哥伦比亚与印度对比
哥伦比亚与印度:35 °C 以上天数
2014 年35 °C 以上天数:哥伦比亚为 4.1 天,印度为 80.6 天(CMIP6 气候模型中位数模拟)。 2014 年印度的数值较高,高出 76.5 天,是哥伦比亚的 20 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 年哥伦比亚为 29 天,印度为 111.9 天。
逐年数据
每年天数 · CMIP6
| 年份 | 哥伦比亚 | 印度 |
|---|---|---|
| 2014 | 4.1 | 80.6 |
| 2013 | 4.8 | 79.2 |
| 2012 | 3.9 | 81.3 |
| 2011 | 3.6 | 79.9 |
| 2010 | 3.9 | 80.8 |
| 2009 | 3.4 | 78.4 |
| 2008 | 3.3 | 77 |
| 2007 | 2.5 | 79 |
| 2006 | 2.9 | 76.2 |
| 2005 | 3.6 | 79.9 |
| 2004 | 3.7 | 80.1 |
| 2003 | 2.7 | 76.9 |
| 2002 | 2.6 | 79.2 |
| 2001 | 3.2 | 80.1 |
| 2000 | 2.6 | 76.6 |
| 1999 | 2 | 77.1 |
| 1998 | 2.7 | 76.9 |
| 1997 | 2.1 | 75.4 |
| 1996 | 1.7 | 72.1 |
| 1995 | 1.8 | 75.4 |
| 1994 | 1.7 | 76.3 |
| 1993 | 1.9 | 72.3 |
| 1992 | 1.1 | 68.7 |
| 1991 | 2.8 | 78.1 |
| 1990 | 2.7 | 76.7 |
| 1989 | 2 | 75.5 |
| 1988 | 1.2 | 73.9 |
| 1987 | 1.7 | 75.3 |
| 1986 | 2.1 | 76.8 |
| 1985 | 1.5 | 73 |
| 1984 | 2.4 | 75.9 |
| 1983 | 1.4 | 74.1 |
| 1982 | 2.2 | 76.1 |
| 1981 | 1.7 | 76.3 |
| 1980 | 1.6 | 75.3 |
| 1979 | 1.7 | 74.4 |
| 1978 | 1.4 | 74.4 |
| 1977 | 1.9 | 74.6 |
| 1976 | 1.5 | 76 |
| 1975 | 0.9 | 71 |
| 1974 | 1.7 | 75.8 |
| 1973 | 1.9 | 77.9 |
| 1972 | 1.3 | 71.1 |
| 1971 | 1.3 | 74.1 |
| 1970 | 1.3 | 73.7 |
| 1969 | 1.4 | 73.3 |
| 1968 | 1.2 | 72.2 |
| 1967 | 1.2 | 71.3 |
| 1966 | 1.6 | 75.3 |
| 1965 | 1.3 | 73.7 |
| 1964 | 1 | 68.9 |
| 1963 | 1 | 70.4 |
| 1962 | 1.4 | 74.2 |
| 1961 | 1.4 | 75.4 |
| 1960 | 1.5 | 75.4 |
| 1959 | 1.3 | 77.9 |
| 1958 | 2 | 74.8 |
| 1957 | 1.4 | 75.5 |
| 1956 | 1.1 | 75.6 |
| 1955 | 1.5 | 74.8 |
| 1954 | 1.6 | 75.3 |
| 1953 | 1.7 | 75.2 |
| 1952 | 2 | 76 |
| 1951 | 1.5 | 76.3 |
| 1950 | 1.1 | 76.5 |
CMIP6 模型中位数(SSP2-4.5,中间路径) · 2015–2100
| 年份 | 哥伦比亚 | 印度 |
|---|---|---|
| 2015 | 4.51.2–11 | 81.664.7–96 |
| 2016 | 4.11.2–10.3 | 8063.1–96.1 |
| 2017 | 4.81.4–13.3 | 79.461.7–95.3 |
| 2018 | 4.51.2–15.1 | 82.461.5–95.7 |
| 2019 | 4.21.4–13.9 | 79.160.2–95.2 |
| 2020 | 4.91.4–11.3 | 82.262.4–99 |
| 2021 | 5.11.7–13 | 82.167.7–96.4 |
| 2022 | 72.1–16.4 | 83.962.8–100.3 |
| 2023 | 51.7–13.8 | 83.164–99.3 |
| 2024 | 72.1–17.4 | 84.766.4–100.3 |
| 2025 | 5.51.7–17.5 | 81.662.4–97.1 |
| 2026 | 6.51.5–16.9 | 84.866.1–101.3 |
| 2027 | 6.62.1–18.5 | 83.563.7–102 |
| 2028 | 6.52.2–16.5 | 82.862.9–99.9 |
| 2029 | 8.53.2–20.7 | 89.566–105.9 |
| 2030 | 7.11.8–16.7 | 84.265.7–100.2 |
| 2031 | 7.92.5–23.1 | 85.466.7–103.4 |
| 2032 | 8.32.7–21.9 | 85.665.5–105.3 |
| 2033 | 6.52–14.8 | 83.865.8–99.8 |
| 2034 | 5.81.8–20.3 | 85.467.4–104.8 |
| 2035 | 7.52.6–24.6 | 85.766.6–105.3 |
| 2036 | 6.72.7–16.1 | 84.565.5–101.8 |
| 2037 | 8.83.8–22.9 | 89.772.2–107.5 |
| 2038 | 104.1–23.3 | 90.269.1–107.1 |
| 2039 | 9.73.1–25.5 | 87.168.5–107.4 |
| 2040 | 8.82.5–27.6 | 87.566.6–106 |
| 2041 | 104–26.4 | 89.171.2–107.8 |
| 2042 | 11.72.7–28.6 | 95.475.7–112.9 |
| 2043 | 9.53.7–28.9 | 91.270.1–111.1 |
| 2044 | 9.23.1–32 | 90.471.6–110.9 |
| 2045 | 10.44.1–31.1 | 91.772–108.5 |
| 2046 | 15.25.4–37.1 | 95.469.1–117 |
| 2047 | 123.8–26.5 | 94.272.6–113.5 |
| 2048 | 11.53.9–32.2 | 97.673.2–120.5 |
| 2049 | 155.1–37.3 | 95.173.8–117.5 |
| 2050 | 12.24.2–31.7 | 94.474.5–117 |
| 2051 | 14.24.6–37.7 | 94.775.8–114.7 |
| 2052 | 14.85.9–37.2 | 96.876.1–116.5 |
| 2053 | 12.84.2–33 | 94.673.9–116.7 |
| 2054 | 13.25.1–34.5 | 96.574.3–117 |
| 2055 | 146.8–42.4 | 99.175.7–118 |
| 2056 | 13.24.8–44.9 | 99.173.7–118.2 |
| 2057 | 15.85–35.5 | 100.371.9–123.9 |
| 2058 | 14.74.7–51.2 | 97.775.1–121.5 |
| 2059 | 20.46.1–44.9 | 102.380.5–127.4 |
| 2060 | 195.8–40.8 | 10380.3–124.4 |
| 2061 | 186.5–44.3 | 100.978.4–122.6 |
| 2062 | 195.5–53.5 | 100.175.4–126.5 |
| 2063 | 18.77.5–45.2 | 100.678.3–122.1 |
| 2064 | 16.87.2–44.7 | 10081–123.3 |
| 2065 | 17.97.2–46.5 | 103.580.5–128.2 |
| 2066 | 17.17.2–46 | 100.780.7–124.6 |
| 2067 | 12.95.8–47.5 | 99.678.4–126 |
| 2068 | 18.55.8–50.1 | 102.780.1–130.1 |
| 2069 | 20.76–54.9 | 10477.5–128.1 |
| 2070 | 18.56–47.8 | 101.179.7–125.6 |
| 2071 | 227.7–51.9 | 105.581.4–132.1 |
| 2072 | 22.47.1–59.2 | 104.384.4–128.2 |
| 2073 | 26.210.7–56.2 | 108.485.4–130 |
| 2074 | 20.46.7–47.1 | 105.182.9–126.8 |
| 2075 | 21.68.3–49.7 | 102.578.8–126 |
| 2076 | 22.28.2–53.3 | 105.582.2–134.6 |
| 2077 | 24.38.5–58.6 | 107.484.1–132.6 |
| 2078 | 22.78.2–57.7 | 107.382.8–127.7 |
| 2079 | 22.76.8–52.8 | 105.379.7–127.2 |
| 2080 | 24.86.6–54.6 | 109.784.5–135.9 |
| 2081 | 24.86.6–60.4 | 108.986.2–134.4 |
| 2082 | 21.86.6–55 | 105.783.4–133.6 |
| 2083 | 24.66.5–59.8 | 110.287.8–135.8 |
| 2084 | 267–72.6 | 111.582.3–138.1 |
| 2085 | 27.16.7–67.4 | 112.381.2–143.6 |
| 2086 | 256.5–68.2 | 107.882.8–141.1 |
| 2087 | 23.55.3–67.3 | 107.980.1–134 |
| 2088 | 30.47.7–74.5 | 107.984.4–143.1 |
| 2089 | 28.48.1–67.5 | 107.981.7–137.3 |
| 2090 | 24.69.4–65.2 | 108.883.9–136.6 |
| 2091 | 28.510–72.1 | 113.486.9–144 |
| 2092 | 29.67.8–70.8 | 113.587.1–142.8 |
| 2093 | 27.99–75.1 | 108.482.7–144.8 |
| 2094 | 25.98–71.7 | 111.386.8–139.5 |
| 2095 | 32.76.7–74.9 | 114.186.4–143.6 |
| 2096 | 25.68–72.4 | 10880.4–140.3 |
| 2097 | 30.18.8–71.7 | 110.385–137.8 |
| 2098 | 30.58.4–84.1 | 113.687.7–141.1 |
| 2099 | 28.29.7–63.4 | 112.585.3–137.7 |
| 2100 | 2910.9–71.6 | 111.986.8–138.7 |
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
哥伦比亚与印度对比
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军用
- 军费开支哥伦比亚:2025 年为 GDP 的 3.2%印度:2025 年为 GDP 的 2.3%
- 军费开支(美元)哥伦比亚:2025 年为 US$137亿印度:2025 年为 US$933亿
- 人均军费开支哥伦比亚:2025 年为 US$273印度:2025 年为 US$63
- 军费占政府支出比例哥伦比亚:2025 年为 9.3%(占政府支出)印度:2025 年为 8.2%(占政府支出)
- 武装部队人员哥伦比亚:2020 年为 428,000 人印度:2020 年为 307万 人
- 国家实力哥伦比亚:2022 年为 0.57%(占全球实力)印度:2022 年为 9.94%(占全球实力)
- 武装部队占劳动力比例哥伦比亚:2020 年为 1.7%(占劳动力)印度:2020 年为 0.58%(占劳动力)
- 武器进口哥伦比亚:2025 年为 2200万 TIV印度:2025 年为 28.6亿 TIV
- 武器出口哥伦比亚:2020 年为 400万 TIV印度:2025 年为 8100万 TIV