| 基于CMIP6的中国未来高温危险性变化评估 |
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| 引用本文： | 郭春华,朱秀芳,张世喆,唐明秀,徐昆.基于CMIP6的中国未来高温危险性变化评估[J].地球信息科学,2022,24(7):1391-1405. |
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| 作者姓名： | 郭春华 朱秀芳 张世喆 唐明秀 徐昆 |
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| 作者单位： | 1.北京师范大学 遥感科学国家重点实验室,北京 1008752.北京师范大学 环境演变与自然灾害教育部重点实验室,北京 1008753.北京师范大学地理科学学部遥感科学与工程研究院,北京 100875 |
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| 基金项目： | 国家重点研发计划项目(2019YFA0606900);国家自然科学基金面上项目(42077436) |
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| 摘 要： | 评估高温灾害的危险性变化,能够为区域高温灾害风险管理和制定减灾措施提供决策依据。本研究选取高温日数、最高温度和平均高温强度3个指标,基于1961—2020年中国2517个气象站点日最高温数据和CMIP6情景模式比较计划中SSP2-4.5情景下12个气候模式提供的2031—2099年未来气候预测数据集,用核密度概率估计方法计算了4个重现期（即5、10、20和50年）下3个指标的取值,对中国未来高温危险性变化进行了评估。结果表明：① 在SSP2.4-5情景下,中国的高温日数呈现出4个危险中心,分别是：西北干旱（半干旱）地区中部、华北和华中地区的交汇区域、西南地区中部和华南地区南部,并且高温日数从这4个中心向外逐渐减少;最高温度在空间上的分布北部大于南部,东部大于西部。平均高温强度的分布则呈现出从华北地区南部、西北干旱（半干旱）地区西部和东部地区西部向我国除青藏高原地区外的其它地区减少的趋势; ② 在SSP2.4-5情景下,随着重现期年限的增长,中国地区3个高温指标均呈增长趋势且增幅较大,并且高值范围也在不断扩大;③ 3个高温指标变化值均呈现出了明显的空间聚集性,3个指标共同显示的热点区域包括西南地区北部和南部、西北干旱（半干旱）地区中部和华北、华中地区的少部分区域,这些地区发生高温灾害的可能最大,同时根据高温日数变化和最高温度变化,东部地区西部发生高温灾害可能也较大,3个指标共同显示的冷点区域包括青藏高原地区东南部、西北干旱（半干旱）地区的西部和我国东南沿海地区,这些地区几乎不会发生高温危险。
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| 关 键 词： | 全球变暖 极端气候 CMIP6 高温危险性 空间分布 共享社会经济路径 核密度函数 热点分析 |
| 收稿时间： | 2021-08-21 |
Hazard Changes Assessment of Future High Temperature in China based on CMIP6 |
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| GUO Chunhua,ZHU Xiufang,ZHANG Shizhe,TANG Mingxiu,XU Kun.Hazard Changes Assessment of Future High Temperature in China based on CMIP6[J].Geo-information Science,2022,24(7):1391-1405. |
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| Authors: | GUO Chunhua ZHU Xiufang ZHANG Shizhe TANG Mingxiu XU Kun |
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| Institution: | 1. State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences, Beijing 100875, China2. Key Laboratory of Environmental Change and Natural Disasters, Ministry of Education, Beijing Normal University, Beijing 100875, China3. Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China |
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| Abstract: | The assessment of hazard changes of high temperature can provide decision basis for regional high temperature risk management and disaster reduction measures. Based on the daily maximum temperature data from 1961 to 2020 and the future climate predictions provided by the 12 climate models in the SSP2-4.5 scenario in the CMIP6 from 2031 to 2099, three indicators were calculated and used to assess the hazard of high temperature, including the number of high temperature days, maximum temperature, and average high temperature intensity. We used the kernel density estimation to calculate the values of the three indicators under four return periods (5, 10, 20, 50 years) of historical and future climate scenarios, and then evaluated the hazard changes of high temperature. The results show that: (1) Under the SSP2.4-5 scenario, the number of high temperature days in China presented four risk centers, including the central part of the arid (semi-arid) area of Northwest China, the intersection area of North China and Central China, the central part of Southwest China, and the southern part of South China. The number of high temperature days gradually decreased outward from these four centers. The spatial distribution of the maximum temperature in the north China was greater than that in the south China, and this distribution in the east China was greater than that in the west China. The distribution of average high temperature intensity showed a decreasing trend from the southern part of North China, the western part of the arid (semi-arid) region of the Northwest China, and the western part of the eastern region to other regions in China except the Qinghai-Tibet Plateau; (2) Under the scenario of SSP2.4-5, with the increase of the return period, the three high temperature indicators in China all showed an increasing trend. The area affected by high temperature expanded, and the values of the three high temperature indicators increased significantly; (3) The changes of the three high temperature indicators showed obvious spatial aggregation. The hotspot areas jointly displayed by the three indicators were: the northern and southern parts of the Southwest China, the central part of the arid (semi-arid) area of the Northwest China, and a small part of the northern and central parts of China, which were most likely to have high-temperature disasters. The change of high temperature days and maximum temperature indicated that high temperature disasters in the western part of the eastern region may also be large. The cold spot areas shown by the three indicators were: the southeast of the Qinghai-Tibet Plateau, the western part of the arid (semi-arid) northwestern region, the western part of the Tibetan Plateau, and the southeast coastal areas of China. There was little risk of high temperature in these areas. |
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| Keywords: | global warming extreme climate CMIP6 high temperature hazard spatial distribution sharing socioeconomic paths kernel density function hot spot analysis |
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