Abstract
Projecting the future extreme high-temperature risk under the background of global warming and urbanization is essential to the collaborative development of Beijing, Tianjin, and Hebei (BTH). In this study, based on the global climate simulation data from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and the fine land-cover data, we use the Weather Research and Forecast (WRF) model coupled with the building effect parameterization (BEP) and building energy model (BEM) at 3-km grid spacing to project the changes in the intensity, frequency, and risk of extreme high temperature over the BTH urban agglomeration. The results show that under the future shared socioeconomic pathway scenario (SSP245), the average extreme high-temperature intensity (EHI) in the BTH will increase by 0.71 °C and 2.12 °C in the middle and late twenty-first century, respectively, compared with that in the reference period (2005–2014), which are 0.23 ℃ and 0.58 ℃ more than that only considering global warming, respectively. The average extreme high-temperature frequency (EHF) will increase by 99 h and 200 h, 53 h and 72 h more than that considering only climate change, respectively. The average high-temperature risk in the BTH for 20-year and 50-year return periods will increase by 1.9 times and 2.4 times in the middle twenty-first century, respectively, and expand to 8.0 times and 12.9 times in the late twenty-first century, respectively. Therefore, it is necessary to take adaptation approaches to reduce the future risk of extreme high-temperature events in the BTH.
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20 July 2023
A Correction to this paper has been published: https://doi.org/10.1007/s00704-023-04559-1
References
Ban N, Rajczak J, Schmidli J, Schär C (2020) Analysis of Alpine precipitation extremes using generalized extreme value theory in convection-resolving climate simulations. Clim Dyn 55(1):61–75. https://doi.org/10.1007/s00382-018-4339-4
Chen F, Kusaka AH, Bornstein BR (2011) The integrated WRF/urban modelling system: development, evaluation, and applications to urban environmental problems. Int J Climatol 31(2):273–288. https://doi.org/10.1002/joc.2158
Coles S (2001) An introduction to statistical modeling of extreme values. Springer, Berlin
Du WP, Quan WJ, Xuan CY (2014) The study of high temperature disaster risk zoning in Beijing-Tianjing-Hebei urban agglomeration. J Nanjing Univ 50(6):829–837. https://doi.org/10.13232/j.cnki.jnju.2014.06.011
Hajat S, Kosatky T (2010) Heat-related mortality: a review and exploration of heterogeneity. J Epidemiol Community Health 64(9):753–760. https://doi.org/10.1136/jech.2009.087999
Han ZY, Shi Y, Wu J, Xu Y (2019) Combined dynamical and statistical downscaling for high-resolution projections of multiple climate variables in the Beijing–Tianjin–Hebei region of China. J Appl Meteorol Climatol 58:2387–2403. https://doi.org/10.1175/JAMC-D-19-0050.1
Hersbach H, Bell B, Berrisford P, Hirahara S, Thépaut J (2020) The ERA5 global reanalysis. Quart J Roy Meteor Soc 146:1999–2049. https://doi.org/10.1002/qj.3803
Hondula DM, Georgescu M, Balling RC (2014) Challenges associated with projecting urbanization- induced heat- related mortality. Sci Total Environ 490:538–544. https://doi.org/10.1289/isee.2014.O-021
Hosking JRM (1990) Analysis and estimation of distributions using linear combinations of order statistics. J Roy Stat Soc: Ser B 52(1):105–124. https://doi.org/10.1111/j.2517-6161.1990.tb01775.x
Iacono MJ, Delamere JS, Mlawer EJ (2008) Radiative forcing by long-lived greenhouse gases: calculations with the AER radiative transfer models. J Geophys Res 113:D13103. https://doi.org/10.1029/2008JD009944
IPCC (2014) Managing the risks of extreme events and disasters to advance climate change adaptation: special report of the intergovernmental panel on climate change. Cambridge: Cambridge University Press. https://doi.org/10.1017/CBO9781139177245
IPCC(2021) Climate change 2021: the physical science basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, United Kingdom and New York, NY, USA: Cambridge University Press. https://doi.org/10.1017/9781009157896
Janjić ZI (1994) The step-mountain eta coordinate model: further developments of the convection, viscous sublayer, and turbulence closure schemes. Mon Wea Rev 122(5):927–945. https://doi.org/10.1175/1520-0493(1994)122%3c0927:TSMECM%3e2.0.CO;2
Jiang XF, Jiang ZH, Li W (2020) Risk estimation of extreme high temperature in eastern China under 1. 5 and 2 ℃ global warming. Trans Atmos Sci 43(6):1056–1064. https://doi.org/10.13878/j.cnki.dqkxxb.20201011001
Kusaka H, Kimura F (2004) Thermal effects of urban canyon structure on the nocturnal heat island: numerical experiment using a mesoscale model coupled with an urban canopy model. J Appl Meteor 43(12):1899–1910. https://doi.org/10.1175/jam2169.1
Kusaka H, Hara M, Takane Y (2012) Urban climate projection by the WRF model at 3-km horizontal grid increment: dynamical downscaling and predicting heat stress in the 2070’s August for Tokyo, Osaka, and Nagoya Metropolises. J Meteorol Soc Jpn 90B:47–63. https://doi.org/10.2151/jmsj.2012-B04
Li D, Bou-Zeid E (2013) Synergistic interactions between urban heat islands and heat waves: the impact in cities is larger than the sum of its parts. J Appl Meteorol Climatol 52(9):2051–2064
Li W, Jiang Z, Zhang X, Li L (2018) On the emergence of anthropogenic signal in extreme precipitation change over China. Geophys Res Lett 45(17):9179–9185. https://doi.org/10.1029/2018GL079133
Li RK, Han ZY, Xu Y (2020) 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. Clim Change Res 16(4):491–504. https://doi.org/10.12006/j.issn.1673-1719.2019.111
Liu Y, Shi CX, Wang HJ (2021) Applicability assessment of CLDAS temperature data in China. Trans Atmos Sci. 44(4):540–548. https://doi.org/10.13878/j.cnki.dqkxxb.20200819001
Luan GJ, Li TT, Yin P, Zhou MG (2015) Heat wave impact on mortality in Beijing in 2010. J Environ Hyg 5(6):525–529. https://doi.org/10.13421/j.cnki.hjwsxzz.2015.06.008
Mahmood R, Pielke RA, Hubbard KG, Niyogi D et al (2014) Land cover changes and their biogeophysical effects on climate. Int J Climatol 34:929–953. https://doi.org/10.1002/joc.3736
Meehl GA, Tebaldi C (2004) More intense, more frequent, and longer lasting heat waves in the 21st century. Science 305:994–997. https://doi.org/10.1126/science.1098704
Niu YL, Yang J, Lin HL (2022) Additional effects of high temperature and heat wave on death of residents in Beijing. Public Health China 38(3):7. https://doi.org/10.11847/zgggws1134217
Quan JP, Xue YK, Duan QY (2021) Numerical investigation and uncertainty analysis of eastern China’s large-scale urbanization effect on regional climate. J Meteor Res 35(6):1023–1040. https://doi.org/10.1007/s13351-021-1033-y
Ren ZH, Yu Y, Zhou FL (2012) Quality detection of surface historical basic meteorological data. J Appl Meteorol Sci 23:739–747. https://doi.org/10.1007/s11783-011-0280-z
Russo S, Dosio A, Graversen RG et al (2014) Magnitude of extreme heat waves in present climate and their projection in a warming world. J Geophys Res: Atmos 119(12):500–512. https://doi.org/10.1002/2014JD022098
Salamanca F, Krpo A, Martilli A (2010) A new building energy model coupled with an urban canopy parameterization for urban climate simulations-part I: formulation, verification, and sensitivity analysis of the model. Theor Appl Climatol 99:331–344. https://doi.org/10.1007/s00704-009-0142-9
Santamouris M (2014) Cooling the cities - a review of reflective and green roof mitigation technologies to fight heat island and improve comfort in urban environments. Sol Energy 103:682–703. https://doi.org/10.1016/j.solener.2012.07.003
Shi Y, Han ZY, Xu Y (2019) Future changes of climate extremes in Xiongan new area and Jing-Jin-Ji district based on high resolution (6.25 km) combined statistical and dynamical downscaling datasets. Clim Change Res 15(2):140–149. https://doi.org/10.12006/j.issn,1673-1719.2018.153
Shi C X, Xie ZH, Qian H, et al. (2011) China land soil moisture EnKF data assimilation based on satellite remote sensing data. Sci China Earth Sci 54:1430–1440. https://doi.org/10.1007/s11430-010-4160-3
Stein U, Alpert P (1993) Factor separation in numerical simulations. J Atmos Sci 50(14):2107–2115. https://doi.org/10.1175/1520-0469(1993)050%3c2107:FSINS%3e2.0.CO;2
Sun Y, Zhang XB, Zwiers FW et al (2014) Rapid increase in the risk of extreme summer heat in eastern China. Nat Clim Change 4:1082–1085. https://doi.org/10.1038/nclimate2410
Sun Y, Zhang XB, Ren GY et al (2016) Contribution of urbanization to warming in China. Nat Clim Change 6:706–709. https://doi.org/10.1038/nclimate2956
Sun Y, Hu T, Zhang XB (2018) Substantial Increase in heat wave risks in china in a future warmer world. Earth’s Future 6(11):1528–1538. https://doi.org/10.1029/2018EF000963
Tang J, Niu X, Wang S, Gao H, Wang X, Wu J (2016) Statistical downscaling and dynamical downscaling of regional climate in China: present climate evaluations and future climate projections. J Geophys Res Atmos 121:2110–2129. https://doi.org/10.1002/2015JD023977
Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of CMIP5 and the experiment design. Bull Am Meteorol Soc 93:485–498. https://doi.org/10.1175/BAMS-D-11-00094.1
Tewari M, Chen F, Wang W, Dudhia J, LeMone MA, Mitchell K, Ek M, Gayno G, Wegiel J, Cuenca RH (2004) Implementation and verification of the unified NOAH land surface model in the WRF model. 20th conference on weather analysis and forecasting/16th conference on numerical weather prediction, 11–15.
Thompson G, Field PR, Rasmussen RM (2008) Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part II: implementation of a new snow parameterization. Mon Wea Rev 136(12):5095–5115. https://doi.org/10.1175/2008MWR2387.1
Tiedtke M (1989) A comprehensive mass flux scheme for cumulus parameterization in large-scale models. Mon Wea Rev 117(8):1779–1800. https://doi.org/10.1175/1520-0493(1989)117%3c1779:ACMFSF%3e2.0.CO;2
Tong RZ, Sun WC, Han Q, Yu JH, Tian ZF (2020) Spatial and temporal variations in extreme precipitation and temperature events in the Beijing–Tianjin–Hebei region of China over the past six decades. Sustainability 12:1415. https://doi.org/10.3390/su12041415
Wang J, Huang B, Fu DJ, Peter M, Zhang XZ (2016) Response of urban heat island to future urban expansion over the Beijing-Tianjin-Hebei metropolitan area. Appl Geogr 70:26–36. https://doi.org/10.1016/j.apgeog.2016.02.010
Wang H, Xiao DP, Zhao YX (2021b) Evaluation and projection of extreme temperature indices in the North China plain based on CMIP6 models. Geogr Geo-Information Sci 37(5):86–94. https://doi.org/10.3969/j.issn.1672-0504.2021.05.012
Wang YJ, Xiang Y, Lu B (2021c) Parameter optimization of multi-layer urban canopy model and simulation of extreme high temperature in Beijing-Tianjin-Hebei urban agglomeration. Clim Environ Res 26(6):1–15. https://doi.org/10.3878/j.issn.1006-9585.2021.20161
Wang YJ, Xiang Y, Song LC, Liang XZ (2022) Quantifying the contribution of urbanization to summer extreme high-temperature events in the Beijing–Tianjin–Hebei urban agglomeration. J Appl Meteorol Climatol 61:669–683. https://doi.org/10.1175/JAMC-D-21-0201.1
Wang YJ, Ren YY, Song LC, Xiang Y (2021a) Responses of extreme high temperatures to urbanization in the Beijing-Tianjin-Hebei urban agglomeration in the context of a changing climate. Meteor Appl. 28. https://doi.org/10.1002/met.2024.
Wu J, Gao XJ, Xu Y (2018) Climate change projection over Xiong’an District and its adjacent areas: an ensemble of RegCM4 simulations. Chin J Atmos Sci 42(3):696–705. https://doi.org/10.3878/j.issn.1006-9895.1712.17244
Xiao RB, Ouyang ZY, Zheng H et al (2007) (2007) Spatial pattern of impervious surfaces and their impacts on land surface temperature in Beijing. China J Environ Sci 19(2):250–256. https://doi.org/10.1016/S1001-0742(07)60041-2
Xu Y, Wu J, Shi T (2015) Change in extreme climate events over China based on CMIP5. Atmos Ocean Sci Lett 8:185–192. https://doi.org/10.3878/AOSL20150006
Xu Z, Han Y, Tam CY, Yang ZL, Fu C (2021) Bias-corrected CMIP6 global dataset for dynamical downscaling of the historical and future climate (1979–2100). Sci Data 8:293. https://doi.org/10.1038/s41597-021-01079-3
Xu XY, Liu LJ, Zhang SW, et al. (2018) China multi-period land use land cover remote sensing monitoring data set (CNLUCC). The Data Registration and Publishing System of the Resource and Environmental Science Data Center of the Chinese Academy of Sciences, http://www.resdc.cn/DOI . Accessed 2 July 2018
Zhai PM, Yuan Y, Yu R, Guo J (2018) Climate change and sustainable development for cities. Chin Sci Bull 64:1995–2001. https://doi.org/10.1360/N972018-00911
Zhang XB, Zwiers FW, Li G (2004) Monte Carlo experiments on the detection of trends in extreme values. J Climate 17(10):1945–1952. https://doi.org/10.1175/1520-0442(2004)017%3c1945:mceotd%3e2.0.co;2
Zhang C, Wang Y, Hamilton K (2011) Improved representation of boundary layer clouds over the southeast Pacific in ARW-WRF using a modified Tiedtke cumulus parameterization scheme. Mon Wea Rev 139:3489–4351. https://doi.org/10.1175/MWR-D-10-05091.1
Zhang Y, Huang G, Wang X, Liu Z (2017) Observed changes in temperature extremes for the Beijing–Tianjin–Hebei region of China. Meteor Appl 24:74–83. https://doi.org/10.1002/met.1606
Zhang GW, Zeng G, Yang XY, Jiang ZH (2021) Future changes in extreme high temperature over China at 1.5℃-5℃ global warming based on CMIP6 simulations. Adv Atmos Sci 38(2):253–267. https://doi.org/10.1007/s00376-020-0182-8
Zhao L, Oleson K, Bou-Zeid E (2021) Global multi-model projections of local urban climates. Nat Clim Chang 11:152–157. https://doi.org/10.1038/s41558-020-00958-8
Zheng ZY, Dong WJ, Yan DD et al (2021) Relative contributions of urbanization and greenhouse gases concentration on future climate over Beijing–Tianjin–Hebei region in China. Clim Dyn. https://doi.org/10.1007/s00382-021-05952-0
Zhou TJ, Yu RC (2006) Twentieth century surface air temperature over China and the globe simulated by coupled climate models. J Clim 19:5843–5858. https://doi.org/10.1175/JCLI3952.1
Zhou D, Zhang L, Hao L (2016) Spatiotemporal trends of urban heat island effect along the urban development intensity gradient in China. Sci Total Environ 544:617–626
Zonato A, Martilli A and Gutierrez E (2021) Exploring the effects of rooftop mitigation strategies on urban temperatures and energy consumption. D Atmospheres: JGR, 126–21. https://doi.org/10.1002/essoar.10506675.1.
Acknowledgements
This work has been supported by the National Natural Science Foundation of China (42075023). We thank the editor and two anonymous reviewers who all made very valuable suggestions for improvement of this manuscript.
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This work was funded by the National Natural Science Foundation of China (Grant No. 42075023).
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YJ Wang and Y Xiang contributed to conception and design of the study. YJ Wang wrote the first draft of the article. Y Xiang and ZY Han performed the statistical analysis. YJ Wang, Y Xiang, and LC Song revised the article. All authors contributed to manuscript revision, read, and approved the submitted version.
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Wang, Y., Xiang, Y., Han, Z. et al. Future extreme high-temperature risk in the Beijing-Tianjin-Hebei urban agglomeration of China based on a regional climate model coupled with urban parameterization scheme. Theor Appl Climatol 153, 621–634 (2023). https://doi.org/10.1007/s00704-023-04481-6
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DOI: https://doi.org/10.1007/s00704-023-04481-6