未来情景下中国高温的人口暴露度变化及影响因素研究
Changes in population exposure to high temperature under a future scenario in China and its influencing factors
查看参考文献37篇
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文摘
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基于RCP 8.5气候情景下21个高分辨率全球气候模式的日最高气温数据和A2r社会经济发展情景下的人口数据,以高温日数和人口数量的乘积构建高温的人口暴露度指标,采用多个气候模式集合平均的方法从网格单元尺度分析未来不同时段中国高温和强危害性高温的人口暴露度变化,并从全国和气象地理分区两种空间尺度研究人口暴露度变化的影响因素。研究表明:未来情景下,中国高温的人口暴露度明显增加,2021-2040年、2041-2060年、2061-2080年和2081-2100年相比基准时段1981-2010年分别增加了1.3、2.0、3.6和5.9倍,强危害性高温的人口暴露度增加更为显著,相比基准时段分别增加了2.0、8.3、24.2和82.7倍。高温的人口暴露度在华北、黄淮、华南、江南、江淮、西南和江汉地区增加较为明显,其中华北、黄淮、华南和江南最为显著;强危害性高温的人口暴露度在华北、黄淮、江南、江淮、西南和江汉等区域增加较为明显,其中华北、黄淮、江南和江淮最为显著;未来情景下人口暴露度的变化主要受气候因子的影响,其次受人口和气候因子的共同影响,单独人口因子的影响很小。全国尺度上,气候因子对未来不同时段人口暴露度变化的影响逐渐减弱,贡献率由70.0%左右逐渐减至60.0%左右。人口和气候因子的共同作用逐渐增强,贡献率由20.0%左右逐渐增至40.0%左右。 |
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其他语种文摘
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Population exposure to high temperature (extremely high temperature) is represented by the multiplication of the population in each grid cell and the projected mean annual number of hot days with daily maximum temperature above 35℃ (40℃) for each corresponding grid cell. Based on daily maximum temperature data from 21 global climate models under the RCP8.5 scenario and population projection data under the A2r socio- economic scenario, population exposures for four future periods (2021-2040,2041-2060,2060-2081 and 2081-2100) in China were projected at the grid cell level. The ensemble mean method was used to calculate the annual number of hot days. The relative importance of population and climate as drives of exposures was evaluated at the national level and the meteorological geographical division level. Compared with the population exposure for the 1981- 2010 base period, population exposure to high temperature (≥ 35℃) over China for four future periods will increase by 1.3, 2.0,3.6 and 5.9 times respectively and population exposure to extremely high temperature (≥ 40°C) will increase by 2.0,8.3, 24.2 and 82.7 times respectively. Population exposure to high temperature will increase significantly in Jianghuai region, Southwest China and Jianghan region, especially in North China, Huanghuai region, South China, Jiangnan region. Population exposure to extremely high temperature will increase significantly in Southwest China and Jianghan region, especially in North China, Huanghuai region, Jiangnan region and Jianghuai region. Climate factors are the most important driver of exposures for Huanghuai region, Jianghuai region, Jianghan region, Jiangnan region, South China and Southwest China, followed by the interact effect of population and climate. At the national level, climate factor is also the most important driver, followed by the interact effect of population and climate. The contribution rate of climate to national- level projected change in exposure will decrease gradually from about 70% to about 60% and the contribution rate of concurrent changes in population and climate will increase gradually from about 20% to about 40% over the four future periods. |
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来源
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地理学报
,2016,71(7):1189-1200 【核心库】
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DOI
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10.11821/dlxb201607008
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关键词
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未来情景
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人口暴露度
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高温
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强危害性高温
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影响因子
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中国
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地址
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中国气象局国家气候中心, 气象灾害预报预警与评估协同创新中心, 北京, 100081
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国家气象中心, 北京, 100081
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语种
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中文 |
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文献类型
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研究性论文 |
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ISSN
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0375-5444 |
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基金
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国家自然科学基金项目
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文献收藏号
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CSCD:5756318
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参考文献 共
37
共2页
|
|
1.
IPCC.
Climate change 2014: Synthesis Report. Contribution of Working Groups I,II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change,2014:151
|
CSCD被引
11
次
|
|
|
|
|
2.
张存杰. IPCC第五次评估报告气候变化对人类福祉影响的新认知.
气候变化研究进展,2014,10(4):246-250
|
CSCD被引
17
次
|
|
|
|
|
3.
李莹. IPCC第五次评估报告对气候变化风险及风险管理的新认识.
气候变化研究进展,2014,10(4):260-267
|
CSCD被引
11
次
|
|
|
|
|
4.
. European heatwave caused 35,000 deaths.
New Scientist,2003
|
CSCD被引
1
次
|
|
|
|
|
5.
国家气候委员会.
中国气候公报2010年,2011
|
CSCD被引
1
次
|
|
|
|
|
6.
中国气象局.
中国气象灾害年鉴2014,2014
|
CSCD被引
1
次
|
|
|
|
|
7.
国家气候委员会.
中国气候公报2014年,2015
|
CSCD被引
1
次
|
|
|
|
|
8.
.
Pakistan Heat Wave 2015: Death Toll Exceeds 1,200 As Karachi Struggles With Continued Extreme Weather During Ramadan. International Business Times
|
CSCD被引
1
次
|
|
|
|
|
9.
Ashish Mehta. Rajasthan's Phalodi sizzles at 51°C, highest ever temperature in country.
The Times of India
|
CSCD被引
1
次
|
|
|
|
|
10.
Ding T. Changes in hot days and heatwaves in China during 1961-2007.
International Journal of Climatology,2010,30(10):1452-1462
|
CSCD被引
54
次
|
|
|
|
|
11.
叶殿秀. 1961-2010年我国夏季高温热浪的时空变化特征.
气候变化研究进展,2013,9(1):15-20
|
CSCD被引
71
次
|
|
|
|
|
12.
李双双. 近54年京津冀地区热浪时空变化特征及影响因素.
应用气象学报,2015,26(5):545-554
|
CSCD被引
21
次
|
|
|
|
|
13.
杨红龙. SRES A2情景下中国区域性高温热浪事件变化特征.
气象与环境学报,2015,31(1):51-59
|
CSCD被引
5
次
|
|
|
|
|
14.
彭海燕. 2003年夏季长江中下游地区异常高温的分析.
气象科学,2005,25(4):355-361
|
CSCD被引
24
次
|
|
|
|
|
15.
You Q. Changes in daily climate extremes in China and their connection to the large scale atmospheric circulation during 1961-2003.
Climate Dynamics,2011,36(11/12):2399-2417
|
CSCD被引
31
次
|
|
|
|
|
16.
Trenberth K E. Climate extremes and climate change: The Russian heat wave and other climate extremes of 2010.
Journal ofGeophsical Research: Atmospheres,2012,117(D17):127-135
|
CSCD被引
10
次
|
|
|
|
|
17.
孙建奇. 我国极端高温事件的年代际变化及其与大气环流的联系.
气候与环境研究,2011,16(2):199-208
|
CSCD被引
41
次
|
|
|
|
|
18.
翟盘茂. 中国北方近50年温度和降水极端事件变化.
地理学报,2003,58(1):1-10
|
CSCD被引
450
次
|
|
|
|
|
19.
Luterbacher J. European seasonal and annual temperature variability, trends, and extremes since 1500.
Science,2004,303:1499-1503
|
CSCD被引
29
次
|
|
|
|
|
20.
史军. 华东地区夏季高温期的气候特征及其变化规律.
地理学报,2008,63(3):237-246
|
CSCD被引
31
次
|
|
|
|
|
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