墨西哥与巴基斯坦对比

墨西哥与巴基斯坦:热带夜

2014 年热带夜:墨西哥为 103.7 夜,巴基斯坦为 149.9 夜(CMIP6 气候模型中位数模拟)。 2014 年巴基斯坦的数值较高,高出 46.2 夜,是墨西哥的 1.45 倍。

每年夜数 · CMIP6

墨西哥与巴基斯坦:热带夜2014 年热带夜:墨西哥为 103.7 夜,巴基斯坦为 149.9 夜(CMIP6 气候模型中位数模拟)。 2014 年巴基斯坦的数值较高,高出 46.2 夜,是墨西哥的 1.45 倍。
墨西哥巴基斯坦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,巴基斯坦的数值都更高。
  • 根据CMIP6 模型中位数(SSP2-4.5,中间路径),2100 年墨西哥为 142.8 夜,巴基斯坦为 177.6 夜。

逐年数据

每年夜数 · CMIP6

年份墨西哥巴基斯坦
2014103.7149.9
2013103.9149.1
2012102.8149.9
2011102.9149
2010101.5148.4
2009101.8147.1
2008100.1147.4
200799.1147.3
200699.8146.7
200599.5146.5
200499.4147.5
200398.3146
200299.1148.3
200198.7145.4
200097.9144.7
199996.6146.9
199895.8144.8
199795.9143.2
199693.8141.2
199592.7141.8
199490.7142.1
199389140.4
199288.5139.9
199193.4142.5
199094.9142.6
198993142.5
198892.1140.3
198792140
198690.2140
198590140
198488138.9
198386.7136.3
198288.7138.5
198191.5141.5
198090.1139.5
197990.8139.1
197889139
197789138.6
197689.1139.2
197586.8136.5
197487.9138.7
197388.6137.8
197287.7137.4
197186.8136
197086.1137.7
196987.5135.9
196887.1136.6
196786.7135.2
196686.4136.9
196584.9138.3
196484.7135.1
196387.1135.7
196288137
196189.5138.6
196089.7139
195989.6138.6
195889.1137.7
195789140
195688.5137.8
195588.2137.5
195488.4137.8
195388.6136.9
195287.8138.1
195189.4136.7
195088.1139.1
CMIP6 模型中位数(SSP2-4.5,中间路径) · 2015–2100
年份墨西哥巴基斯坦
2015106.192.9–118.4152.4138.7–163.3
2016107.695–120150.6139.4–161.7
2017108.696.3–119.7151.3138.8–162
201810997.1–120.4152.6139.8–161.8
2019110.396.4–123151.8138.1–163.3
2020109.696.8–121.8153.1140.3–164.5
2021111.296.9–124.1154.1140.6–165.7
2022109.796–121.7153.1140.2–165.7
2023111.898.6–125.3155.5141.6–165.9
2024112.298.2–126.2156.8143.3–168.7
202511298.8–125.8155.7141.5–168.6
2026113.999.5–126.4155.3142.2–167.9
2027112.999.5–128.3156.8140.9–168.9
2028115.2101.9–127.9156.5143.9–167.6
2029115.6101.9–127.9155.9141.5–167.7
2030115.9101.7–128.4156.3143.3–167.6
2031116.1100.9–129.9157.3144.4–168.1
2032116.1102.5–129.1157.4142.5–169.6
2033117.5102.5–131.1157.7143.6–169.4
2034117.2104.1–130.3159.5145.6–173.4
2035116.4103.1–130.3159.3146.7–170.3
2036117.8105.1–131159.1147.5–171.4
2037118.6104.3–131.7162.2147.5–175.3
2038121107.5–133.4161.4148.6–171.7
2039121.3107–135.6160148.2–170.4
2040121.9108.1–135.1160.9147.9–171.3
2041121.3105.3–134.6160.8146.7–171.7
2042122.7107.7–137.1162.3149.9–175.6
2043122.4108.1–137.5161.1150.3–173.8
2044121.9108–135.6163150.9–173.9
2045123.1108.8–139.2163149.5–174.8
2046123.8109.9–139.4163.5149.8–177.2
2047125.6111.9–139.4165.2151.8–178.5
2048126.3113–140.3166.4154.1–179.4
2049127111.2–140.8165150.8–178.2
2050128.1112.6–142.5166.9153.5–179.3
2051127.2113.4–140.8165.6150.8–180
2052126.2112.5–141.1166.7155.4–179.5
2053128.4111.5–142.7166.3154.2–178.8
2054128.1113.1–142.3167.5153.9–181
2055129114.4–144.7167.8155.8–182.5
2056129.5114.3–143.8166.3153.6–179.7
2057130.3113.5–145.9168.3153.9–182.3
2058128.8114.1–144.5167.5157–179.9
2059129.5114.5–144.5170.9155.9–183.8
2060131.4115.5–146.5170.3156.5–181.8
2061131.3118.4–146.8168.8156.8–179.6
2062130.8115.1–146.3171.6153.5–186.5
2063131.2115.6–147.9171.3159.5–183.8
2064132.2116.5–147.8170.2155.3–184.5
2065133.1115.8–148.6169.7156.4–185
2066133.3117.2–149.4172.2157–186.7
2067133.3117–150.3173.2160.2–186.3
2068134.6117.4–150.5172.6157.7–186.3
2069134.4117.8–150.9171.8157.3–185.6
2070134.2116.7–151.8171.7158.9–186.7
2071136.3120.8–152.7173.4158.7–186.8
2072137.2119.9–153.1174161.5–186.9
2073136.1119.1–153.6175.6157.2–187.9
2074136119.7–152.2173.5160–186.3
2075135.2118.5–151.8173161.5–187.8
2076136.3117.4–154.7172.9158.8–186.7
2077136.8119.6–152.8175161.3–187.8
2078137.1118.3–154.2174.4160.9–189.3
2079138.1121.3–157.1172.5158.3–187.7
2080137.6120.5–157.9175.1160.7–189.2
2081137.8120.7–154.7174.9159.2–190.2
2082138.7121.6–155.2175.4161.8–190.6
2083139.2119.3–158.8176.5161.8–190.8
2084140.3123.4–157.5176.1160.9–190.5
2085139.2120.9–157.2176.3161.9–193.6
2086139.8121–157.8173.9160–189.1
2087139.5121.5–157.4176161–193.1
2088141121.8–159.9175.4160.5–195.2
2089140.4121.7–158.9177.2164.2–191.7
2090141.2123–160.7177.7163.9–192.2
2091141.8123–161.6177.8162.8–192.2
2092141.3122.5–159.3178.7162.9–192.9
2093141.4124.3–161177164–191
2094142123.3–159.6176.8164.1–190.7
2095143.5124–160.7178.6163.8–193.1
2096142.3125–161.3177.8163.7–192.5
2097141.2123.2–160177.3160.4–192.5
2098142.2122.5–162178.7162.6–195.7
2099141.8122.4–160.7176159.9–193.2
2100142.8124.3–160.8177.6162.2–193

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

墨西哥与巴基斯坦对比

健康

人口与社会

经济

教育

政治

冲突

军用

气候

技术与环境