印度与尼日利亚对比
印度与尼日利亚:热带夜
2014 年热带夜:印度为 215.5 夜,尼日利亚为 303.9 夜(CMIP6 气候模型中位数模拟)。 2014 年尼日利亚的数值较高,高出 88.4 夜,是印度的 1.41 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,尼日利亚的数值都更高。
- 根据CMIP6 模型中位数(SSP2-4.5,中间路径),2100 年印度为 250.4 夜,尼日利亚为 332.3 夜。
逐年数据
每年夜数 · CMIP6
| 年份 | 印度 | 尼日利亚 |
|---|---|---|
| 2014 | 215.5 | 303.9 |
| 2013 | 216 | 299.5 |
| 2012 | 217.4 | 304.4 |
| 2011 | 215.5 | 301.5 |
| 2010 | 215.2 | 301.4 |
| 2009 | 213.7 | 297.8 |
| 2008 | 213.7 | 297.4 |
| 2007 | 213.3 | 298 |
| 2006 | 212.5 | 297.2 |
| 2005 | 212.9 | 295.4 |
| 2004 | 214.9 | 294.1 |
| 2003 | 212.1 | 298.4 |
| 2002 | 213 | 295.2 |
| 2001 | 212.2 | 299.1 |
| 2000 | 210.8 | 293.9 |
| 1999 | 211.9 | 290.3 |
| 1998 | 210.3 | 289.9 |
| 1997 | 209.5 | 292.5 |
| 1996 | 207 | 290 |
| 1995 | 208.8 | 289.1 |
| 1994 | 207.1 | 287.4 |
| 1993 | 206 | 284 |
| 1992 | 203.5 | 281.6 |
| 1991 | 208 | 289.1 |
| 1990 | 209.9 | 290.6 |
| 1989 | 208 | 287.2 |
| 1988 | 207.3 | 288 |
| 1987 | 207.2 | 285.6 |
| 1986 | 207.2 | 289.1 |
| 1985 | 205.9 | 284.2 |
| 1984 | 206.7 | 284.4 |
| 1983 | 203.3 | 281.9 |
| 1982 | 205.7 | 283.5 |
| 1981 | 208.4 | 286.4 |
| 1980 | 205.3 | 282.8 |
| 1979 | 205.5 | 283 |
| 1978 | 204.1 | 282.8 |
| 1977 | 205.9 | 285.9 |
| 1976 | 204.6 | 281.7 |
| 1975 | 202.6 | 280.4 |
| 1974 | 205.3 | 280.7 |
| 1973 | 205.6 | 284.3 |
| 1972 | 203.8 | 284.1 |
| 1971 | 203.6 | 279.3 |
| 1970 | 202.6 | 282 |
| 1969 | 203.6 | 278.7 |
| 1968 | 201.7 | 279.6 |
| 1967 | 201.6 | 278.1 |
| 1966 | 203.9 | 279.4 |
| 1965 | 202.4 | 279 |
| 1964 | 201.2 | 276.8 |
| 1963 | 201.8 | 275.1 |
| 1962 | 203.8 | 284.1 |
| 1961 | 206.3 | 281.9 |
| 1960 | 206 | 285.9 |
| 1959 | 206.1 | 282.8 |
| 1958 | 204 | 281.4 |
| 1957 | 206.5 | 283.3 |
| 1956 | 204 | 280.8 |
| 1955 | 203.2 | 280.4 |
| 1954 | 206 | 284.1 |
| 1953 | 204 | 280.4 |
| 1952 | 204.4 | 280.8 |
| 1951 | 204.3 | 284.2 |
| 1950 | 206.3 | 279.3 |
CMIP6 模型中位数(SSP2-4.5,中间路径) · 2015–2100
| 年份 | 印度 | 尼日利亚 |
|---|---|---|
| 2015 | 216.9202.1–228.4 | 301.7282.9–317.3 |
| 2016 | 215202.5–227.6 | 301.4280.8–312.5 |
| 2017 | 216.2202.1–228.5 | 302.5284.3–316.3 |
| 2018 | 218.3204.5–231.4 | 302.2286.8–316.5 |
| 2019 | 217.4202.3–230.4 | 300.1281.2–318 |
| 2020 | 217.6203.1–232 | 303283.7–318.5 |
| 2021 | 219.7204.5–233.2 | 305.1287.1–318.4 |
| 2022 | 220.5205.8–233 | 305.1286–319.5 |
| 2023 | 220.4206.4–233.7 | 305286.4–318.9 |
| 2024 | 222.6208.1–236.3 | 310.6295.1–321.5 |
| 2025 | 219.7202.7–236.2 | 307.5292–319.4 |
| 2026 | 220.2204.2–236.8 | 306.9286.1–322.9 |
| 2027 | 223.6207.1–239 | 307.4288–321.7 |
| 2028 | 222.1205.1–235.7 | 310.7293.4–322.6 |
| 2029 | 225.4210–240.3 | 311.4293.3–324.4 |
| 2030 | 222.7207.3–235.6 | 310.6294.1–322.5 |
| 2031 | 223.8207.1–237.8 | 312.2295–324.8 |
| 2032 | 225.9210–240.5 | 310290–323.5 |
| 2033 | 223.8209.3–235.4 | 313292.9–327 |
| 2034 | 225.8210–242.7 | 311.7294.8–325.1 |
| 2035 | 224.9210.6–240.9 | 314.5296.7–327.7 |
| 2036 | 224.2210.4–240.3 | 313.5294.1–326.5 |
| 2037 | 228.3213.6–242.6 | 315.6297.9–327.1 |
| 2038 | 228.1212.6–241.4 | 316.9301.2–329.6 |
| 2039 | 227.4211.9–242.8 | 315.4299.4–328.2 |
| 2040 | 227212.2–242 | 315.6297.9–327.4 |
| 2041 | 227.1210.7–240.5 | 316.8300.6–330.2 |
| 2042 | 229.1214.8–244.2 | 316.3300.3–330.3 |
| 2043 | 228.9214.5–244 | 316298.4–326 |
| 2044 | 229.7214.7–244.2 | 317.9299.5–329.6 |
| 2045 | 231.2216.6–246.1 | 316.6300.5–330.4 |
| 2046 | 232.6216.2–248.6 | 317.8302.2–330.7 |
| 2047 | 232.4215.5–250 | 319.8301.5–333 |
| 2048 | 235.6218.5–254.3 | 320.1302.4–331.7 |
| 2049 | 235.3218.3–249.9 | 320.7305.3–331.7 |
| 2050 | 233.5216.6–253.4 | 320.9304–335 |
| 2051 | 234.9218.6–249.9 | 321304.1–334.2 |
| 2052 | 234219.4–249.3 | 320.9303.9–333.5 |
| 2053 | 234.9219.5–252.5 | 320.9303.3–334.1 |
| 2054 | 237.2219.4–253.1 | 324.5304.5–338.2 |
| 2055 | 237.2220.7–256.6 | 322.2308.4–333.7 |
| 2056 | 238.8221.2–254.9 | 324.8305.8–336.3 |
| 2057 | 235.7218.5–253.8 | 326310.8–337.3 |
| 2058 | 238.8222.8–254.1 | 322.9307.4–334.2 |
| 2059 | 241.1224.9–256.2 | 325.7308.9–339 |
| 2060 | 240.9225.6–255.6 | 323.8307.5–337 |
| 2061 | 237.9220.6–253.7 | 324.4307.1–338.2 |
| 2062 | 239.9221.2–259.7 | 325.7308–339.3 |
| 2063 | 239.9222.3–256.1 | 326.6308.7–337 |
| 2064 | 240223.8–256.3 | 326.2311–339.8 |
| 2065 | 240.9222.9–257.3 | 327.6313.5–339.4 |
| 2066 | 241.5224.1–258.6 | 328.4311.3–339.6 |
| 2067 | 241.6224.8–257.9 | 326308.4–340.5 |
| 2068 | 242.3224.6–262.1 | 329.3311.2–342.9 |
| 2069 | 242.8225.4–259.1 | 326.6309.9–338.1 |
| 2070 | 240.1225.3–257.1 | 327.4312.6–340.7 |
| 2071 | 241.8223.7–260.2 | 329.1313–341.1 |
| 2072 | 242.6225.4–259.2 | 330.3315.9–342.1 |
| 2073 | 243.8228–259.3 | 326.3309.5–341 |
| 2074 | 241.6226.9–258.4 | 326.7311.4–341.4 |
| 2075 | 243.5226.4–258.2 | 328.9314.5–341.8 |
| 2076 | 243.2224.6–259.9 | 329.6313.4–342 |
| 2077 | 243.7226.6–264.6 | 332.5314.4–343.3 |
| 2078 | 246.7230.1–261.5 | 329.8312.8–342.1 |
| 2079 | 244.9226.4–261.3 | 329.6313.8–340.6 |
| 2080 | 246.7227–263.7 | 331.7314.1–344.8 |
| 2081 | 247.5230.4–264.8 | 329.3311.6–343.7 |
| 2082 | 244.6227.9–262.3 | 329.3313.3–341.4 |
| 2083 | 247.5227.8–265.1 | 330.4315–342.2 |
| 2084 | 247.3228.3–266.8 | 330.5311.9–344.2 |
| 2085 | 246.5227.5–267.1 | 332.7316.6–343.6 |
| 2086 | 245.6227.7–266.5 | 331.9316.3–343.6 |
| 2087 | 245.5226.7–266.2 | 330.7314.9–342.4 |
| 2088 | 247229.4–266.9 | 331.7313.2–342.7 |
| 2089 | 248.1230–266.3 | 331.1318.3–341.9 |
| 2090 | 249228.9–266.4 | 332.4312.7–343.9 |
| 2091 | 248.9230–266.5 | 333.3311–345.8 |
| 2092 | 249.8232.3–267.8 | 332.7313.8–345.7 |
| 2093 | 248.1229–266.5 | 334.2318.6–344.9 |
| 2094 | 249.1230.6–268.4 | 334.2317.3–345.6 |
| 2095 | 251233.2–270 | 333.8319.3–346.3 |
| 2096 | 247.8231.4–269.3 | 332.5317.4–346.7 |
| 2097 | 251.1231.4–268 | 331.8312.6–344.6 |
| 2098 | 252.3230.8–268.3 | 332.3316–343.7 |
| 2099 | 248.7230.1–268.3 | 333316.9–344.2 |
| 2100 | 250.4232.9–266.7 | 332.3318.5–345 |
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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
印度与尼日利亚对比
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军用
- 军费开支印度:2025 年为 GDP 的 2.3%尼日利亚:2025 年为 GDP 的 0.91%
- 军费开支(美元)印度:2025 年为 US$933亿尼日利亚:2025 年为 US$17.6亿
- 人均军费开支印度:2025 年为 US$63尼日利亚:2025 年为 US$9
- 军费占政府支出比例印度:2025 年为 8.2%(占政府支出)尼日利亚:2025 年为 5.9%(占政府支出)
- 武装部队人员印度:2020 年为 307万 人尼日利亚:2020 年为 223,000 人
- 国家实力印度:2022 年为 9.94%(占全球实力)尼日利亚:2022 年为 1.16%(占全球实力)
- 武装部队占劳动力比例印度:2020 年为 0.58%(占劳动力)尼日利亚:2020 年为 0.23%(占劳动力)
- 武器进口印度:2025 年为 28.6亿 TIV尼日利亚:2025 年为 2100万 TIV
- 武器出口印度:2025 年为 8100万 TIV尼日利亚:2022 年为 400万 TIV