印度与南非对比
印度与南非:35 °C 以上天数
2014 年35 °C 以上天数:印度为 80.6 天,南非为 13.2 天(CMIP6 气候模型中位数模拟)。 2014 年印度的数值较高,高出 67.4 天,是南非的 6.12 倍。
每年天数 · CMIP6
数据解读
- 自 1950 年以来,在两国都有数据的每一年,印度的数值都更高。
- 根据CMIP6 模型中位数(SSP2-4.5,中间路径),2100 年印度为 111.9 天,南非为 40 天。
逐年数据
每年天数 · CMIP6
| 年份 | 印度 | 南非 |
|---|---|---|
| 2014 | 80.6 | 13.2 |
| 2013 | 79.2 | 15.2 |
| 2012 | 81.3 | 14.3 |
| 2011 | 79.9 | 11.7 |
| 2010 | 80.8 | 12.5 |
| 2009 | 78.4 | 13.2 |
| 2008 | 77 | 12.3 |
| 2007 | 79 | 12 |
| 2006 | 76.2 | 11.9 |
| 2005 | 79.9 | 13.3 |
| 2004 | 80.1 | 13.9 |
| 2003 | 76.9 | 12.6 |
| 2002 | 79.2 | 12.6 |
| 2001 | 80.1 | 11.1 |
| 2000 | 76.6 | 11.8 |
| 1999 | 77.1 | 11 |
| 1998 | 76.9 | 12.1 |
| 1997 | 75.4 | 11.3 |
| 1996 | 72.1 | 9.4 |
| 1995 | 75.4 | 8.3 |
| 1994 | 76.3 | 8.5 |
| 1993 | 72.3 | 7.8 |
| 1992 | 68.7 | 7.1 |
| 1991 | 78.1 | 11.1 |
| 1990 | 76.7 | 10.1 |
| 1989 | 75.5 | 11.5 |
| 1988 | 73.9 | 9.4 |
| 1987 | 75.3 | 11.5 |
| 1986 | 76.8 | 10.2 |
| 1985 | 73 | 8.7 |
| 1984 | 75.9 | 8.7 |
| 1983 | 74.1 | 8.6 |
| 1982 | 76.1 | 10.5 |
| 1981 | 76.3 | 9.7 |
| 1980 | 75.3 | 10.3 |
| 1979 | 74.4 | 9.4 |
| 1978 | 74.4 | 9.5 |
| 1977 | 74.6 | 9.3 |
| 1976 | 76 | 9 |
| 1975 | 71 | 9.3 |
| 1974 | 75.8 | 8.8 |
| 1973 | 77.9 | 9.5 |
| 1972 | 71.1 | 8.4 |
| 1971 | 74.1 | 8.7 |
| 1970 | 73.7 | 8.2 |
| 1969 | 73.3 | 8.9 |
| 1968 | 72.2 | 8.4 |
| 1967 | 71.3 | 6.9 |
| 1966 | 75.3 | 7.8 |
| 1965 | 73.7 | 7.9 |
| 1964 | 68.9 | 6.4 |
| 1963 | 70.4 | 7 |
| 1962 | 74.2 | 8.9 |
| 1961 | 75.4 | 10.5 |
| 1960 | 75.4 | 9.7 |
| 1959 | 77.9 | 8.9 |
| 1958 | 74.8 | 9.3 |
| 1957 | 75.5 | 10.3 |
| 1956 | 75.6 | 7.9 |
| 1955 | 74.8 | 10.3 |
| 1954 | 75.3 | 9.1 |
| 1953 | 75.2 | 7.8 |
| 1952 | 76 | 9 |
| 1951 | 76.3 | 8.4 |
| 1950 | 76.5 | 8.7 |
CMIP6 模型中位数(SSP2-4.5,中间路径) · 2015–2100
| 年份 | 印度 | 南非 |
|---|---|---|
| 2015 | 81.664.7–96 | 14.76.8–24 |
| 2016 | 8063.1–96.1 | 13.46.3–22.2 |
| 2017 | 79.461.7–95.3 | 15.27.7–24.6 |
| 2018 | 82.461.5–95.7 | 179.7–28 |
| 2019 | 79.160.2–95.2 | 14.67.1–24.5 |
| 2020 | 82.262.4–99 | 15.77.4–23.5 |
| 2021 | 82.167.7–96.4 | 17.99.4–27 |
| 2022 | 83.962.8–100.3 | 189.8–29 |
| 2023 | 83.164–99.3 | 18.69.9–27.1 |
| 2024 | 84.766.4–100.3 | 17.89.8–28 |
| 2025 | 81.662.4–97.1 | 16.57.5–29.6 |
| 2026 | 84.866.1–101.3 | 20.19.3–29.4 |
| 2027 | 83.563.7–102 | 19.910.5–32.8 |
| 2028 | 82.862.9–99.9 | 19.110.6–33.2 |
| 2029 | 89.566–105.9 | 21.412.9–33.6 |
| 2030 | 84.265.7–100.2 | 21.19.6–33.9 |
| 2031 | 85.466.7–103.4 | 20.79.9–35.4 |
| 2032 | 85.665.5–105.3 | 2110.9–34.8 |
| 2033 | 83.865.8–99.8 | 22.612.2–34.2 |
| 2034 | 85.467.4–104.8 | 24.615–35.1 |
| 2035 | 85.766.6–105.3 | 20.911.7–31.3 |
| 2036 | 84.565.5–101.8 | 21.111.9–32.2 |
| 2037 | 89.772.2–107.5 | 23.412.6–35.2 |
| 2038 | 90.269.1–107.1 | 26.216–36.7 |
| 2039 | 87.168.5–107.4 | 22.611.4–35.9 |
| 2040 | 87.566.6–106 | 21.510.9–35.4 |
| 2041 | 89.171.2–107.8 | 23.213.2–35.8 |
| 2042 | 95.475.7–112.9 | 25.214.3–39.6 |
| 2043 | 91.270.1–111.1 | 25.613.4–39.9 |
| 2044 | 90.471.6–110.9 | 26.113.1–41.6 |
| 2045 | 91.772–108.5 | 26.813.1–40.3 |
| 2046 | 95.469.1–117 | 25.914.5–40.8 |
| 2047 | 94.272.6–113.5 | 26.713.7–41.4 |
| 2048 | 97.673.2–120.5 | 29.215.6–45.4 |
| 2049 | 95.173.8–117.5 | 29.117.3–44.7 |
| 2050 | 94.474.5–117 | 29.815.7–48.6 |
| 2051 | 94.775.8–114.7 | 27.913.6–45.2 |
| 2052 | 96.876.1–116.5 | 31.918.3–46.8 |
| 2053 | 94.673.9–116.7 | 28.318.6–43.2 |
| 2054 | 96.574.3–117 | 30.717.3–50.1 |
| 2055 | 99.175.7–118 | 2917.8–48 |
| 2056 | 99.173.7–118.2 | 29.916–45.3 |
| 2057 | 100.371.9–123.9 | 3117.4–50.1 |
| 2058 | 97.775.1–121.5 | 31.115.7–46 |
| 2059 | 102.380.5–127.4 | 3216.1–49.9 |
| 2060 | 10380.3–124.4 | 33.419.8–49.7 |
| 2061 | 100.978.4–122.6 | 32.718.1–51.1 |
| 2062 | 100.175.4–126.5 | 31.317.7–52.6 |
| 2063 | 100.678.3–122.1 | 32.317.4–50.7 |
| 2064 | 10081–123.3 | 31.817.7–47.6 |
| 2065 | 103.580.5–128.2 | 31.716.2–50.7 |
| 2066 | 100.780.7–124.6 | 34.318.4–55.3 |
| 2067 | 99.678.4–126 | 32.617.2–52.4 |
| 2068 | 102.780.1–130.1 | 33.819.2–54.3 |
| 2069 | 10477.5–128.1 | 34.621.3–52.4 |
| 2070 | 101.179.7–125.6 | 32.819.3–51.1 |
| 2071 | 105.581.4–132.1 | 35.320.8–55.8 |
| 2072 | 104.384.4–128.2 | 37.820.3–54.6 |
| 2073 | 108.485.4–130 | 4425.6–62.1 |
| 2074 | 105.182.9–126.8 | 38.320.7–55.8 |
| 2075 | 102.578.8–126 | 34.218.7–53.1 |
| 2076 | 105.582.2–134.6 | 38.822.9–57.5 |
| 2077 | 107.484.1–132.6 | 39.124.2–60.3 |
| 2078 | 107.382.8–127.7 | 35.521.6–53.4 |
| 2079 | 105.379.7–127.2 | 40.522.9–59.7 |
| 2080 | 109.784.5–135.9 | 40.523.4–59 |
| 2081 | 108.986.2–134.4 | 38.523.8–59.9 |
| 2082 | 105.783.4–133.6 | 38.622.5–58.2 |
| 2083 | 110.287.8–135.8 | 38.518.7–62.9 |
| 2084 | 111.582.3–138.1 | 39.720.6–58.4 |
| 2085 | 112.381.2–143.6 | 4021.5–65.8 |
| 2086 | 107.882.8–141.1 | 40.823–63.7 |
| 2087 | 107.980.1–134 | 38.324.6–58.6 |
| 2088 | 107.984.4–143.1 | 42.222.6–66.3 |
| 2089 | 107.981.7–137.3 | 41.624.2–59.1 |
| 2090 | 108.883.9–136.6 | 42.326.1–66.4 |
| 2091 | 113.486.9–144 | 43.324.2–63.6 |
| 2092 | 113.587.1–142.8 | 43.928–61.6 |
| 2093 | 108.482.7–144.8 | 37.521.3–63.4 |
| 2094 | 111.386.8–139.5 | 41.325.4–65.9 |
| 2095 | 114.186.4–143.6 | 43.522.9–70.2 |
| 2096 | 10880.4–140.3 | 41.224.1–66.8 |
| 2097 | 110.385–137.8 | 37.723.7–60.9 |
| 2098 | 113.687.7–141.1 | 46.425.1–71.4 |
| 2099 | 112.585.3–137.7 | 42.525.1–64.8 |
| 2100 | 111.986.8–138.7 | 4022.2–64.5 |
分别查看各国
须知
- 一年中最高气温超过 35 °C 的天数,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
印度与南非对比
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军用
- 军费开支印度:2025 年为 GDP 的 2.3%南非:2025 年为 GDP 的 0.74%
- 军费开支(美元)印度:2025 年为 US$933亿南非:2025 年为 US$30亿
- 人均军费开支印度:2025 年为 US$63南非:2025 年为 US$49
- 军费占政府支出比例印度:2025 年为 8.2%(占政府支出)南非:2025 年为 2.2%(占政府支出)
- 武装部队人员印度:2020 年为 307万 人南非:2020 年为 89,000 人
- 国家实力印度:2022 年为 9.94%(占全球实力)南非:2022 年为 0.55%(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)南非:2020 年为 0.4%(占劳动力)
- 武器进口印度:2025 年为 28.6亿 TIV南非:2025 年为 3000万 TIV
- 武器出口印度:2025 年为 8100万 TIV南非:2025 年为 1.15亿 TIV