巴西与泰国对比

巴西与泰国：热带夜

2014 年热带夜：巴西为 282 夜，泰国为 286.4 夜（CMIP6 气候模型中位数模拟）。 2014 年泰国的数值较高，高出 4.4 夜，是巴西的 1.02 倍。

在地图上查看其变化

每年夜数 · CMIP6

巴西与泰国：热带夜2014 年热带夜：巴西为 282 夜，泰国为 286.4 夜（CMIP6 气候模型中位数模拟）。 2014 年泰国的数值较高，高出 4.4 夜，是巴西的 1.02 倍。
巴西泰国CMIP6 模型中位数（SSP2-4.5，中间路径）模型第 10–90 百分位

数据解读

  • 自 1950 年以来，在两国都有数据的每一年，泰国的数值都更高。
  • 根据CMIP6 模型中位数（SSP2-4.5，中间路径），2100 年巴西为 317.3 夜，泰国为 320.8 夜。

逐年数据

每年夜数 · CMIP6

年份巴西泰国
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 模型中位数（SSP2-4.5，中间路径） · 2015–2100
年份巴西泰国
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

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须知

  • 一年中气温始终高于 20 °C 的夜晚数，取全国平均。
  • 来源：World Bank Climate Change Knowledge Portal, CMIP6 multi-model median。
  • 虚线为CMIP6 模型中位数（SSP2-4.5，中间路径），是一种情景而非预报；阴影带为其模型第 10–90 百分位。
  • 气候模型值为 CMIP6 各模型的中位数：2014 年及以前按历史排放模拟，此后按中间路径 SSP2-4.5 模拟；它们并非实测数据。

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