伊拉克与巴基斯坦对比
伊拉克与巴基斯坦:热带夜
2014 年热带夜:伊拉克为 151.4 夜,巴基斯坦为 149.9 夜(CMIP6 气候模型中位数模拟)。 2014 年伊拉克的数值较高,高出 1.5 夜,是巴基斯坦的 1.01 倍。
每年夜数 · CMIP6
数据解读
- 自 2011 年超过巴基斯坦以来,伊拉克的数值一直更高。
- 根据CMIP6 模型中位数(SSP2-4.5,中间路径),2100 年伊拉克为 178.7 夜,巴基斯坦为 177.6 夜。
逐年数据
每年夜数 · CMIP6
| 年份 | 伊拉克 | 巴基斯坦 |
|---|---|---|
| 2014 | 151.4 | 149.9 |
| 2013 | 151.3 | 149.1 |
| 2012 | 150.9 | 149.9 |
| 2011 | 150.7 | 149 |
| 2010 | 147.5 | 148.4 |
| 2009 | 146.1 | 147.1 |
| 2008 | 149.9 | 147.4 |
| 2007 | 148 | 147.3 |
| 2006 | 146.9 | 146.7 |
| 2005 | 147.7 | 146.5 |
| 2004 | 147.3 | 147.5 |
| 2003 | 145.4 | 146 |
| 2002 | 147.2 | 148.3 |
| 2001 | 146.6 | 145.4 |
| 2000 | 148 | 144.7 |
| 1999 | 143.6 | 146.9 |
| 1998 | 142.6 | 144.8 |
| 1997 | 144.5 | 143.2 |
| 1996 | 143.7 | 141.2 |
| 1995 | 139.1 | 141.8 |
| 1994 | 141.6 | 142.1 |
| 1993 | 136.6 | 140.4 |
| 1992 | 136.6 | 139.9 |
| 1991 | 138.1 | 142.5 |
| 1990 | 142.7 | 142.6 |
| 1989 | 141.6 | 142.5 |
| 1988 | 138.2 | 140.3 |
| 1987 | 138.9 | 140 |
| 1986 | 140.9 | 140 |
| 1985 | 137.6 | 140 |
| 1984 | 135.5 | 138.9 |
| 1983 | 135 | 136.3 |
| 1982 | 135.8 | 138.5 |
| 1981 | 140.3 | 141.5 |
| 1980 | 139.4 | 139.5 |
| 1979 | 136.4 | 139.1 |
| 1978 | 136.8 | 139 |
| 1977 | 137.7 | 138.6 |
| 1976 | 138.2 | 139.2 |
| 1975 | 134.1 | 136.5 |
| 1974 | 135.8 | 138.7 |
| 1973 | 136.1 | 137.8 |
| 1972 | 135.8 | 137.4 |
| 1971 | 133.7 | 136 |
| 1970 | 135.5 | 137.7 |
| 1969 | 135.5 | 135.9 |
| 1968 | 134.3 | 136.6 |
| 1967 | 133.6 | 135.2 |
| 1966 | 132.9 | 136.9 |
| 1965 | 132.9 | 138.3 |
| 1964 | 134.2 | 135.1 |
| 1963 | 135.1 | 135.7 |
| 1962 | 137.7 | 137 |
| 1961 | 138.2 | 138.6 |
| 1960 | 136.7 | 139 |
| 1959 | 139 | 138.6 |
| 1958 | 138.4 | 137.7 |
| 1957 | 137.3 | 140 |
| 1956 | 136.6 | 137.8 |
| 1955 | 134.7 | 137.5 |
| 1954 | 136.1 | 137.8 |
| 1953 | 137 | 136.9 |
| 1952 | 136.7 | 138.1 |
| 1951 | 137.3 | 136.7 |
| 1950 | 134.9 | 139.1 |
CMIP6 模型中位数(SSP2-4.5,中间路径) · 2015–2100
| 年份 | 伊拉克 | 巴基斯坦 |
|---|---|---|
| 2015 | 154.9143.1–165.8 | 152.4138.7–163.3 |
| 2016 | 152.1139.6–164.5 | 150.6139.4–161.7 |
| 2017 | 154.1139.5–165.6 | 151.3138.8–162 |
| 2018 | 153.8142.3–165 | 152.6139.8–161.8 |
| 2019 | 153.3141–166.9 | 151.8138.1–163.3 |
| 2020 | 155.1144.8–166 | 153.1140.3–164.5 |
| 2021 | 155.8144.5–166.5 | 154.1140.6–165.7 |
| 2022 | 155.9144.3–168 | 153.1140.2–165.7 |
| 2023 | 157.1146.3–168.5 | 155.5141.6–165.9 |
| 2024 | 155.9145.1–167.7 | 156.8143.3–168.7 |
| 2025 | 156.8145.6–168.8 | 155.7141.5–168.6 |
| 2026 | 158.1147.1–168.2 | 155.3142.2–167.9 |
| 2027 | 159.5148.6–169.8 | 156.8140.9–168.9 |
| 2028 | 157.5145.7–169.4 | 156.5143.9–167.6 |
| 2029 | 158.5149.2–167.6 | 155.9141.5–167.7 |
| 2030 | 157.9147.4–169.7 | 156.3143.3–167.6 |
| 2031 | 160.1146.9–171.3 | 157.3144.4–168.1 |
| 2032 | 160146.9–170.6 | 157.4142.5–169.6 |
| 2033 | 161.6151.5–172 | 157.7143.6–169.4 |
| 2034 | 161148.9–173.9 | 159.5145.6–173.4 |
| 2035 | 161.8150.3–173.6 | 159.3146.7–170.3 |
| 2036 | 158.5146.3–170.6 | 159.1147.5–171.4 |
| 2037 | 161.2148.6–173.5 | 162.2147.5–175.3 |
| 2038 | 165.1152.5–174.7 | 161.4148.6–171.7 |
| 2039 | 164.6152.2–177.7 | 160148.2–170.4 |
| 2040 | 165153.4–175.3 | 160.9147.9–171.3 |
| 2041 | 163.3151.3–172.5 | 160.8146.7–171.7 |
| 2042 | 165.3153–176.8 | 162.3149.9–175.6 |
| 2043 | 164.6151.4–175.3 | 161.1150.3–173.8 |
| 2044 | 164.6155.1–175.2 | 163150.9–173.9 |
| 2045 | 167.8152.2–180.1 | 163149.5–174.8 |
| 2046 | 167.6155–178.8 | 163.5149.8–177.2 |
| 2047 | 167.1156.3–177.3 | 165.2151.8–178.5 |
| 2048 | 165.8155.5–179.1 | 166.4154.1–179.4 |
| 2049 | 165.6155.1–177.4 | 165150.8–178.2 |
| 2050 | 170.8156.9–181.9 | 166.9153.5–179.3 |
| 2051 | 168.8155.6–181.1 | 165.6150.8–180 |
| 2052 | 167.8154.5–180.4 | 166.7155.4–179.5 |
| 2053 | 168.5155.9–181.1 | 166.3154.2–178.8 |
| 2054 | 170.4155.7–181.4 | 167.5153.9–181 |
| 2055 | 171.2158.7–181.8 | 167.8155.8–182.5 |
| 2056 | 168152.6–178.6 | 166.3153.6–179.7 |
| 2057 | 169.2153.3–186.3 | 168.3153.9–182.3 |
| 2058 | 170.7157.1–185.5 | 167.5157–179.9 |
| 2059 | 169.8157–187.5 | 170.9155.9–183.8 |
| 2060 | 169.5157.6–185.7 | 170.3156.5–181.8 |
| 2061 | 170.2157.8–180.7 | 168.8156.8–179.6 |
| 2062 | 171.2158.1–183 | 171.6153.5–186.5 |
| 2063 | 170.6156.5–183.6 | 171.3159.5–183.8 |
| 2064 | 170159.8–182.8 | 170.2155.3–184.5 |
| 2065 | 171.7160.9–185.3 | 169.7156.4–185 |
| 2066 | 172.4158.6–184.5 | 172.2157–186.7 |
| 2067 | 173.9161.8–187.6 | 173.2160.2–186.3 |
| 2068 | 172.6159.5–185 | 172.6157.7–186.3 |
| 2069 | 174.3160.8–186.2 | 171.8157.3–185.6 |
| 2070 | 175.4162.3–186.7 | 171.7158.9–186.7 |
| 2071 | 176.1161.2–188.9 | 173.4158.7–186.8 |
| 2072 | 174.1161.5–187.3 | 174161.5–186.9 |
| 2073 | 176.1159.9–190.6 | 175.6157.2–187.9 |
| 2074 | 173.5157.8–187 | 173.5160–186.3 |
| 2075 | 171.3158.5–186.4 | 173161.5–187.8 |
| 2076 | 173.1158.7–186.3 | 172.9158.8–186.7 |
| 2077 | 176.7160.9–189.8 | 175161.3–187.8 |
| 2078 | 176.2163.1–188.2 | 174.4160.9–189.3 |
| 2079 | 175.2160.1–188 | 172.5158.3–187.7 |
| 2080 | 172.9160.7–190.1 | 175.1160.7–189.2 |
| 2081 | 175.5161.6–189.9 | 174.9159.2–190.2 |
| 2082 | 176.9162.8–188.2 | 175.4161.8–190.6 |
| 2083 | 179.2161.9–191.4 | 176.5161.8–190.8 |
| 2084 | 179.7164.5–191 | 176.1160.9–190.5 |
| 2085 | 177.9162.3–190.8 | 176.3161.9–193.6 |
| 2086 | 178.4166.1–189.5 | 173.9160–189.1 |
| 2087 | 178.1161.8–194.1 | 176161–193.1 |
| 2088 | 178.1163.4–194.1 | 175.4160.5–195.2 |
| 2089 | 177.5162.8–192.6 | 177.2164.2–191.7 |
| 2090 | 176.1163.1–191.9 | 177.7163.9–192.2 |
| 2091 | 178.7163.4–192.6 | 177.8162.8–192.2 |
| 2092 | 179.2164.9–191.4 | 178.7162.9–192.9 |
| 2093 | 179.3165.6–193.8 | 177164–191 |
| 2094 | 181.3169.4–192.9 | 176.8164.1–190.7 |
| 2095 | 181.2166.6–193.1 | 178.6163.8–193.1 |
| 2096 | 179.2163.8–193.2 | 177.8163.7–192.5 |
| 2097 | 180.7162.7–194.4 | 177.3160.4–192.5 |
| 2098 | 178.4163.1–199 | 178.7162.6–195.7 |
| 2099 | 178.9166.7–191.9 | 176159.9–193.2 |
| 2100 | 178.7165.8–191.9 | 177.6162.2–193 |
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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
伊拉克与巴基斯坦对比
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军用
- 军费开支伊拉克:2025 年为 GDP 的 2.45%巴基斯坦:2025 年为 GDP 的 2.91%
- 军费开支(美元)伊拉克:2025 年为 US$64亿巴基斯坦:2025 年为 US$115亿
- 人均军费开支伊拉克:2025 年为 US$139巴基斯坦:2025 年为 US$47
- 军费占政府支出比例伊拉克:2025 年为 5.5%(占政府支出)巴基斯坦:2025 年为 13.8%(占政府支出)
- 武装部队人员伊拉克:2020 年为 459,000 人巴基斯坦:2020 年为 943,000 人
- 国家实力伊拉克:2022 年为 0.54%(占全球实力)巴基斯坦:2022 年为 1.77%(占全球实力)
- 武装部队占劳动力比例伊拉克:2020 年为 4.4%(占劳动力)巴基斯坦:2020 年为 1.25%(占劳动力)
- 武器进口伊拉克:2025 年为 5500万 TIV巴基斯坦:2025 年为 7.06亿 TIV
- 武器出口伊拉克:1989 年为 2000万 TIV巴基斯坦:2025 年为 1.27亿 TIV