巴基斯坦与苏丹对比
巴基斯坦与苏丹:热带夜
2014 年热带夜:巴基斯坦为 149.9 夜,苏丹为 242.4 夜(CMIP6 气候模型中位数模拟)。 2014 年苏丹的数值较高,高出 92.5 夜,是巴基斯坦的 1.62 倍。
每年夜数 · CMIP6
数据解读
- 自 1950 年以来,在两国都有数据的每一年,苏丹的数值都更高。
- 根据CMIP6 模型中位数(SSP2-4.5,中间路径),2100 年巴基斯坦为 177.6 夜,苏丹为 278.6 夜。
逐年数据
每年夜数 · CMIP6
| 年份 | 巴基斯坦 | 苏丹 |
|---|---|---|
| 2014 | 149.9 | 242.4 |
| 2013 | 149.1 | 240 |
| 2012 | 149.9 | 242 |
| 2011 | 149 | 242.2 |
| 2010 | 148.4 | 240.6 |
| 2009 | 147.1 | 236.5 |
| 2008 | 147.4 | 237.1 |
| 2007 | 147.3 | 236 |
| 2006 | 146.7 | 235.3 |
| 2005 | 146.5 | 233.1 |
| 2004 | 147.5 | 233.5 |
| 2003 | 146 | 235.6 |
| 2002 | 148.3 | 234.7 |
| 2001 | 145.4 | 235.4 |
| 2000 | 144.7 | 230.5 |
| 1999 | 146.9 | 232.1 |
| 1998 | 144.8 | 230.2 |
| 1997 | 143.2 | 231.1 |
| 1996 | 141.2 | 227.3 |
| 1995 | 141.8 | 227.8 |
| 1994 | 142.1 | 225.3 |
| 1993 | 140.4 | 219.1 |
| 1992 | 139.9 | 217.8 |
| 1991 | 142.5 | 227.3 |
| 1990 | 142.6 | 227.5 |
| 1989 | 142.5 | 223 |
| 1988 | 140.3 | 223.1 |
| 1987 | 140 | 224.4 |
| 1986 | 140 | 226.4 |
| 1985 | 140 | 222.1 |
| 1984 | 138.9 | 220.7 |
| 1983 | 136.3 | 217 |
| 1982 | 138.5 | 216.3 |
| 1981 | 141.5 | 223.2 |
| 1980 | 139.5 | 220 |
| 1979 | 139.1 | 218.4 |
| 1978 | 139 | 218.5 |
| 1977 | 138.6 | 219.3 |
| 1976 | 139.2 | 220.1 |
| 1975 | 136.5 | 215.7 |
| 1974 | 138.7 | 217.5 |
| 1973 | 137.8 | 220 |
| 1972 | 137.4 | 217.9 |
| 1971 | 136 | 215.5 |
| 1970 | 137.7 | 217 |
| 1969 | 135.9 | 213.1 |
| 1968 | 136.6 | 214 |
| 1967 | 135.2 | 210.3 |
| 1966 | 136.9 | 213.3 |
| 1965 | 138.3 | 213.4 |
| 1964 | 135.1 | 213.2 |
| 1963 | 135.7 | 210.1 |
| 1962 | 137 | 217 |
| 1961 | 138.6 | 216.9 |
| 1960 | 139 | 221.3 |
| 1959 | 138.6 | 217.8 |
| 1958 | 137.7 | 216.2 |
| 1957 | 140 | 218.9 |
| 1956 | 137.8 | 217.2 |
| 1955 | 137.5 | 216.6 |
| 1954 | 137.8 | 218.9 |
| 1953 | 136.9 | 215.1 |
| 1952 | 138.1 | 216.4 |
| 1951 | 136.7 | 218.5 |
| 1950 | 139.1 | 214.1 |
CMIP6 模型中位数(SSP2-4.5,中间路径) · 2015–2100
| 年份 | 巴基斯坦 | 苏丹 |
|---|---|---|
| 2015 | 152.4138.7–163.3 | 242.3225.3–258.3 |
| 2016 | 150.6139.4–161.7 | 241.4223.3–254.1 |
| 2017 | 151.3138.8–162 | 243.2223–259.1 |
| 2018 | 152.6139.8–161.8 | 242.3226.4–258.5 |
| 2019 | 151.8138.1–163.3 | 243.1221.7–260.4 |
| 2020 | 153.1140.3–164.5 | 244.9225.9–261.7 |
| 2021 | 154.1140.6–165.7 | 245.6229.1–261.5 |
| 2022 | 153.1140.2–165.7 | 249230.9–262.3 |
| 2023 | 155.5141.6–165.9 | 247.1226.8–263.1 |
| 2024 | 156.8143.3–168.7 | 251.5232.8–266.1 |
| 2025 | 155.7141.5–168.6 | 250.7232.4–265.6 |
| 2026 | 155.3142.2–167.9 | 250230.8–266.6 |
| 2027 | 156.8140.9–168.9 | 248.8228.4–265.6 |
| 2028 | 156.5143.9–167.6 | 249232.2–263.1 |
| 2029 | 155.9141.5–167.7 | 251.4232.1–266 |
| 2030 | 156.3143.3–167.6 | 252235.4–266.7 |
| 2031 | 157.3144.4–168.1 | 253.3234.4–268.9 |
| 2032 | 157.4142.5–169.6 | 250.4232.3–266.6 |
| 2033 | 157.7143.6–169.4 | 254.4235.3–269.4 |
| 2034 | 159.5145.6–173.4 | 254.2234.5–269.9 |
| 2035 | 159.3146.7–170.3 | 258.3240.2–273.6 |
| 2036 | 159.1147.5–171.4 | 252.8235.6–268.6 |
| 2037 | 162.2147.5–175.3 | 256.7238.6–271.8 |
| 2038 | 161.4148.6–171.7 | 258.7244.8–272.1 |
| 2039 | 160148.2–170.4 | 259.5241.2–272.5 |
| 2040 | 160.9147.9–171.3 | 256.4236.7–272.3 |
| 2041 | 160.8146.7–171.7 | 259.1241.2–274.5 |
| 2042 | 162.3149.9–175.6 | 258.7242–274.7 |
| 2043 | 161.1150.3–173.8 | 258.1240.3–272.6 |
| 2044 | 163150.9–173.9 | 257.3238.6–274.7 |
| 2045 | 163149.5–174.8 | 258.8243.1–276.2 |
| 2046 | 163.5149.8–177.2 | 261.1241.7–277.6 |
| 2047 | 165.2151.8–178.5 | 261.4244.6–278.4 |
| 2048 | 166.4154.1–179.4 | 260.4243.4–279.5 |
| 2049 | 165150.8–178.2 | 261.9242.6–275.9 |
| 2050 | 166.9153.5–179.3 | 263.5245.4–279 |
| 2051 | 165.6150.8–180 | 263.6246.6–278.8 |
| 2052 | 166.7155.4–179.5 | 263.8248.7–278.3 |
| 2053 | 166.3154.2–178.8 | 262.8242.4–279.2 |
| 2054 | 167.5153.9–181 | 266.3246.6–282.9 |
| 2055 | 167.8155.8–182.5 | 265.6245.9–281.4 |
| 2056 | 166.3153.6–179.7 | 264.1242.6–281.5 |
| 2057 | 168.3153.9–182.3 | 266.4247.6–283.4 |
| 2058 | 167.5157–179.9 | 265.2247.4–280.3 |
| 2059 | 170.9155.9–183.8 | 268.4249.6–285.4 |
| 2060 | 170.3156.5–181.8 | 263.6246.7–284.4 |
| 2061 | 168.8156.8–179.6 | 266.9250.1–281.9 |
| 2062 | 171.6153.5–186.5 | 266.4248.5–285 |
| 2063 | 171.3159.5–183.8 | 267.1246.9–283.4 |
| 2064 | 170.2155.3–184.5 | 269250.7–284.7 |
| 2065 | 169.7156.4–185 | 269.8253.9–285.6 |
| 2066 | 172.2157–186.7 | 271.4251.9–287.3 |
| 2067 | 173.2160.2–186.3 | 270.9250–288.4 |
| 2068 | 172.6157.7–186.3 | 271.9251.7–289.8 |
| 2069 | 171.8157.3–185.6 | 268251.1–286.2 |
| 2070 | 171.7158.9–186.7 | 269252.3–285.8 |
| 2071 | 173.4158.7–186.8 | 275.5254.1–290.4 |
| 2072 | 174161.5–186.9 | 273.3254.2–289.4 |
| 2073 | 175.6157.2–187.9 | 271.9250.7–287.6 |
| 2074 | 173.5160–186.3 | 270.9249–290.1 |
| 2075 | 173161.5–187.8 | 273.1254–291.9 |
| 2076 | 172.9158.8–186.7 | 272.7255.2–290.9 |
| 2077 | 175161.3–187.8 | 274.6255.2–292.7 |
| 2078 | 174.4160.9–189.3 | 274.3255.6–291.2 |
| 2079 | 172.5158.3–187.7 | 272255.2–288.5 |
| 2080 | 175.1160.7–189.2 | 276.6256–292.2 |
| 2081 | 174.9159.2–190.2 | 275.1254.1–294.8 |
| 2082 | 175.4161.8–190.6 | 272.9258.7–288.6 |
| 2083 | 176.5161.8–190.8 | 276.5258–291.7 |
| 2084 | 176.1160.9–190.5 | 275.4253–294.9 |
| 2085 | 176.3161.9–193.6 | 275.3258.6–291.6 |
| 2086 | 173.9160–189.1 | 276.4256.9–296.6 |
| 2087 | 176161–193.1 | 273.5253.1–293.3 |
| 2088 | 175.4160.5–195.2 | 273.6253.7–293.6 |
| 2089 | 177.2164.2–191.7 | 276.5256.6–293.4 |
| 2090 | 177.7163.9–192.2 | 276.6256.4–294.6 |
| 2091 | 177.8162.8–192.2 | 277.4256.4–296.6 |
| 2092 | 178.7162.9–192.9 | 274.6254.3–294.9 |
| 2093 | 177164–191 | 278261.9–292.1 |
| 2094 | 176.8164.1–190.7 | 280.3262.2–294 |
| 2095 | 178.6163.8–193.1 | 277.5262.6–294 |
| 2096 | 177.8163.7–192.5 | 279.7258.7–297 |
| 2097 | 177.3160.4–192.5 | 276.3254.1–297.6 |
| 2098 | 178.7162.6–195.7 | 277.7258.2–295.5 |
| 2099 | 176159.9–193.2 | 276.8257.2–296.2 |
| 2100 | 177.6162.2–193 | 278.6259–294.3 |
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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
巴基斯坦与苏丹对比
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军用
- 军费开支巴基斯坦:2025 年为 GDP 的 2.91%苏丹:2021 年为 GDP 的 0.93%
- 军费开支(美元)巴基斯坦:2025 年为 US$115亿苏丹:2021 年为 US$27.7亿
- 人均军费开支巴基斯坦:2025 年为 US$47苏丹:2021 年为 US$8
- 军费占政府支出比例巴基斯坦:2025 年为 13.8%(占政府支出)苏丹:2021 年为 9.5%(占政府支出)
- 武装部队人员巴基斯坦:2020 年为 943,000 人苏丹:2020 年为 144,000 人
- 国家实力巴基斯坦:2022 年为 1.77%(占全球实力)苏丹:2022 年为 0.37%(占全球实力)
- 武装部队占劳动力比例巴基斯坦:2020 年为 1.25%(占劳动力)苏丹:2020 年为 1.34%(占劳动力)
- 武器进口巴基斯坦:2025 年为 7.06亿 TIV苏丹:2025 年为 5700万 TIV
- 武器出口巴基斯坦:2025 年为 1.27亿 TIV苏丹:2013 年为 1100万 TIV