印度与缅甸对比
印度与缅甸:35 °C 以上天数
2014 年35 °C 以上天数:印度为 80.6 天,缅甸为 28.8 天(CMIP6 气候模型中位数模拟)。 2014 年印度的数值较高,高出 51.8 天,是缅甸的 2.8 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,缅甸为 57 天。
逐年数据
每年天数 · CMIP6
| 年份 | 印度 | 缅甸 |
|---|---|---|
| 2014 | 80.6 | 28.8 |
| 2013 | 79.2 | 28 |
| 2012 | 81.3 | 27.5 |
| 2011 | 79.9 | 27.4 |
| 2010 | 80.8 | 27.9 |
| 2009 | 78.4 | 27.3 |
| 2008 | 77 | 27.2 |
| 2007 | 79 | 25.4 |
| 2006 | 76.2 | 26.8 |
| 2005 | 79.9 | 25 |
| 2004 | 80.1 | 28.3 |
| 2003 | 76.9 | 26.4 |
| 2002 | 79.2 | 27.4 |
| 2001 | 80.1 | 28.4 |
| 2000 | 76.6 | 26.6 |
| 1999 | 77.1 | 26.1 |
| 1998 | 76.9 | 26.4 |
| 1997 | 75.4 | 26.2 |
| 1996 | 72.1 | 24.7 |
| 1995 | 75.4 | 25.5 |
| 1994 | 76.3 | 24.3 |
| 1993 | 72.3 | 23.9 |
| 1992 | 68.7 | 20.5 |
| 1991 | 78.1 | 28.4 |
| 1990 | 76.7 | 26.3 |
| 1989 | 75.5 | 23.8 |
| 1988 | 73.9 | 23.9 |
| 1987 | 75.3 | 25.4 |
| 1986 | 76.8 | 26.6 |
| 1985 | 73 | 23.6 |
| 1984 | 75.9 | 25.8 |
| 1983 | 74.1 | 23.2 |
| 1982 | 76.1 | 24 |
| 1981 | 76.3 | 23.8 |
| 1980 | 75.3 | 23.5 |
| 1979 | 74.4 | 25.3 |
| 1978 | 74.4 | 23.4 |
| 1977 | 74.6 | 24.1 |
| 1976 | 76 | 25.6 |
| 1975 | 71 | 22.3 |
| 1974 | 75.8 | 24.1 |
| 1973 | 77.9 | 25.9 |
| 1972 | 71.1 | 23.1 |
| 1971 | 74.1 | 22.8 |
| 1970 | 73.7 | 24.2 |
| 1969 | 73.3 | 23.6 |
| 1968 | 72.2 | 23.4 |
| 1967 | 71.3 | 24.1 |
| 1966 | 75.3 | 23.1 |
| 1965 | 73.7 | 23.3 |
| 1964 | 68.9 | 21.7 |
| 1963 | 70.4 | 23.2 |
| 1962 | 74.2 | 24.4 |
| 1961 | 75.4 | 24.9 |
| 1960 | 75.4 | 26.2 |
| 1959 | 77.9 | 22.6 |
| 1958 | 74.8 | 25 |
| 1957 | 75.5 | 24.1 |
| 1956 | 75.6 | 25 |
| 1955 | 74.8 | 24.5 |
| 1954 | 75.3 | 25.3 |
| 1953 | 75.2 | 23.5 |
| 1952 | 76 | 26.1 |
| 1951 | 76.3 | 25 |
| 1950 | 76.5 | 23.8 |
CMIP6 模型中位数(SSP2-4.5,中间路径) · 2015–2100
| 年份 | 印度 | 缅甸 |
|---|---|---|
| 2015 | 81.664.7–96 | 28.518.5–40 |
| 2016 | 8063.1–96.1 | 27.717.3–38.7 |
| 2017 | 79.461.7–95.3 | 2818.9–38.5 |
| 2018 | 82.461.5–95.7 | 29.818.6–41.2 |
| 2019 | 79.160.2–95.2 | 28.218.2–41.3 |
| 2020 | 82.262.4–99 | 28.616.8–41.4 |
| 2021 | 82.167.7–96.4 | 29.719.4–40.8 |
| 2022 | 83.962.8–100.3 | 31.521.7–42.7 |
| 2023 | 83.164–99.3 | 30.319.9–41.6 |
| 2024 | 84.766.4–100.3 | 31.720.1–43.7 |
| 2025 | 81.662.4–97.1 | 32.721.3–44.8 |
| 2026 | 84.866.1–101.3 | 30.420.3–44.5 |
| 2027 | 83.563.7–102 | 30.620.4–44.2 |
| 2028 | 82.862.9–99.9 | 31.119–43 |
| 2029 | 89.566–105.9 | 32.720.9–46.4 |
| 2030 | 84.265.7–100.2 | 32.619.3–45 |
| 2031 | 85.466.7–103.4 | 32.721.3–45.2 |
| 2032 | 85.665.5–105.3 | 32.119.6–45.4 |
| 2033 | 83.865.8–99.8 | 33.322–43.8 |
| 2034 | 85.467.4–104.8 | 31.322–43.5 |
| 2035 | 85.766.6–105.3 | 32.620.4–49 |
| 2036 | 84.565.5–101.8 | 33.322.7–46.1 |
| 2037 | 89.772.2–107.5 | 34.322.6–49.1 |
| 2038 | 90.269.1–107.1 | 35.824.4–49.9 |
| 2039 | 87.168.5–107.4 | 34.920.9–49 |
| 2040 | 87.566.6–106 | 3419.2–51.2 |
| 2041 | 89.171.2–107.8 | 3724.8–51.9 |
| 2042 | 95.475.7–112.9 | 36.223–49.9 |
| 2043 | 91.270.1–111.1 | 34.522.6–47.7 |
| 2044 | 90.471.6–110.9 | 38.222.1–51.2 |
| 2045 | 91.772–108.5 | 35.924.2–50.7 |
| 2046 | 95.469.1–117 | 38.425.2–57.1 |
| 2047 | 94.272.6–113.5 | 38.824.1–56.1 |
| 2048 | 97.673.2–120.5 | 40.323.4–58.7 |
| 2049 | 95.173.8–117.5 | 40.625.2–58.1 |
| 2050 | 94.474.5–117 | 40.226.5–55.3 |
| 2051 | 94.775.8–114.7 | 41.726.7–56.8 |
| 2052 | 96.876.1–116.5 | 40.326.3–61.2 |
| 2053 | 94.673.9–116.7 | 41.323.6–61.5 |
| 2054 | 96.574.3–117 | 42.426.8–60.9 |
| 2055 | 99.175.7–118 | 41.428.5–61.9 |
| 2056 | 99.173.7–118.2 | 42.726.5–63 |
| 2057 | 100.371.9–123.9 | 43.226.3–63.2 |
| 2058 | 97.775.1–121.5 | 44.526.8–63.6 |
| 2059 | 102.380.5–127.4 | 43.729.1–65.3 |
| 2060 | 10380.3–124.4 | 47.428.3–66 |
| 2061 | 100.978.4–122.6 | 46.729.5–64.5 |
| 2062 | 100.175.4–126.5 | 47.126.7–68.6 |
| 2063 | 100.678.3–122.1 | 46.929–66.4 |
| 2064 | 10081–123.3 | 47.527.9–66.1 |
| 2065 | 103.580.5–128.2 | 42.427.5–62.4 |
| 2066 | 100.780.7–124.6 | 46.228.9–68.8 |
| 2067 | 99.678.4–126 | 4125.7–63.6 |
| 2068 | 102.780.1–130.1 | 46.928–70.4 |
| 2069 | 10477.5–128.1 | 48.729.3–70.1 |
| 2070 | 101.179.7–125.6 | 43.927.5–64.3 |
| 2071 | 105.581.4–132.1 | 4926.2–71.9 |
| 2072 | 104.384.4–128.2 | 4730.6–71.4 |
| 2073 | 108.485.4–130 | 50.732.2–74 |
| 2074 | 105.182.9–126.8 | 4829.2–74 |
| 2075 | 102.578.8–126 | 49.126.1–68.9 |
| 2076 | 105.582.2–134.6 | 47.826.5–72.8 |
| 2077 | 107.484.1–132.6 | 51.832.9–76.1 |
| 2078 | 107.382.8–127.7 | 50.632.4–75.6 |
| 2079 | 105.379.7–127.2 | 50.730.5–77 |
| 2080 | 109.784.5–135.9 | 53.331.3–79.5 |
| 2081 | 108.986.2–134.4 | 5235.5–79.1 |
| 2082 | 105.783.4–133.6 | 52.532.5–76.8 |
| 2083 | 110.287.8–135.8 | 51.730.5–79.6 |
| 2084 | 111.582.3–138.1 | 52.329.5–76 |
| 2085 | 112.381.2–143.6 | 51.232.9–79.4 |
| 2086 | 107.882.8–141.1 | 52.632.5–80.1 |
| 2087 | 107.980.1–134 | 51.732.6–77.9 |
| 2088 | 107.984.4–143.1 | 50.727.2–88.8 |
| 2089 | 107.981.7–137.3 | 52.731.4–86.3 |
| 2090 | 108.883.9–136.6 | 54.231.1–80.1 |
| 2091 | 113.486.9–144 | 53.235.4–80.1 |
| 2092 | 113.587.1–142.8 | 54.135.4–85.5 |
| 2093 | 108.482.7–144.8 | 5235.6–85 |
| 2094 | 111.386.8–139.5 | 54.434.9–80.7 |
| 2095 | 114.186.4–143.6 | 58.535.1–84.3 |
| 2096 | 10880.4–140.3 | 52.832.3–84.1 |
| 2097 | 110.385–137.8 | 53.230.4–82.8 |
| 2098 | 113.687.7–141.1 | 55.334.4–83.8 |
| 2099 | 112.585.3–137.7 | 55.233.1–84.7 |
| 2100 | 111.986.8–138.7 | 5734.6–84 |
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
印度与缅甸对比
健康
人口与社会
经济
教育
政治
冲突
军用
- 军费开支印度:2025 年为 GDP 的 2.3%缅甸:2024 年为 GDP 的 6.61%
- 军费开支(美元)印度:2025 年为 US$933亿缅甸:2024 年为 US$46.5亿
- 人均军费开支印度:2025 年为 US$63缅甸:2024 年为 US$92
- 军费占政府支出比例印度:2025 年为 8.2%(占政府支出)缅甸:2024 年为 32%(占政府支出)
- 武装部队人员印度:2020 年为 307万 人缅甸:2020 年为 463,000 人
- 国家实力印度:2022 年为 9.94%(占全球实力)缅甸:2022 年为 0.53%(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)缅甸:2020 年为 1.94%(占劳动力)
- 武器进口印度:2025 年为 28.6亿 TIV缅甸:2025 年为 7000万 TIV