印度与苏丹对比
印度与苏丹:热带夜
2014 年热带夜:印度为 215.5 夜,苏丹为 242.4 夜(CMIP6 气候模型中位数模拟)。 2014 年苏丹的数值较高,高出 26.9 夜,是印度的 1.12 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,苏丹的数值都更高。
- 根据CMIP6 模型中位数(SSP2-4.5,中间路径),2100 年印度为 250.4 夜,苏丹为 278.6 夜。
逐年数据
每年夜数 · CMIP6
| 年份 | 印度 | 苏丹 |
|---|---|---|
| 2014 | 215.5 | 242.4 |
| 2013 | 216 | 240 |
| 2012 | 217.4 | 242 |
| 2011 | 215.5 | 242.2 |
| 2010 | 215.2 | 240.6 |
| 2009 | 213.7 | 236.5 |
| 2008 | 213.7 | 237.1 |
| 2007 | 213.3 | 236 |
| 2006 | 212.5 | 235.3 |
| 2005 | 212.9 | 233.1 |
| 2004 | 214.9 | 233.5 |
| 2003 | 212.1 | 235.6 |
| 2002 | 213 | 234.7 |
| 2001 | 212.2 | 235.4 |
| 2000 | 210.8 | 230.5 |
| 1999 | 211.9 | 232.1 |
| 1998 | 210.3 | 230.2 |
| 1997 | 209.5 | 231.1 |
| 1996 | 207 | 227.3 |
| 1995 | 208.8 | 227.8 |
| 1994 | 207.1 | 225.3 |
| 1993 | 206 | 219.1 |
| 1992 | 203.5 | 217.8 |
| 1991 | 208 | 227.3 |
| 1990 | 209.9 | 227.5 |
| 1989 | 208 | 223 |
| 1988 | 207.3 | 223.1 |
| 1987 | 207.2 | 224.4 |
| 1986 | 207.2 | 226.4 |
| 1985 | 205.9 | 222.1 |
| 1984 | 206.7 | 220.7 |
| 1983 | 203.3 | 217 |
| 1982 | 205.7 | 216.3 |
| 1981 | 208.4 | 223.2 |
| 1980 | 205.3 | 220 |
| 1979 | 205.5 | 218.4 |
| 1978 | 204.1 | 218.5 |
| 1977 | 205.9 | 219.3 |
| 1976 | 204.6 | 220.1 |
| 1975 | 202.6 | 215.7 |
| 1974 | 205.3 | 217.5 |
| 1973 | 205.6 | 220 |
| 1972 | 203.8 | 217.9 |
| 1971 | 203.6 | 215.5 |
| 1970 | 202.6 | 217 |
| 1969 | 203.6 | 213.1 |
| 1968 | 201.7 | 214 |
| 1967 | 201.6 | 210.3 |
| 1966 | 203.9 | 213.3 |
| 1965 | 202.4 | 213.4 |
| 1964 | 201.2 | 213.2 |
| 1963 | 201.8 | 210.1 |
| 1962 | 203.8 | 217 |
| 1961 | 206.3 | 216.9 |
| 1960 | 206 | 221.3 |
| 1959 | 206.1 | 217.8 |
| 1958 | 204 | 216.2 |
| 1957 | 206.5 | 218.9 |
| 1956 | 204 | 217.2 |
| 1955 | 203.2 | 216.6 |
| 1954 | 206 | 218.9 |
| 1953 | 204 | 215.1 |
| 1952 | 204.4 | 216.4 |
| 1951 | 204.3 | 218.5 |
| 1950 | 206.3 | 214.1 |
CMIP6 模型中位数(SSP2-4.5,中间路径) · 2015–2100
| 年份 | 印度 | 苏丹 |
|---|---|---|
| 2015 | 216.9202.1–228.4 | 242.3225.3–258.3 |
| 2016 | 215202.5–227.6 | 241.4223.3–254.1 |
| 2017 | 216.2202.1–228.5 | 243.2223–259.1 |
| 2018 | 218.3204.5–231.4 | 242.3226.4–258.5 |
| 2019 | 217.4202.3–230.4 | 243.1221.7–260.4 |
| 2020 | 217.6203.1–232 | 244.9225.9–261.7 |
| 2021 | 219.7204.5–233.2 | 245.6229.1–261.5 |
| 2022 | 220.5205.8–233 | 249230.9–262.3 |
| 2023 | 220.4206.4–233.7 | 247.1226.8–263.1 |
| 2024 | 222.6208.1–236.3 | 251.5232.8–266.1 |
| 2025 | 219.7202.7–236.2 | 250.7232.4–265.6 |
| 2026 | 220.2204.2–236.8 | 250230.8–266.6 |
| 2027 | 223.6207.1–239 | 248.8228.4–265.6 |
| 2028 | 222.1205.1–235.7 | 249232.2–263.1 |
| 2029 | 225.4210–240.3 | 251.4232.1–266 |
| 2030 | 222.7207.3–235.6 | 252235.4–266.7 |
| 2031 | 223.8207.1–237.8 | 253.3234.4–268.9 |
| 2032 | 225.9210–240.5 | 250.4232.3–266.6 |
| 2033 | 223.8209.3–235.4 | 254.4235.3–269.4 |
| 2034 | 225.8210–242.7 | 254.2234.5–269.9 |
| 2035 | 224.9210.6–240.9 | 258.3240.2–273.6 |
| 2036 | 224.2210.4–240.3 | 252.8235.6–268.6 |
| 2037 | 228.3213.6–242.6 | 256.7238.6–271.8 |
| 2038 | 228.1212.6–241.4 | 258.7244.8–272.1 |
| 2039 | 227.4211.9–242.8 | 259.5241.2–272.5 |
| 2040 | 227212.2–242 | 256.4236.7–272.3 |
| 2041 | 227.1210.7–240.5 | 259.1241.2–274.5 |
| 2042 | 229.1214.8–244.2 | 258.7242–274.7 |
| 2043 | 228.9214.5–244 | 258.1240.3–272.6 |
| 2044 | 229.7214.7–244.2 | 257.3238.6–274.7 |
| 2045 | 231.2216.6–246.1 | 258.8243.1–276.2 |
| 2046 | 232.6216.2–248.6 | 261.1241.7–277.6 |
| 2047 | 232.4215.5–250 | 261.4244.6–278.4 |
| 2048 | 235.6218.5–254.3 | 260.4243.4–279.5 |
| 2049 | 235.3218.3–249.9 | 261.9242.6–275.9 |
| 2050 | 233.5216.6–253.4 | 263.5245.4–279 |
| 2051 | 234.9218.6–249.9 | 263.6246.6–278.8 |
| 2052 | 234219.4–249.3 | 263.8248.7–278.3 |
| 2053 | 234.9219.5–252.5 | 262.8242.4–279.2 |
| 2054 | 237.2219.4–253.1 | 266.3246.6–282.9 |
| 2055 | 237.2220.7–256.6 | 265.6245.9–281.4 |
| 2056 | 238.8221.2–254.9 | 264.1242.6–281.5 |
| 2057 | 235.7218.5–253.8 | 266.4247.6–283.4 |
| 2058 | 238.8222.8–254.1 | 265.2247.4–280.3 |
| 2059 | 241.1224.9–256.2 | 268.4249.6–285.4 |
| 2060 | 240.9225.6–255.6 | 263.6246.7–284.4 |
| 2061 | 237.9220.6–253.7 | 266.9250.1–281.9 |
| 2062 | 239.9221.2–259.7 | 266.4248.5–285 |
| 2063 | 239.9222.3–256.1 | 267.1246.9–283.4 |
| 2064 | 240223.8–256.3 | 269250.7–284.7 |
| 2065 | 240.9222.9–257.3 | 269.8253.9–285.6 |
| 2066 | 241.5224.1–258.6 | 271.4251.9–287.3 |
| 2067 | 241.6224.8–257.9 | 270.9250–288.4 |
| 2068 | 242.3224.6–262.1 | 271.9251.7–289.8 |
| 2069 | 242.8225.4–259.1 | 268251.1–286.2 |
| 2070 | 240.1225.3–257.1 | 269252.3–285.8 |
| 2071 | 241.8223.7–260.2 | 275.5254.1–290.4 |
| 2072 | 242.6225.4–259.2 | 273.3254.2–289.4 |
| 2073 | 243.8228–259.3 | 271.9250.7–287.6 |
| 2074 | 241.6226.9–258.4 | 270.9249–290.1 |
| 2075 | 243.5226.4–258.2 | 273.1254–291.9 |
| 2076 | 243.2224.6–259.9 | 272.7255.2–290.9 |
| 2077 | 243.7226.6–264.6 | 274.6255.2–292.7 |
| 2078 | 246.7230.1–261.5 | 274.3255.6–291.2 |
| 2079 | 244.9226.4–261.3 | 272255.2–288.5 |
| 2080 | 246.7227–263.7 | 276.6256–292.2 |
| 2081 | 247.5230.4–264.8 | 275.1254.1–294.8 |
| 2082 | 244.6227.9–262.3 | 272.9258.7–288.6 |
| 2083 | 247.5227.8–265.1 | 276.5258–291.7 |
| 2084 | 247.3228.3–266.8 | 275.4253–294.9 |
| 2085 | 246.5227.5–267.1 | 275.3258.6–291.6 |
| 2086 | 245.6227.7–266.5 | 276.4256.9–296.6 |
| 2087 | 245.5226.7–266.2 | 273.5253.1–293.3 |
| 2088 | 247229.4–266.9 | 273.6253.7–293.6 |
| 2089 | 248.1230–266.3 | 276.5256.6–293.4 |
| 2090 | 249228.9–266.4 | 276.6256.4–294.6 |
| 2091 | 248.9230–266.5 | 277.4256.4–296.6 |
| 2092 | 249.8232.3–267.8 | 274.6254.3–294.9 |
| 2093 | 248.1229–266.5 | 278261.9–292.1 |
| 2094 | 249.1230.6–268.4 | 280.3262.2–294 |
| 2095 | 251233.2–270 | 277.5262.6–294 |
| 2096 | 247.8231.4–269.3 | 279.7258.7–297 |
| 2097 | 251.1231.4–268 | 276.3254.1–297.6 |
| 2098 | 252.3230.8–268.3 | 277.7258.2–295.5 |
| 2099 | 248.7230.1–268.3 | 276.8257.2–296.2 |
| 2100 | 250.4232.9–266.7 | 278.6259–294.3 |
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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
印度与苏丹对比
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军用
- 军费开支印度:2025 年为 GDP 的 2.3%苏丹:2021 年为 GDP 的 0.93%
- 军费开支(美元)印度:2025 年为 US$933亿苏丹:2021 年为 US$27.7亿
- 人均军费开支印度:2025 年为 US$63苏丹:2021 年为 US$8
- 军费占政府支出比例印度:2025 年为 8.2%(占政府支出)苏丹:2021 年为 9.5%(占政府支出)
- 武装部队人员印度:2020 年为 307万 人苏丹:2020 年为 144,000 人
- 国家实力印度:2022 年为 9.94%(占全球实力)苏丹:2022 年为 0.37%(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)苏丹:2020 年为 1.34%(占劳动力)
- 武器进口印度:2025 年为 28.6亿 TIV苏丹:2025 年为 5700万 TIV
- 武器出口印度:2025 年为 8100万 TIV苏丹:2013 年为 1100万 TIV