澳大利亚与缅甸对比
澳大利亚与缅甸:热带夜
2014 年热带夜:澳大利亚为 135.1 夜,缅甸为 201.9 夜(CMIP6 气候模型中位数模拟)。 2014 年缅甸的数值较高,高出 66.8 夜,是澳大利亚的 1.49 倍。
每年夜数 · 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 年澳大利亚为 169.9 夜,缅甸为 244.5 夜。
逐年数据
每年夜数 · CMIP6
| 年份 | 澳大利亚 | 缅甸 |
|---|---|---|
| 2014 | 135.1 | 201.9 |
| 2013 | 133 | 203.1 |
| 2012 | 132.9 | 201.1 |
| 2011 | 133.8 | 200.9 |
| 2010 | 134.2 | 202.1 |
| 2009 | 132.3 | 199 |
| 2008 | 132.3 | 198.1 |
| 2007 | 131.8 | 196.8 |
| 2006 | 128.1 | 198.2 |
| 2005 | 131.7 | 196.7 |
| 2004 | 131.4 | 200.3 |
| 2003 | 132.4 | 195.3 |
| 2002 | 129.7 | 196.6 |
| 2001 | 130.1 | 199 |
| 2000 | 129.7 | 197.2 |
| 1999 | 127.7 | 196.4 |
| 1998 | 128.1 | 194.7 |
| 1997 | 124.5 | 194.4 |
| 1996 | 123.9 | 191.4 |
| 1995 | 123.5 | 189.9 |
| 1994 | 124.4 | 188.8 |
| 1993 | 121.4 | 185 |
| 1992 | 116.7 | 180.5 |
| 1991 | 125.1 | 191.7 |
| 1990 | 126 | 191.5 |
| 1989 | 124.4 | 190.3 |
| 1988 | 125.2 | 190.7 |
| 1987 | 124.4 | 190.8 |
| 1986 | 124.9 | 189.7 |
| 1985 | 122.7 | 187.4 |
| 1984 | 123.9 | 189.1 |
| 1983 | 122.5 | 182.3 |
| 1982 | 124.7 | 186.7 |
| 1981 | 123.3 | 188.4 |
| 1980 | 123.7 | 189 |
| 1979 | 122.3 | 189.8 |
| 1978 | 121.2 | 186.8 |
| 1977 | 120.6 | 187.4 |
| 1976 | 121.1 | 185.8 |
| 1975 | 118.6 | 183.6 |
| 1974 | 119.7 | 185.5 |
| 1973 | 123.3 | 187.5 |
| 1972 | 120.2 | 184.8 |
| 1971 | 120.4 | 185.2 |
| 1970 | 118.5 | 184.9 |
| 1969 | 120.9 | 184.3 |
| 1968 | 118.9 | 183.7 |
| 1967 | 118.6 | 182.3 |
| 1966 | 116.5 | 186.2 |
| 1965 | 116.5 | 182.9 |
| 1964 | 112.5 | 180.6 |
| 1963 | 114.6 | 183.1 |
| 1962 | 119 | 187.3 |
| 1961 | 121.9 | 188.7 |
| 1960 | 120.7 | 187.1 |
| 1959 | 120.3 | 186.5 |
| 1958 | 121.8 | 187.1 |
| 1957 | 119.8 | 186.2 |
| 1956 | 119.9 | 187.9 |
| 1955 | 117.5 | 185.9 |
| 1954 | 119.9 | 185.7 |
| 1953 | 118.5 | 185 |
| 1952 | 119.9 | 186.1 |
| 1951 | 119.6 | 187.1 |
| 1950 | 119.7 | 186.9 |
CMIP6 模型中位数(SSP2-4.5,中间路径) · 2015–2100
| 年份 | 澳大利亚 | 缅甸 |
|---|---|---|
| 2015 | 134116.5–149.2 | 205.2186.4–218.9 |
| 2016 | 134.8118.2–149.5 | 202.8186.2–218.8 |
| 2017 | 136.1119.3–149.5 | 203.4187.3–218.3 |
| 2018 | 135.8118.4–151.4 | 205.5190.9–219.9 |
| 2019 | 136.6120.4–151.6 | 205.2186.7–222.2 |
| 2020 | 136119.8–151.4 | 208.2188.5–223.5 |
| 2021 | 135.6118.6–151.2 | 208.9191.2–224.1 |
| 2022 | 138.6121.4–155.1 | 209.9192.7–224.9 |
| 2023 | 139.9123.6–155.2 | 210.6195.4–226.3 |
| 2024 | 141122.4–156.1 | 210.4193.8–226.6 |
| 2025 | 140.9121–159.3 | 209.9190.3–225.2 |
| 2026 | 139.4121.9–157.8 | 212.2194.3–229.2 |
| 2027 | 140.4123.7–156 | 212195.6–228.6 |
| 2028 | 141.8125.4–158.7 | 213195.7–227.2 |
| 2029 | 143.3126–159.6 | 215.3193.9–231.9 |
| 2030 | 140.1125.4–156.4 | 212.2194.9–228 |
| 2031 | 141.6124.9–156.9 | 214.3197.2–230.8 |
| 2032 | 144.6128.6–162.1 | 215199.3–229.6 |
| 2033 | 144.5127.7–159.2 | 215.5198.5–230.1 |
| 2034 | 145129.2–161.3 | 214.9198.4–233.5 |
| 2035 | 146.5128.8–161.8 | 214.9199.2–236.8 |
| 2036 | 146.9128–161.8 | 216198.7–234.5 |
| 2037 | 146.6130.1–161.6 | 217.2199.6–233.5 |
| 2038 | 148.2131.2–164 | 220.7204.8–235.1 |
| 2039 | 146.9130.3–163.4 | 219.7202.4–234.3 |
| 2040 | 147.9130.6–164.6 | 219.2202.9–235 |
| 2041 | 147.6130.5–165.9 | 220.7203.9–237.7 |
| 2042 | 148.2129.2–166.5 | 221.8206.1–237.2 |
| 2043 | 148.9133.1–164.5 | 219.5201.2–237.1 |
| 2044 | 151135.2–166.3 | 221.4201.6–239.6 |
| 2045 | 149.3130.9–164.3 | 223.7204.8–241.5 |
| 2046 | 151.2135.4–168 | 223.2203.2–240.2 |
| 2047 | 153.5132.9–169.5 | 224.3204.8–241.1 |
| 2048 | 154.1135.2–171.4 | 226.4207.7–243.8 |
| 2049 | 153.4137.4–170.1 | 226.5205.6–242.8 |
| 2050 | 152.3133–170.6 | 227209.3–243.5 |
| 2051 | 154.5135.1–171.8 | 229.6212.4–246.4 |
| 2052 | 154.7134.9–175.7 | 227.2208.6–244.2 |
| 2053 | 153.8131.8–173 | 227.9208.7–247.7 |
| 2054 | 152.8134.4–172.4 | 227.9209.6–244.8 |
| 2055 | 155.2138.1–174.8 | 229.1209.8–247.5 |
| 2056 | 157139.6–173.6 | 231.8210.4–252.1 |
| 2057 | 156.3137.4–172.3 | 228.6208–249 |
| 2058 | 158.3135.3–176.6 | 228.9208.8–249 |
| 2059 | 157.9138.9–174.7 | 230.7212.1–247.5 |
| 2060 | 158.4140.3–176.8 | 234.7211.1–251.5 |
| 2061 | 158.8139.6–175.2 | 231.9213–247.7 |
| 2062 | 156.1137.8–172.7 | 233.6209.9–252.8 |
| 2063 | 160.2140–178.6 | 233215.4–250.5 |
| 2064 | 159.3140.5–177.9 | 233.2214.4–251.3 |
| 2065 | 156.7136.6–177.5 | 230.7210.4–250.7 |
| 2066 | 161.6142–180.6 | 233.5214.3–251.2 |
| 2067 | 162.5141.6–183.1 | 233211.5–253.3 |
| 2068 | 162.4140.7–180.1 | 233.1212.4–253.7 |
| 2069 | 162.6143.3–181 | 236.1214.6–253.4 |
| 2070 | 161.4140.4–180.3 | 235.9218.1–253.4 |
| 2071 | 162.1142–181.9 | 235.1213.5–259.6 |
| 2072 | 162.2142.3–179.6 | 234.6216.1–252.6 |
| 2073 | 163.8144.6–182.6 | 237.2219.3–257.2 |
| 2074 | 162.2142.8–181.6 | 236.4215.7–254.5 |
| 2075 | 163.1143–180.9 | 236.7214.4–255.9 |
| 2076 | 163.2141.1–181.9 | 237.7216–257 |
| 2077 | 163.1141.8–181.8 | 237.5219–258.7 |
| 2078 | 163.9145.8–181.3 | 238.5217.3–257.8 |
| 2079 | 163.3143.1–186 | 238.5219.2–257.2 |
| 2080 | 163142.7–183.3 | 240.5218.8–258.3 |
| 2081 | 165.8142.9–185.5 | 240219.7–260.9 |
| 2082 | 164.6145–182.6 | 239.4217.1–258.8 |
| 2083 | 164.2141.3–187 | 239.1220.5–259.2 |
| 2084 | 165.6140.7–187.4 | 241.3218.9–260.6 |
| 2085 | 167.2143.9–187.5 | 241.7218.8–260.7 |
| 2086 | 166.8143.2–186.2 | 241.7217.4–262.7 |
| 2087 | 165.2142.9–189.1 | 241.4220.5–260.9 |
| 2088 | 167.5146–187.8 | 241.8222.3–261.5 |
| 2089 | 166.3146.9–184.2 | 240.7221.2–262.6 |
| 2090 | 168.6145–188.3 | 241.3224.4–260.9 |
| 2091 | 168.3145.8–186.5 | 242.6220.3–262.5 |
| 2092 | 167.4144.8–185.2 | 241.9221.6–263.3 |
| 2093 | 168.2146–191.5 | 242.1220.2–260 |
| 2094 | 169146.8–187.2 | 242.5222.5–261.9 |
| 2095 | 169.5146.9–189.1 | 244.9224–264.4 |
| 2096 | 170.2145.3–192.8 | 243.9221.1–266.5 |
| 2097 | 168.2145.9–187.4 | 243.6219.6–264.2 |
| 2098 | 169.7149.4–190.8 | 243.6222.5–263.9 |
| 2099 | 167.1147.5–187.7 | 244.7221.8–263.5 |
| 2100 | 169.9149.9–190.1 | 244.5223.2–261.8 |
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须知
- 一年中气温始终高于 20 °C 的夜晚数,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
澳大利亚与缅甸对比
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军用
- 军费开支澳大利亚:2025 年为 GDP 的 1.92%缅甸:2024 年为 GDP 的 6.61%
- 军费开支(美元)澳大利亚:2025 年为 US$353亿缅甸:2024 年为 US$46.5亿
- 人均军费开支澳大利亚:2025 年为 US$1,316缅甸:2024 年为 US$92
- 军费占政府支出比例澳大利亚:2025 年为 4.9%(占政府支出)缅甸:2024 年为 32%(占政府支出)
- 武装部队人员澳大利亚:2020 年为 60,000 人缅甸:2020 年为 463,000 人
- 国家实力澳大利亚:2022 年为 0.6%(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澳大利亚:2020 年为 0.44%(占劳动力)缅甸:2020 年为 1.94%(占劳动力)
- 武器进口澳大利亚:2025 年为 8.26亿 TIV缅甸:2025 年为 7000万 TIV