298.8292.6275.1–308.7
2019283.7262.9–302.4291.2272.8–306.7
2020286.3264.2–303.6292.4272.2–307.8
2021287.9266.9–302.3294.2276.3–308.5
2022289.6270.7–303.9294.8277.2–311.9
2023287.3270.5–303296.1281.9–310.2
2024291.5268.2–304.6291.7275.5–307.8
2025289.6269.1–304.8294276.7–306.3
2026289.7266.2–305.1296278–309.3
2027289.5269.1–303.7295.8276.3–311.1
2028291.2271.9–306297279.3–315.1
2029295.5276.3–309.6300.3278.7–315
2030292.2269.1–306.9297.7280.2–312.5
2031295.3274–308.9300.8282.8–317.1
2032296.8280.1–310.9297.2280.9–311.4
2033295.4273.3–308.5300281.9–315.8
2034294.3273.5–311.8300.8279–318.4
2035295.2274.4–310.9298.6281.5–317.8
2036295.1278.2–307.8300.4279.9–316.9
2037299.9283.7–311.3299.8280–315.1
2038299.6283.5–312.1304.8286.6–319.3
2039296.9279.9–311300.9281.9–317
2040298.5279.4–311.9301.6284.5–315.7
2041301.4282.2–314.1303.1285.1–318.8
2042300.6277.8–314.7303.8290–318.3
2043298.2279.8–312.7302285.8–317.9
2044300.3281.2–315.6304.3286.3–318.8
2045299.9279.7–313.1307.1287.7–324.2
2046302.8285.8–316.6305.9285.2–321
2047303.3285–316.4306.6289.5–321.5
2048304.4287.3–317.3309.4288.9–325.7
2049305.4288.4–318.5308.1287.2–324.1
2050303.9283.9–316.2307.5291–321.5
2051305.7287.5–317.5312.8294.8–326
2052305.9290.6–318.8310.9291.2–323.4
2053305.3285.9–317.5308.3289.5–323.3
2054306.1288.4–319.3308.8292.8–322.3
2055306.4290.8–319.8308.6290.6–325.1
2056306.7288.3–321.1311.3294.9–326.3
2057306.4287.6–321.1308.5286.7–326.4
2058309291–321.4308291.1–325.1
2059310.4290.7–322.8312.7295.8–325.3
2060311.6294.4–325.1313.1294.6–328.6
2061307.5290.3–321.2314.4289.5–331.1
2062309291.5–322.8311.4293.4–330.3
2063310.8290.6–322.8314.8296.9–327.3
2064309.6290.2–321.1312.5295.4–330
2065311.1291.6–323.1309.6293.1–329.3
2066312.2294.2–324.2312.1292.6–328
2067310.9291.5–323.3311.2289.6–329.4
2068311.5293–324.5310.8292.4–330.6
2069310.6292.2–323.6315.5296.1–329.5
2070312.7293–325.3314.2292.9–330.7
2071311.5292.1–323.9313.8296.4–330.8
2072313.3297.8–325.5314.8296–329.6
2073313.8298.6–327.2316.5299.9–332.1
2074311.4292.4–323.5317.2295–333.8
2075310.9291.5–324.5317.2295.3–331.4
2076314.2294.4–327.7317.4299.8–335
2077315.1295.6–326.8317.5297.3–335.9
2078314.9296.3–327.1315.9297.1–331.6
2079313292.8–326.8318.5300.5–332.3
2080311.9294.7–325.4319.8297.4–337.5
2081313.5295.5–325.6317.7299.5–334.9
2082315296.8–327.1317.9294.8–333
2083315.7295.3–328.7315297.8–332.9
2084316.6298.5–327.9316295.5–330
2085317.4299.3–329.8322.6300.8–334.7
2086315.8294–328319.4295.2–335.2
2087316.5297.9–328.5320.2302.2–334.7
2088315.6297.3–328.8321.4299.8–334.8
2089315296.6–327.9318.9296.4–338.1
2090316298.9–330.3317.6304–337.4
2091317298.1–330.6320.1299.8–336.1
2092317.6298.7–330.7316.3298–336.4
2093316.3297.5–328.6316.1297.7–333.7
2094316.4300.2–327.5323.4301.8–337.3
2095319299.8–330.7322.9302.4–338.9
2096317.7299.5–330.8319.6302.8–336.7
2097316.9297.8–329319.8299.1–336
2098318.6299.9–331.9320.5303.9–336
2099318.1300–330.2319.9303.1–334.4
2100317.3298.4–330.3320.8301.1–336.7

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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