马拉维与南苏丹对比
马拉维与南苏丹:热带夜
2014 年热带夜:马拉维为 138.1 夜,南苏丹为 343.7 夜(CMIP6 气候模型中位数模拟)。 2014 年南苏丹的数值较高,高出 205.6 夜,是马拉维的 2.49 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,南苏丹的数值都更高。
- 根据CMIP6 模型中位数(SSP2-4.5,中间路径),2100 年马拉维为 213.3 夜,南苏丹为 361.1 夜。
逐年数据
每年夜数 · CMIP6
| 年份 | 马拉维 | 南苏丹 |
|---|---|---|
| 2014 | 138.1 | 343.7 |
| 2013 | 138.9 | 342.1 |
| 2012 | 137.4 | 342.8 |
| 2011 | 136.6 | 343.3 |
| 2010 | 132.4 | 341.9 |
| 2009 | 131.4 | 338.8 |
| 2008 | 130.8 | 339.8 |
| 2007 | 132.8 | 336.9 |
| 2006 | 129.8 | 339 |
| 2005 | 129.5 | 336.8 |
| 2004 | 133.4 | 336.6 |
| 2003 | 132.5 | 338.9 |
| 2002 | 130.3 | 336.6 |
| 2001 | 134 | 338.7 |
| 2000 | 126.6 | 334.6 |
| 1999 | 124.8 | 331.8 |
| 1998 | 127.1 | 333.6 |
| 1997 | 124.2 | 334.5 |
| 1996 | 121.3 | 330.7 |
| 1995 | 120.9 | 329.6 |
| 1994 | 118.4 | 328.2 |
| 1993 | 113.8 | 321.9 |
| 1992 | 114.1 | 321.5 |
| 1991 | 120.9 | 329.1 |
| 1990 | 123.8 | 332.6 |
| 1989 | 120.5 | 329 |
| 1988 | 120.8 | 330.5 |
| 1987 | 118.8 | 328.8 |
| 1986 | 121.1 | 330.4 |
| 1985 | 115.7 | 327.1 |
| 1984 | 116.2 | 324.1 |
| 1983 | 115.5 | 322.8 |
| 1982 | 115.1 | 322.8 |
| 1981 | 119 | 329.9 |
| 1980 | 116.9 | 326.5 |
| 1979 | 117.7 | 325.3 |
| 1978 | 114.8 | 324.1 |
| 1977 | 117.2 | 325 |
| 1976 | 114 | 322.6 |
| 1975 | 114 | 320.8 |
| 1974 | 115.2 | 320.1 |
| 1973 | 114.7 | 325.4 |
| 1972 | 113.8 | 324.7 |
| 1971 | 114.1 | 319.9 |
| 1970 | 115.8 | 322.3 |
| 1969 | 114.3 | 318.5 |
| 1968 | 110 | 318.3 |
| 1967 | 109.8 | 315.6 |
| 1966 | 111 | 317.2 |
| 1965 | 111.5 | 316.5 |
| 1964 | 106.3 | 312.9 |
| 1963 | 107.5 | 309.8 |
| 1962 | 114.7 | 323.9 |
| 1961 | 116 | 323.3 |
| 1960 | 117.4 | 324.8 |
| 1959 | 115.6 | 321.5 |
| 1958 | 116.4 | 320.2 |
| 1957 | 114.6 | 322.1 |
| 1956 | 111.4 | 319.5 |
| 1955 | 115.2 | 317 |
| 1954 | 113.9 | 322.4 |
| 1953 | 113 | 321.6 |
| 1952 | 113.5 | 322.4 |
| 1951 | 113.5 | 323.9 |
| 1950 | 112.8 | 318.6 |
CMIP6 模型中位数(SSP2-4.5,中间路径) · 2015–2100
| 年份 | 马拉维 | 南苏丹 |
|---|---|---|
| 2015 | 145.7126.7–164.3 | 342.7327.5–353.3 |
| 2016 | 139.8122.6–157.4 | 342.1325.1–351.1 |
| 2017 | 143.6127–161.8 | 344.5326.9–353.7 |
| 2018 | 143.1124.6–162.6 | 344.1324.9–353.7 |
| 2019 | 143.8128.3–161.4 | 344.5324–354.2 |
| 2020 | 145127.3–165.8 | 344.9326.9–355.6 |
| 2021 | 145.3128–163.6 | 347.3329.2–355.3 |
| 2022 | 151130.1–169.1 | 344.5326.1–355.5 |
| 2023 | 149.2129.5–171.2 | 347.2329.2–357 |
| 2024 | 152.4130–172.6 | 350.4331.6–357.9 |
| 2025 | 151.1129.7–170.4 | 347.1330.2–357.7 |
| 2026 | 151.1126.2–173.8 | 347.6329.5–356.7 |
| 2027 | 154.4135.1–173.1 | 346327.1–356.1 |
| 2028 | 155.5134.6–174 | 349.4333.5–357.3 |
| 2029 | 159.8139.2–178.5 | 348.6337.5–355.6 |
| 2030 | 155.6138.7–174.5 | 349.3335.5–357.2 |
| 2031 | 161.1143.4–177.6 | 351.3336.3–357.6 |
| 2032 | 160.8141.3–181.3 | 348.6333.6–357.7 |
| 2033 | 161.3135.6–179.2 | 350.8336.7–359.4 |
| 2034 | 160.9138.9–185 | 350.2337.5–358.3 |
| 2035 | 164.3140.4–189.5 | 352.3331–359.5 |
| 2036 | 163.8137.6–182.5 | 352340.8–359.1 |
| 2037 | 166.9144.6–189.2 | 351.9337.4–359.5 |
| 2038 | 172.2148.4–191.2 | 354.3341.9–359.6 |
| 2039 | 164.8147.3–188.2 | 353.8340.6–360 |
| 2040 | 164.4146.4–189.7 | 353.5338.7–360.2 |
| 2041 | 169.3143.7–192.5 | 353.2336.9–360.2 |
| 2042 | 169.8146.3–197.8 | 351.9336.9–360.3 |
| 2043 | 167.5147.1–193.2 | 353.2336.2–359.5 |
| 2044 | 172.6148.3–197 | 353.7339.9–361.1 |
| 2045 | 170.4153.1–194.7 | 353.7342.2–360.6 |
| 2046 | 176.3151.2–203.1 | 355.4342.2–361 |
| 2047 | 178.7157.8–200.9 | 355.7344–361.2 |
| 2048 | 180.7158.9–204.1 | 354.7342.5–361.6 |
| 2049 | 176.7154–205.4 | 356.7346.4–361.1 |
| 2050 | 180.4155.2–204.9 | 356.8345.6–361.7 |
| 2051 | 182.9162.1–207.4 | 356345.4–361.8 |
| 2052 | 184.9163.2–209.6 | 355.3343.8–362.2 |
| 2053 | 177.2149.6–207.4 | 355.5343.4–361.7 |
| 2054 | 185.1157.1–208 | 356.9345.2–362.1 |
| 2055 | 186.1159.2–212.6 | 356.9345–361.7 |
| 2056 | 184.6158.5–214.3 | 356.7345.7–363.1 |
| 2057 | 184.7159.3–212.3 | 358.4349.2–362.6 |
| 2058 | 188.5163.8–214.7 | 357.7345–361.8 |
| 2059 | 189.8166.9–218.5 | 358.6348.4–362.6 |
| 2060 | 192.6163.4–218.1 | 357.7344.7–363.8 |
| 2061 | 186.4158.8–216.8 | 358.2345.8–362.7 |
| 2062 | 192.3163.9–218.6 | 357.4345.2–363.1 |
| 2063 | 189.2165.2–220.3 | 358.8347.3–362.6 |
| 2064 | 197161.3–218 | 358.9348.4–363.3 |
| 2065 | 193.1165.8–225.2 | 359.1351.1–362.9 |
| 2066 | 201170.1–224.7 | 359.2350.6–363.1 |
| 2067 | 193.1164.2–223.7 | 358.6347.6–363.3 |
| 2068 | 198.6163.7–227.1 | 359.9350.3–364.4 |
| 2069 | 199.4165.8–225 | 357.6347–363.1 |
| 2070 | 198.7168–226.7 | 359.5350.4–363 |
| 2071 | 199.3167.9–233.5 | 359.6350.5–363.2 |
| 2072 | 202.3173.2–226.2 | 360.2348.4–364.2 |
| 2073 | 205.4168.2–233.4 | 359.3350.3–363.5 |
| 2074 | 198.2175.4–227.7 | 359.5348–363.3 |
| 2075 | 199.6168.9–228.3 | 359.9348.5–363.4 |
| 2076 | 198.8169.8–225.2 | 359.4350.5–364 |
| 2077 | 202172.2–231.2 | 359.9352.2–363.7 |
| 2078 | 203.5175.8–232.1 | 360351.2–363.4 |
| 2079 | 203.6172.2–236.1 | 359.8350.9–363.3 |
| 2080 | 204.7172.2–234.3 | 361.3349.8–364.6 |
| 2081 | 202.2174.9–231.3 | 360.4350.5–363.9 |
| 2082 | 204.3178.3–228.6 | 360.3351–363.3 |
| 2083 | 205.8170.7–238.3 | 360351.1–363.8 |
| 2084 | 208.1175.9–235.6 | 360.8350.8–364.9 |
| 2085 | 211.1179.4–241 | 361352.3–363.7 |
| 2086 | 203.3175.4–237 | 360.4352.5–364.1 |
| 2087 | 205.3181.2–237.7 | 359.7347.8–363.8 |
| 2088 | 211.6174.7–240.6 | 360.3352.2–364.7 |
| 2089 | 208.9180.5–237.8 | 360.2352.3–363.8 |
| 2090 | 209.4173.4–243 | 360.2351.3–363.9 |
| 2091 | 214.3179.7–244.8 | 360.9351.6–364.1 |
| 2092 | 213.2176.8–242.3 | 360.7352.4–364.7 |
| 2093 | 212.5177.8–242.2 | 361.6354.1–363.9 |
| 2094 | 211.8179.7–241.5 | 361.3353.8–364.3 |
| 2095 | 216.9185–243.8 | 361.5353.2–364 |
| 2096 | 213.1182–247.6 | 361.1352.8–365.1 |
| 2097 | 210.8174.9–242.1 | 360.7351.9–364.2 |
| 2098 | 214.4181.5–242.2 | 361.5352.6–364.1 |
| 2099 | 213.5181.9–244.1 | 361.2352.4–364.1 |
| 2100 | 213.3181.5–246.3 | 361.1352.6–364.2 |
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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
马拉维与南苏丹对比
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冲突
军用
- 军费开支马拉维:2025 年为 GDP 的 1.15%南苏丹:2025 年为 GDP 的 3.43%
- 军费开支(美元)马拉维:2025 年为 US$1.28亿南苏丹:2025 年为 US$1.03亿
- 人均军费开支马拉维:2025 年为 US$7南苏丹:2025 年为 US$15
- 军费占政府支出比例马拉维:2025 年为 3.8%(占政府支出)南苏丹:2025 年为 18.4%(占政府支出)
- 武装部队人员马拉维:2020 年为 15,000 人南苏丹:2020 年为 53,000 人
- 国家实力马拉维:2022 年为 0.07%(占全球实力)南苏丹:2022 年为 0.08%(占全球实力)
- 武装部队占劳动力比例马拉维:2020 年为 0.2%(占劳动力)南苏丹:2020 年为 1.18%(占劳动力)
- 武器进口马拉维:2024 年为 200万 TIV南苏丹:2022 年为 1000万 TIV