泰国与越南对比

泰国与越南：热带夜

2014 年热带夜：泰国为 286.4 夜，越南为 242.6 夜（CMIP6 气候模型中位数模拟）。 2014 年泰国的数值较高，高出 43.8 夜，是越南的 1.18 倍。

在地图上查看其变化

每年夜数 · CMIP6

泰国与越南：热带夜2014 年热带夜：泰国为 286.4 夜，越南为 242.6 夜（CMIP6 气候模型中位数模拟）。 2014 年泰国的数值较高，高出 43.8 夜，是越南的 1.18 倍。
泰国越南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 年泰国为 320.8 夜，越南为 282.7 夜。

逐年数据

每年夜数 · CMIP6

年份泰国越南
2014286.4242.6
2013289.1243.6
2012286.6242.4
2011286.4241.9
2010286240.9
2009286.9240
2008284.8238.7
2007283.2238
2006284.3239.1
2005281.7238.3
2004287.5241.8
2003279.3235.9
2002280236.3
2001285.5241.3
2000283.4237
1999282.5236
1998282.7236.8
1997284.8237.1
1996279.8231.4
1995278.7229.9
1994280.8231.2
1993275.6225.3
1992268.8221.9
1991276.7229.8
1990279.2233.1
1989278.4229.2
1988279.6231.7
1987277.9230.3
1986277.5230.9
1985276228
1984277.9230.2
1983273.9224.9
1982275.7227.5
1981277.8229.7
1980277.9228.1
1979278228.9
1978276227.5
1977277.3229
1976275.6225.8
1975274.4225.8
1974276225.7
1973280.6230.3
1972273.9225.6
1971273.2225.5
1970276226.1
1969274.8227
1968277227.1
1967270.3222.9
1966274.4226.8
1965275.8225.4
1964271.9222.6
1963274.7224.3
1962275.4227.8
1961278.5229.4
1960276.2228.5
1959274.4226.2
1958278.7230.9
1957277.4228.4
1956277228.7
1955274.6227.5
1954275226.3
1953275.4228.2
1952275.1228.5
1951277.5229.2
1950276.2228.9
CMIP6 模型中位数（SSP2-4.5，中间路径） · 2015–2100
年份泰国越南
2015290.6272.2–305245.8228.2–261.9
2016287.1269.8–306.2243.7226.1–259.4
2017290273.8–302.8247229.6–260.7
2018292.6275.1–308.7248.3228.8–263.3
2019291.2272.8–306.7248.1229.2–262.8
2020292.4272.2–307.8248.8229.6–262.9
2021294.2276.3–308.5251.1233.1–266.7
2022294.8277.2–311.9252.4234.3–266.8
2023296.1281.9–310.2253.4235.4–266.6
2024291.7275.5–307.8251.3236.7–264.6
2025294276.7–306.3252.4234.1–266.2
2026296278–309.3252.8232.9–268.5
2027295.8276.3–311.1253.2237–268.8
2028297279.3–315.1254.3237.7–272
2029300.3278.7–315258.1238.5–273.7
2030297.7280.2–312.5256.4240.9–270.1
2031300.8282.8–317.1255.9241.2–270.9
2032297.2280.9–311.4257.4239.1–269.9
2033300281.9–315.8258.3241.7–273.2
2034300.8279–318.4258.8240.7–275.8
2035298.6281.5–317.8258.6242.5–275.4
2036300.4279.9–316.9258.3239.1–273.8
2037299.8280–315.1258.7242.4–272.4
2038304.8286.6–319.3263.4247.6–277.3
2039300.9281.9–317260.2243.7–275.9
2040301.6284.5–315.7260.1242.8–274.8
2041303.1285.1–318.8263.1245.9–279
2042303.8290–318.3263.6246.5–279
2043302285.8–317.9261.4245.5–279.6
2044304.3286.3–318.8263.2245.2–277.6
2045307.1287.7–324.2265.5249.6–281.4
2046305.9285.2–321264.9246.8–278.1
2047306.6289.5–321.5266.9250.4–282.9
2048309.4288.9–325.7268250.5–283.8
2049308.1287.2–324.1267.8251.2–284.8
2050307.5291–321.5268251.2–283.5
2051312.8294.8–326270.9254.3–285.4
2052310.9291.2–323.4268.3253.3–282.8
2053308.3289.5–323.3268.5250.1–283.2
2054308.8292.8–322.3269.1250.8–283.6
2055308.6290.6–325.1270249.8–284.2
2056311.3294.9–326.3271.7254.8–290.1
2057308.5286.7–326.4270253–285.7
2058308291.1–325.1270.7252.9–286.5
2059312.7295.8–325.3273257.6–286.6
2060313.1294.6–328.6275.7255.6–290.6
2061314.4289.5–331.1273.5254.6–289.5
2062311.4293.4–330.3271.4253.1–290.2
2063314.8296.9–327.3272.9252.7–288.1
2064312.5295.4–330273.8254.4–288.1
2065309.6293.1–329.3272255.8–286.3
2066312.1292.6–328271.8254.4–287.3
2067311.2289.6–329.4270.8252.1–287.6
2068310.8292.4–330.6275.6256.9–292.4
2069315.5296.1–329.5276.4258.5–292
2070314.2292.9–330.7274.6257.8–292.8
2071313.8296.4–330.8276.2255.2–294.1
2072314.8296–329.6277256.8–291.4
2073316.5299.9–332.1278.5262.7–293.4
2074317.2295–333.8277.6259.2–295.8
2075317.2295.3–331.4276.4258.7–294.4
2076317.4299.8–335279.2258.5–296.6
2077317.5297.3–335.9280264.9–294.7
2078315.9297.1–331.6278.4258.6–295.5
2079318.5300.5–332.3279.3263.4–293.6
2080319.8297.4–337.5280261.4–296.5
2081317.7299.5–334.9280.4261.2–297.9
2082317.9294.8–333280.9258.9–296
2083315297.8–332.9278.4261.6–295
2084316295.5–330279257.4–293.8
2085322.6300.8–334.7281.3265–297.8
2086319.4295.2–335.2282.3260.2–297.5
2087320.2302.2–334.7281.9261.3–300.7
2088321.4299.8–334.8283.1265.7–301.5
2089318.9296.4–338.1280.8261.2–301.6
2090317.6304–337.4280.5264.5–297.6
2091320.1299.8–336.1283.3264.3–298.9
2092316.3298–336.4281.1261.5–299.4
2093316.1297.7–333.7280.5263.4–296.1
2094323.4301.8–337.3283264.5–300
2095322.9302.4–338.9285.5264.5–302.9
2096319.6302.8–336.7281.8264.8–299.8
2097319.8299.1–336281.5263.3–299
2098320.5303.9–336282.5263.9–301.5
2099319.9303.1–334.4282.7265.1–298
2100320.8301.1–336.7282.7261–298.2

分别查看各国

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。

各图层如何运作、数据从何而来，以及它们无法告诉你什么： 历年各国数据

泰国与越南对比

健康

人口与社会

经济

教育

政治

冲突

军用

气候

技术与环境