孟加拉国与泰国对比

孟加拉国与泰国：热带夜

2014 年热带夜：孟加拉国为 239.9 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 46.5 夜，是孟加拉国的 1.19 倍。

在地图上查看其变化

每年夜数 · CMIP6

孟加拉国与泰国：热带夜2014 年热带夜：孟加拉国为 239.9 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 46.5 夜，是孟加拉国的 1.19 倍。
孟加拉国泰国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 年孟加拉国为 269.8 夜，泰国为 320.8 夜。

逐年数据

每年夜数 · CMIP6

年份孟加拉国泰国
2014239.9286.4
2013239289.1
2012240.4286.6
2011240.5286.4
2010241286
2009238.6286.9
2008236.7284.8
2007236.9283.2
2006238.9284.3
2005238.2281.7
2004238.7287.5
2003236.5279.3
2002237.6280
2001237.2285.5
2000239.3283.4
1999236.6282.5
1998235.5282.7
1997235.5284.8
1996235.5279.8
1995234.8278.7
1994233.6280.8
1993232.2275.6
1992228268.8
1991233.4276.7
1990234.6279.2
1989235.7278.4
1988235.1279.6
1987236.4277.9
1986233.8277.5
1985233.6276
1984236277.9
1983230273.9
1982232.3275.7
1981235.9277.8
1980233.4277.9
1979234.5278
1978231.8276
1977232.3277.3
1976232275.6
1975231.3274.4
1974232.9276
1973232.1280.6
1972234.3273.9
1971233.1273.2
1970232.5276
1969231.1274.8
1968229.7277
1967231.6270.3
1966232.6274.4
1965232.5275.8
1964229.3271.9
1963233.8274.7
1962231.9275.4
1961236.2278.5
1960232.5276.2
1959234.7274.4
1958233.5278.7
1957232.5277.4
1956234.3277
1955230.2274.6
1954232.9275
1953234275.4
1952230.3275.1
1951233.5277.5
1950233.9276.2
CMIP6 模型中位数（SSP2-4.5，中间路径） · 2015–2100
年份孟加拉国泰国
2015241.2227–252.4290.6272.2–305
2016237.9226.6–249.3287.1269.8–306.2
2017239.8227.5–249.4290273.8–302.8
2018242.6227–255292.6275.1–308.7
2019241.2228.5–251.9291.2272.8–306.7
2020242.6229.3–254.4292.4272.2–307.8
2021244.3230.4–254.4294.2276.3–308.5
2022243.8231.5–254.2294.8277.2–311.9
2023243.5229.2–254.5296.1281.9–310.2
2024245.7231.4–257.1291.7275.5–307.8
2025242.5229.4–253.9294276.7–306.3
2026243.9227.9–258.1296278–309.3
2027247.1232.8–259.3295.8276.3–311.1
2028244.2232.2–254.4297279.3–315.1
2029247.3232.7–261.5300.3278.7–315
2030243.9232.3–255.2297.7280.2–312.5
2031245.1232.9–262.3300.8282.8–317.1
2032247.6236.6–257.8297.2280.9–311.4
2033245.3231.7–256.6300281.9–315.8
2034246.3233.2–260.4300.8279–318.4
2035246.4234.1–260.9298.6281.5–317.8
2036246.2228.6–260.4300.4279.9–316.9
2037247.7235.4–260.2299.8280–315.1
2038249232.1–261304.8286.6–319.3
2039250234.4–262.7300.9281.9–317
2040248.9235.5–260.5301.6284.5–315.7
2041248.3235.6–261.9303.1285.1–318.8
2042252.5237.8–265.3303.8290–318.3
2043250.8234.7–263.4302285.8–317.9
2044251.8239.1–265.2304.3286.3–318.8
2045250.3239.1–265.6307.1287.7–324.2
2046252.7238.9–267.8305.9285.2–321
2047253.9239.4–267.1306.6289.5–321.5
2048254.4242.7–270.5309.4288.9–325.7
2049253.7241.5–266.4308.1287.2–324.1
2050254.6242.5–269.2307.5291–321.5
2051254.7242.7–270.1312.8294.8–326
2052252.3240.2–264.6310.9291.2–323.4
2053256.9244–272.4308.3289.5–323.3
2054255.9243.5–269.4308.8292.8–322.3
2055255.2243.8–273.6308.6290.6–325.1
2056259.1243.6–274.7311.3294.9–326.3
2057257.5243.8–273.7308.5286.7–326.4
2058257.4245.6–274.2308291.1–325.1
2059258.3246.4–271.8312.7295.8–325.3
2060259.2246.1–272.6313.1294.6–328.6
2061257.7244.8–270.1314.4289.5–331.1
2062260.4246.4–274.2311.4293.4–330.3
2063259.9241.3–272.7314.8296.9–327.3
2064260.2245.9–273.1312.5295.4–330
2065259.8244.1–274.7309.6293.1–329.3
2066260246.6–274.6312.1292.6–328
2067261.4246.4–272.3311.2289.6–329.4
2068260.1245.8–274.8310.8292.4–330.6
2069263.3249.8–276.6315.5296.1–329.5
2070259.6245.8–272.6314.2292.9–330.7
2071262.2248.7–283.4313.8296.4–330.8
2072259.8248.6–276314.8296–329.6
2073264.1246.6–277.2316.5299.9–332.1
2074262.6248.3–276.6317.2295–333.8
2075263.6248.5–277.7317.2295.3–331.4
2076261.6251–276.9317.4299.8–335
2077264251.1–279.8317.5297.3–335.9
2078265251.6–281.6315.9297.1–331.6
2079263.8248.5–277.2318.5300.5–332.3
2080266.8251.9–280.3319.8297.4–337.5
2081266.5250.7–279.9317.7299.5–334.9
2082264249.6–277.3317.9294.8–333
2083266.6251.8–282.8315297.8–332.9
2084264.8253.1–284.2316295.5–330
2085265.4251.2–283.4322.6300.8–334.7
2086268.2252.8–285.3319.4295.2–335.2
2087265.7251.3–280.8320.2302.2–334.7
2088267.2252.6–284.4321.4299.8–334.8
2089267.1252.5–286.7318.9296.4–338.1
2090267.9254.9–283.4317.6304–337.4
2091267.7254.5–282.2320.1299.8–336.1
2092268.8253.6–281.7316.3298–336.4
2093268.1251.7–287316.1297.7–333.7
2094267.4251.3–287.4323.4301.8–337.3
2095270.8253.7–284.9322.9302.4–338.9
2096267.9250.5–285.8319.6302.8–336.7
2097269.3254.9–287.3319.8299.1–336
2098268.8252.4–284.9320.5303.9–336
2099269253–282.3319.9303.1–334.4
2100269.8254.8–284.9320.8301.1–336.7

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

孟加拉国与泰国对比

健康

人口与社会

经济

教育

政治

冲突

军用

气候

技术与环境