巴西与危地马拉对比

巴西与危地马拉：热带夜

2014 年热带夜：巴西为 282 夜，危地马拉为 171.5 夜（CMIP6 气候模型中位数模拟）。 2014 年巴西的数值较高，高出 110.5 夜，是危地马拉的 1.64 倍。

在地图上查看其变化

每年夜数 · CMIP6

巴西与危地马拉：热带夜2014 年热带夜：巴西为 282 夜，危地马拉为 171.5 夜（CMIP6 气候模型中位数模拟）。 2014 年巴西的数值较高，高出 110.5 夜，是危地马拉的 1.64 倍。
巴西危地马拉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，巴西的数值都更高。
  • 根据CMIP6 模型中位数（SSP2-4.5，中间路径），2100 年巴西为 317.3 夜，危地马拉为 230.9 夜。

逐年数据

每年夜数 · CMIP6

年份巴西危地马拉
2014282171.5
2013282.5175.4
2012283.6171.8
2011281.8173.8
2010280.2171.4
2009279.2169.7
2008277.8167.8
2007275.9167.6
2006275.4167.2
2005275.8169.2
2004278.5167.4
2003276.7165.8
2002276.2165.7
2001277.7165.3
2000273.3163.6
1999271162
1998273.6162.1
1997270.9160.7
1996264.7157.9
1995263.8153.5
1994263.6155
1993258.3153.5
1992252.8151.7
1991269.7158.2
1990272.5161.9
1989267.1157.5
1988264.7154.2
1987265.9154.7
1986265.7153.1
1985260.6150.7
1984261.1150.3
1983255.9148.1
1982264.8151.3
1981265.3156.7
1980262152.3
1979261.2153
1978259.7149.5
1977261.8150.6
1976258.4151.6
1975252.4147.4
1974258.7150.2
1973261149.6
1972258.1148.2
1971256.5145.5
1970254.5145.4
1969255.1146.8
1968254.9146.7
1967250.3142.7
1966254.5145.9
1965250.8143.3
1964246.2143
1963241.9144.6
1962257.9149.5
1961258.2149.8
1960260.3151.4
1959255.7147.4
1958258.1150.5
1957256.6151.1
1956251.2148.2
1955257.2152.5
1954258150.4
1953255.2149.3
1952255.8150.3
1951256.3151.8
1950254.4148.6
CMIP6 模型中位数（SSP2-4.5，中间路径） · 2015–2100
年份巴西危地马拉
2015284257.6–299.5177158.4–194.6
2016281.5261.4–296.3178.5161.5–195.5
2017284.1263.2–297.8182.8166.6–194.9
2018283.4263.1–298.8180.2165–196.4
2019283.7262.9–302.4181.7159.3–199.5
2020286.3264.2–303.6184.1161.8–198.6
2021287.9266.9–302.3184.4168.1–201.1
2022289.6270.7–303.9184.6165.7–198.4
2023287.3270.5–303186.3171.1–207
2024291.5268.2–304.6187.1169.9–207.3
2025289.6269.1–304.8187.4169.7–205.6
2026289.7266.2–305.1190.9167.9–211
2027289.5269.1–303.7188.4171.9–210.9
2028291.2271.9–306191.3175.8–208.5
2029295.5276.3–309.6192.7175.6–208.8
2030292.2269.1–306.9191173.3–207.9
2031295.3274–308.9193.6175.3–211.4
2032296.8280.1–310.9196.4178.7–214.8
2033295.4273.3–308.5193.3174.6–208.5
2034294.3273.5–311.8193.5176.2–214.4
2035295.2274.4–310.9193.4177.7–212.8
2036295.1278.2–307.8194.4180.5–212.8
2037299.9283.7–311.3199.1180–216.2
2038299.6283.5–312.1202.6184.8–216.7
2039296.9279.9–311199181.6–217.6
2040298.5279.4–311.9200.3182.9–217.9
2041301.4282.2–314.1200.7182.1–218.2
2042300.6277.8–314.7201.6180.3–222.1
2043298.2279.8–312.7201.7182.3–226.4
2044300.3281.2–315.6200.8184.9–222.3
2045299.9279.7–313.1203.6188.7–225.1
2046302.8285.8–316.6204.5183.3–227.5
2047303.3285–316.4207.5190.3–227.5
2048304.4287.3–317.3208.2188.9–229.4
2049305.4288.4–318.5210.1188.7–227.4
2050303.9283.9–316.2211.4188–230.7
2051305.7287.5–317.5208.1192.7–229.2
2052305.9290.6–318.8207.7193–227.9
2053305.3285.9–317.5209.8186.4–233.2
2054306.1288.4–319.3209.5194.7–233.2
2055306.4290.8–319.8212.4196.2–231.9
2056306.7288.3–321.1210.9193.3–233.1
2057306.4287.6–321.1213.3193.7–232.4
2058309291–321.4211.7192.3–238.1
2059310.4290.7–322.8214.5190.4–237.4
2060311.6294.4–325.1217.4196.4–237.3
2061307.5290.3–321.2213.9193.7–237.1
2062309291.5–322.8216.3194–237.3
2063310.8290.6–322.8217.2197–240.8
2064309.6290.2–321.1214.5196.1–236.1
2065311.1291.6–323.1217.3195.2–238.2
2066312.2294.2–324.2217.9195.2–240
2067310.9291.5–323.3216.4194.1–241.1
2068311.5293–324.5221200.2–243.4
2069310.6292.2–323.6219.9200.5–241.9
2070312.7293–325.3219.6199.9–242.6
2071311.5292.1–323.9222.5203.4–243
2072313.3297.8–325.5225.6200.4–247.6
2073313.8298.6–327.2224.4199.9–248.3
2074311.4292.4–323.5222.5202.2–245.9
2075310.9291.5–324.5220.2198.7–242.6
2076314.2294.4–327.7222.9195.4–247.7
2077315.1295.6–326.8225.2198.3–245.9
2078314.9296.3–327.1225.4197.8–245.9
2079313292.8–326.8223.4199.3–248.2
2080311.9294.7–325.4223.4201–250.7
2081313.5295.5–325.6224202.5–246.6
2082315296.8–327.1224.8201.3–245.9
2083315.7295.3–328.7225202.3–250.5
2084316.6298.5–327.9227.9204.9–249.9
2085317.4299.3–329.8227.7199.2–251.3
2086315.8294–328224.9202.3–249.6
2087316.5297.9–328.5227.1197.8–249.7
2088315.6297.3–328.8230.6207.4–255.5
2089315296.6–327.9229.7207.4–251.4
2090316298.9–330.3228.2210–253.4
2091317298.1–330.6231.4206.6–255.4
2092317.6298.7–330.7230.1207.4–253.4
2093316.3297.5–328.6226.7206–255.5
2094316.4300.2–327.5227.3204.4–251.7
2095319299.8–330.7231.1207.7–255.5
2096317.7299.5–330.8229.9206.3–255.5
2097316.9297.8–329226.7208.6–249.5
2098318.6299.9–331.9231.1202.9–259.1
2099318.1300–330.2230.2203.1–256.5
2100317.3298.4–330.3230.9208.9–255.1

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

巴西与危地马拉对比

健康

人口与社会

经济

教育

政治

冲突

军用

气候

技术与环境