阿根廷与缅甸对比
阿根廷与缅甸:35 °C 以上天数
2014 年35 °C 以上天数:阿根廷为 13.5 天,缅甸为 28.8 天(CMIP6 气候模型中位数模拟)。 2014 年缅甸的数值较高,高出 15.3 天,是阿根廷的 2.14 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,缅甸的数值都更高。
- 根据CMIP6 模型中位数(SSP2-4.5,中间路径),2100 年阿根廷为 27.2 天,缅甸为 57 天。
逐年数据
每年天数 · CMIP6
| 年份 | 阿根廷 | 缅甸 |
|---|---|---|
| 2014 | 13.5 | 28.8 |
| 2013 | 13.2 | 28 |
| 2012 | 14.1 | 27.5 |
| 2011 | 13.3 | 27.4 |
| 2010 | 13.1 | 27.9 |
| 2009 | 12.6 | 27.3 |
| 2008 | 12.4 | 27.2 |
| 2007 | 11.7 | 25.4 |
| 2006 | 11.3 | 26.8 |
| 2005 | 11.5 | 25 |
| 2004 | 12 | 28.3 |
| 2003 | 11.2 | 26.4 |
| 2002 | 12.7 | 27.4 |
| 2001 | 12.7 | 28.4 |
| 2000 | 10.9 | 26.6 |
| 1999 | 11 | 26.1 |
| 1998 | 11.3 | 26.4 |
| 1997 | 12.8 | 26.2 |
| 1996 | 11.6 | 24.7 |
| 1995 | 9.6 | 25.5 |
| 1994 | 9.2 | 24.3 |
| 1993 | 9.1 | 23.9 |
| 1992 | 9 | 20.5 |
| 1991 | 9.6 | 28.4 |
| 1990 | 8.8 | 26.3 |
| 1989 | 10.9 | 23.8 |
| 1988 | 10.5 | 23.9 |
| 1987 | 10.5 | 25.4 |
| 1986 | 10.4 | 26.6 |
| 1985 | 10.7 | 23.6 |
| 1984 | 9.6 | 25.8 |
| 1983 | 9.6 | 23.2 |
| 1982 | 9.5 | 24 |
| 1981 | 10.9 | 23.8 |
| 1980 | 10.8 | 23.5 |
| 1979 | 11.2 | 25.3 |
| 1978 | 10.6 | 23.4 |
| 1977 | 9.7 | 24.1 |
| 1976 | 10.4 | 25.6 |
| 1975 | 9.5 | 22.3 |
| 1974 | 10.5 | 24.1 |
| 1973 | 9 | 25.9 |
| 1972 | 9.7 | 23.1 |
| 1971 | 9.2 | 22.8 |
| 1970 | 9.9 | 24.2 |
| 1969 | 9.4 | 23.6 |
| 1968 | 9 | 23.4 |
| 1967 | 8.6 | 24.1 |
| 1966 | 9.9 | 23.1 |
| 1965 | 8.9 | 23.3 |
| 1964 | 7.1 | 21.7 |
| 1963 | 8.8 | 23.2 |
| 1962 | 11 | 24.4 |
| 1961 | 10.2 | 24.9 |
| 1960 | 11.1 | 26.2 |
| 1959 | 10.5 | 22.6 |
| 1958 | 9.6 | 25 |
| 1957 | 10.7 | 24.1 |
| 1956 | 10.6 | 25 |
| 1955 | 9.8 | 24.5 |
| 1954 | 9.6 | 25.3 |
| 1953 | 10.9 | 23.5 |
| 1952 | 10.7 | 26.1 |
| 1951 | 10.5 | 25 |
| 1950 | 9.4 | 23.8 |
CMIP6 模型中位数(SSP2-4.5,中间路径) · 2015–2100
| 年份 | 阿根廷 | 缅甸 |
|---|---|---|
| 2015 | 14.78.1–22.2 | 28.518.5–40 |
| 2016 | 13.66.8–22 | 27.717.3–38.7 |
| 2017 | 14.26.9–22.4 | 2818.9–38.5 |
| 2018 | 13.35.6–21.6 | 29.818.6–41.2 |
| 2019 | 14.56.9–23 | 28.218.2–41.3 |
| 2020 | 15.88.4–24.8 | 28.616.8–41.4 |
| 2021 | 15.38–24.6 | 29.719.4–40.8 |
| 2022 | 13.36.7–22.2 | 31.521.7–42.7 |
| 2023 | 14.99.3–22.1 | 30.319.9–41.6 |
| 2024 | 15.87.6–24 | 31.720.1–43.7 |
| 2025 | 16.410.2–23.4 | 32.721.3–44.8 |
| 2026 | 189.4–26.5 | 30.420.3–44.5 |
| 2027 | 16.49.1–26.2 | 30.620.4–44.2 |
| 2028 | 15.67.6–26.5 | 31.119–43 |
| 2029 | 15.97.3–26.3 | 32.720.9–46.4 |
| 2030 | 16.39.8–27.4 | 32.619.3–45 |
| 2031 | 15.88.1–24.9 | 32.721.3–45.2 |
| 2032 | 15.78.9–25.4 | 32.119.6–45.4 |
| 2033 | 16.18.4–25.7 | 33.322–43.8 |
| 2034 | 16.110.4–24.9 | 31.322–43.5 |
| 2035 | 16.810.2–27.5 | 32.620.4–49 |
| 2036 | 18.310.9–27.9 | 33.322.7–46.1 |
| 2037 | 18.310.2–27.3 | 34.322.6–49.1 |
| 2038 | 16.89.1–24.7 | 35.824.4–49.9 |
| 2039 | 17.39–25.8 | 34.920.9–49 |
| 2040 | 18.311.3–28.3 | 3419.2–51.2 |
| 2041 | 19.310.7–29.3 | 3724.8–51.9 |
| 2042 | 189.9–27.1 | 36.223–49.9 |
| 2043 | 189.5–27.5 | 34.522.6–47.7 |
| 2044 | 19.49.7–30.2 | 38.222.1–51.2 |
| 2045 | 20.511.8–30.1 | 35.924.2–50.7 |
| 2046 | 19.612–28.2 | 38.425.2–57.1 |
| 2047 | 19.78.9–31.3 | 38.824.1–56.1 |
| 2048 | 19.610.8–28.6 | 40.323.4–58.7 |
| 2049 | 20.411.6–30.7 | 40.625.2–58.1 |
| 2050 | 19.910.4–28.4 | 40.226.5–55.3 |
| 2051 | 20.511.5–30.8 | 41.726.7–56.8 |
| 2052 | 21.610.9–33.4 | 40.326.3–61.2 |
| 2053 | 20.411.3–30.2 | 41.323.6–61.5 |
| 2054 | 19.712–31.9 | 42.426.8–60.9 |
| 2055 | 21.310.1–34.1 | 41.428.5–61.9 |
| 2056 | 20.510.5–32.4 | 42.726.5–63 |
| 2057 | 21.711.5–34.7 | 43.226.3–63.2 |
| 2058 | 21.312.4–33.2 | 44.526.8–63.6 |
| 2059 | 21.612.4–32.7 | 43.729.1–65.3 |
| 2060 | 2211.2–33.3 | 47.428.3–66 |
| 2061 | 2310.8–34.9 | 46.729.5–64.5 |
| 2062 | 2212.1–32.7 | 47.126.7–68.6 |
| 2063 | 24.114–33.8 | 46.929–66.4 |
| 2064 | 24.115.3–39.2 | 47.527.9–66.1 |
| 2065 | 23.712.7–34.9 | 42.427.5–62.4 |
| 2066 | 24.715.7–35.1 | 46.228.9–68.8 |
| 2067 | 24.912.6–37 | 4125.7–63.6 |
| 2068 | 23.712.3–36.3 | 46.928–70.4 |
| 2069 | 2514–35.5 | 48.729.3–70.1 |
| 2070 | 24.413.3–37.1 | 43.927.5–64.3 |
| 2071 | 23.913.1–36.8 | 4926.2–71.9 |
| 2072 | 23.814.5–38.9 | 4730.6–71.4 |
| 2073 | 25.314.6–39.1 | 50.732.2–74 |
| 2074 | 24.511.6–36.9 | 4829.2–74 |
| 2075 | 25.312.9–38.6 | 49.126.1–68.9 |
| 2076 | 23.713.4–36.3 | 47.826.5–72.8 |
| 2077 | 24.615.3–36.4 | 51.832.9–76.1 |
| 2078 | 26.213.8–39.4 | 50.632.4–75.6 |
| 2079 | 23.913.4–36.8 | 50.730.5–77 |
| 2080 | 25.413.8–40.8 | 53.331.3–79.5 |
| 2081 | 2514.2–38 | 5235.5–79.1 |
| 2082 | 2413.3–36.3 | 52.532.5–76.8 |
| 2083 | 25.415–39.2 | 51.730.5–79.6 |
| 2084 | 22.512.1–36 | 52.329.5–76 |
| 2085 | 23.412.2–36.2 | 51.232.9–79.4 |
| 2086 | 25.214–39.1 | 52.632.5–80.1 |
| 2087 | 2614.4–37.2 | 51.732.6–77.9 |
| 2088 | 26.214.9–40.2 | 50.727.2–88.8 |
| 2089 | 27.315.3–38.2 | 52.731.4–86.3 |
| 2090 | 27.115.8–41.5 | 54.231.1–80.1 |
| 2091 | 28.315.4–42.5 | 53.235.4–80.1 |
| 2092 | 29.316–42 | 54.135.4–85.5 |
| 2093 | 2615.1–44.6 | 5235.6–85 |
| 2094 | 27.714.9–41 | 54.434.9–80.7 |
| 2095 | 26.713.8–43.1 | 58.535.1–84.3 |
| 2096 | 27.114.5–42.2 | 52.832.3–84.1 |
| 2097 | 2715.3–41.1 | 53.230.4–82.8 |
| 2098 | 26.715.9–42.2 | 55.334.4–83.8 |
| 2099 | 27.615.3–41 | 55.233.1–84.7 |
| 2100 | 27.214.1–45.4 | 5734.6–84 |
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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
阿根廷与缅甸对比
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军用
- 军费开支阿根廷:2025 年为 GDP 的 0.56%缅甸:2024 年为 GDP 的 6.61%
- 军费开支(美元)阿根廷:2025 年为 US$37.3亿缅甸:2024 年为 US$46.5亿
- 人均军费开支阿根廷:2025 年为 US$85缅甸:2024 年为 US$92
- 军费占政府支出比例阿根廷:2025 年为 1.8%(占政府支出)缅甸:2024 年为 32%(占政府支出)
- 武装部队人员阿根廷:2020 年为 103,000 人缅甸:2020 年为 463,000 人
- 国家实力阿根廷:2022 年为 0.45%(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阿根廷:2020 年为 0.53%(占劳动力)缅甸:2020 年为 1.94%(占劳动力)
- 武器进口阿根廷:2025 年为 1.08亿 TIV缅甸:2025 年为 7000万 TIV