阿根廷与泰国：热带夜

2014 年热带夜：阿根廷为 42.6 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 243.8 夜，是阿根廷的 6.72 倍。

每年夜数 · CMIP6

阿根廷泰国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 年阿根廷为 65.2 夜，泰国为 320.8 夜。

逐年数据

每年夜数 · CMIP6

年份	阿根廷	泰国
2014	42.6	286.4
2013	41.8	289.1
2012	41	286.6
2011	41.5	286.4
2010	40.8	286
2009	41.6	286.9
2008	41.4	284.8
2007	39.6	283.2
2006	39.3	284.3
2005	40.1	281.7
2004	40.6	287.5
2003	38.7	279.3
2002	40.2	280
2001	39.5	285.5
2000	39.5	283.4
1999	38.4	282.5
1998	38.8	282.7
1997	38.5	284.8
1996	37	279.8
1995	35.7	278.7
1994	34.7	280.8
1993	34.3	275.6
1992	33.4	268.8
1991	36.8	276.7
1990	36	279.2
1989	37	278.4
1988	36.9	279.6
1987	36.1	277.9
1986	36.4	277.5
1985	36	276
1984	34.7	277.9
1983	35.2	273.9
1982	35.7	275.7
1981	37.3	277.8
1980	36.3	277.9
1979	37.5	278
1978	35.7	276
1977	35.1	277.3
1976	35.7	275.6
1975	34.8	274.4
1974	35.4	276
1973	34.1	280.6
1972	35.5	273.9
1971	34.7	273.2
1970	34.9	276
1969	33.4	274.8
1968	34.6	277
1967	33	270.3
1966	34.3	274.4
1965	32.2	275.8
1964	30.3	271.9
1963	32	274.7
1962	35.2	275.4
1961	35.6	278.5
1960	35.6	276.2
1959	34.5	274.4
1958	33.3	278.7
1957	35.7	277.4
1956	35	277
1955	34.4	274.6
1954	33.6	275
1953	35.8	275.4
1952	34.4	275.1
1951	35.4	277.5
1950	33.9	276.2

CMIP6 模型中位数（SSP2-4.5，中间路径） · 2015–2100

年份	阿根廷	泰国
2015	44.433.5–53.4	290.6272.2–305
2016	43.532.4–53.4	287.1269.8–306.2
2017	42.834.2–52.6	290273.8–302.8
2018	42.632.7–52.6	292.6275.1–308.7
2019	43.633.4–53.1	291.2272.8–306.7
2020	45.735.5–57.2	292.4272.2–307.8
2021	45.634.3–56.4	294.2276.3–308.5
2022	44.633.3–56.4	294.8277.2–311.9
2023	44.934.9–53.9	296.1281.9–310.2
2024	45.834.2–57.5	291.7275.5–307.8
2025	46.536–58.1	294276.7–306.3
2026	47.936.7–57	296278–309.3
2027	46.434.5–56.3	295.8276.3–311.1
2028	46.133.5–58.5	297279.3–315.1
2029	48.336–58.8	300.3278.7–315
2030	47.835.4–60.3	297.7280.2–312.5
2031	46.935.5–58.7	300.8282.8–317.1
2032	4837.9–59.7	297.2280.9–311.4
2033	47.436.7–57.6	300281.9–315.8
2034	47.837.8–58.9	300.8279–318.4
2035	47.339–59	298.6281.5–317.8
2036	48.939.2–58.4	300.4279.9–316.9
2037	5141.2–60.9	299.8280–315.1
2038	48.737.9–59.7	304.8286.6–319.3
2039	48.839.1–58.4	300.9281.9–317
2040	50.440.4–62.3	301.6284.5–315.7
2041	50.740.4–61.7	303.1285.1–318.8
2042	5037.3–61.1	303.8290–318.3
2043	50.340.5–61	302285.8–317.9
2044	5240–65	304.3286.3–318.8
2045	52.441.3–65.6	307.1287.7–324.2
2046	51.941.2–62.7	305.9285.2–321
2047	51.542–64	306.6289.5–321.5
2048	54.542.6–65	309.4288.9–325.7
2049	53.942.8–64.9	308.1287.2–324.1
2050	52.543.6–62.4	307.5291–321.5
2051	55.743.3–67.4	312.8294.8–326
2052	55.642.2–67	310.9291.2–323.4
2053	55.342.6–67.6	308.3289.5–323.3
2054	54.743–66.7	308.8292.8–322.3
2055	55.942.3–67.2	308.6290.6–325.1
2056	57.642.8–68.6	311.3294.9–326.3
2057	56.842.3–68.7	308.5286.7–326.4
2058	54.841.5–68.1	308291.1–325.1
2059	57.844.9–70.3	312.7295.8–325.3
2060	56.943.4–67.9	313.1294.6–328.6
2061	56.542.3–70.6	314.4289.5–331.1
2062	57.345.8–68.5	311.4293.4–330.3
2063	58.244.6–70.2	314.8296.9–327.3
2064	58.845.8–71.5	312.5295.4–330
2065	58.746–71.1	309.6293.1–329.3
2066	59.947–68.9	312.1292.6–328
2067	58.944.8–71.5	311.2289.6–329.4
2068	57.844.3–70.3	310.8292.4–330.6
2069	59.445.4–70.5	315.5296.1–329.5
2070	61.348.2–74.3	314.2292.9–330.7
2071	60.148.7–73.2	313.8296.4–330.8
2072	60.146.7–73.7	314.8296–329.6
2073	60.847.6–73.5	316.5299.9–332.1
2074	60.447.6–73.7	317.2295–333.8
2075	61.347.9–73.9	317.2295.3–331.4
2076	61.146.8–75.8	317.4299.8–335
2077	60.647.1–73.1	317.5297.3–335.9
2078	63.449.4–75.6	315.9297.1–331.6
2079	59.948.7–73	318.5300.5–332.3
2080	6348.1–75.5	319.8297.4–337.5
2081	61.448.7–73.5	317.7299.5–334.9
2082	61.747.7–75	317.9294.8–333
2083	62.148.2–75.6	315297.8–332.9
2084	62.950.3–76	316295.5–330
2085	6246.9–76.4	322.6300.8–334.7
2086	62.749.4–76.4	319.4295.2–335.2
2087	61.847.9–74.9	320.2302.2–334.7
2088	64.450.8–77.5	321.4299.8–334.8
2089	62.749.3–76.3	318.9296.4–338.1
2090	64.152.5–77.1	317.6304–337.4
2091	64.451.7–78.3	320.1299.8–336.1
2092	65.650.8–78.3	316.3298–336.4
2093	62.749.7–76.8	316.1297.7–333.7
2094	63.950.1–78.5	323.4301.8–337.3
2095	62.948.8–77.9	322.9302.4–338.9
2096	64.749.4–78.9	319.6302.8–336.7
2097	64.552.4–80.1	319.8299.1–336
2098	65.652.7–78.7	320.5303.9–336
2099	65.249.3–80.2	319.9303.1–334.4
2100	65.250–81.7	320.8301.1–336.7

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

阿根廷与泰国对比

健康

人口与社会

经济

教育

政治

冲突

冲突死亡人数阿根廷：2022 年为 0 人死亡泰国：2022 年为 46 人死亡