气候变化研究进展 ›› 2024, Vol. 20 ›› Issue (3): 265-277.doi: 10.12006/j.issn.1673-1719.2023.286
陈婷婷1,2, 余文君1,2(), 李艳忠1,2, 白鹏3, 星寅聪1,2, 黄曼捷1,2, 邵伟1,2
收稿日期:
2023-12-28
修回日期:
2024-02-21
出版日期:
2024-05-30
发布日期:
2024-05-08
通讯作者:
余文君,女,讲师,作者简介:
陈婷婷,女,本科生
基金资助:
CHEN Ting-Ting1,2, YU Wen-Jun1,2(), LI Yan-Zhong1,2, BAI Peng3, XING Yin-Cong1,2, HUANG Man-Jie1,2, SHAO Wei1,2
Received:
2023-12-28
Revised:
2024-02-21
Online:
2024-05-30
Published:
2024-05-08
摘要:
体感温度(Apparent Temperature,AP)描述了人体实际感受到的温度。文中基于我国1960—2019年的空气温度、相对湿度和风速等气象要素数据,估算并分析了4个典型气候区(湿润区、过渡区、干旱区和青藏高原)AP的时空变化格局及其高温风险。结果发现:(1)在空间分布上,AP由东南沿海向西北内陆递减,平均AP值由湿润区(约为17.0℃)逐渐向干旱区(约为7.0℃)和青藏高原区(约为0.6℃)递减;(2)全国AP呈现显著上升的趋势,4个典型气候区的上升速率分别为0.29℃/(10 a)、0.27℃/(10 a)、0.15℃/(10 a)和0.13℃/(10 a);(3)气温变化对AP的贡献率最高,约为92.4%,其次是风速及相对湿度,约为5.6%和2.0%;(4)典型气候区的高温风险天数变化呈现空间异质性,湿润区和青藏高原大部分地区均呈现显著增长趋势。
陈婷婷, 余文君, 李艳忠, 白鹏, 星寅聪, 黄曼捷, 邵伟. 中国1960—2019年体感温度的时空变化及其风险分析[J]. 气候变化研究进展, 2024, 20(3): 265-277.
CHEN Ting-Ting, YU Wen-Jun, LI Yan-Zhong, BAI Peng, XING Yin-Cong, HUANG Man-Jie, SHAO Wei. The spatiotemporal changes and risk analysis of apparent temperature in China from 1960 to 2019[J]. Climate Change Research, 2024, 20(3): 265-277.
图1 研究区气象站点分布和高程(DEM)示意图 注:本文图基于自然资源部地图技术审查中心标准地图服务网站下载的审图号为GS(2019)1822号的标准地图制作,底图无修改,下同。
Fig. 1 Distribution of the meteorological stations and DEM on the study region
图5 1960—2019年体感温度和平均气温在不同季节上趋势值的空间分布 注:“×”表示变化趋势显著(P<0.05),下同。
Fig. 5 The spatial distribution of AP and AT trends in different temporal scales from 1960 to 2019. (“×” represent where observed trends are significant (P<0.05))
图7 1960—2019年体感温度与平均气温在不同季节上差值绝对值的趋势值空间分布
Fig. 7 Spatial distribution of the difference between the trend change of AP and AT in different spatial scales from 1960 to 2019
图8 1960—2019年中国典型气候区域的高温风险面积占比(a)和天数(b)的时间变化
Fig. 8 The proportion of annual high temperature risk area (a) and temporal variation of days (b) in typical climate regions of China from 1960 to 2019
图9 1960—2019年中国高温风险天数变化趋势的空间分布 注:白色区域表示并未出现高温风险地区。
Fig. 9 Spatial distribution of the trend of high temperature risk days in China from 1960 to 2019. (The white area indicates that there is no high temperature risk area)
图11 去趋势前后体感温度的变化特征 注:AP代表去趋势前的体感温度值;APdeT、APdeRH、APdeU分别代表去除温度、相对湿度和风速年际变化趋势的体感温度值。
Fig. 11 The change characteristics of AP before and after detrending. (AP represents the apparent temperature value before detrending. APdeT, APdeRH, and APdeU represent the apparent temperature values calculated by detrending air temperature, relative humidity, and wind speed, respectively)
图12 平均气温、相对湿度和风速在不同季节上分别对AP相对贡献率的空间分布
Fig. 12 The spatial distribution of the relative contribution rates of the average air temperature (a-c), relative humidity (d-f), and wind speed (g-i) to AP in different timescales, respectively
[1] | Steadman R G. A universal scale of apparent temperature[J]. Journal of Applied Meteorology and Climatology, 1984, 23 (12): 1674-1687 |
[2] | Zhu X, Huang G, Zhou X, et al. Projection of apparent temperature using statistical downscaling approach in the Pearl River Delta[J]. Theoretical and Applied Climatology, 2021, 144: 1253-1266 |
[3] |
Camilo M, Bénédicte D, Iain R C, et al. Global risk of deadly heat[J]. Nature Climate Change, 2017, 7 (7): 501-506
doi: 10.1038/NCLIMATE3322 |
[4] | Hoang T L T, Dao H N, Cu P T, et al. Assessing heat index changes in the context of climate change: a case study of Hanoi (Vietnam)[J]. Frontiers in Earth Science, 2022, 10: 897601 |
[5] | Zhu J, Wang S, Huang G. Assessing climate change impacts on human-perceived temperature extremes and underlying uncertainties[J]. Journal of Geophysical Research: Atmospheres, 2019, 124 (7): 3800-3821 |
[6] | Li Q X, Sun W B, Yun X, et al. An updated evaluation of the global mean land surface air temperature and surface temperature trends based on CLSAT and CMST[J]. Climate Dynamics, 2021, 56: 635-650 |
[7] | Fan X W, Duan Q Y, Shen C W, et al. Global surface air temperatures in CMIP6: historical performance and future changes[J]. Environmental Research Letters, 2020, 15 (10): 104056 |
[8] | Huang J Y, Li Q X, Song Z Y. Historical global land surface air apparent temperature and its future changes based on CMIP6 projections[J]. Science of The Total Environment, 2021, 816: 151656 |
[9] | Li Y, Gao Q J, You Q, et al. Intraseasonal oscillation features of the two types of persistent high temperature events over Jiangnan region[J]. Atmosphere, 2023, 14 (1): 185 |
[10] | Luo M, Lau N C. Heat waves in southern China: synoptic behavior, long-term change, and urbanization effects[J]. Journal of Climate, 2017, 30 (2): 703-720 |
[11] | Wajid N, Asad A, Shah F, et al. Future risk assessment by estimating historical heat wave trends with projected heat accumulation using simclim climate model in Pakistan[J]. Atmospheric Research, 2018, 205: 118-133 |
[12] |
黄晓军, 王博, 刘萌萌, 等. 中国城市高温特征及社会脆弱性评价[J]. 地理研究, 2020, 39 (7): 1534-1547.
doi: 10.11821/dlyj020190608 |
Huang X J, Wang B, Liu M M, et al. Characteristics of urban extreme heat and assessment of social vulnerability in China[J]. Geographical Research, 2020, 39 (7): 1534-1547 (in Chinese)
doi: 10.11821/dlyj020190608 |
|
[13] |
闫慧敏, 陈伟娜, 杨方兴, 等. 过去50年内蒙古极端气候事件时空格局特征[J]. 地理研究, 2014, 33 (1): 13-22.
doi: 10.11821/dlyj201401002 |
Yan H M, Chen W N, Yang F X, et al. The spatial and temporal analysis of extreme climatic events in Inner Mongolia during the past 50 years[J]. Geographical Research, 2014, 33 (1): 13-22 (in Chinese)
doi: 10.11821/dlyj201401002 |
|
[14] |
张蕾, 黄大鹏, 杨冰韵. RCP4.5情景下中国人口对高温暴露度预估研究[J]. 地理研究, 2016, 35 (12): 2238-2248.
doi: 10.11821/dlyj201612004 |
Zhang L, Huang D P, Yang B Y. Future population exposure to high temperature in China under RCP4.5 scenario[J]. Geographical Research. 2016, 35 (12): 2238-2248 (in Chinese)
doi: 10.11821/dlyj201612004 |
|
[15] | Zhou C, Zhang D, Cao Y, et al. Spatio-temporal evolution and factors of climate comfort for urban human settlements in the Guangdong-Hong Kong-Macau Greater Bay area[J]. Frontiers in Environmental Science, 2022, 10: 1001064 |
[16] | Zhang J T, Ren G Y, You Q L. Detection and projection of climatic comfort changes in China’s Mainland in a warming world[J]. Advances in Climate Change Research, 2022, 13 (4): 507-516 |
[17] | Zhang D, Zhou C, Zhou Y, et al. Spatiotemporal relationship characteristic of climate comfort of urban human settlement environment and population density in China[J]. Meta-Scenario Computation for Social-Geographical Sustainability, 2023, 16648714: 269 |
[18] | Zhang Y, Xiao F, Mei H, et al. Comprehensive analysis of climate-related comfort in southern China: climatology, trend, and interannual variations[J]. Urban Climate, 2022, 46: 101349 |
[19] | 杨文婷, 王汶. 基于体感温度的中国户外高温风险分布研究[J]. 气候与环境研究, 2021, 26 (6): 637-647. |
Yang W T, Wang W. Apparent temperature-based outdoor heat-risk distribution in China[J]. Climatic and Environmental Research, 2021, 26 (6): 637-647 (in Chinese) | |
[20] | 孟长青, 刘柯莹, 钟德钰, 等. 高相对湿度对热浪及寒潮事件的放大效应评估: 以嘉陵江流域为例[J]. 水力发电学报, 2023, 42 (4): 11-24. |
Meng C Q, Liu K Y, Zhong D Y, et al. Evaluation of amplification effect of high relative humidity on heat wave and cold wave events: a case study of Jialing River basin[J]. Journal of Hydroelectric Power, 2023, 42 (4): 11-24 (in Chinese) | |
[21] |
李卓群, 刘星才. 1961—2019年辽宁省高温天气变化特征[J]. 应用生态学报, 2021, 32 (11): 4059-4067.
doi: 10.13287/j.1001-9332.202111.038 |
Li Z Q, Liu X C. Characteristics of high temperature weather changes in Liaoning province from 1961 to 2019[J]. Journal of Applied Ecology, 2021, 32 (11): 4059-4067 (in Chinese) | |
[22] | Wu J, Gao X J, Filippo G, et al. Changes of effective temperature and cold/hot days in late 10as over China based on a high resolution gridded observation dataset[J]. International Journal of Climatology, 2017, 37: 788-800 |
[23] | Li J, Chen Y D, Gan T Y, et al. Elevated increases in human-perceived temperature under climate warming[J]. Nature Climate Change, 2018, 8 (1): 43-47 |
[24] | Li W, Hao X, Wang L, et al. Detection and attribution of changes in thermal discomfort over China during 1961-2014 and future projections[J]. Advances in Atmospheric Sciences, 2022, 39 (3): 456-470 |
[25] | Wang Y J, Chen L T, Song Z Y, et al. Human-perceived temperature changes over South China: long-term trends and urbanization effects[J]. Atmospheric Research, 2018, 215: 116-127 |
[26] |
Yan Y, Yue S, Hui C, et al. Advances on assessment of bioclimatic comfort conditions at home and abroad[J]. Advances in Earth Science, 2013, 28: 1119-1125
doi: 10.11867/j.issn.1001-8166.2013.10.1119 |
[27] | Ma P, Wang S G, Shang K Z, et al. The impact of meteorological comfort conditions on respiratory disease[J]. China Environmental Science, 2018, 38: 374-382 |
[28] | Yang J J. The comprehensive evaluation of suitability of summer tourism base in China[J]. Resources Science, 2016, 38: 11-16 |
[29] | Fischer E M, Knutti R. Robust projections of combined humidity and temperature extremes[J]. Nature Climate Change, 2013, 3 (2): 126-130 |
[30] |
Mora C, Dousset B, Caldwell L, et al. Global risk of deadly heat[J]. Nature Climate Change, 2017, 7 (7): 501-506
doi: 10.1038/NCLIMATE3322 |
[31] | Robinson S, Turrell E S, Gerking S D. Physiological equivalent conditions of air temperature and humidity[J]. American Journal of Physiology-Legacy Content, 1945, 143 (1): 21-32 |
[32] | Akimovich N N, Balalla O A. Sultry weathers at the south of Primorye and their influence on human body[J]. Izvestia ASc USSR Geogr, 1971, 4: 94-100 |
[33] | Rohles Jr F H, Nevins R G. The nature of thermal comfort for sedentary man[J]. ASHRAE Transactions, 1971, 77 (1): 239-246 |
[34] | Weiss M. The humisery and other measures of summer discomfort[J]. National Weather Digest, 1982, 7 (2): 10-18 |
[35] |
Jendritzky G de Dear R, Havenith G. UTCI-Why another thermal index?[J]. International Journal of Biometeorology, 2012, 56 (3): 421-428
doi: 10.1007/s00484-011-0513-7 pmid: 22187087 |
[36] | Sheridan S C. The redevelopment of a weather-type classification scheme for North America[J]. International Journal of Climatology, 2002, 22 (1): 51-68 |
[37] | 中国气象局. 中华人民共和国气象行业标准QX/T 500—2019: 避暑旅游气候适宜度评定方法[J]. 北京: 气象出版社, 2019. |
China Meteorological Administration. The meteorological industry standard of the People’s Republic of China QX/T 500-2019: evaluation method for climate suitability for summer tourism[J]. Beijing: China Meteorological Press, 2019 (in Chinese) | |
[38] | 毛飞, 孙涵, 杨红龙. 干湿气候区划研究进展[J]. 地理科学进展, 2011, 30 (1): 17-26. |
Mao F, Sun H, Yang H L. Research progress in dry/wet climate zoning[J]. Progress in Geographic Science, 2011, 30 (1): 17-26 (in Chinese) | |
[39] |
彭振华, 李艳忠, 余文君, 等. 遥感降水产品在中国不同气候区的适用性研究[J]. 地球信息科学学报, 2021, 23 (7): 1296-1311.
doi: 10.12082/dqxxkx.2021.200348 |
Peng Z H, Li Y Z, Yu W J, et al. Study on the applicability of remote sensing precipitation products in different climate regions of China[J]. Journal of Earth Information Science, 2021, 23 (7): 1296-1311 (in Chinese) | |
[40] | 刘志红, Tim R, Van N, 等. 专用气候数据空间插值软件ANUSPLIN及其应用[J]. 气象, 2008 (2): 92-100. |
Liu Z H, Tim R, Van N, et al. Introduction of the professional interpolation software for meteorology data: ANUSPLINN[J]. Meteorology, 2008 (2): 92-100 (in Chinese) | |
[41] |
谭剑波, 李爱农, 雷光斌. 青藏高原东南缘气象要素Anusplin和Cokriging空间插值对比分析[J]. 高原气象, 2016, 35 (4): 875-886.
doi: 10.7522/j.issn.1000-0534.2015.00037 |
Tan J B, Li A N, Lei G B. Comparative analysis of Anusplin and Cokriging spatial interpolation of meteorological elements in the southeastern margin of Qinghai-Xizang Plateau[J]. Plateau Meteorology, 2016, 35 (4): 875-886 (in Chinese) | |
[42] | 钱永兰, 吕厚荃, 张艳红. 基于ANUSPLIN软件的逐日气象要素插值方法应用与评估[J]. 气象与环境学报, 2010, 26 (2): 7-15. |
Qian Y L, Lyu H Q, Zhang Y H. Application and evaluation of daily meteorological element interpolation method based on ANUSPLIN software[J]. Journal of Meteorology and Environment, 2010, 26 (2): 7-15 (in Chinese) | |
[43] | Bai P. Comparison of remote sensing evapotranspiration models: consistency, merits, and pitfalls[J]. Hydrology, 2023, 617: 128856 |
[44] | Li Y Z, Zhuang J C, Bai P, et al. Evaluation of three long-term remotely sensed precipitation estimates for meteorological drought monitoring over China[J]. Remote Sensing, 2022, 15, 86: 1-19 |
[45] | Yang Z, Bai P, Li Y. Quantifying the effect of vegetation greening on evapotranspiration and its components on the Loess Plateau[J]. Hydrology, 2022, 613: 128446 |
[46] | Bai P, Liu X, Zhang Y, et al. Assessing the impacts of vegetation greenness change on evapotranspiration and water yield in China[J]. Water Resource Research, 2020, 56 |
[47] | 庄稼成, 星寅聪, 李艳忠, 等. 基于改进ABCD模型的黄河源区径流变化与归因[J]. 南水北调与水利科技(中英文), 2022, 20 (5): 953-965. |
Zhuang J C, Xing Y C, Li Y Z, et al. Attribution analysis of runoff change based on the ABCD model coupled with the snowmelt module in the source region of the Yellow River[J]. South to North Water Diversion and Water Conservancy Technology (Chinese and English), 2022, 20 (5): 953-965 (in Chinese) | |
[48] | 江振蓝, 荆长伟, 李丹, 等. 运用Mann-Kendall方法探究地表植被变化趋势及其对地形因子的响应机制: 以太湖苕溪流域为例[J]. 浙江大学学报(农业与生命科学版), 2011, 37 (6): 684-692. |
Jiang Z L, Jing C W, Li D, et al. Dynamics of vegetation and its responses to terrain factors with Mann-Kendall approach: a case study in Tiao xi watershed, Tai Hu lake[J]. Journal of Zhejiang University (Agriculture and Life Sciences Edition), 2011, 37 (6): 684-692 (in Chinese) | |
[49] | 张建云, 章四龙, 王金星, 等. 近50年来中国六大流域年际径流变化趋势研究[J]. 水科学进展, 2007 (2): 230-234. |
Zhang J Y, Zhang S L, Wang J X, et al. Study on runoff trends of the six larger basins in China over the past 50 years[J]. Progress in Water Science, 2007 (2): 230-234 (in Chinese) | |
[50] | Sivapragasam C, Natarajan N. Comparison of trends in apparent and air temperature for climate change assessment[J]. Modeling Earth Systems and Environment, 2021, 7: 261-271 |
[51] |
Yoram E, Daniel S. Thermal comfort and the heat stress indices[J]. Industrial Health, 2006, 44 (3): 388-398
doi: 10.2486/indhealth.44.388 pmid: 16922182 |
[52] | Zhu J X, Huang G D, Wang X Q, et al. Investigation of changes in extreme temperature and humidity over China through a dynamical downscaling approach[J]. Earth’s Future, 2017, 5 (11): 1136-1155 |
[53] | Wang S, Zhu J X. Amplified or exaggerated changes in perceived temperature extremes under global warming[J]. Climate Dynamics: Observational, Theoretical and Computational Research on the Climate System, 2020, 54 (7): 117-127 |
[54] | Wang Z, Zhang A, Liu M. Daily spatial distribution of apparent temperature comfort zone in China based on heat index[J]. Remote Sensing, 2022, 14 (19): 4999 |
[55] | Jin J, Xu Z, Cao R, et al. Long-term apparent temperature, extreme temperature exposure, and depressive symptoms: a longitudinal study in China[J]. International Journal of Environmental Research and Public Health, 2023, 20 (4): 3229 |
[1] | 王玮, 王欢, 左志燕. 中国冬季大范围极端冷、暖日的时空变化特征[J]. 气候变化研究进展, 2023, 19(4): 418-430. |
[2] | 玛地尼亚提·地里夏提,玉素甫江·如素力,海日古丽·纳麦提,肉克亚木·艾克木. 天山新疆段植被物候特征及其气候响应[J]. 气候变化研究进展, 2019, 15(6): 624-632. |
[3] | 刘洁,王宁练,花婷. 1960—2016年中国北方半干旱区盛夏降水时空变化及其水汽输送特征分析[J]. 气候变化研究进展, 2019, 15(3): 257-269. |
[4] | 李宁,白蕤,李玮,张蕾,易克贤,陈淼,陈歆. 未来气候变化背景下我国橡胶树寒害事件的变化特征[J]. 气候变化研究进展, 2018, 14(4): 402-410. |
[5] | 周建琴, 黄玮, 朱勇, 李蒙, 周波涛. 云南气候舒适度分布和变化特征及未来变化趋势预估[J]. 气候变化研究进展, 2018, 14(2): 144-154. |
[6] | 胡浩林, 任福民. CMIP5模式集合对中国区域性低温事件的模拟与预估[J]. 气候变化研究进展, 2016, 12(5): 396-406. |
[7] | 蔡新斌, 买尔燕古丽·阿不都热合曼, 江晓珩, 林宣龙, 田润炜, 布早拉木. 新疆湿地资源时空变化特征及其原因分析研究[J]. 气候变化研究进展, 2015, 11(6): 395-401. |
[8] | 陆采荣, 杨虎. 冻融变化与水工混凝土耐久性[J]. 气候变化研究进展, 2015, 11(5): 308-312. |
[9] | 王艳君, 高超, 王安乾, 王豫燕, 张飞跃, 翟建青, 李修仓, 苏布达. 中国暴雨洪涝灾害的暴露度与脆弱性时空变化特征[J]. 气候变化研究进展, 2014, 10(6): 391-398. |
[10] | 钱维宏 李进. 北京地区长期增暖中的一个减缓期[J]. 气候变化研究进展, 2012, 8(3): 178-182. |
[11] | 李明财 熊明明 杨艳娟 任雨. 环渤海地区1961—2010年太阳总辐射时空变化特征[J]. 气候变化研究进展, 2012, 8(2): 119-123. |
[12] | 王平;黄耀;张稳. 1955-2005年中国稻田甲烷排放估算[J]. 气候变化研究进展, 2009, 5(05): 291-297. |
[13] | 高留喜 杨成芳 冯桂力 吕环宇. 山东省雷暴时空变化特征[J]. 气候变化研究进展, 2007, 03(04): 239-242. |
[14] | 简茂球 秦晓昊 乔云亭 温之平. 中国南方春季大尺度大气水汽汇时空变化特征[J]. 气候变化研究进展, 2007, 03(02): 74-079. |
[15] | 何慧 覃志年 李艳兰 廖雪萍. 广西月平均温度异常的时空特征及其变化[J]. 气候变化研究进展, 2007, 03(02): 95-099. |
阅读次数 | ||||||
全文 |
|
|||||
摘要 |
|
|||||
|