高分辨率区域气候变化降尺度数据对京津冀地区高温GDP和人口暴露度的集合预估

doi:10.12006/j.issn.1673-1719.2019.111

摘要/Abstract

摘要：

基于RCP4.5（中等温室气体排放）情景下5个全球模式模拟结果的降尺度数据,及SSP2社会经济路径下的GDP和人口密度数据,对21世纪京津冀地区（北京、天津和河北的统称）未来2021—2040年（近期)、2046—2065年（中期)、2080—2099年（末期）的高温GDP和人口暴露度进行多模式集合预估。结果表明：未来京津冀地区热事件将增加,21世纪末期京津冀东南部平原和沿海地区的闷热事件、中部平原地区的高温事件出现频率明显增加。GDP和人口暴露度大值区主要分布在北京、天津、保定、石家庄和邯郸等经济发达、交通便利、人口聚集的城市及其周边地区。21世纪京津冀地区的GDP暴露度区域平均值持续增加,21世纪末期多年平均值约为参照时段的58.9倍;各城市的区域平均值也表现出一致增加。京津冀地区人口暴露度区域平均值在21世纪中期达到最大,为参照时段的2.3倍;北京、秦皇岛、张家口、承德和唐山人口暴露度区域平均值将持续增长,其他城市人口暴露度区域平均值在21世纪中期达到最大。GDP暴露度的变化主要取决于非线性因子,且其贡献率随时间逐渐增加,到21世纪末期可达70.9%。21世纪近期和中期人口暴露度的变化主要取决于非线性因子,气候因子在末期占主导地位。

关键词: 区域模式, 气候变化, 高温, 暴露度, 京津冀地区

Abstract:

Future changes of GDP and population exposure to high temperature in the 21st century over Jing-Jin-Ji district were investigated based on the high resolution (6.25 km) combined statistical and dynamical downscaling datasets under RCP4.5 scenario, as well as the population and GDP data under SSP2. Results show that, the high resolution downscaling projection could well reproduce the hazard distributions of high temperature events in the Jing-Jin-Ji district. At the end of the 21st century, the significantly increased frequency of sultry events over the southeastern plain and coastal areas of Jing-Jin-Ji district will be found, so will hot events in the midland of Hebei. The areas with high population and GDP exposure are mainly located in cities such as Beijing, Tianjin, Baoding, Shijiazhuang and Handan etc., who are featured by developed economy, convenient transportation and large population, and their surrounding areas. In the 21st century, the increasing GDP exposure over Jing-Jin-Ji district can also be found. The exposure average by the end of the 21st century is about 58.9 times of the reference period. All the cities also present a consistent increase. In the mid-21st century, the regional average of population exposure in Jing-Jin-Ji district will reach its maximum value as 2.3 times that of the reference period. But the average population exposure in Beijing, Zhangjiakou, Chengde and Tangshan will grow continuously in the 21st century. The change of GDP exposure mainly subjects to non-linear factors, and the contribution rate increases with time reaching 70.9% at the end of the 21st century. While the change of population exposure in the near and medium term of the 21st century mainly subjects to non-linear factors. The contribution rate of climate factors increases with time, reaching a dominant position at the end of the 21st century.

Key words: Regional climate model, Climate change, High temperature, Exposure, Jing-Jin-Ji district

李柔珂, 韩振宇, 徐影, 石英, 吴佳. 高分辨率区域气候变化降尺度数据对京津冀地区高温GDP和人口暴露度的集合预估[J]. 气候变化研究进展, 2020, 16(4): 491-504.

LI Rou-Ke, HAN Zhen-Yu, XU Ying, SHI Ying, WU Jia. An ensemble projection of GDP and population exposure to high temperature events over Jing-Jin-Ji district based on high resolution combined dynamical and statistical downscaling datasets[J]. Climate Change Research, 2020, 16(4): 491-504.

图/表 10

图1 夏半年TX、TN和RH集合平均模拟误差的泰勒图(a)及集合平均模拟的HD和SD与观测的差(b、c) 注：标记“+”指模拟与观测的差值达到0.05显著性水平的点。

Fig. 1 Taylor diagram for ensemble average simulation error of TX, TN and RH in the summer half year by using the station data (a); differences of HD (b) and SD (c) over the entire Jing-Jin-Ji area between the ensemble mean and observation

表1 降尺度数据5个集合成员和集合平均模拟的京津冀HD和SD与观测的空间相关系数、平均误差和均方根误差

Table 1 The correlation coefficient, mean error and root mean square error of HD and SD over the entire Jing-Jin-Ji area between the simulations and observation

图2 京津冀地区致灾危险度在参照时段（1986—2005年）以及未来变化的空间分布（相对于参照时段）

Fig. 2 Spatial distribution of the present day (1986-2005) and spatial distribution of the future change of hazard factor over the Jing-Jin-Ji district compared with the present day

图3 京津冀地区区域平均致灾危险度相对于参照时段的变化值序列(a、b)和区域平均GDP和人口的数值序列(c) 注：图(c)中蓝色五角星表示GDP增长过程中数值开始超过参照时段平均值1倍的时间点,红色五角星表示人口下降过程中数值开始低于参照时段平均值的时间点。

Fig. 3 Future change of hazard factor over the Jing-Jin-Ji district compared with the present day (a, b), and regional average value of GDP and population (c) (In (c), blue star indicates the time point when the regional average value exceeds 1 time of average value of the reference period in the process of GDP rising, and red star indicates the time point when the regional average value is lower than the value of reference period in the process of population falling)

图4 京津冀GDP 和人口密度在参照时段和21世纪各时段的变化的空间分布

Fig. 4 Spatial distribution of GDP and population in the present day and the change of future periods in the 21st century over the Jing-Jin-Ji district

图5 京津冀GDP和人口高温暴露度在参照时段和21世纪各时段的变化空间分布

Fig. 5 Spatial distribution of GDP and population exposure in the present day and the change of future periods in the 21st century over the Jing-Jin-Ji district

表2 京津冀主要城市的GDP和人口暴露度相对于参照时段的倍率的时空变化

Table 2 Regional mean changes of GDP/population exposures ratio compared with the present day (1986—2005) over some cities of Jing-Jin-Ji district, respectively

图6 集合平均模拟的京津冀地区及各城市的GDP/人口暴露度注：竖线表示 5个模式间的1个标准差范围。

Fig. 6 Regional mean GDP/population exposures over cities of Jing-Jin-Ji district, respectively

表3 京津冀地区高温的GDP/人口暴露度变化的影响因子贡献率分析

Table 3 Analysis of the influencing factors of population exposure and GDP exposure to high temperature in Jing-Jin-Ji district

图7 21世纪近期、中期、末期京津冀地区及各城市GDP/人口暴露度影响因子贡献率

Fig. 7 Contribution rates of influencing factors of GDP/population exposures in cities of Jing-Jin-Ji district during three future periods

参考文献 37

[1]	Blumberg G, Dphil S M. Assessing the potential impact of heat waves in cities: implications for hazard preparation and planning[J]. Procedia Economics and Finance, 2014,18:727-735. DOI: 10.1016/S2212-5671(14) 00996-4
[2]	Zhao Y, Sultan B, Vautard R, et al. Potential escalation of heat-related working costs with climate and socioeconomic changes in China[J]. PNAS, 2016,113(17):4640-4645. DOI: 10.1073/pnas.1521828113
[3]	郭建平, 高素华. 高温、高CO₂对农作物影响的试验研究[J]. 中国生态农业学报, 2002,10(1):17-20.
	Guo J P, Gao S H. The experimental study on impacts of high temperature and high CO₂ concentration on crops[J]. Chinese Journal of Eco-Agriculture, 2002,10(1):17-20 (in Chinese)
[4]	李琪, 任景全, 王连喜. 未来气候变化情景下江苏水稻高温热害模拟研究I: 评估孕穗—抽穗期高温热害对水稻产量的影响[J]. 中国农业气象, 2014,35(1):91-96.
	Li Q, Ren J Q, Wang L X. Simulation of the heat injury on rice production in Jiangsu province under the climate change scenarios I: impact assessment of the heat injury on rice yield from booting to heading stage[J]. Chinese Journal of Agrometeorology, 2014,35(1):91-96 (in Chinese)
[5]	Kalkstein S, Scott J G. An evaluation of climate/mortality relationships in large U.S. cities and the possible impacts of a climate change[J]. Environmental Health Perspectives, 1997,105(1):84-93. DOI: 10.1289/ehp.9710584
[6]	Chen K, Bi J, Chen J, , et al. Influence of heat wave definitions to the added effect of heat waves on daily mortality in Nanjing, China[J]. Science of the Total Environment, 2015,506/507:18-25. DOI: 10.1016/j.scitotenv.2014.10.092
[7]	刘珂, 许吟隆, 陶生才, 等. 多模式集合对中国气温的模拟效果及未来30年中国气温变化预估[J]. 高原气象, 2011,30(2):363-370.
	Liu K, Xu Y L, Tao S C, et al. Validation of multi-model ensemble to air temperature of china and projection of air temperature change in China for the next three decades[J]. Plateau Meteorology, 2011,30(2):363-370 (in Chinese)
[8]	姚遥, 罗勇, 黄建斌. 8个CMIP5模式对中国极端气温的模拟和预估[J]. 气候变化研究进展, 2012 (4):250-256.
	Yao Y, Luo Y, Huang J B. Evaluation and projection of temperature extremes over china based on 8 modeling data from CMIP5[J]. Climate Change Research, 2012 (4):250-256 (in Chinese)
[9]	吴绍洪. 综合风险防范[M]. 北京: 科学出版社, 2011.
	Wu S H. Integrated risk governance [M]. Beijing: Science Press, 2011 (in Chinese)
[10]	董思言, 徐影, 周波涛, 等. 基于CMIP5模式的中国地区未来高温灾害风险预估[J]. 气候变化研究进展, 2014,10(5):365-369.
	Dong S Y, Xu Y, Zhou B T, et al. Projected risk of extreme heat in China based on CMIP5 models[J]. Climate Change Research, 2014,10(5):365-369 (in Chinese)
[11]	黄大鹏, 张蕾, 高歌. 未来情景下中国高温的人口暴露度变化及影响因素研究[J]. 地理学报, 2016,71(7):1189-1200. DOI: 10.11821/dlxb201607008.
	Huang D P, Zhang L, Gao G. Changes in population exposure to high temperature under a future scenario in China and its influencing factors[J]. Acta Geographica Sinica, 2016,71(7):1189-1200. DOI: 10.11821/dlxb201607008 (in Chinese)
[12]	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.
	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)
[13]	Gao X J, Shi Y, Song R V, et al. Reduction of future monsoon precipitation over China: comparison between a high resolution RCM simulation and the driving GCM[J]. Meteorology and Atmospheric Physics, 2008,100(1-4):73-86. DOI: 10.1007/s00703-008-0296-5
[14]	Gao X J, Shi Y, Zhang D F, et al. Uncertainties in monsoon precipitation projections over China: results from two high resolution RCM simulations[J]. Climate Research, 2012,52:213-226. DOI: 10.3354/cr01084
[15]	吴婕, 高学杰, 徐影. RegCM4模式对雄安及周边区域气候变化的集合预估[J]. 大气科学, 2018,42(3):696-705.
	Wu J, Gao X J, Xu Y. Climate change projection over Xiong'an district and its adjacent areas: an ensemble of RegCM4 simulations[J]. Chinese Journal of Atmospheric Sciences, 2018,42(3):696-705 (in Chinese)
[16]	高学杰, 石英, 张冬峰, 等. RegCM3对21世纪中国区域气候变化的高分辨率模拟[J]. 科学通报, 2012,57(5):374-381.
	Gao X J, Shi Y, Zhang D F, et al. High resolution simulation of regional climate change in China in the 21st century by RegCM3[J]. Chinese Science Bulletin, 2012,57(5):374-381 (in Chinese)
[17]	郎咸梅, 隋月. 全球变暖2℃情景下中国平均气候和极端气候事件变化预估[J]. 科学通报, 2013,58(8):734-742.
	Lang X M, Sui Y. Prediction of changes of average climate and extreme climate events in China under the scenario of global warming at 2℃[J]. Chinese Science Bulletin, 2013,58(8):734-742 (in Chinese)
[18]	李东欢, 邹立维, 周天军. 全球1.5℃温升背景下中国极端事件变化的区域模式预估[J]. 地球科学进展, 2017 (4):446-457.
	Li D H, Zou L W, Zhou T J. Changes of extreme indices over China in response to 1.5℃global warming projected by a regional climate model[J]. Advances in Earth Science, 2017 (4):446-457 (in Chinese)
[19]	石英, 韩振宇, 徐影, 等. 6.25 km高分辨率降尺度数据对雄安新区及整个京津冀地区未来极端气候事件的预估[J]. 气候变化研究进展, 2019,15(2):140-149.
	Shi Y, Han Z Y, Xu Y, et al. Future changes of climate extremes in Xiong'an New Area and Jing-Jin-Ji district based on high resolution (6.25 km) combined statistical and dynamical downscaling datasets[J]. Climate Change Research, 2019,15(2):140-149 (in Chinese)
[20]	van Vuuren D P, Edmonds J, Kainuma M, et al. The representative concentration pathways: an overview[J]. Climatic Change, 2011,109:5-31. DOI: 10.1007/s10584-011-0148-z
[21]	Kriegler E, O'Neill B C, Hallegatte S, et al. The need for and use of socio-economic scenarios for climate change analysis: a new approach based on shared socio-economic pathways[J]. Global Environmental Change, 2012,22(4):807-822. DOI: 10.1016/j.gloenvcha.2012.05.005
[22]	O'Neill B C, Kriegler E, Riahi K, et al. A new scenario framework for climate change research: the concept of shared socioeconomic pathways[J]. Climatic Change, 2014,122(3):387-400. DOI: 10.1007/s10584-013-0905-2 doi: 10.1007/s10584-013-0905-2 URL
[23]	Han Z Y, Shi Y, Wu J, et al. Combined dynamical and statistical downscaling for high-resolution projections of multiple climate variables in the Beijing-Tianjin-Hebei region of China[J]. Journal of Applied Meteorology and Climatology, 2019,58(11):2387-2403. DOI: 10.1175/JAMC-D-19-0050.1
[24]	Jones B, O'Neill B C. Spatially explicit global population scenarios consistent with the Shared Socioeconomic Pathways[J]. Environmental Research Letters, 2016,11(8):084003. DOI: 10.1088/1748-9326/11/8/084003
[25]	Geiger T, Murakami D, Frieler K, et al. Spatially-explicit Gross Cell Product (GCP) time series: past observations (1850-2000) harmonized with future projections according the Shared Socioeconomic Pathways (2010-2100) [DB/OL]. GFZ Data Services. 2017 [2019-05-16]. http://dataservices.gfz-potsdam.de/pik/showshort.php?id=escidoc:2740907
[26]	Murakami D, Yamagata Y. Estimation of gridded population and GDP scenarios with spatially explicit statistical downscaling[J]. Sustainability, 2019,11(7):2106. DOI: 10.3390/su11072106
[27]	张可慧, 李正涛, 刘剑锋, 等. 河北地区高温热浪时空特征及其对工业、交通的影响研究[J]. 地理与地理信息科学, 2011,27(6):90-95.
	Zhang K H, Li Z T, Liu J F, et al. Temporal-spatial feature analysis on the high-temperature and heatwaves in Hebei and its influence on industry and transportation[J]. Geography and Geo-Information Science, 2011,27(6):90-95 (in Chinese)
[28]	Wu J, Gao X J, Giorgi F, et al. Changes of effective temperature and cold/hot days in late decades over China based on a high-resolution gridded observation dataset[J]. International Journal of Climatology, 2017,37(1):788-800. DOI: 10.1002/joc.5038
[29]	Cheng X S, Su H. Effects of climatic temperature stress on cardiovascular diseases[J]. European Journal of Internal Medicine, 2010,21(3):164-167. DOI: 10.1016/j.ejim.2010.03.001 URL pmid: 20493415
[30]	邢娟娟. 井下高温作业的矿工生理、生化测定研究[J]. 中国安全科学学报, 2001,11(4):45-47.
	Xing J J. Study on physiological and biological changes for miners exposed to heat in underground Pit[J]. China Safety Science Journal, 2001,11(4):45-47 (in Chinese)
[31]	Robinson P J. On the definition of a heat wave[J]. Journal of Applied Meteorology, 2001,40(4):762-775. DOI: 10.1175/1520-0450(2001)040< 0762:OTDOAH>2.0.CO;2
[32]	Ding T, Qian W H. Geographical patterns and temporal variations of regional dry and wet heatwave events in China during 1960-2008[J]. Advances in Atmospheric Sciences, 2011,28(2):322-337. DOI: 10.1007/s00376-010-9236-7
[33]	IPCC. Managing the risks of extreme events and disasters to advance climate change adaptation. A special report of working groups I and II of the intergovernmental panel on climate change [M]. Cambridge: Cambridge University Press, 2012: 582
[34]	Jones B, O'Neill B C, McDaniel L, et al. Future population exposure to US heat extremes[J]. Nature Climate Change, 2015,5(7):652-655. DOI: 10.1038/nclimate2631
[35]	Jones B, Tebaldi C, Brian C, et al. Avoiding population exposure to heat-related extremes: demographic change vs climate change[J]. Climatic Change, 2018,146(3-4):1-15. DOI: 10.1007/s10584-017-2133-7
[36]	Harrington L J, Otto F E L. Changing population dynamics and uneven temperature emergence combine to exacerbate regional exposure to heat extremes under 1.5 ℃ and 2℃of warming[J]. Environmental Research Letters, 2018,13(3):034011. DOI: 10.1088/1748-9326/aaaa99
[37]	Park T-W, Deng Y, Cai M. Feedback attribution of the El Niño-Southern Oscillation: related atmospheric and surface temperature anomalies[J]. Journal of Geophysical Research: Atmospheres, 2012,117(D23), D23101. DOI: 10.1029/2012JD018468