缅甸与泰国对比
缅甸与泰国:35 °C 以上天数
2014 年35 °C 以上天数:缅甸为 28.8 天,泰国为 39.9 天(CMIP6 气候模型中位数模拟)。 2014 年泰国的数值较高,高出 11.1 天,是缅甸的 1.39 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,泰国的数值都更高。
- 根据CMIP6 模型中位数(SSP2-4.5,中间路径),2100 年缅甸为 57 天,泰国为 95.9 天。
逐年数据
每年天数 · CMIP6
| 年份 | 缅甸 | 泰国 |
|---|---|---|
| 2014 | 28.8 | 39.9 |
| 2013 | 28 | 42.1 |
| 2012 | 27.5 | 40.1 |
| 2011 | 27.4 | 39.9 |
| 2010 | 27.9 | 39.8 |
| 2009 | 27.3 | 37.9 |
| 2008 | 27.2 | 36 |
| 2007 | 25.4 | 33 |
| 2006 | 26.8 | 39.2 |
| 2005 | 25 | 34.9 |
| 2004 | 28.3 | 39.4 |
| 2003 | 26.4 | 34.1 |
| 2002 | 27.4 | 36.3 |
| 2001 | 28.4 | 37.8 |
| 2000 | 26.6 | 32.5 |
| 1999 | 26.1 | 29.7 |
| 1998 | 26.4 | 33.3 |
| 1997 | 26.2 | 33.7 |
| 1996 | 24.7 | 31.3 |
| 1995 | 25.5 | 33.5 |
| 1994 | 24.3 | 33.5 |
| 1993 | 23.9 | 30.4 |
| 1992 | 20.5 | 25.5 |
| 1991 | 28.4 | 35.4 |
| 1990 | 26.3 | 37.7 |
| 1989 | 23.8 | 29.3 |
| 1988 | 23.9 | 30.6 |
| 1987 | 25.4 | 31.2 |
| 1986 | 26.6 | 34.3 |
| 1985 | 23.6 | 29.3 |
| 1984 | 25.8 | 32.4 |
| 1983 | 23.2 | 30.4 |
| 1982 | 24 | 33.1 |
| 1981 | 23.8 | 29.5 |
| 1980 | 23.5 | 28.8 |
| 1979 | 25.3 | 32.4 |
| 1978 | 23.4 | 26.1 |
| 1977 | 24.1 | 31.5 |
| 1976 | 25.6 | 29.7 |
| 1975 | 22.3 | 25 |
| 1974 | 24.1 | 31.5 |
| 1973 | 25.9 | 34.5 |
| 1972 | 23.1 | 30.3 |
| 1971 | 22.8 | 27.1 |
| 1970 | 24.2 | 28.7 |
| 1969 | 23.6 | 29.9 |
| 1968 | 23.4 | 29.4 |
| 1967 | 24.1 | 28.3 |
| 1966 | 23.1 | 25.4 |
| 1965 | 23.3 | 27.1 |
| 1964 | 21.7 | 26.6 |
| 1963 | 23.2 | 26 |
| 1962 | 24.4 | 28.7 |
| 1961 | 24.9 | 31.1 |
| 1960 | 26.2 | 32.9 |
| 1959 | 22.6 | 24.4 |
| 1958 | 25 | 33 |
| 1957 | 24.1 | 27.8 |
| 1956 | 25 | 27.1 |
| 1955 | 24.5 | 31.2 |
| 1954 | 25.3 | 30.3 |
| 1953 | 23.5 | 28.2 |
| 1952 | 26.1 | 34.1 |
| 1951 | 25 | 33 |
| 1950 | 23.8 | 28.5 |
CMIP6 模型中位数(SSP2-4.5,中间路径) · 2015–2100
| 年份 | 缅甸 | 泰国 |
|---|---|---|
| 2015 | 28.518.5–40 | 41.123.5–66.3 |
| 2016 | 27.717.3–38.7 | 38.915.7–61.2 |
| 2017 | 2818.9–38.5 | 41.420.8–64.9 |
| 2018 | 29.818.6–41.2 | 43.521.7–65.5 |
| 2019 | 28.218.2–41.3 | 40.916.5–61.9 |
| 2020 | 28.616.8–41.4 | 45.617.3–66.6 |
| 2021 | 29.719.4–40.8 | 43.720.7–70.7 |
| 2022 | 31.521.7–42.7 | 47.826.4–73.3 |
| 2023 | 30.319.9–41.6 | 41.822.3–67.2 |
| 2024 | 31.720.1–43.7 | 44.726.9–70.9 |
| 2025 | 32.721.3–44.8 | 50.227.3–77 |
| 2026 | 30.420.3–44.5 | 44.425.2–80.5 |
| 2027 | 30.620.4–44.2 | 47.126.2–75.6 |
| 2028 | 31.119–43 | 51.123.1–73.3 |
| 2029 | 32.720.9–46.4 | 56.733.9–84.7 |
| 2030 | 32.619.3–45 | 50.726.1–78.9 |
| 2031 | 32.721.3–45.2 | 50.531.5–81.2 |
| 2032 | 32.119.6–45.4 | 49.823.6–85.7 |
| 2033 | 33.322–43.8 | 53.432–73.7 |
| 2034 | 31.322–43.5 | 51.531.6–83.4 |
| 2035 | 32.620.4–49 | 52.228.9–89.2 |
| 2036 | 33.322.7–46.1 | 53.335.2–77.4 |
| 2037 | 34.322.6–49.1 | 57.632–82.5 |
| 2038 | 35.824.4–49.9 | 62.238.5–91.5 |
| 2039 | 34.920.9–49 | 55.127.8–78.8 |
| 2040 | 3419.2–51.2 | 53.728.4–91.3 |
| 2041 | 3724.8–51.9 | 6337.5–86.6 |
| 2042 | 36.223–49.9 | 54.826.4–94.7 |
| 2043 | 34.522.6–47.7 | 57.129.8–85.3 |
| 2044 | 38.222.1–51.2 | 57.336.7–95.7 |
| 2045 | 35.924.2–50.7 | 54.934.2–90.2 |
| 2046 | 38.425.2–57.1 | 62.736.4–102.1 |
| 2047 | 38.824.1–56.1 | 63.635–98.5 |
| 2048 | 40.323.4–58.7 | 72.237.4–110.4 |
| 2049 | 40.625.2–58.1 | 66.240.8–98.3 |
| 2050 | 40.226.5–55.3 | 64.536.9–95.3 |
| 2051 | 41.726.7–56.8 | 66.135.5–97.1 |
| 2052 | 40.326.3–61.2 | 74.339.3–108.1 |
| 2053 | 41.323.6–61.5 | 64.635–106.7 |
| 2054 | 42.426.8–60.9 | 69.144.2–112.1 |
| 2055 | 41.428.5–61.9 | 71.445.3–107.6 |
| 2056 | 42.726.5–63 | 70.536.5–117.7 |
| 2057 | 43.226.3–63.2 | 7240–104.6 |
| 2058 | 44.526.8–63.6 | 73.140.6–107.3 |
| 2059 | 43.729.1–65.3 | 77.251.2–121.4 |
| 2060 | 47.428.3–66 | 80.946–117 |
| 2061 | 46.729.5–64.5 | 77.335.3–117.8 |
| 2062 | 47.126.7–68.6 | 73.535.5–125.8 |
| 2063 | 46.929–66.4 | 83.640.3–122.6 |
| 2064 | 47.527.9–66.1 | 74.746.9–111.8 |
| 2065 | 42.427.5–62.4 | 69.939.5–124.8 |
| 2066 | 46.228.9–68.8 | 80.250.7–122.7 |
| 2067 | 4125.7–63.6 | 69.437.9–116 |
| 2068 | 46.928–70.4 | 76.445.8–131.7 |
| 2069 | 48.729.3–70.1 | 77.747.4–128.7 |
| 2070 | 43.927.5–64.3 | 73.839.2–129.5 |
| 2071 | 4926.2–71.9 | 80.642.2–131.6 |
| 2072 | 4730.6–71.4 | 81.347.5–135.3 |
| 2073 | 50.732.2–74 | 88.854.2–133.4 |
| 2074 | 4829.2–74 | 8444.3–128.1 |
| 2075 | 49.126.1–68.9 | 82.838.3–123.9 |
| 2076 | 47.826.5–72.8 | 82.937.1–132.9 |
| 2077 | 51.832.9–76.1 | 8849.4–132.4 |
| 2078 | 50.632.4–75.6 | 91.252–137 |
| 2079 | 50.730.5–77 | 85.749.5–144.4 |
| 2080 | 53.331.3–79.5 | 88.552.7–143.6 |
| 2081 | 5235.5–79.1 | 87.150.9–135.1 |
| 2082 | 52.532.5–76.8 | 89.953.5–133.6 |
| 2083 | 51.730.5–79.6 | 87.145.8–149.7 |
| 2084 | 52.329.5–76 | 8447.7–131 |
| 2085 | 51.232.9–79.4 | 93.853.8–141.8 |
| 2086 | 52.632.5–80.1 | 87.745.8–139.2 |
| 2087 | 51.732.6–77.9 | 86.447.4–139.2 |
| 2088 | 50.727.2–88.8 | 90.441.7–153.2 |
| 2089 | 52.731.4–86.3 | 90.149–151.2 |
| 2090 | 54.231.1–80.1 | 87.853.3–154.1 |
| 2091 | 53.235.4–80.1 | 8648.2–148.6 |
| 2092 | 54.135.4–85.5 | 92.650.2–149.9 |
| 2093 | 5235.6–85 | 89.854.6–159 |
| 2094 | 54.434.9–80.7 | 95.549.6–143.6 |
| 2095 | 58.535.1–84.3 | 103.458.2–156.3 |
| 2096 | 52.832.3–84.1 | 89.951.3–155.4 |
| 2097 | 53.230.4–82.8 | 94.550.2–142.6 |
| 2098 | 55.334.4–83.8 | 89.851.6–149.6 |
| 2099 | 55.233.1–84.7 | 88.244.7–147.4 |
| 2100 | 5734.6–84 | 95.951.1–153.3 |
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
缅甸与泰国对比
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人口与社会
经济
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冲突
军用
- 军费开支缅甸:2024 年为 GDP 的 6.61%泰国:2025 年为 GDP 的 1.04%
- 军费开支(美元)缅甸:2024 年为 US$46.5亿泰国:2025 年为 US$55.7亿
- 人均军费开支缅甸:2024 年为 US$92泰国:2025 年为 US$84
- 军费占政府支出比例缅甸:2024 年为 32%(占政府支出)泰国:2025 年为 4.4%(占政府支出)
- 武装部队人员缅甸:2020 年为 463,000 人泰国:2020 年为 455,000 人
- 国家实力缅甸:2022 年为 0.53%(占全球实力)泰国:2022 年为 0.79%(占全球实力)
- 武装部队占劳动力比例缅甸:2020 年为 1.94%(占劳动力)泰国:2020 年为 1.13%(占劳动力)
- 武器进口缅甸:2025 年为 7000万 TIV泰国:2025 年为 3300万 TIV