Abstract
Few studies have investigated the spatial patterns of the air temperature urban heat island (AUHI) and its controlling factors. In this study, the data generated by an urban climate model were used to investigate the spatial variations of the AUHI across China and the underlying climate and ecological drivers. A total of 355 urban clusters were used. We performed an attribution analysis of the AUHI to elucidate the mechanisms underlying its formation. The results show that the midday AUHI is negatively correlated with climate wetness (humid: 0.34 K; semi-humid: 0.50 K; semi-arid: 0.73 K). The annual mean midnight AUHI does not show discernible spatial patterns, but is generally stronger than the midday AUHI. The urban–rural difference in convection efficiency is the largest contributor to the midday AUHI in the humid (0.32 ± 0.09 K) and the semi-arid (0.36 ± 0.11 K) climate zones. The release of anthropogenic heat from urban land is the dominant contributor to the midnight AUHI in all three climate zones. The rural vegetation density is the most important driver of the daytime and nighttime AUHI spatial variations. A spatial covariance analysis revealed that this vegetation influence is manifested mainly through its regulation of heat storage in rural land.
摘 要
与地表温度城市热岛相比,当前对气温城市热岛(Air temperature urban heat island, AUHI)的空间变化特征及其影响机制的认知还较为薄弱。本研究基于耦合陆面过程模型CLM5的地球系统模式CESM2模拟的SSP585情景下的气候数据,以中国355个城市为研究对象,对比相同格点内城市次网格与郊区次网格的数据,分析了中国AUHI的空间变化特征,利用发展的AUHI归因分析方法量化了生物物理因子对AUHI的贡献,揭示了气候因子和生态因子对AUHI空间格局的影响机制。结果表明,白天AUHI随着干燥度增加而增强,AUHI在半干旱区最强(0.73±0.13 K),半湿润区次之(0.50±0.14 K),湿润区最弱(0.34±0.14);夜间AUHI虽然强于白天结果,但无明显的空间变化特征。城郊对流效率差异是湿润区(0.32±0.09 K)和半干旱区(0.36±0.11 K)白天AUHI最主要的生物物理贡献因子,城市人为热释放对三个气候区夜间AUHI的贡献最大。郊区叶面积指数是AUHI空间格局的主控因子,它主要通过调节郊区下垫面热量的存储和释放来影响AUHI的空间格局。
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References
Adebayo, Y. R., 1987: A note on the effect of urbanization on temperature in Ibadan. J. Climatol., 7(2), 185–192, https://doi.org/10.1002/joc.3370070209.
Arnfield, A. J., 2003: Two decades of urban climate research: A review of turbulence, exchanges of energy and water, and the urban heat island. International Journal of Climatology, 23(1), 1–26, https://doi.org/10.1002/joc.859.
Azen, R., and D. V. Budescu, 2003: The dominance analysis approach for comparing predictors in multiple regression. Psychological Methods, 8(2), 129–148, https://doi.org/10.1037/1082-989X.8.2.129.
Bergeron, O., and I. B. Strachan, 2012: Wintertime radiation and energy budget along an urbanization gradient in Montreal, Canada. International Journal of Climatology, 32(1), 137–152, https://doi.org/10.1002/joc.2246.
Bounoua, L., and Coauthors, 2015: Impact of urbanization on US surface climate. Environmental Research Letters, 10(8), 084010, https://doi.org/10.1088/1748-9326/10/8/084010.
Budescu, D. V., 1993: Dominance analysis: A new approach to the problem of relative importance of predictors in multiple regression. Psychological Bulletin, 114(3), 542–551, https://doi.org/10.1037/0033-2909.114.3.542.
Cao, C., X. Lee, S. D. Liu, N. Schultz, W. Xiao, M. Zhang, and L. Zhao, 2016: Urban heat islands in China enhanced by haze pollution. Nature Communications, 7, 12509, https://doi.org/10.1038/ncomms12509.
Christen, A., and R. Vogt, 2004: Energy and radiation balance of a central European city. International Journal of Climatology, 24(11), 1395–1421, https://doi.org/10.1002/joc.1074.
Danabasoglu, G., and Coauthors, 2020: The community earth system model version 2 (CESM2). Journal of Advances in Modeling Earth Systems, 12, e2019MS001916, https://doi.org/10.1029/2019MS001916.
Du, H. L., and Coauthors, 2021: Simultaneous investigation of surface and canopy urban heat islands over global cities. ISPRS Journal of Photogrammetry and Remote Sensing, 8, 67–83, https://doi.org/10.1016/jisprsjprs.202L09.003.
Guo, W. D., X. Q. Wang, J. N. Sun, A. J. Ding, and J. Zou, 2016: Comparison of land-atmosphere interaction at different surface types in the mid- to lower reaches of the Yangtze River valley. Atmospheric Chemistry and Physics, 16(15), 9875–9890, https://doi.org/10.5194/acp-16-9875-2016.
He, C. Y., Z. F. Liu, J. G. Wu, X. H. Pan, Z. H. Fang, J. W. Li, and B. A. Bryan, 2021: Future global urban water scarcity and potential solutions. Nature Communications, 2, 4667, https://doi.org/10.1038/s41467-021-25026-3.
Imhoff, M. L., P. Zhang, R. E. Wolfe, and L. Bounoua, 2010: Remote sensing of the urban heat island effect across biomes in the continental USA. Remote Sensing of Environment, 4 (3), 504–513, https://doi.org/10.1016/j.rse.2009.10.008.
Jänicke, B., F. Meier, D. Fenner, U. Fehrenbach, A. Holtmann, and D. Scherer, 2017: Urban-rural differences in near-surface air temperature as resolved by the Central Europe Refined analysis (CER): Sensitivity to planetary boundary layer schemes and urban canopy models. International Journal of Climatology, 37(4), 2063–2079, https://doi.org/10.1002/joc.4835.
Jauregui, E., 1997: Heat island development in Mexico City. Atmos. Environ., 11(22), 3821–3831, https://doi.org/10.1016/S1352-2310(97)00136-2.
Juang, J.-Y., G. Katul, M. Siqueira, P. Stoy, and K. Novick, 2007: Separating the effects of albedo from eco-physiological changes on surface temperature along a successional chronosequence in the southeastern United States. Geophys. Res. Lett., 34, L21408, https://doi.org/10.1029/2007GL031296.
Lawrence, D. M., and Coauthors, 2019a: The Community Land Model version 5: Description of new features, benchmarking, and impact of forcing uncertainty. Journal of Advances in Modeling Earth Systems, 11, 4245–4287, https://doi.org/10.1029/2018MS001583.
Lawrence, D., and Coauthors, 2019b: CLM5 documentation. Technical Report, National Center for Atmospheric Research. [Available online from https://www2.cesm.ucar.edu/models/cesm2/land/CLM50_Tech_Note.pdf]
Li, D., W. L. Liao, A. J. Rigden, X. P. Liu, D. G. Wang, S. Malyshev, and E. Shevliakova, 2019: Urban heat island: Aerodynamics or imperviousness. Science Advances, 5, eaau4299, https://doi.org/10.1126/sciadv.aau4299.
Li, H. D., F. Meier, X. Lee, T. Chakraborty, J. F. Liu, M. Schaap, and S. Sodoudi, 2018: Interaction between urban heat island and urban pollution island during summer in Berlin. Science of the Total Environment, 636, 818–828, https://doi.org/10.1016/j.scitotenv.2018.04.254.
Li, L., and Y. Zha, 2019: Satellite-based spatiotemporal trends of canopy urban heat islands and associated drivers in China’s 32 major cities. Remote Sensing, 11 (1), 102, https://doi.org/10.3390/rs11010102.
Liu, Y., Q. Li, L. Yang, K. K. Mu, M. Y. Zhang, and J. P. Liu, 2020: Urban heat island effects of various urban morphologies under regional climate conditions. Science of the Total Environment, 343, 140589, https://doi.org/10.1016/j.scitotenv.2020.140589.
Manoli, G., S. Fatichi, E. Bou-Zeid, and G. G. Katul, 2020: Seasonal hysteresis of surface urban heat islands. Proceedings of the National Academy of Sciences of the United States of America, 113(13), 7082–7089, https://doi.org/10.1073/pnas.1917554117.
Manoli, G., and Coauthors, 2019: Magnitude of urban heat islands largely explained by climate and population. Nature, 533, 55–60, https://doi.org/10.1038/s41586-019-1512-9.
Oke, T. R., 1981: Canyon geometry and the nocturnal urban heat island: Comparison of scale model and field observations. J. Climatol., 1 (3), 237–254, https://doi.org/10.1002/joc.3370010304.
Oke, T. R., and G. B. Maxwell, 1975: Urban heat island dynamics in Montreal and Vancouver. Atmos. Environ. (1967), 9(2), 191–200, https://doi.org/10.1016/0004-6981(75)90067-0.
Oleson, K. W., and J. Feddema, 2020: Parameterization and surface data improvements and new capabilities for the Community Land Model Urban (CLMU). Journal of Advances in Modeling Earth Systems, 12, e2018MS001586, https://doi.org/10.1029/2018MS001586.
Oleson, K. W., G. B. Bonan, J. Feddema, and T. Jackson, 2011: An examination of urban heat island characteristics in a global climate model. International Journal of Climatology, 31, 1848–1865, https://doi.org/10.1002/joc.2201.
Oliver, S. A., H. R. Oliver, J. S. Wallace, and A. M. Roberts, 1987: Soil heat flux and temperature variation with vegetation, soil type and climate. Agricultural and Forest Meteorology, 32(2–3), 257–269, https://doi.org/10.1016/016168-1923(87)90042-6.
Peng, S. J., Z. L. Feng, H. X. Liao, B. Huang, S. L. Peng, and T. Zhou, 2019: Spatial-temporal pattern of, and driving forces for, urban heat island in China. Ecological Indicators, 96, 127–132, https://doi.org/10.1016/j.ecolind.2018.08.059.
Peng, S. S., and Coauthors, 2012: Surface urban heat island across 419 global big cities. Environ. Sci. Technol., 46(2), 696–703, https://doi.org/10.1021/es2030438.
Qian, Y., and Coauthors, 2022: Urbanization impact on regional climate and extreme weather: Current understanding, uncertainties, and future research directions. Adv. Atmos. Sci., 39, 819–860, https://doi.org/10.1007/s00376-021-1371-9.
Rigden, A. J., and D. Li, 2017: Attribution of surface temperature anomalies induced by land use and land cover changes. Geophys. Res. Lett., 44, 6814–6822, https://doi.org/10.1002/2017GL073811.
Roth, M., 2007: Review of urban climate research in (sub)tropical regions. International Journal of Climatology, 27(14), 1859–1873, https://doi.org/10.1002/joc.1591.
Smoliak, B. V., P. K. Snyder, T. E. Twine, P. M. Mykleby, and W. F. Hertel, 2015: Dense network observations of the Twin Cities canopy-layer urban heat island. J. Appl. Meteorol. Climatol., 54(9), 1899–1917, https://doi.org/10.1175/JAMC-D-14-0239.1.
Swenson, S. C., S. P. Burns, and D. M. Lawrence, 2019: The impact of biomass heat storage on the canopy energy balance and atmospheric stability in the community land model. Journal of Advances in Modeling Earth Systems, 11, 83–98, https://doi.org/10.1029/2018MS001476.
Venter, Z. S., T. Chakraborty, and X. Lee, 2021: Crowdsourced air temperatures contrast satellite measures of the urban heat island and its mechanisms. Science Advances, 2, eabb9569, https://doi.org/10.1126/sciadv.abb9569.
Wang, L., and D. Li, 2021: Urban heat islands during heat waves: A comparative study between Boston and Phoenix. J. Appl. Meteorol. Climatol., 60(5), 621–641, https://doi.org/10.1175/JAMC-D-20-0132.1.
Wang, L. L., Z. Q. Gao, S. G. Miao, X. F. Guo, T. Sun, M. F. Liu, and D. Li, 2015: Contrasting characteristics of the surface energy balance between the urban and rural areas of Beijing. Adv. Atmos. Sci., 32, 505–514, https://doi.org/10.1007/s00376-014-3222-4.
Wang, L. M., F. Q. Tian, X. F. Wang, Y. Z. Yang, and Z. W. Wei, 2020: Attribution of the land surface temperature response to land-use conversions from bare land. Global and Planetary Change, 123, 103268, https://doi.org/10.1016/j.gloplacha.2020.103268.
Wang, S. P., Z. H. Wang, Y. C. Zhang, and Y. F. Fan, 2022: Characteristics of urban heat island in China and its influences on building energy consumption. Applied Sciences, 13(15), 7678, https://doi.org/10.3390/app12157678.
Zhang, K. E., and Coauthors, 2023: A global dataset on subgrid land surface climate (2015–2100) from the Community Earth System Model. Geoscience Data Journal, 16(2), 208–219, https://doi.org/10.1002/gdj3.153.
Zhang, N., Z. Q. Gao, X. M. Wang, and Y. Chen, 2010: Modeling the impact of urbanization on the local and regional climate in Yangtze River Delta, China. Theor. Appl. Climatol., 163(3–4), 331–342, https://doi.org/10.1007/s00704-010-0263-1.
Zhao, L., and Coauthors, 2021: Global multi-model projections of local urban climates. Nature Climate Change, 11, 152–157, https://doi.org/10.1038/s41558-020-00958-8.
Zhao, L. X., X. Lee, R. B. Smith, and K. Oleson, 2014: Strong contributions of local background climate to urban heat islands. Nature, 511, 216–219, https://doi.org/10.1038/nature13462.
Zhou, D. C., S. L. Sun, Y. Li, L. X. Zhang, and L. Huang, 2023: A multi-perspective study of atmospheric urban heat island effect in China based on national meteorological observations: Facts and uncertainties. Science of the Total Environment, 854, 158638, https://doi.org/10.1016/j.scitotenv.2022.158638.
Ziter, C. D., E. J. Pedersen, C. J. Kucharik, and M. G. Turner, 2019: Scale-dependent interactions between tree canopy cover and impervious surfaces reduce daytime urban heat during summer. Proceedings of the National Academy of Sciences of the United States of America, 116(15), 7575–7580, https://doi.org/10.1073/pnas.1817561116.
Acknowledgements
This research was supported by the National Key R&D Program of China (Grant No. 2019YFA0607202), the National Natural Science Foundation of China (Grant Nos. 42021004 and 42005143). Heng LYU acknowledges support by the Postgraduate Research & Practice Innovation Program of Jiangsu Province (Grant No. KYCX21_0978). Wei WANG acknowledges support by the Open Research Fund Program of the Key Laboratory of Urban Meteorology, China Meteorological Administration (Grant No. LUM-2023-12), and the 333 Project of Jiangsu Province (Grant No. BRA2022023). We thank Long LI, Yan LIU, Decheng ZHOU, Yifan FAN and Shijia PENG for providing the data for Fig. 3.
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• The annual mean midday AUHI is negatively correlated with the precipitation gradient across China.
• The urban–rural difference in convection efficiency is the largest contributor to the midday AUHI in humid and semi-arid climate zones.
• The rural LAI is the most important driver of the AUHI spatial variations, mainly via its regulation of heat storage in rural land.
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Lyu, H., Wang, W., Zhang, K. et al. Factors Influencing the Spatial Variability of Air Temperature Urban Heat Island Intensity in Chinese Cities. Adv. Atmos. Sci. 41, 817–829 (2024). https://doi.org/10.1007/s00376-023-3012-y
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DOI: https://doi.org/10.1007/s00376-023-3012-y