喀麦隆与马拉维对比
喀麦隆与马拉维:热带夜
2014 年热带夜:喀麦隆为 238.7 夜,马拉维为 138.1 夜(CMIP6 气候模型中位数模拟)。 2014 年喀麦隆的数值较高,高出 100.6 夜,是马拉维的 1.73 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,喀麦隆的数值都更高。
- 根据CMIP6 模型中位数(SSP2-4.5,中间路径),2100 年喀麦隆为 324.2 夜,马拉维为 213.3 夜。
逐年数据
每年夜数 · CMIP6
| 年份 | 喀麦隆 | 马拉维 |
|---|---|---|
| 2014 | 238.7 | 138.1 |
| 2013 | 236.7 | 138.9 |
| 2012 | 242.2 | 137.4 |
| 2011 | 237.6 | 136.6 |
| 2010 | 230.4 | 132.4 |
| 2009 | 226.6 | 131.4 |
| 2008 | 223.1 | 130.8 |
| 2007 | 222.1 | 132.8 |
| 2006 | 220.1 | 129.8 |
| 2005 | 216.1 | 129.5 |
| 2004 | 217.6 | 133.4 |
| 2003 | 221 | 132.5 |
| 2002 | 215.5 | 130.3 |
| 2001 | 223 | 134 |
| 2000 | 210 | 126.6 |
| 1999 | 200.5 | 124.8 |
| 1998 | 203.5 | 127.1 |
| 1997 | 202 | 124.2 |
| 1996 | 190 | 121.3 |
| 1995 | 187.3 | 120.9 |
| 1994 | 183.1 | 118.4 |
| 1993 | 165.2 | 113.8 |
| 1992 | 156.7 | 114.1 |
| 1991 | 192.6 | 120.9 |
| 1990 | 200.7 | 123.8 |
| 1989 | 187.5 | 120.5 |
| 1988 | 185.8 | 120.8 |
| 1987 | 182.7 | 118.8 |
| 1986 | 188.9 | 121.1 |
| 1985 | 173.2 | 115.7 |
| 1984 | 172.9 | 116.2 |
| 1983 | 165.5 | 115.5 |
| 1982 | 176.3 | 115.1 |
| 1981 | 182.6 | 119 |
| 1980 | 173.6 | 116.9 |
| 1979 | 175.8 | 117.7 |
| 1978 | 172.8 | 114.8 |
| 1977 | 175.7 | 117.2 |
| 1976 | 164.5 | 114 |
| 1975 | 159.5 | 114 |
| 1974 | 166.8 | 115.2 |
| 1973 | 170.4 | 114.7 |
| 1972 | 165.2 | 113.8 |
| 1971 | 161.3 | 114.1 |
| 1970 | 164.4 | 115.8 |
| 1969 | 160 | 114.3 |
| 1968 | 157.8 | 110 |
| 1967 | 147.9 | 109.8 |
| 1966 | 156.5 | 111 |
| 1965 | 151.6 | 111.5 |
| 1964 | 143.1 | 106.3 |
| 1963 | 145.9 | 107.5 |
| 1962 | 171.8 | 114.7 |
| 1961 | 167.2 | 116 |
| 1960 | 176.2 | 117.4 |
| 1959 | 166.5 | 115.6 |
| 1958 | 169.2 | 116.4 |
| 1957 | 168.2 | 114.6 |
| 1956 | 160 | 111.4 |
| 1955 | 163.7 | 115.2 |
| 1954 | 167.6 | 113.9 |
| 1953 | 168.2 | 113 |
| 1952 | 165.7 | 113.5 |
| 1951 | 172.8 | 113.5 |
| 1950 | 162.8 | 112.8 |
CMIP6 模型中位数(SSP2-4.5,中间路径) · 2015–2100
| 年份 | 喀麦隆 | 马拉维 |
|---|---|---|
| 2015 | 242.2196.9–270.8 | 145.7126.7–164.3 |
| 2016 | 237.7192.1–259.9 | 139.8122.6–157.4 |
| 2017 | 245.8205.7–271.3 | 143.6127–161.8 |
| 2018 | 245.7208–270.5 | 143.1124.6–162.6 |
| 2019 | 243.7200.2–275.6 | 143.8128.3–161.4 |
| 2020 | 246.4210.4–278 | 145127.3–165.8 |
| 2021 | 255.2220.2–279.6 | 145.3128–163.6 |
| 2022 | 255.8221–285.7 | 151130.1–169.1 |
| 2023 | 254.5221.7–284.1 | 149.2129.5–171.2 |
| 2024 | 263225.8–286.8 | 152.4130–172.6 |
| 2025 | 257.3222.7–293.9 | 151.1129.7–170.4 |
| 2026 | 260.4211.9–292 | 151.1126.2–173.8 |
| 2027 | 258.5223.8–291.5 | 154.4135.1–173.1 |
| 2028 | 266.9235.5–294.7 | 155.5134.6–174 |
| 2029 | 269.9242.3–293.5 | 159.8139.2–178.5 |
| 2030 | 266.6229–293.5 | 155.6138.7–174.5 |
| 2031 | 273.2229.9–298.5 | 161.1143.4–177.6 |
| 2032 | 269.8240.6–296.2 | 160.8141.3–181.3 |
| 2033 | 271.8235.6–303.4 | 161.3135.6–179.2 |
| 2034 | 268.1239.2–299.1 | 160.9138.9–185 |
| 2035 | 277.5241.5–302.7 | 164.3140.4–189.5 |
| 2036 | 275.7235.1–301.3 | 163.8137.6–182.5 |
| 2037 | 283.5252.3–305.9 | 166.9144.6–189.2 |
| 2038 | 285.8258–307.9 | 172.2148.4–191.2 |
| 2039 | 280.9250.4–305.8 | 164.8147.3–188.2 |
| 2040 | 279.5252.1–310.6 | 164.4146.4–189.7 |
| 2041 | 284.5254.6–309.9 | 169.3143.7–192.5 |
| 2042 | 284.8253.6–313.2 | 169.8146.3–197.8 |
| 2043 | 281.2251.4–309.8 | 167.5147.1–193.2 |
| 2044 | 283.8244.4–312.3 | 172.6148.3–197 |
| 2045 | 286.8253.7–311.4 | 170.4153.1–194.7 |
| 2046 | 296.1260.6–318.1 | 176.3151.2–203.1 |
| 2047 | 294.3260.7–316.6 | 178.7157.8–200.9 |
| 2048 | 297.3265.6–318.1 | 180.7158.9–204.1 |
| 2049 | 298.3267.8–318.1 | 176.7154–205.4 |
| 2050 | 296.9267.9–322.7 | 180.4155.2–204.9 |
| 2051 | 297.8266.7–319.9 | 182.9162.1–207.4 |
| 2052 | 298.7270.1–322.3 | 184.9163.2–209.6 |
| 2053 | 297.7261.3–321.7 | 177.2149.6–207.4 |
| 2054 | 301.2268.2–324.6 | 185.1157.1–208 |
| 2055 | 301.3276.5–324.3 | 186.1159.2–212.6 |
| 2056 | 302.1270.4–329.2 | 184.6158.5–214.3 |
| 2057 | 305.4277.7–327.4 | 184.7159.3–212.3 |
| 2058 | 302.6272.1–325.2 | 188.5163.8–214.7 |
| 2059 | 306.2279.6–330.1 | 189.8166.9–218.5 |
| 2060 | 306.1276.1–331.1 | 192.6163.4–218.1 |
| 2061 | 304.4267.2–329.7 | 186.4158.8–216.8 |
| 2062 | 307.6266.2–332.2 | 192.3163.9–218.6 |
| 2063 | 309.1272.5–328.8 | 189.2165.2–220.3 |
| 2064 | 308.5278.7–331 | 197161.3–218 |
| 2065 | 311.3284.1–329.6 | 193.1165.8–225.2 |
| 2066 | 311.9284–332.7 | 201170.1–224.7 |
| 2067 | 310.1271.7–335.6 | 193.1164.2–223.7 |
| 2068 | 312.9278.7–335 | 198.6163.7–227.1 |
| 2069 | 310.4278.4–333.4 | 199.4165.8–225 |
| 2070 | 309.8281.2–334.9 | 198.7168–226.7 |
| 2071 | 316.4282.3–336 | 199.3167.9–233.5 |
| 2072 | 315.4289.2–335.3 | 202.3173.2–226.2 |
| 2073 | 314282.6–339.5 | 205.4168.2–233.4 |
| 2074 | 311.7281.9–336.8 | 198.2175.4–227.7 |
| 2075 | 314.8285.3–335.3 | 199.6168.9–228.3 |
| 2076 | 315.2284.7–338.4 | 198.8169.8–225.2 |
| 2077 | 318.2289.7–341.4 | 202172.2–231.2 |
| 2078 | 315.8286.4–338.5 | 203.5175.8–232.1 |
| 2079 | 317.7287.8–337.5 | 203.6172.2–236.1 |
| 2080 | 321.5285.9–341.7 | 204.7172.2–234.3 |
| 2081 | 318.7290–339 | 202.2174.9–231.3 |
| 2082 | 316.2289–337.7 | 204.3178.3–228.6 |
| 2083 | 317.7288.2–341.6 | 205.8170.7–238.3 |
| 2084 | 318.5284.5–342.1 | 208.1175.9–235.6 |
| 2085 | 323.4294–342.7 | 211.1179.4–241 |
| 2086 | 319.5292.3–343 | 203.3175.4–237 |
| 2087 | 318292.7–342.5 | 205.3181.2–237.7 |
| 2088 | 320.4288.1–342 | 211.6174.7–240.6 |
| 2089 | 320.1289.6–340.7 | 208.9180.5–237.8 |
| 2090 | 322.4292.8–342.6 | 209.4173.4–243 |
| 2091 | 322.6289.9–345 | 214.3179.7–244.8 |
| 2092 | 324.9291.6–343.6 | 213.2176.8–242.3 |
| 2093 | 322.3299.7–342.6 | 212.5177.8–242.2 |
| 2094 | 324299.3–343.4 | 211.8179.7–241.5 |
| 2095 | 326.4300.2–346.4 | 216.9185–243.8 |
| 2096 | 322.1294.4–346.4 | 213.1182–247.6 |
| 2097 | 320.9287.4–342.5 | 210.8174.9–242.1 |
| 2098 | 323.1293.8–344.4 | 214.4181.5–242.2 |
| 2099 | 322.6295.7–344.2 | 213.5181.9–244.1 |
| 2100 | 324.2293.6–346.6 | 213.3181.5–246.3 |
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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
喀麦隆与马拉维对比
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人口与社会
经济
教育
政治
冲突
军用
- 军费开支喀麦隆:2025 年为 GDP 的 1.06%马拉维:2025 年为 GDP 的 1.15%
- 军费开支(美元)喀麦隆:2025 年为 US$5.8亿马拉维:2025 年为 US$1.28亿
- 人均军费开支喀麦隆:2025 年为 US$21马拉维:2025 年为 US$7
- 军费占政府支出比例喀麦隆:2025 年为 6.5%(占政府支出)马拉维:2025 年为 3.8%(占政府支出)
- 武装部队人员喀麦隆:2020 年为 34,000 人马拉维:2020 年为 15,000 人
- 国家实力喀麦隆:2022 年为 0.16%(占全球实力)马拉维:2022 年为 0.07%(占全球实力)
- 武装部队占劳动力比例喀麦隆:2020 年为 0.34%(占劳动力)马拉维:2020 年为 0.2%(占劳动力)
- 武器进口喀麦隆:2023 年为 200万 TIV马拉维:2024 年为 200万 TIV