The contribution of intensified urbanization effects on surface warming trends in China
Historical temperature records are often partially biased by the urban heat island (UHI) effect. However, the exact magnitude of these biases is an ongoing, controversial scientific question, especially in regions like China where urbanization has greatly increased in recent decades. Previous studies have mainly used statistical information and selected static population targets, or urban areas in a particular year, to classify urban-rural stations and estimate the influence of urbanization on observed warming trends. However, there is a lack of consideration for the dynamic processes of urbanization. The Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), and Pearl River Delta (PRD) are three major urban agglomerations in China which were selected to investigate the spatiotemporal heterogeneity of urban expansion effects on observed warming trends in this study. Based on remote sensing (RS) data, urban area expansion processes were taken into consideration and the relationship between urban expansion rates and warming trends was investigated using data from 975 meteorological stations throughout China. Although urban areas constitute less than 1% of land in China, more than 90% of the meteorological stations experienced urban land use change and the average urban expansion rate was 0.33%/a. There was also a significant positive relationship between observed warming trends and urban expansion rates. Background warming, without the influence of urbanization and extra warming induced by urbanization processes, was estimated using a linear regression model based on observed warming trends and urban expansion rates. On average, urbanization led to an additional annual warming of 0.034 ± 0.005 °C/10a. This urbanization warming effect was 0.050 ± 0.007 °C/10a for minimum temperatures and 0.008 ± 0.004 °C/10a for maximum temperatures. Moreover, it appeared that urbanization induced greater warming on the minimum temperature during the cold season and maximum temperature during the warm season.
KeywordsRemote sensing Urbanization Surface air temperature Warming Climate change
We would like to acknowledge meteorological station data provider, China National Meteorological Information Center. We also acknowledge the European Space Agency (ESA) for providing the CCI land cover product.
This work was funded by the National Natural Science Foundation of China (Grant # 41675079 and Grant # 41861124005).
- Connolly R, Connolly M (2014a) Urbanization bias I. Is it a negligible problem for global temperature estimates? Open peer rev. J., 28 (Clim. Sci.), ver. 0.2 (non peer reviewed draft). URL: http://oprj.net/articles/climate-science/28
- Connolly R, Connolly M (2014b) Urbanization bias II. An assessment of the NASA GISS urbanization adjustment method, open peer rev. J., 31 (Clim. Sci.), ver. 0.1 (non peer reviewed draft). URL: http://oprj.net/articles/climate-science/31
- Connolly R, Connolly M (2014c) Urbanization bias III. Estimating the extent of bias in the historical climatology network datasets, open peer rev. J., 34 (Clim. Sci.), ver. 0.1 (non peer reviewed draft). URL: http://oprj.net/articles/climate-science/34
- Hartmann DL, Tank AMGK, Rusticucci M (2013) 2013: Observations: atmosphere and surface. In: Climate change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.Google Scholar
- He YT, Jia GS (2012) A dynamic method for quantifying natural warming in urban areas. Atmos Oceanic Sci Lett 5(5):408–413Google Scholar
- Jones PD, Lister DH, Li Q (2008) Urbanization effects in large-scale temperature records, with an emphasis on China. J Geophys Res, 113(D16). doi: https://doi.org/10.1029/2008jd009916
- Li Q, Huang J (2013) Effects of urbanization on extreme warmest night temperatures during summer near Bohai. Acta Meteorol Sin 27(06):808–818Google Scholar
- Lowry WP (1977) Empirical estimation of urban effects on climate - a problem analysis. J Appl Meteorol 16(2):129–135Google Scholar
- McKitrick RR, Michaels PJ (2007) Quantifying the influence of anthropogenic surface processes and inhomogeneities on gridded global climate data. J Geophys Res, 112(D24). doi: https://doi.org/10.1029/2007jd008465
- Ren GY, Zhang AY, Zhou ZYJX, Ren YY, Zhou YQ (2010) Principles and procedures for selecting reference surface air temperature stations in China. Meteorol Sci Technol 38(01):78–85 in ChineseGoogle Scholar
- Ren GY, Li J, Ren YY, Chu ZY, Zhang AY, Zhou YQ, Zhang L, Zhang Y, Bian T (2015) An integrated procedure to determine a reference station network for evaluating and adjusting urban bias in surface air temperature data. J Appl Meteorol Climatol 54(6):1248–1266. https://doi.org/10.1175/jamc-d-14-0295.1 Google Scholar
- Wang H, Shi GY, Zhang XY, Gong SL, Tan SC, Chen B, Li T (2015b) Mesoscale modelling study of the interactions between aerosols and PBL meteorology during a haze episode in China Jing–Jin–Ji and its near surrounding region – part 2: Aerosols’ radiative feedback effects. Atmos Chem Phys 15(6):3277–3287. https://doi.org/10.5194/acp-15-3277-2015 Google Scholar
- Zhang J, Dong W, Wu L, Wei J, Chen P, LEE DK (2005) Impact of land use changes on surface warming in China. Adv Atmos Sci 22:343–348Google Scholar
- Zhang AY, Ren GY, Zhou XJ, Chu ZY, Ren YY, Tang GL (2010) On the urbanization effect on surface air temperature trends over China. Acta Meteorol Sin 68(06):957–966 in ChineseGoogle Scholar