• Cao, W., Q. Q. Shao, J. Y. Liu, and Z. W. Hu, 2013: Impact of land use/land cover and its change on climate warming in Beijing area (in Chinese). Climate Environ. Res., 18, 451460.

    • Search Google Scholar
    • Export Citation
  • Chen, F., and J. Dudhia, 2001: Coupling an advanced land surface–hydrology model with the Penn State–NCAR MM5 modeling system. Part I: Model description and implementation. Mon. Wea. Rev., 129, 569585, doi:10.1175/1520-0493(2001)129<0569:CAALSH>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, F., M. Tewari, H. Kusaka, and T. T. Warner, 2006: Current status of urban modeling in the community Weather Research and Forecast (WRF) Model. Sixth Symp. on the Urban Environment, Atlanta, GA, Amer. Meteor. Soc., J1.4. [Available online at https://ams.confex.com/ams/Annual2006/techprogram/paper_98678.htm.]

  • Ding, H. Y., Z. F. Zheng, and W. D. Liu, 2010: Warming trend and seasonal variation in Beijing during 1951–2008 (in Chinese). Adv. Climate Change Res., 6, 187191.

    • Search Google Scholar
    • Export Citation
  • Gallo, K., T. Owen, and D. Easterling, 1999: Temperature trends of the U.S. Historical Climatology Network based on satellite-designated land use/land cover. J. Climate, 12, 13441348, doi:10.1175/1520-0442(1999)012<1344:TTOTUS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Guo, Y.-R., and S. Chen, 1994: Terrain and land use for the fifth-generation Penn State/NCAR Mesoscale Modeling System (MM5): Program TERRAIN. NCAR Tech. Note NCAR/TN-397+IA, 114 pp., doi:10.5065/D68C9T67.

    • Crossref
    • Export Citation
  • He, Y. T., G. S. Jia, Y. H. Hu, and Z. J. Zhou, 2013: Detecting urban warming signals in climate records. Adv. Atmos. Sci., 30, 11431153, doi:10.1007/s00376-012-2135-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hu, Y., G. Jia, C. Pohl, Q. Feng, Y. He, H. Gao, R. Xu, J. van Genderen, and J. Feng, 2015: Improved monitoring of urbanization processes in China for regional climate impact assessment. Environ. Earth Sci., 73, 83878404, doi:10.1007/s12665-014-4000-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hu, Y.-C., W. Dong, and Y. He, 2010: Impact of land surface forcings on mean and extreme temperature in eastern China. J. Geophys. Res., 115, D19117, doi:10.1029/2009JD013368.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ichinose, T., K. Shimodozono, and K. Hanaki, 1999: Impact of anthropogenic heat on urban climate in Tokyo. Atmos. Environ., 33, 38973909, doi:10.1016/S1352-2310(99)00132-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • IPCC, 2013: Climate Change 2013: The Physical Science Basis. Cambridge University Press, 1535 pp., doi:10.1017/CBO9781107415324.

    • Crossref
    • Export Citation
  • Jia, G., R. Xu, Y. Hu, and Y. He, 2015: Multi-scale remote sensing estimates of urban fractions and road widths for regional models. Climatic Change, 129, 543554, doi:10.1007/s10584-014-1114-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jones, P. D., D. H. Lister, and Q. Li, 2008: Urbanization effects in large-scale temperature records with an emphasis on China. J. Geophys. Res., 113, D16122, doi:10.1029/2008JD009916.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jones, P. D., D. H. Lister, T. J. Osborn, C. Harpham, M. Salmon, and C. P. Morice, 2012: Hemispheric and large-scale land surface air temperature variations: An extensive revision and an update to 2010. J. Geophys. Res., 117, D05127, doi:10.1029/2011JD017139.

    • Search Google Scholar
    • Export Citation
  • Kalnay, E., and M. Cai, 2003: Impact of urbanization and land-use change on climate. Nature, 423, 528531, doi:10.1038/nature01675.

  • Kanamitsu, M., W. Ebisuzaki, J. Woollen, S.-K. Yang, J. J. Hnilo, M. Fiorino, and G. L. Potter, 2002: NCEP–DOE AMIP-II reanalysis (R-2). Bull. Amer. Meteor. Soc., 83, 16311643, doi:10.1175/BAMS-83-11-1631.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, Q. X., W. Li, P. Si, X. R. Gao, W. J. Dong, P. D. Jones, J. Y. Huang, and L. J. Cao, 2010: Assessment of surface air warming in northeast China, with emphasis on the impacts of urbanization. Theor. Appl. Climatol., 99, 469478, doi:10.1007/s00704-009-0155-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, J. Y., M. L. Liu, H. Q. Tian, D. F. Zhuang, Z. X. Zhang, W. Zhang, X. M. Tang, and X. Z. Deng, 2005: Spatial and temporal patterns of China’s cropland during 1990–2000: An analysis based on Landsat TM data. Remote Sens. Environ., 98, 442456, doi:10.1016/j.rse.2005.08.012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, W. D., B. Z. Zhang, H. L. You, and P. Yang, 2014: Preliminary analysis of urbanization effects on temperature change in Beijing during 1978–2008 (in Chinese). Meteor. Mon., 40, 94100.

    • Search Google Scholar
    • Export Citation
  • Ma, X. M., R. K. Li, K. Luo, R. M. Zhang, Z. S. Wang, and Q. B. Xu, 2016: Association between temperature and mortality in three cities in China (in Chinese). Basic Clin. Med., 36, 805810.

    • Search Google Scholar
    • Export Citation
  • Matsuura, K., and C. J. Willmott, 2012: Terrestrial air temperature: 1900–2010 gridded monthly time series (version 3.01). University of Delaware Dept. of Geography Tech. Doc. [Available online at http://climate.geog.udel.edu/~climate/html_pages/Global2011/README.GlobalTsT2011.html.]

  • Meng, Z. Y., and D. Yao, 2014: Damage survey, radar, and environment analyses on the first-ever documented tornado in Beijing during the heavy rainfall event of 21 July 2012. Wea. Forecasting, 29, 702724, doi:10.1175/WAF-D-13-00052.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ren, G. Y., Y. Zhou, Z. Chu, J. Zhou, A. Zhang, J. Guo, and X. Liu, 2008: Urbanization effects on observed surface air temperature trends in north China. J. Climate, 21, 13331348, doi:10.1175/2007JCLI1348.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shem, W., and M. Shepherd, 2009: On the impact of urbanization on summertime thunderstorms in Atlanta: Two numerical model case studies. Atmos. Res., 92, 172189, doi:10.1016/j.atmosres.2008.09.013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., and Coauthors, 2008: A description of the Advanced Research WRF version 3. NCAR Tech. Note NCAR/TN-475+STR, 113 pp., doi:10.5065/D68S4MVH.

    • Crossref
    • Export Citation
  • Thielen, J., W. Wobrock, A. Gadian, P. G. Mestayer, and J.-D. Creutin, 2000: The possible influence of urban surfaces on rainfall development: A sensitivity study in 2D in the meso-γ-scale. Atmos. Res., 54, 1539, doi:10.1016/S0169-8095(00)00041-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yan, Z. W., Z. Li, Q. X. Li, and P. D. Jones, 2010: Effects of site change and urbanization in the Beijing temperature series 1977–2006. Int. J. Climatol., 30, 12261234, doi:10.1002/joc.1971.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yan, Z. W., J. Wang, Z. Li, and W. D. Liu, 2014: Effects of urbanization based on homogenized daily observations (in Chinese). Adv. Meteor. Sci. Tech., 4, 4148.

    • Search Google Scholar
    • Export Citation
  • Yang, X. C., Y. L. Hou, and B. D. Chen, 2011: Observed surface warming induced by urbanization in east China. J. Geophys. Res., 116, D14113, doi:10.1029/2010JD015452.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, L., G. Y. Ren, J. Liu, Y. Q. Zhou, Y. Y. Ren, A. Y. Zhang, and Y. W. Feng, 2011: Urban effect on trends of extreme temperature indices at Beijing meteorological station (in Chinese). Chin. J. Geophys., 54, 11501159.

    • Search Google Scholar
    • Export Citation
  • Zhao, D. M., and J. Wu, 2017: The influence of urban surface expansion in China on regional climate. J. Climate, 30, 10611080, doi:10.1175/JCLI-D-15-0604.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhou, J. P., Z. B. Sun, D. H. Ni, and Z. X. Li, 2013: Impact of meteorological station relocation on homogeneity of annual mean temperature in China (in Chinese). Trans. Atmos. Sci., 36, 139146.

    • Search Google Scholar
    • Export Citation
  • Zhou, L., R. E. Dickinson, Y. Tian, J. Fang, Q. Li, R. K. Kaufmann, C. J. Tucker, and R. B. Myneli, 2004: Evidence for a significant urbanization effect on climate in China. Proc. Natl. Acad. Sci. USA, 101, 95409544, doi:10.1073/pnas.0400357101.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhuang, B. L., and Coauthors, 2014: Optical properties and radiative forcing of urban aerosols in Nanjing, China. Atmos. Environ., 83, 4352, doi:10.1016/j.atmosenv.2013.10.052.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • View in gallery

    (a) Model domain (black outline) and terrain elevation (m) with nested domains, all in China [white squares labeled Beijing–Tianjin–Hebei (BTH), Yangtze River delta (YRD), and Pearl River delta (PRD)], (b) terrain elevation in BTH (m), including the city of Beijing, and (c) terrain elevation over Beijing (m). Also shown are spatial distributions for different land-use categories in Beijing under (d) U1980, (e) U1990, (f) U2000, (g) U2010, and (h) U2016, with red color denoting grid cells with urban surface. The land-use categories given in the key are 1) evergreen needleleaf forests, 2) deciduous broadleaf forests, 3) mixed forests, 4) closed shrublands, 5) open shrublands, 6) woody savannas, 7) grasslands, 8) croplands, 9) urban and built-up areas, 10) cropland/natural vegetation mosaics, 11) barren or sparsely vegetated areas, and 12) water.

  • View in gallery

    Spatial distributions of the surface air temperature for the observed data [(a) CRU and (b) UDEL] and simulated results [(c) EX1 and (d) EX2] between 1980 and 2014.

  • View in gallery

    Spatial distributions of annual averaged urban-related warming (°C) (a) between 1980 and 1989, (b) between 1990 and 1999, (c) between 2000 and 2009, (d) between 2010 and 2016, and (e) between 1980 and 2016 in Beijing [(a), (b), and (c) depict 10-yr averages; (d) shows 7-yr averages; (e) shows 37-yr averages; the shaded areas passed the 90% confidence-level t test].

  • View in gallery

    Annual variation in urban-related warming between 1980 and 2016.

  • View in gallery

    Changes (between EX1 and EX2) in the PDF for the daily SAT maximum/minimum (Tmax/Tmin), daily mean SAT (Tmean), and DTR in the summer across (a) the whole of Beijing, (b) the plains areas of Beijing, (c) U2U, and (d) N2U.

  • View in gallery

    Time series of annual averages in surface air temperature and the trends for EX1 and EX2 for (a) the whole of Beijing, (b) the plains areas of Beijing, (c) U2U, and (d) N2U.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 104 34 4
PDF Downloads 79 38 2

Contribution of Urban Surface Expansion to Regional Warming in Beijing, China

View More View Less
  • 1 Chinese Academy of Sciences Key Laboratory of Regional Climate-Environment for Temperate East Asia, Institute of Atmospheric Physics, Beijing, China
  • | 2 Department of Atmospheric Science, Yunnan University, Kunming, China
Full access

Abstract

The contribution of urban surface expansion to regional warming as detected from meteorological observational station data may vary with considerable uncertainty because of the spatial heterogeneity of such data—a situation that promotes a requirement for numerical model-based investigations. Satellite-based images from 1980 to 2016 that have fine resolution over three city clusters and that display the urban surface expansion in China from rapid economic development and anthropogenic activity were used to perform 37-yr nested dynamical downscaling using the Weather Research and Forecasting (WRF) Model. The urban surface areas in Beijing, China, expressed marked expansion in the last 37 years. The contribution of urban surface expansion to regional warming was approximately 22% of the overall warming in Beijing and was stronger in the plains areas of Beijing (42%). The contributions to land-use grids that changed from nonurban (in 1980) to urban (in 2016; N2U) were much stronger than those to grids that were classified as urban in both time periods (U2U), which were closer to the values of urban areas (including N2U and U2U) because of the intense increase in urban surface areas. Urban-related warming expressed marked annual variation and was greater in the warm seasons and smaller in the cold seasons. The greater increase in surface air temperature (SAT) minimum and the weaker SAT maximum accounted for the decreased diurnal temperature range.

Denotes content that is immediately available upon publication as open access.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Deming Zhao, zhaodm@tea.ac.cn

Abstract

The contribution of urban surface expansion to regional warming as detected from meteorological observational station data may vary with considerable uncertainty because of the spatial heterogeneity of such data—a situation that promotes a requirement for numerical model-based investigations. Satellite-based images from 1980 to 2016 that have fine resolution over three city clusters and that display the urban surface expansion in China from rapid economic development and anthropogenic activity were used to perform 37-yr nested dynamical downscaling using the Weather Research and Forecasting (WRF) Model. The urban surface areas in Beijing, China, expressed marked expansion in the last 37 years. The contribution of urban surface expansion to regional warming was approximately 22% of the overall warming in Beijing and was stronger in the plains areas of Beijing (42%). The contributions to land-use grids that changed from nonurban (in 1980) to urban (in 2016; N2U) were much stronger than those to grids that were classified as urban in both time periods (U2U), which were closer to the values of urban areas (including N2U and U2U) because of the intense increase in urban surface areas. Urban-related warming expressed marked annual variation and was greater in the warm seasons and smaller in the cold seasons. The greater increase in surface air temperature (SAT) minimum and the weaker SAT maximum accounted for the decreased diurnal temperature range.

Denotes content that is immediately available upon publication as open access.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Deming Zhao, zhaodm@tea.ac.cn

1. Introduction

Beijing, the capital of China, is located in one of the three city clusters in eastern China that have high economic vitality, and it has faced several climatic and environmental problems in the past several decades (Ma et al. 2016; Meng and Yao 2014). With the rapid economic development and population explosion, marked urban surface expansion occurred in Beijing between the 1980s and 2010s. The evaluation of the effect on the regional climate, especially on the land surface air temperature at 2 m (SAT) that is highly connected to the daily lives and activities of humans, is of great importance.

The contribution of land-use changes to the globally averaged SAT trend is unlikely to be more than 10%, but the effect on the regional scale in an area with rapid economic development and intense anthropogenic activities may be greater (IPCC 2013). With the rapid development in China, particularly in the eastern region in the 1980s–2010s, urban surface areas have greatly increased (Liu et al. 2005), and many studies using different methods have been performed to detect the impact of urbanization on SAT (Jones et al. 2008; Li et al. 2010; Ren et al. 2008; Yan et al. 2014); they revealed contributions of urban-related warming that range from less than 10% up to 37.9% over eastern China. These studies include urban meteorological observations minus rural meteorological observations (UMR) (Gallo et al. 1999; Yang et al. 2011) and observations minus reanalysis (OMR) (Hu et al. 2010; Kalnay and Cai 2003; Zhou et al. 2004). The contribution of urban-related warming may vary (Cao et al. 2013; Ding et al. 2010) and cause substantial uncertainty as a result of several aspects, such as the relocation of meteorological stations (Zhou et al. 2013), difficulties in identifying rural sites (Yan et al. 2010), heterogeneity of observation site distributions, scarcity of stations in western China, and different resolution of the reanalysis data.

Nested numerical simulations using regional climate models (RCMs) can provide fine homogenous grid values at the regional scale (Zhao and Wu 2017). The urban surface data of the commonly used RCMs, such as MM5 (Guo and Chen 1994) and WRF (Skamarock et al. 2008), are from two satellite-based datasets, which were collected between April 1992 and March 1993 (U1992) and in 2001 (U2001). These two fixed-in-time datasets can be used for short-term simulations of seasonal and annual studies but are inadequate for long-term studies.

To identify the contribution of urban-related warming in Beijing, satellite-derived urban surface data for 1980, 1990, 2000, 2010, and 2016 in China (He et al. 2013; Hu et al. 2015; Jia et al. 2015), rather than fixed-in-time urban data, were used to perform numerical simulations. With the reconstructed annual land-use data in the coarse mesh and nested domains of the model, nested simulations using the fine-resolution dynamical downscaling method allow one to perform sensitivity studies and obtain homogenous grid values, as compared with the heterogeneous observed data from the meteorological stations, and can reveal the contribution of urban-related warming in Beijing.

2. Experimental design and data

a. Experimental design

The central latitude and longitude of the simulated domain covering eastern Asia were 35°N and 108.5°E, respectively. The horizontal coarse mesh had 259 longitudinal grid points and 199 latitudinal grid points, including a 15-gridpoint buffer zone that was not used in the analysis (Fig. 1a). The horizontal grid spacing was 30 km for the coarse domain, and the time step was 60 s. The first nested domain (10-km resolution) covered most of eastern China, with 222 and 312 grid points in the longitudinal and latitudinal directions, respectively. The second three nested domains (3.3-km resolution) covered three city clusters in China (Fig. 1b), with 150 and 120 grid points in the longitudinal and latitudinal directions, respectively. Feedback from the nested domain to its parent domain was considered in the integrations. The air pressure at the top of the model was 10 hPa, and there were 51 levels in the vertical direction.

Fig. 1.
Fig. 1.

(a) Model domain (black outline) and terrain elevation (m) with nested domains, all in China [white squares labeled Beijing–Tianjin–Hebei (BTH), Yangtze River delta (YRD), and Pearl River delta (PRD)], (b) terrain elevation in BTH (m), including the city of Beijing, and (c) terrain elevation over Beijing (m). Also shown are spatial distributions for different land-use categories in Beijing under (d) U1980, (e) U1990, (f) U2000, (g) U2010, and (h) U2016, with red color denoting grid cells with urban surface. The land-use categories given in the key are 1) evergreen needleleaf forests, 2) deciduous broadleaf forests, 3) mixed forests, 4) closed shrublands, 5) open shrublands, 6) woody savannas, 7) grasslands, 8) croplands, 9) urban and built-up areas, 10) cropland/natural vegetation mosaics, 11) barren or sparsely vegetated areas, and 12) water.

Citation: Journal of Applied Meteorology and Climatology 56, 6; 10.1175/JAMC-D-17-0019.1

Two numerical experiments (1980–2016), which differed only in the land-use data over China, were performed using WRF, version 3.4. The International Geosphere–Biosphere Programme (IGBP)-modified 20-category land-use categories were adopted in the model. The first experiment (EX1) was conducted using the fixed-in-time land-use data (in which the urban data are based on U1980), and the second experiment (EX2) was based on varied land-use data for each year. The integrations were composed of a series of restarts using the default urban-related model parameters for individual years starting from 1 July of the previous year, which were used as “spinup” time when results for the current year were analyzed.

The unified Noah land surface model, in which urban effects were considered in computing surface temperature, heat fluxes, and the momentum exchanges between the atmosphere and land surface (urban canopy model with the default parameters; Chen et al. 2006), was used in simulations. Thermal and kinetic parameters were determined on the basis of the vegetation data and soil texture through a four-layer soil model. Therefore, sensible and latent heat fluxes from land surface to the boundary layer could be objectively described and the feedback of land surface forcing could be considered (Chen and Dudhia 2001). Other physical parameterization schemes included the WRF single-moment six-class graupel microphysics scheme, the Community Atmosphere Model shortwave and longwave radiation schemes, the Yonsei University boundary layer scheme, and the Grell 3D ensemble cumulus scheme (for 30- and 10-km resolutions integrations only).

b. Data

1) Satellite-based urban surface data and their reconstruction

On the basis of the integrated data from census information, multiple-source satellite images, and national land-cover datasets obtained from the Chinese Data Sharing Infrastructure of Earth System Science, the general trends of the urbanization dynamics and their spatial patterns over China were determined. Meanwhile, socioeconomic activities for urbanization and population growth were further described by the nighttime light datasets from the Defense Meteorological Satellite Program Operational Linescan System. The datasets that showed the best results in representing urban land cover and the evaluations were then selected and combined to construct five urban cover images over China (U1980, U1990, U2000, U2010, and U2016; Hu et al. 2015; Jia et al. 2015). To avoid the error from spatial-scale transformation and to provide more accurate urban surface information at model grid cells for the nested integrations, the fractional urban cover was calculated at the model resolutions of 30, 10, and 3.3 km, respectively, using 1-km urban data.

With the five fractional satellite-based images on each model grid cell, urban data for individual years during 1980 and 2016 were reconstructed. To display the growth of urban areas, the increase in fractional urban areas was assumed to increase linearly for each time period (1980–89, 1990–99, 2000–09, and 2010–16), and the use of annual fractional urban areas could avoid unrealistic discontinuity-induced spurious results during long-term climate studies.

With the reconstructed 37-yr fractional urban data and other land-use data, annual fractional land-use data were obtained. Because only one land-use category was needed to be assigned to each grid cell of the WRF Model (before WRF, version 3.6), the dominant land-use category at each grid cell was assigned on the basis of the land-use category with the largest fraction; a grid cell that showed the largest fraction as water category but with that fraction being less than 50% was assigned to the category with the second largest fraction (Guo and Chen 1994).

2) Driving data

The identical driving data, including sea surface temperatures and atmospheric data, were used in the integrations, in which the forcing was only applied at the boundaries. The initial conditions and time-varying boundary conditions were provided by the National Centers for Environmental Prediction–U.S. Department of Energy Atmospheric Model Intercomparison Project (AMIP-II) reanalysis (R-2) dataset during 1979 and 2016 (Kanamitsu et al. 2002). The reanalysis data with a resolution of 2.5° × 2.5° were interpolated into the WRF Model domain with the bilinear method and were updated every 6 h.

3. Results

a. Urban grid cells in Beijing

The default urban data in the commonly used RCMs (such as MM5 and WRF) were unchanged and could not represent the rapid urbanization that has occurred in Beijing. In addition, the default urban data in Beijing from U1992 (26 urban cells) and U2001 (230 urban cells) were notably different. The reconstructed urban data showed the urban surface expansion in Beijing during the last 37 years (Figs. 1d–h) and demonstrated the importance of including changes in the urban grid cells in long-term climate studies. The increase was less between U1980 (20 urban cells) and U1990 (67 urban cells), whereas much more intense changes occurred between U1990 (67 urban cells) and U2000 (183 urban cells), between U2000 and U2010 (262 urban cells), and between U2010 and U2016 (302 urban cells). The numbers of urban grid cells between U2000 (183 urban cells) and U2001 (230 urban cells) were similar, which demonstrated the reliability of the U2000.

b. Performance on surface air temperature simulations

The spatial distributions of the simulated values of SAT with the reconstructed land-use data instead of the default fixed-in-time data were similar to those of the observed values (Fig. 2) from the Climate Research Unit (CRU; Jones et al. 2012) and the University of Delaware [UDEL; Matsuura and Willmott (2012), obtained online from the NOAA/OAR/ESRL Physical Sciences Division at https://www.esrl.noaa.gov/psd/data/gridded/data.UDel_AirT_Precip.html], especially in eastern China, including Beijing. Area-averaged biases over Beijing, which were −3.03° (CRU) and −2.54°C (UDEL) from EX1, decreased to values of −2.78° (CRU) and −2.29°C (UDEL) from EX2.

Fig. 2.
Fig. 2.

Spatial distributions of the surface air temperature for the observed data [(a) CRU and (b) UDEL] and simulated results [(c) EX1 and (d) EX2] between 1980 and 2014.

Citation: Journal of Applied Meteorology and Climatology 56, 6; 10.1175/JAMC-D-17-0019.1

c. Spatial distribution of urban-related warming

Urban-related warming showed varied intensity in different areas and was more intense during 2010–16 and weaker during 1980–89 (Fig. 3). Greater warming was detected in areas outside the second ring road than in areas inside it, which have been occupied by urban surfaces since the 1980s.

Fig. 3.
Fig. 3.

Spatial distributions of annual averaged urban-related warming (°C) (a) between 1980 and 1989, (b) between 1990 and 1999, (c) between 2000 and 2009, (d) between 2010 and 2016, and (e) between 1980 and 2016 in Beijing [(a), (b), and (c) depict 10-yr averages; (d) shows 7-yr averages; (e) shows 37-yr averages; the shaded areas passed the 90% confidence-level t test].

Citation: Journal of Applied Meteorology and Climatology 56, 6; 10.1175/JAMC-D-17-0019.1

The 37-yr area-averaged annual urban-related warming was 0.25°C* (here one, two, three, and four asterisks denote passing the 80%, 90%, 95%, and 99% confidence-level t test, respectively) for the whole of Beijing; the value was 0.38°C*** in the grids that were classified as urban for both time periods (U2U) but was much larger (1.09°C****) in the grids whose classification switched from nonurban to urban surfaces over the two time periods (N2U). The warming was 1.03°C**** in urban areas (including N2U and U2U), which was similar to the values in N2U because of the intense increase in urban areas during 1980–2016.

Urban-related warming did show varied intensity because of differences in the expanded urban areas. Area-averaged annual urban-related warming across the whole of Beijing was 0.10°, 0.20°, 0.30°*, and 0.43°C* during 1980–89, 1990–99, 2000–09, and 2010–16, respectively (10-yr area averaged for the former three time periods and 7-yr area averaged for the last one; hereinafter the same definitions are used for multiple-year area-averaged annual values); the corresponding values were smaller (0.14°, 0.35°*, 0.47°***, and 0.67°C**) in U2U and much larger (0.85°, 1.18°***, 1.60°****, and 1.95°C****) in N2U. The warming was 0.64°, 1.09°***, 1.51°****, and 1.86°C**** in urban areas.

Because of the special geographic characteristics for the plains areas of Beijing in the southeastern region and surrounding mountain areas in the northwestern region (Fig. 1c), respectively accounting for approximately 62% and 38% of the total area, urban-related warming was mainly concentrated in the plains areas. The area-averaged annual values were 0.52°C**** during 1980–2016 and 0.14°, 0.42°***, 0.72°****, and 0.93°C*** during 1980–89, 1990–99, 2000–09, and 2010–16—almost 2 times the values across the whole of Beijing, except during 1980–89.

d. Annual variation in urban-related warming

Urban-related warming over Beijing showed obvious annual variation (Fig. 4), which was stronger during warm seasons, with maximum values in July, and weaker during cold seasons. The values in the plains areas were similar to that in U2U, especially during warm seasons.

Fig. 4.
Fig. 4.

Annual variation in urban-related warming between 1980 and 2016.

Citation: Journal of Applied Meteorology and Climatology 56, 6; 10.1175/JAMC-D-17-0019.1

e. Changes in diurnal temperature range

Changes in the daily SAT maximum/minimum, daily mean SAT, and diurnal temperature range (DTR) in summer were further analyzed using probability density functions (PDF; Fig. 5). Urban-related warming revealed increased SAT maximum and minimum, but the latter was greater and resulted in a decrease of DTR. Furthermore, changes in the SAT maximum were similar in different areas, but changes in the SAT minimum varied and resulted in different intensities of DTR changes.

Fig. 5.
Fig. 5.

Changes (between EX1 and EX2) in the PDF for the daily SAT maximum/minimum (Tmax/Tmin), daily mean SAT (Tmean), and DTR in the summer across (a) the whole of Beijing, (b) the plains areas of Beijing, (c) U2U, and (d) N2U.

Citation: Journal of Applied Meteorology and Climatology 56, 6; 10.1175/JAMC-D-17-0019.1

Across the whole of Beijing, the DTR decreased by −0.27°C because of a larger increase in the SAT minimum (0.36°C) than in the SAT maximum (0.09°C). For the plains areas, the DTR decreased by −0.71°C because of a larger increase in the SAT minimum (0.83°C) than in the maximum (0.12°C), which were all stronger than those across the whole of Beijing.

The DTR decreased −0.38°, −1.68°, and −1.57°C in U2U, N2U, and urban areas, respectively; the corresponding SAT maximums increased by 0.19°, 0.14°, and 0.15°C. The increases in the SAT minimum were much greater (0.57°, 1.82°, and 1.72°C). Similar intensities for the increase in SAT maximum were detected for U2U and N2U; marked differences for the SAT minimum changes were found, however, which contributed to the greater DTR in N2U and urban areas because of the intense increase in urban areas.

f. The contribution to regional warming

The contribution to regional warming was further explored across the whole of Beijing and the plains areas. The 37-yr averaged SAT increasing trend across the whole of Beijing was 0.37°C (10 yr)−1 for the unchanged urban surface data (EX1). With the reconstructed annual data showing urban surface expansion (EX2), the trend increased to 0.48°C (10 yr)−1. The contribution of urban-related warming was approximately 0.11°C (10 yr)−1, which accounted for 22% of the overall warming across the whole of Beijing (Fig. 6a).

Fig. 6.
Fig. 6.

Time series of annual averages in surface air temperature and the trends for EX1 and EX2 for (a) the whole of Beijing, (b) the plains areas of Beijing, (c) U2U, and (d) N2U.

Citation: Journal of Applied Meteorology and Climatology 56, 6; 10.1175/JAMC-D-17-0019.1

The SAT-increase trends over the plains areas under EX1 and EX2 were 0.39°C (10 yr)−1 and 0.67°C (10 yr)−1 (Fig. 6b), respectively, which were more intense than those across the whole of Beijing. The contribution was 0.28°C (10 yr)−1, which accounted for approximately 42% of the overall warming. For U2U and N2U areas (Figs. 6c,d), the contributions were 31% and 62%, respectively.

4. Conclusions and discussion

On the basis of reconstructed satellite-based urban surface images for 1980 and 2016, the contribution of urban surface expansion to regional warming in Beijing was investigated using the WRF Model. These results provide a basis for impact evaluation of urban surface expansion on SAT changes using homogenous model grid values.

The 37-yr annual urban-related warming in Beijing was 0.25°C for the whole area and was much stronger (0.52°C) for the plains areas. The warming in urban areas was similar to the values for N2U because of the intense increase in urban areas during 1980–2016.

The contribution of regional warming was more intense during the warm seasons. Further analysis of the summer results showed that a larger increased SAT minimum and a smaller increased SAT maximum accounted for the decreased DTR. The DTR decreased by −0.27° and −0.71°C across the whole of Beijing and the plains areas, respectively.

The contribution of urban-related warming accounted for approximately 22% of overall warming across the whole of Beijing from 1980 to 2016. The contribution in the plains areas of Beijing was more intense, however, accounting for approximately 42% of the overall warming.

Previous studies that were based on meteorological station data revealed that urban-related warming, which mainly resulted from the increased SAT minimum (Ding et al. 2010; Zhang et al. 2011), concentrated on the urban areas and southeastern part of Beijing (Liu et al. 2014). Simulated results using the reconstructed satellite-based urban surface data were consistent with the observed ones, but differences in the contribution of urban-related warming to overall warming could be detected between the observations and the simulations. In fact, urbanization-induced climate/environmental issues are caused by urban surface expansion and other aspects, such as building density and height (Shem and Shepherd 2009; Thielen et al. 2000), aerosol emissions (Zhuang et al. 2014), and anthropogenic heat release (Ichinose et al. 1999), that are not covered here because of the difficulties in monitoring and the large uncertainties. Therefore, more studies are necessary.

Acknowledgments

This work was supported by the National Key Research and Development Program of China under Grant 2016YFA0600403, the Chinese Academy of Sciences Strategic Priority Program under Grant XDA05090206, the National Key Basic Research Program on Global Change under Grant 2011CB952003, the Chinese Natural Science Foundation under Grant 41675149, and the Jiangsu Collaborative Innovation Center for Climatic Change. The UDel_AirT_Precip data were provided by the NOAA/OAR/ESRL Physical Sciences Division (https://www.esrl.noaa.gov/psd/data/gridded/data.UDel_AirT_Precip.html). The authors thank the reviewers for their numerous valuable comments to improve the manuscript.

REFERENCES

  • Cao, W., Q. Q. Shao, J. Y. Liu, and Z. W. Hu, 2013: Impact of land use/land cover and its change on climate warming in Beijing area (in Chinese). Climate Environ. Res., 18, 451460.

    • Search Google Scholar
    • Export Citation
  • Chen, F., and J. Dudhia, 2001: Coupling an advanced land surface–hydrology model with the Penn State–NCAR MM5 modeling system. Part I: Model description and implementation. Mon. Wea. Rev., 129, 569585, doi:10.1175/1520-0493(2001)129<0569:CAALSH>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, F., M. Tewari, H. Kusaka, and T. T. Warner, 2006: Current status of urban modeling in the community Weather Research and Forecast (WRF) Model. Sixth Symp. on the Urban Environment, Atlanta, GA, Amer. Meteor. Soc., J1.4. [Available online at https://ams.confex.com/ams/Annual2006/techprogram/paper_98678.htm.]

  • Ding, H. Y., Z. F. Zheng, and W. D. Liu, 2010: Warming trend and seasonal variation in Beijing during 1951–2008 (in Chinese). Adv. Climate Change Res., 6, 187191.

    • Search Google Scholar
    • Export Citation
  • Gallo, K., T. Owen, and D. Easterling, 1999: Temperature trends of the U.S. Historical Climatology Network based on satellite-designated land use/land cover. J. Climate, 12, 13441348, doi:10.1175/1520-0442(1999)012<1344:TTOTUS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Guo, Y.-R., and S. Chen, 1994: Terrain and land use for the fifth-generation Penn State/NCAR Mesoscale Modeling System (MM5): Program TERRAIN. NCAR Tech. Note NCAR/TN-397+IA, 114 pp., doi:10.5065/D68C9T67.

    • Crossref
    • Export Citation
  • He, Y. T., G. S. Jia, Y. H. Hu, and Z. J. Zhou, 2013: Detecting urban warming signals in climate records. Adv. Atmos. Sci., 30, 11431153, doi:10.1007/s00376-012-2135-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hu, Y., G. Jia, C. Pohl, Q. Feng, Y. He, H. Gao, R. Xu, J. van Genderen, and J. Feng, 2015: Improved monitoring of urbanization processes in China for regional climate impact assessment. Environ. Earth Sci., 73, 83878404, doi:10.1007/s12665-014-4000-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hu, Y.-C., W. Dong, and Y. He, 2010: Impact of land surface forcings on mean and extreme temperature in eastern China. J. Geophys. Res., 115, D19117, doi:10.1029/2009JD013368.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ichinose, T., K. Shimodozono, and K. Hanaki, 1999: Impact of anthropogenic heat on urban climate in Tokyo. Atmos. Environ., 33, 38973909, doi:10.1016/S1352-2310(99)00132-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • IPCC, 2013: Climate Change 2013: The Physical Science Basis. Cambridge University Press, 1535 pp., doi:10.1017/CBO9781107415324.

    • Crossref
    • Export Citation
  • Jia, G., R. Xu, Y. Hu, and Y. He, 2015: Multi-scale remote sensing estimates of urban fractions and road widths for regional models. Climatic Change, 129, 543554, doi:10.1007/s10584-014-1114-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jones, P. D., D. H. Lister, and Q. Li, 2008: Urbanization effects in large-scale temperature records with an emphasis on China. J. Geophys. Res., 113, D16122, doi:10.1029/2008JD009916.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jones, P. D., D. H. Lister, T. J. Osborn, C. Harpham, M. Salmon, and C. P. Morice, 2012: Hemispheric and large-scale land surface air temperature variations: An extensive revision and an update to 2010. J. Geophys. Res., 117, D05127, doi:10.1029/2011JD017139.

    • Search Google Scholar
    • Export Citation
  • Kalnay, E., and M. Cai, 2003: Impact of urbanization and land-use change on climate. Nature, 423, 528531, doi:10.1038/nature01675.

  • Kanamitsu, M., W. Ebisuzaki, J. Woollen, S.-K. Yang, J. J. Hnilo, M. Fiorino, and G. L. Potter, 2002: NCEP–DOE AMIP-II reanalysis (R-2). Bull. Amer. Meteor. Soc., 83, 16311643, doi:10.1175/BAMS-83-11-1631.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, Q. X., W. Li, P. Si, X. R. Gao, W. J. Dong, P. D. Jones, J. Y. Huang, and L. J. Cao, 2010: Assessment of surface air warming in northeast China, with emphasis on the impacts of urbanization. Theor. Appl. Climatol., 99, 469478, doi:10.1007/s00704-009-0155-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, J. Y., M. L. Liu, H. Q. Tian, D. F. Zhuang, Z. X. Zhang, W. Zhang, X. M. Tang, and X. Z. Deng, 2005: Spatial and temporal patterns of China’s cropland during 1990–2000: An analysis based on Landsat TM data. Remote Sens. Environ., 98, 442456, doi:10.1016/j.rse.2005.08.012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, W. D., B. Z. Zhang, H. L. You, and P. Yang, 2014: Preliminary analysis of urbanization effects on temperature change in Beijing during 1978–2008 (in Chinese). Meteor. Mon., 40, 94100.

    • Search Google Scholar
    • Export Citation
  • Ma, X. M., R. K. Li, K. Luo, R. M. Zhang, Z. S. Wang, and Q. B. Xu, 2016: Association between temperature and mortality in three cities in China (in Chinese). Basic Clin. Med., 36, 805810.

    • Search Google Scholar
    • Export Citation
  • Matsuura, K., and C. J. Willmott, 2012: Terrestrial air temperature: 1900–2010 gridded monthly time series (version 3.01). University of Delaware Dept. of Geography Tech. Doc. [Available online at http://climate.geog.udel.edu/~climate/html_pages/Global2011/README.GlobalTsT2011.html.]

  • Meng, Z. Y., and D. Yao, 2014: Damage survey, radar, and environment analyses on the first-ever documented tornado in Beijing during the heavy rainfall event of 21 July 2012. Wea. Forecasting, 29, 702724, doi:10.1175/WAF-D-13-00052.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ren, G. Y., Y. Zhou, Z. Chu, J. Zhou, A. Zhang, J. Guo, and X. Liu, 2008: Urbanization effects on observed surface air temperature trends in north China. J. Climate, 21, 13331348, doi:10.1175/2007JCLI1348.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shem, W., and M. Shepherd, 2009: On the impact of urbanization on summertime thunderstorms in Atlanta: Two numerical model case studies. Atmos. Res., 92, 172189, doi:10.1016/j.atmosres.2008.09.013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., and Coauthors, 2008: A description of the Advanced Research WRF version 3. NCAR Tech. Note NCAR/TN-475+STR, 113 pp., doi:10.5065/D68S4MVH.

    • Crossref
    • Export Citation
  • Thielen, J., W. Wobrock, A. Gadian, P. G. Mestayer, and J.-D. Creutin, 2000: The possible influence of urban surfaces on rainfall development: A sensitivity study in 2D in the meso-γ-scale. Atmos. Res., 54, 1539, doi:10.1016/S0169-8095(00)00041-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yan, Z. W., Z. Li, Q. X. Li, and P. D. Jones, 2010: Effects of site change and urbanization in the Beijing temperature series 1977–2006. Int. J. Climatol., 30, 12261234, doi:10.1002/joc.1971.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yan, Z. W., J. Wang, Z. Li, and W. D. Liu, 2014: Effects of urbanization based on homogenized daily observations (in Chinese). Adv. Meteor. Sci. Tech., 4, 4148.

    • Search Google Scholar
    • Export Citation
  • Yang, X. C., Y. L. Hou, and B. D. Chen, 2011: Observed surface warming induced by urbanization in east China. J. Geophys. Res., 116, D14113, doi:10.1029/2010JD015452.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, L., G. Y. Ren, J. Liu, Y. Q. Zhou, Y. Y. Ren, A. Y. Zhang, and Y. W. Feng, 2011: Urban effect on trends of extreme temperature indices at Beijing meteorological station (in Chinese). Chin. J. Geophys., 54, 11501159.

    • Search Google Scholar
    • Export Citation
  • Zhao, D. M., and J. Wu, 2017: The influence of urban surface expansion in China on regional climate. J. Climate, 30, 10611080, doi:10.1175/JCLI-D-15-0604.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhou, J. P., Z. B. Sun, D. H. Ni, and Z. X. Li, 2013: Impact of meteorological station relocation on homogeneity of annual mean temperature in China (in Chinese). Trans. Atmos. Sci., 36, 139146.

    • Search Google Scholar
    • Export Citation
  • Zhou, L., R. E. Dickinson, Y. Tian, J. Fang, Q. Li, R. K. Kaufmann, C. J. Tucker, and R. B. Myneli, 2004: Evidence for a significant urbanization effect on climate in China. Proc. Natl. Acad. Sci. USA, 101, 95409544, doi:10.1073/pnas.0400357101.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhuang, B. L., and Coauthors, 2014: Optical properties and radiative forcing of urban aerosols in Nanjing, China. Atmos. Environ., 83, 4352, doi:10.1016/j.atmosenv.2013.10.052.

    • Crossref
    • Search Google Scholar
    • Export Citation
Save