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基于CMIP5的逐日最高温度模拟资料、GGI情景数据库逐年代人口数据,在RCP4.5情景下,以对应栅格高温日数与人口数量的乘积作为人口对高温的暴露度指标,通过多模式集合平均预估未来中国人口对不同强度高温的暴露度变化。结果表明:相比于基准时段(1981-2010年),中国人口对高温和强危害性高温的暴露度从2021-2040年开始明显增加,至2081-2100年暴露度分别增加了5.7倍和17.5倍;除了中国西部部分地区外,全国大部地区人群均受高温的影响,在21世纪中后期中东部大部人口对高温的暴露度超过10.0×106人?d;相比基准时段,随着年代的增长,中国人口对强危害性高温的暴露度在范围和强度上均有明显增加;2081-2100年,人口对高温和强危害性高温的暴露度增幅减缓。从气象地理区域上看,未来各时段人口对高温、强危害性高温的暴露度均有一定程度增加,但增加明显的区域主要集中在华北、黄淮、江南和江淮地区,华南地区对强危害性高温的暴露度增幅较小。高温日数变化对全国人口对高温暴露度的变化所产生的作用最明显。多模式集合的预估结果可以为防控未来高温风险提供重要的参考价值。
Based on the daily maximum temperature simulation data of CMIP5 and the population data of GGI scenario database by year, under the RCP4.5 scenario, the product of the number of days corresponding to grid high temperature and the population is taken as the index of population exposure to high temperature. Estimating the Change of Population Exposure to Different Temperatures and High Temperatures in the Future. The results show that compared with the baseline period (1981-2010), the exposure of China’s population to high temperature and high temperature and high temperature significantly increased from 2021 to 2040, and its exposure increased by 5.7 times and 17.5% respectively from 2081 to 2100 In addition to some parts of western China, most of the population in the country are subject to the effects of high temperatures. In the middle and late 21st century, most of the population in the central and eastern regions exposed to high temperatures exceeded 10.0 × 106 d. Compared with the baseline period, In the 1980s and the year 2100, the population exposure to high temperature and high temperature harmfulness slowed down. From the perspective of meteorological and geographical regions, the population exposure to high temperature and high temperature and high temperature will increase to some extent in future periods, but the obvious increase areas are mainly concentrated in North China, Huanghuai, Jiangnan and Jianghuai regions and in southern China The increase in exposure to high temperatures is small. Changes in high-temperature days on the national population on the impact of high temperature exposure has the most obvious effect. The result of multi-model ensemble prediction can provide important reference value for preventing and controlling future high temperature risk.