马拉维与苏丹对比
马拉维与苏丹:热带夜
2014 年热带夜:马拉维为 138.1 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 104.3 夜,是马拉维的 1.76 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 年马拉维为 213.3 夜,苏丹为 278.6 夜。
逐年数据
每年夜数 · CMIP6
| 年份 | 马拉维 | 苏丹 |
|---|---|---|
| 2014 | 138.1 | 242.4 |
| 2013 | 138.9 | 240 |
| 2012 | 137.4 | 242 |
| 2011 | 136.6 | 242.2 |
| 2010 | 132.4 | 240.6 |
| 2009 | 131.4 | 236.5 |
| 2008 | 130.8 | 237.1 |
| 2007 | 132.8 | 236 |
| 2006 | 129.8 | 235.3 |
| 2005 | 129.5 | 233.1 |
| 2004 | 133.4 | 233.5 |
| 2003 | 132.5 | 235.6 |
| 2002 | 130.3 | 234.7 |
| 2001 | 134 | 235.4 |
| 2000 | 126.6 | 230.5 |
| 1999 | 124.8 | 232.1 |
| 1998 | 127.1 | 230.2 |
| 1997 | 124.2 | 231.1 |
| 1996 | 121.3 | 227.3 |
| 1995 | 120.9 | 227.8 |
| 1994 | 118.4 | 225.3 |
| 1993 | 113.8 | 219.1 |
| 1992 | 114.1 | 217.8 |
| 1991 | 120.9 | 227.3 |
| 1990 | 123.8 | 227.5 |
| 1989 | 120.5 | 223 |
| 1988 | 120.8 | 223.1 |
| 1987 | 118.8 | 224.4 |
| 1986 | 121.1 | 226.4 |
| 1985 | 115.7 | 222.1 |
| 1984 | 116.2 | 220.7 |
| 1983 | 115.5 | 217 |
| 1982 | 115.1 | 216.3 |
| 1981 | 119 | 223.2 |
| 1980 | 116.9 | 220 |
| 1979 | 117.7 | 218.4 |
| 1978 | 114.8 | 218.5 |
| 1977 | 117.2 | 219.3 |
| 1976 | 114 | 220.1 |
| 1975 | 114 | 215.7 |
| 1974 | 115.2 | 217.5 |
| 1973 | 114.7 | 220 |
| 1972 | 113.8 | 217.9 |
| 1971 | 114.1 | 215.5 |
| 1970 | 115.8 | 217 |
| 1969 | 114.3 | 213.1 |
| 1968 | 110 | 214 |
| 1967 | 109.8 | 210.3 |
| 1966 | 111 | 213.3 |
| 1965 | 111.5 | 213.4 |
| 1964 | 106.3 | 213.2 |
| 1963 | 107.5 | 210.1 |
| 1962 | 114.7 | 217 |
| 1961 | 116 | 216.9 |
| 1960 | 117.4 | 221.3 |
| 1959 | 115.6 | 217.8 |
| 1958 | 116.4 | 216.2 |
| 1957 | 114.6 | 218.9 |
| 1956 | 111.4 | 217.2 |
| 1955 | 115.2 | 216.6 |
| 1954 | 113.9 | 218.9 |
| 1953 | 113 | 215.1 |
| 1952 | 113.5 | 216.4 |
| 1951 | 113.5 | 218.5 |
| 1950 | 112.8 | 214.1 |
CMIP6 模型中位数(SSP2-4.5,中间路径) · 2015–2100
| 年份 | 马拉维 | 苏丹 |
|---|---|---|
| 2015 | 145.7126.7–164.3 | 242.3225.3–258.3 |
| 2016 | 139.8122.6–157.4 | 241.4223.3–254.1 |
| 2017 | 143.6127–161.8 | 243.2223–259.1 |
| 2018 | 143.1124.6–162.6 | 242.3226.4–258.5 |
| 2019 | 143.8128.3–161.4 | 243.1221.7–260.4 |
| 2020 | 145127.3–165.8 | 244.9225.9–261.7 |
| 2021 | 145.3128–163.6 | 245.6229.1–261.5 |
| 2022 | 151130.1–169.1 | 249230.9–262.3 |
| 2023 | 149.2129.5–171.2 | 247.1226.8–263.1 |
| 2024 | 152.4130–172.6 | 251.5232.8–266.1 |
| 2025 | 151.1129.7–170.4 | 250.7232.4–265.6 |
| 2026 | 151.1126.2–173.8 | 250230.8–266.6 |
| 2027 | 154.4135.1–173.1 | 248.8228.4–265.6 |
| 2028 | 155.5134.6–174 | 249232.2–263.1 |
| 2029 | 159.8139.2–178.5 | 251.4232.1–266 |
| 2030 | 155.6138.7–174.5 | 252235.4–266.7 |
| 2031 | 161.1143.4–177.6 | 253.3234.4–268.9 |
| 2032 | 160.8141.3–181.3 | 250.4232.3–266.6 |
| 2033 | 161.3135.6–179.2 | 254.4235.3–269.4 |
| 2034 | 160.9138.9–185 | 254.2234.5–269.9 |
| 2035 | 164.3140.4–189.5 | 258.3240.2–273.6 |
| 2036 | 163.8137.6–182.5 | 252.8235.6–268.6 |
| 2037 | 166.9144.6–189.2 | 256.7238.6–271.8 |
| 2038 | 172.2148.4–191.2 | 258.7244.8–272.1 |
| 2039 | 164.8147.3–188.2 | 259.5241.2–272.5 |
| 2040 | 164.4146.4–189.7 | 256.4236.7–272.3 |
| 2041 | 169.3143.7–192.5 | 259.1241.2–274.5 |
| 2042 | 169.8146.3–197.8 | 258.7242–274.7 |
| 2043 | 167.5147.1–193.2 | 258.1240.3–272.6 |
| 2044 | 172.6148.3–197 | 257.3238.6–274.7 |
| 2045 | 170.4153.1–194.7 | 258.8243.1–276.2 |
| 2046 | 176.3151.2–203.1 | 261.1241.7–277.6 |
| 2047 | 178.7157.8–200.9 | 261.4244.6–278.4 |
| 2048 | 180.7158.9–204.1 | 260.4243.4–279.5 |
| 2049 | 176.7154–205.4 | 261.9242.6–275.9 |
| 2050 | 180.4155.2–204.9 | 263.5245.4–279 |
| 2051 | 182.9162.1–207.4 | 263.6246.6–278.8 |
| 2052 | 184.9163.2–209.6 | 263.8248.7–278.3 |
| 2053 | 177.2149.6–207.4 | 262.8242.4–279.2 |
| 2054 | 185.1157.1–208 | 266.3246.6–282.9 |
| 2055 | 186.1159.2–212.6 | 265.6245.9–281.4 |
| 2056 | 184.6158.5–214.3 | 264.1242.6–281.5 |
| 2057 | 184.7159.3–212.3 | 266.4247.6–283.4 |
| 2058 | 188.5163.8–214.7 | 265.2247.4–280.3 |
| 2059 | 189.8166.9–218.5 | 268.4249.6–285.4 |
| 2060 | 192.6163.4–218.1 | 263.6246.7–284.4 |
| 2061 | 186.4158.8–216.8 | 266.9250.1–281.9 |
| 2062 | 192.3163.9–218.6 | 266.4248.5–285 |
| 2063 | 189.2165.2–220.3 | 267.1246.9–283.4 |
| 2064 | 197161.3–218 | 269250.7–284.7 |
| 2065 | 193.1165.8–225.2 | 269.8253.9–285.6 |
| 2066 | 201170.1–224.7 | 271.4251.9–287.3 |
| 2067 | 193.1164.2–223.7 | 270.9250–288.4 |
| 2068 | 198.6163.7–227.1 | 271.9251.7–289.8 |
| 2069 | 199.4165.8–225 | 268251.1–286.2 |
| 2070 | 198.7168–226.7 | 269252.3–285.8 |
| 2071 | 199.3167.9–233.5 | 275.5254.1–290.4 |
| 2072 | 202.3173.2–226.2 | 273.3254.2–289.4 |
| 2073 | 205.4168.2–233.4 | 271.9250.7–287.6 |
| 2074 | 198.2175.4–227.7 | 270.9249–290.1 |
| 2075 | 199.6168.9–228.3 | 273.1254–291.9 |
| 2076 | 198.8169.8–225.2 | 272.7255.2–290.9 |
| 2077 | 202172.2–231.2 | 274.6255.2–292.7 |
| 2078 | 203.5175.8–232.1 | 274.3255.6–291.2 |
| 2079 | 203.6172.2–236.1 | 272255.2–288.5 |
| 2080 | 204.7172.2–234.3 | 276.6256–292.2 |
| 2081 | 202.2174.9–231.3 | 275.1254.1–294.8 |
| 2082 | 204.3178.3–228.6 | 272.9258.7–288.6 |
| 2083 | 205.8170.7–238.3 | 276.5258–291.7 |
| 2084 | 208.1175.9–235.6 | 275.4253–294.9 |
| 2085 | 211.1179.4–241 | 275.3258.6–291.6 |
| 2086 | 203.3175.4–237 | 276.4256.9–296.6 |
| 2087 | 205.3181.2–237.7 | 273.5253.1–293.3 |
| 2088 | 211.6174.7–240.6 | 273.6253.7–293.6 |
| 2089 | 208.9180.5–237.8 | 276.5256.6–293.4 |
| 2090 | 209.4173.4–243 | 276.6256.4–294.6 |
| 2091 | 214.3179.7–244.8 | 277.4256.4–296.6 |
| 2092 | 213.2176.8–242.3 | 274.6254.3–294.9 |
| 2093 | 212.5177.8–242.2 | 278261.9–292.1 |
| 2094 | 211.8179.7–241.5 | 280.3262.2–294 |
| 2095 | 216.9185–243.8 | 277.5262.6–294 |
| 2096 | 213.1182–247.6 | 279.7258.7–297 |
| 2097 | 210.8174.9–242.1 | 276.3254.1–297.6 |
| 2098 | 214.4181.5–242.2 | 277.7258.2–295.5 |
| 2099 | 213.5181.9–244.1 | 276.8257.2–296.2 |
| 2100 | 213.3181.5–246.3 | 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
马拉维与苏丹对比
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军用
- 军费开支马拉维:2025 年为 GDP 的 1.15%苏丹:2021 年为 GDP 的 0.93%
- 军费开支(美元)马拉维:2025 年为 US$1.28亿苏丹:2021 年为 US$27.7亿
- 人均军费开支马拉维:2025 年为 US$7苏丹:2021 年为 US$8
- 军费占政府支出比例马拉维:2025 年为 3.8%(占政府支出)苏丹:2021 年为 9.5%(占政府支出)
- 武装部队人员马拉维:2020 年为 15,000 人苏丹:2020 年为 144,000 人
- 国家实力马拉维:2022 年为 0.07%(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马拉维:2020 年为 0.2%(占劳动力)苏丹:2020 年为 1.34%(占劳动力)
- 武器进口马拉维:2024 年为 200万 TIV苏丹:2025 年为 5700万 TIV
- 武器出口马拉维:2000 年为 100万 TIV苏丹:2013 年为 1100万 TIV