马拉维与塞内加尔对比
马拉维与塞内加尔:热带夜
2014 年热带夜:马拉维为 138.1 夜,塞内加尔为 302.1 夜(CMIP6 气候模型中位数模拟)。 2014 年塞内加尔的数值较高,高出 164 夜,是马拉维的 2.19 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,塞内加尔的数值都更高。
- 根据CMIP6 模型中位数(SSP2-4.5,中间路径),2100 年马拉维为 213.3 夜,塞内加尔为 347.3 夜。
逐年数据
每年夜数 · CMIP6
| 年份 | 马拉维 | 塞内加尔 |
|---|---|---|
| 2014 | 138.1 | 302.1 |
| 2013 | 138.9 | 300.9 |
| 2012 | 137.4 | 304.1 |
| 2011 | 136.6 | 302.1 |
| 2010 | 132.4 | 301.5 |
| 2009 | 131.4 | 302.1 |
| 2008 | 130.8 | 298.3 |
| 2007 | 132.8 | 298.5 |
| 2006 | 129.8 | 298.6 |
| 2005 | 129.5 | 298.1 |
| 2004 | 133.4 | 294.7 |
| 2003 | 132.5 | 296.1 |
| 2002 | 130.3 | 296 |
| 2001 | 134 | 298.9 |
| 2000 | 126.6 | 295 |
| 1999 | 124.8 | 293.2 |
| 1998 | 127.1 | 292.5 |
| 1997 | 124.2 | 288.5 |
| 1996 | 121.3 | 285.1 |
| 1995 | 120.9 | 285.9 |
| 1994 | 118.4 | 283.7 |
| 1993 | 113.8 | 282 |
| 1992 | 114.1 | 277.6 |
| 1991 | 120.9 | 286.7 |
| 1990 | 123.8 | 292.5 |
| 1989 | 120.5 | 282.1 |
| 1988 | 120.8 | 282.4 |
| 1987 | 118.8 | 281.8 |
| 1986 | 121.1 | 283 |
| 1985 | 115.7 | 278.1 |
| 1984 | 116.2 | 274.6 |
| 1983 | 115.5 | 276.3 |
| 1982 | 115.1 | 281.3 |
| 1981 | 119 | 277.9 |
| 1980 | 116.9 | 277.6 |
| 1979 | 117.7 | 281 |
| 1978 | 114.8 | 280.7 |
| 1977 | 117.2 | 278 |
| 1976 | 114 | 274.4 |
| 1975 | 114 | 278.4 |
| 1974 | 115.2 | 273.8 |
| 1973 | 114.7 | 274.5 |
| 1972 | 113.8 | 273.5 |
| 1971 | 114.1 | 273.4 |
| 1970 | 115.8 | 271.3 |
| 1969 | 114.3 | 271.6 |
| 1968 | 110 | 272.6 |
| 1967 | 109.8 | 275.4 |
| 1966 | 111 | 273.3 |
| 1965 | 111.5 | 272.8 |
| 1964 | 106.3 | 271.8 |
| 1963 | 107.5 | 272.8 |
| 1962 | 114.7 | 280.2 |
| 1961 | 116 | 276.9 |
| 1960 | 117.4 | 279.2 |
| 1959 | 115.6 | 277.4 |
| 1958 | 116.4 | 278.8 |
| 1957 | 114.6 | 274.4 |
| 1956 | 111.4 | 273.2 |
| 1955 | 115.2 | 273.4 |
| 1954 | 113.9 | 275 |
| 1953 | 113 | 271.7 |
| 1952 | 113.5 | 271 |
| 1951 | 113.5 | 272.9 |
| 1950 | 112.8 | 270.8 |
CMIP6 模型中位数(SSP2-4.5,中间路径) · 2015–2100
| 年份 | 马拉维 | 塞内加尔 |
|---|---|---|
| 2015 | 145.7126.7–164.3 | 298.9275.9–321 |
| 2016 | 139.8122.6–157.4 | 302.7276.5–319 |
| 2017 | 143.6127–161.8 | 302.9285.4–318.6 |
| 2018 | 143.1124.6–162.6 | 305.6289.2–321.5 |
| 2019 | 143.8128.3–161.4 | 307.6280.6–328.1 |
| 2020 | 145127.3–165.8 | 305.2276.2–322.9 |
| 2021 | 145.3128–163.6 | 307.6286–325.6 |
| 2022 | 151130.1–169.1 | 312.9289.6–334.3 |
| 2023 | 149.2129.5–171.2 | 307.5292.2–328.6 |
| 2024 | 152.4130–172.6 | 313.2294.6–326.9 |
| 2025 | 151.1129.7–170.4 | 311.8289–329.4 |
| 2026 | 151.1126.2–173.8 | 308.3285.2–328.6 |
| 2027 | 154.4135.1–173.1 | 310.7287.8–330 |
| 2028 | 155.5134.6–174 | 316.3289.2–331.6 |
| 2029 | 159.8139.2–178.5 | 315.7291.2–334.2 |
| 2030 | 155.6138.7–174.5 | 311.5289.1–329.1 |
| 2031 | 161.1143.4–177.6 | 318.8296.3–335 |
| 2032 | 160.8141.3–181.3 | 316295.7–332.4 |
| 2033 | 161.3135.6–179.2 | 316.7289.6–336.4 |
| 2034 | 160.9138.9–185 | 315.4291.6–335.1 |
| 2035 | 164.3140.4–189.5 | 316.8291.1–335.3 |
| 2036 | 163.8137.6–182.5 | 320.8298.5–335.3 |
| 2037 | 166.9144.6–189.2 | 324.5303.1–337.7 |
| 2038 | 172.2148.4–191.2 | 323.5304.2–339.7 |
| 2039 | 164.8147.3–188.2 | 323.3300.9–337 |
| 2040 | 164.4146.4–189.7 | 322.4298.9–339.5 |
| 2041 | 169.3143.7–192.5 | 320.2300.1–343.6 |
| 2042 | 169.8146.3–197.8 | 320302.1–339.9 |
| 2043 | 167.5147.1–193.2 | 320.4303.7–340.4 |
| 2044 | 172.6148.3–197 | 326.9301.5–340.9 |
| 2045 | 170.4153.1–194.7 | 324.1304.1–340 |
| 2046 | 176.3151.2–203.1 | 325.9304.7–340.6 |
| 2047 | 178.7157.8–200.9 | 329301.4–347 |
| 2048 | 180.7158.9–204.1 | 331.1314.3–344.8 |
| 2049 | 176.7154–205.4 | 331.3308.3–343.7 |
| 2050 | 180.4155.2–204.9 | 327.6306.5–341.9 |
| 2051 | 182.9162.1–207.4 | 328307.1–344 |
| 2052 | 184.9163.2–209.6 | 325.4308.1–344.2 |
| 2053 | 177.2149.6–207.4 | 331.8304.9–347.2 |
| 2054 | 185.1157.1–208 | 330.1310.8–345.2 |
| 2055 | 186.1159.2–212.6 | 330.1305.5–344 |
| 2056 | 184.6158.5–214.3 | 331.4307.4–349.8 |
| 2057 | 184.7159.3–212.3 | 337306.4–351.5 |
| 2058 | 188.5163.8–214.7 | 333313.2–350.2 |
| 2059 | 189.8166.9–218.5 | 334315.6–353.5 |
| 2060 | 192.6163.4–218.1 | 334.5316.4–351.1 |
| 2061 | 186.4158.8–216.8 | 333.7307.5–352.9 |
| 2062 | 192.3163.9–218.6 | 333.3312.4–350.2 |
| 2063 | 189.2165.2–220.3 | 335.4313.2–352.8 |
| 2064 | 197161.3–218 | 335.4307.1–351.1 |
| 2065 | 193.1165.8–225.2 | 334.8314.1–352.8 |
| 2066 | 201170.1–224.7 | 337.3312.9–351 |
| 2067 | 193.1164.2–223.7 | 335.5309.9–349.9 |
| 2068 | 198.6163.7–227.1 | 335.5313.5–354 |
| 2069 | 199.4165.8–225 | 336.1316.9–351.8 |
| 2070 | 198.7168–226.7 | 333.3312.9–352.9 |
| 2071 | 199.3167.9–233.5 | 335.7321.1–351.9 |
| 2072 | 202.3173.2–226.2 | 342.3321–354.6 |
| 2073 | 205.4168.2–233.4 | 339.4320.1–356.1 |
| 2074 | 198.2175.4–227.7 | 335.9317.1–351.8 |
| 2075 | 199.6168.9–228.3 | 338.4320.7–354.1 |
| 2076 | 198.8169.8–225.2 | 338.7321.9–352 |
| 2077 | 202172.2–231.2 | 342.8314.8–357.3 |
| 2078 | 203.5175.8–232.1 | 343325.8–355.4 |
| 2079 | 203.6172.2–236.1 | 341.2322.3–357.6 |
| 2080 | 204.7172.2–234.3 | 340.9317.7–358.1 |
| 2081 | 202.2174.9–231.3 | 339.8315.5–357.1 |
| 2082 | 204.3178.3–228.6 | 341.6316.4–354.9 |
| 2083 | 205.8170.7–238.3 | 340.8324.8–356.9 |
| 2084 | 208.1175.9–235.6 | 340.4322.8–354.9 |
| 2085 | 211.1179.4–241 | 346.8325.6–357 |
| 2086 | 203.3175.4–237 | 341.7324.4–357.6 |
| 2087 | 205.3181.2–237.7 | 343.9325.9–357.7 |
| 2088 | 211.6174.7–240.6 | 341.5321.6–358.2 |
| 2089 | 208.9180.5–237.8 | 343.2324.3–355.2 |
| 2090 | 209.4173.4–243 | 342320.6–356 |
| 2091 | 214.3179.7–244.8 | 341.7319.8–356.4 |
| 2092 | 213.2176.8–242.3 | 339.7318.4–357.7 |
| 2093 | 212.5177.8–242.2 | 345326.8–356.4 |
| 2094 | 211.8179.7–241.5 | 344.4324.3–357.6 |
| 2095 | 216.9185–243.8 | 346.3320.6–359.4 |
| 2096 | 213.1182–247.6 | 344.4323.1–359.2 |
| 2097 | 210.8174.9–242.1 | 341.4321.3–353.8 |
| 2098 | 214.4181.5–242.2 | 345322.3–357 |
| 2099 | 213.5181.9–244.1 | 344.6322.7–357.5 |
| 2100 | 213.3181.5–246.3 | 347.3323.6–359.2 |
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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
马拉维与塞内加尔对比
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冲突
军用
- 军费开支马拉维:2025 年为 GDP 的 1.15%塞内加尔:2025 年为 GDP 的 1.56%
- 军费开支(美元)马拉维:2025 年为 US$1.28亿塞内加尔:2025 年为 US$5.33亿
- 人均军费开支马拉维:2025 年为 US$7塞内加尔:2025 年为 US$30
- 军费占政府支出比例马拉维:2025 年为 3.8%(占政府支出)塞内加尔:2025 年为 5.2%(占政府支出)
- 武装部队人员马拉维:2020 年为 15,000 人塞内加尔:2020 年为 19,000 人
- 国家实力马拉维:2022 年为 0.07%(占全球实力)塞内加尔:2022 年为 0.1%(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)塞内加尔:2020 年为 0.37%(占劳动力)
- 武器进口马拉维:2024 年为 200万 TIV塞内加尔:2024 年为 7900万 TIV
- 武器出口马拉维:2000 年为 100万 TIV塞内加尔:1976 年为 300万 TIV