意大利与缅甸对比
意大利与缅甸:35 °C 以上天数
2014 年35 °C 以上天数:意大利为 0.5 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 28.3 天,是意大利的 59 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 年意大利为 9.6 天,缅甸为 57 天。
逐年数据
每年天数 · CMIP6
| 年份 | 意大利 | 缅甸 |
|---|---|---|
| 2014 | 0.5 | 28.8 |
| 2013 | 0.2 | 28 |
| 2012 | 0.2 | 27.5 |
| 2011 | 0.3 | 27.4 |
| 2010 | 0.4 | 27.9 |
| 2009 | 0.3 | 27.3 |
| 2008 | 0.3 | 27.2 |
| 2007 | 0.1 | 25.4 |
| 2006 | 0.2 | 26.8 |
| 2005 | 0.1 | 25 |
| 2004 | 0.3 | 28.3 |
| 2003 | 0.2 | 26.4 |
| 2002 | 0.1 | 27.4 |
| 2001 | 0.1 | 28.4 |
| 2000 | 0.2 | 26.6 |
| 1999 | 0.1 | 26.1 |
| 1998 | 0.1 | 26.4 |
| 1997 | 0.1 | 26.2 |
| 1996 | 0.1 | 24.7 |
| 1995 | 0.1 | 25.5 |
| 1994 | 0 | 24.3 |
| 1993 | 0 | 23.9 |
| 1992 | 0 | 20.5 |
| 1991 | 0 | 28.4 |
| 1990 | 0.1 | 26.3 |
| 1989 | 0 | 23.8 |
| 1988 | 0 | 23.9 |
| 1987 | 0 | 25.4 |
| 1986 | 0 | 26.6 |
| 1985 | 0 | 23.6 |
| 1984 | 0 | 25.8 |
| 1983 | 0 | 23.2 |
| 1982 | 0 | 24 |
| 1981 | 0 | 23.8 |
| 1980 | 0.1 | 23.5 |
| 1979 | 0 | 25.3 |
| 1978 | 0 | 23.4 |
| 1977 | 0 | 24.1 |
| 1976 | 0 | 25.6 |
| 1975 | 0 | 22.3 |
| 1974 | 0 | 24.1 |
| 1973 | 0 | 25.9 |
| 1972 | 0 | 23.1 |
| 1971 | 0 | 22.8 |
| 1970 | 0 | 24.2 |
| 1969 | 0.1 | 23.6 |
| 1968 | 0 | 23.4 |
| 1967 | 0 | 24.1 |
| 1966 | 0 | 23.1 |
| 1965 | 0 | 23.3 |
| 1964 | 0.1 | 21.7 |
| 1963 | 0 | 23.2 |
| 1962 | 0 | 24.4 |
| 1961 | 0 | 24.9 |
| 1960 | 0 | 26.2 |
| 1959 | 0.1 | 22.6 |
| 1958 | 0 | 25 |
| 1957 | 0 | 24.1 |
| 1956 | 0 | 25 |
| 1955 | 0.1 | 24.5 |
| 1954 | 0 | 25.3 |
| 1953 | 0.1 | 23.5 |
| 1952 | 0.1 | 26.1 |
| 1951 | 0 | 25 |
| 1950 | 0.1 | 23.8 |
CMIP6 模型中位数(SSP2-4.5,中间路径) · 2015–2100
| 年份 | 意大利 | 缅甸 |
|---|---|---|
| 2015 | 0.40.1–1.7 | 28.518.5–40 |
| 2016 | 0.40–2.6 | 27.717.3–38.7 |
| 2017 | 0.60–3.2 | 2818.9–38.5 |
| 2018 | 0.60.1–3.2 | 29.818.6–41.2 |
| 2019 | 0.80.1–2.4 | 28.218.2–41.3 |
| 2020 | 0.60–3 | 28.616.8–41.4 |
| 2021 | 0.70–3.2 | 29.719.4–40.8 |
| 2022 | 0.90–4.3 | 31.521.7–42.7 |
| 2023 | 0.70–3.4 | 30.319.9–41.6 |
| 2024 | 1.10–3.9 | 31.720.1–43.7 |
| 2025 | 0.90.1–4.3 | 32.721.3–44.8 |
| 2026 | 10.1–6.1 | 30.420.3–44.5 |
| 2027 | 0.90–3.8 | 30.620.4–44.2 |
| 2028 | 1.40.2–5 | 31.119–43 |
| 2029 | 1.20.1–5.5 | 32.720.9–46.4 |
| 2030 | 0.90.1–4.8 | 32.619.3–45 |
| 2031 | 10.1–4.1 | 32.721.3–45.2 |
| 2032 | 1.20.3–4.7 | 32.119.6–45.4 |
| 2033 | 1.60–4.3 | 33.322–43.8 |
| 2034 | 1.70.2–6.9 | 31.322–43.5 |
| 2035 | 1.90.2–6.5 | 32.620.4–49 |
| 2036 | 1.90.2–7 | 33.322.7–46.1 |
| 2037 | 1.90.4–6.2 | 34.322.6–49.1 |
| 2038 | 1.90.2–7.5 | 35.824.4–49.9 |
| 2039 | 20.3–5.6 | 34.920.9–49 |
| 2040 | 2.20.3–6.7 | 3419.2–51.2 |
| 2041 | 2.10.3–7 | 3724.8–51.9 |
| 2042 | 1.80.2–6.9 | 36.223–49.9 |
| 2043 | 2.10.2–5.7 | 34.522.6–47.7 |
| 2044 | 2.40.4–6.4 | 38.222.1–51.2 |
| 2045 | 2.10.2–6.2 | 35.924.2–50.7 |
| 2046 | 2.70.5–8.8 | 38.425.2–57.1 |
| 2047 | 3.30.9–8.1 | 38.824.1–56.1 |
| 2048 | 2.10.1–9.3 | 40.323.4–58.7 |
| 2049 | 2.80.3–9.4 | 40.625.2–58.1 |
| 2050 | 3.80.8–8.5 | 40.226.5–55.3 |
| 2051 | 3.20.6–11.6 | 41.726.7–56.8 |
| 2052 | 2.90.2–9.6 | 40.326.3–61.2 |
| 2053 | 30.6–9.8 | 41.323.6–61.5 |
| 2054 | 3.70.4–11.5 | 42.426.8–60.9 |
| 2055 | 3.30.3–10.2 | 41.428.5–61.9 |
| 2056 | 3.40.9–11.1 | 42.726.5–63 |
| 2057 | 3.10.6–8.5 | 43.226.3–63.2 |
| 2058 | 4.80.6–12.3 | 44.526.8–63.6 |
| 2059 | 4.20.6–12.5 | 43.729.1–65.3 |
| 2060 | 3.80.9–11.3 | 47.428.3–66 |
| 2061 | 4.51.2–10.3 | 46.729.5–64.5 |
| 2062 | 4.50.8–10.7 | 47.126.7–68.6 |
| 2063 | 5.70.8–13.2 | 46.929–66.4 |
| 2064 | 5.51–13.9 | 47.527.9–66.1 |
| 2065 | 4.80.9–13 | 42.427.5–62.4 |
| 2066 | 4.40.6–12.7 | 46.228.9–68.8 |
| 2067 | 7.31.3–17 | 4125.7–63.6 |
| 2068 | 5.71.2–13.8 | 46.928–70.4 |
| 2069 | 6.11.5–11.5 | 48.729.3–70.1 |
| 2070 | 5.71.3–15.6 | 43.927.5–64.3 |
| 2071 | 7.11.2–15.6 | 4926.2–71.9 |
| 2072 | 6.52.1–14.7 | 4730.6–71.4 |
| 2073 | 4.51.1–11.8 | 50.732.2–74 |
| 2074 | 5.30.7–12.8 | 4829.2–74 |
| 2075 | 5.81.3–18 | 49.126.1–68.9 |
| 2076 | 5.81–15.9 | 47.826.5–72.8 |
| 2077 | 5.81.5–14.7 | 51.832.9–76.1 |
| 2078 | 5.41.4–20.3 | 50.632.4–75.6 |
| 2079 | 6.71.2–20 | 50.730.5–77 |
| 2080 | 6.41.8–17.3 | 53.331.3–79.5 |
| 2081 | 61.5–16.9 | 5235.5–79.1 |
| 2082 | 9.42.3–18.9 | 52.532.5–76.8 |
| 2083 | 6.11.1–15.3 | 51.730.5–79.6 |
| 2084 | 7.72.6–20.2 | 52.329.5–76 |
| 2085 | 8.22.7–17.8 | 51.232.9–79.4 |
| 2086 | 8.12.4–18.1 | 52.632.5–80.1 |
| 2087 | 8.12–20 | 51.732.6–77.9 |
| 2088 | 10.63.1–22.1 | 50.727.2–88.8 |
| 2089 | 9.23–22.4 | 52.731.4–86.3 |
| 2090 | 8.62.9–17.7 | 54.231.1–80.1 |
| 2091 | 9.73–19.4 | 53.235.4–80.1 |
| 2092 | 6.70.5–17.5 | 54.135.4–85.5 |
| 2093 | 6.42–17.4 | 5235.6–85 |
| 2094 | 7.62–19.9 | 54.434.9–80.7 |
| 2095 | 9.71.3–19.2 | 58.535.1–84.3 |
| 2096 | 7.61.6–21.4 | 52.832.3–84.1 |
| 2097 | 8.61.8–18.9 | 53.230.4–82.8 |
| 2098 | 9.92.4–19 | 55.334.4–83.8 |
| 2099 | 9.51.7–19.7 | 55.233.1–84.7 |
| 2100 | 9.62.4–20.4 | 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
意大利与缅甸对比
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军用
- 军费开支意大利:2025 年为 GDP 的 1.89%缅甸:2024 年为 GDP 的 6.61%
- 军费开支(美元)意大利:2025 年为 US$454亿缅甸:2024 年为 US$46.5亿
- 人均军费开支意大利:2025 年为 US$813缅甸:2024 年为 US$92
- 军费占政府支出比例意大利:2025 年为 3.7%(占政府支出)缅甸:2024 年为 32%(占政府支出)
- 武装部队人员意大利:2020 年为 338,000 人缅甸:2020 年为 463,000 人
- 国家实力意大利:2022 年为 0.96%(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意大利:2020 年为 1.35%(占劳动力)缅甸:2020 年为 1.94%(占劳动力)
- 武器进口意大利:2025 年为 5.66亿 TIV缅甸:2025 年为 7000万 TIV