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Analysis of the Effects of Advection and Urban Fraction on Urban Heat Island Intensity using Unified Model for Seoul Metropolitan Area, Korea

통합모델을 활용한 이류와 도시비율이 서울 수도권 지역의 도시열섬강도에 미치는 영향 분석

  • Hong, Seon-Ok (Applied Meteorology Research Division, National Institute of Meteorological Sciences) ;
  • Kim, Do-Hyoung (Applied Meteorology Research Division, National Institute of Meteorological Sciences) ;
  • Byon, Jae-Young (Applied Meteorology Research Division, National Institute of Meteorological Sciences) ;
  • Park, HyangSuk (Applied Meteorology Research Division, National Institute of Meteorological Sciences) ;
  • Ha, Jong-Chul (Applied Meteorology Research Division, National Institute of Meteorological Sciences)
  • 홍선옥 (국립기상과학원 응용기상연구과) ;
  • 김도형 (국립기상과학원 응용기상연구과) ;
  • 변재영 (국립기상과학원 응용기상연구과) ;
  • 박향숙 (국립기상과학원 응용기상연구과) ;
  • 하종철 (국립기상과학원 응용기상연구과)
  • Received : 2019.07.02
  • Accepted : 2019.09.20
  • Published : 2019.11.30

Abstract

This study investigates the impacts of urban land-use fraction and temperature advection on the urban heat island intensity over the Seoul metropolitan area using the UM (Unified Model) with the MORUSES (Met Office Reading Urban Surface Exchange Scheme) during the heat wave over the region from 2 to 8, August 2016. Two simulations are performed with two different land-use type, the urban (urban simulation) and the urban surfaces replaced with grass (rural simulation), in order to calculate the urban heat island intensity defined as the 1.5-m temperature difference between the urban and the rural simulations. The land-use type for the urban simulation is obtained from Korea Ministry of Environment (2007) land-use data after it is converted into the types used in the UM. It is found that the urban heat island intensity over high urban-fraction regions in the metropolitan area is as large as 1℃ in daytime and 3.2℃ in nighttime, i.e., the effects of urban heat island is much larger for night than day. It is also found that the magnitude of urban heat island intensity increases linearly with urban land-use fraction. Spatially, the estimated the urban heat island intensities are systematically larger in the downwind regions of the metropolitan area than in the upwind area due to the effects of temperature advection. Results of this study indicate that urban surface fraction in the city area and temperature advection play a key role in determining the spatial distribution and magnitude of urban heat island intensity.

Keywords

References

  1. Arnfield, A. J., 2003: Two decades of urban climate research: a review of turbulence, exchanges of energy and water, and the urban heat island. Int. J. Climatol., 23, 1-26 https://doi.org/10.1002/joc.859
  2. Best, M. J., 2005: Representing urban areas within operational numerical weather prediction models. Bound.-Layer Meteor., 114, 91-109. https://doi.org/10.1007/s10546-004-4834-5
  3. Best, M. J., and Coauthors, 2011: The Joint UK Land Environment Simulator (JULES), model description - Part 1: Energy and water fluxes. Geosci. Model Dev., 4, 677-699, doi:10.5194/gmd-4-677-2011.
  4. Bohnenstengel, S. I., S. Evans, P. A. Clark, and S. E. Belcher, 2011: Simulations of the London urban heat island. Q. J. R. Meteorol. Soc., 137, 1625-1640, doi:10.1002/qj.855.
  5. Chen, F., and Coauthors, 2011: The integrated WRF/urban modelling system: development, evaluation, and applications to urban environmental problems. Int. J. Climatol., 31, 273-288, doi:10.1002/joc.2158.
  6. Chen, F., X. Yang, and W. Zhu, 2014: WRF simulations of urban heat island under hot-weather synoptic conditions: The case study of Hangzhou City, China. Atmos. Res., 138, 364-377, doi:10.1016/j.atmosres.2013.12.005.
  7. Davies, T., M. J. P. Cullen, A. J. Malcolm, M. H. Mawson, A. Staniforth, A. A. White, and N. Wood, 2005: A new dynamical core for the Met Office's global and regional modelling of the atmosphere. Q. J. R. Meteorol. Soc., 131, 1759-1782. https://doi.org/10.1256/qj.04.101
  8. Edwards, J. M., and A. Slingo, 1996: Studies with a flexible new radiation code. I: Choosing a configuration for a large-scale model. Q. J. R. Meteorol. Soc., 122, 689-719. https://doi.org/10.1002/qj.49712253107
  9. Grimmond, C. S. B., and T. R. Oke, 1999: Heat storage in urban areas: local-scale observations and evaluation of a simple model, J. Appl. Meteorol., 38, 922-940. https://doi.org/10.1175/1520-0450(1999)038<0922:HSIUAL>2.0.CO;2
  10. Hong, J.-W., J. Hong, S.-E. Lee, and J. Lee, 2013: Spatial distribution of Urban Heat Island based on Local Climate Zone of Automatic Weather Station in Seoul metropolitan area. Atmosphere, 23, 413-424 (in Korean with English abstract). https://doi.org/10.14191/Atmos.2013.23.4.413
  11. Hong, S.-O., J.-Y. Byon, H. Park, Y.-G. Lee, B.-J. Kim, and J.-C. Ha, 2018: Sensitivity analysis of near surface air temperature to land cover change and urban parameterization scheme using Unified Model, Atmosphere, 28, 427-441 (in Korean with English abstract). https://doi.org/10.14191/ATMOS.2018.28.4.427
  12. Kanda, M., T. Kawai, M. Kanega, R. Moriwaki, K. Narita, and A. Hagishima, 2005: A simple energy balance model for regular building arrays. Bound.-Layer Meteor., 116, 423-443. https://doi.org/10.1007/s10546-004-7956-x
  13. Kim, Y.-H., and J.-J. Baik, 2004: Daily maximum urban heat island intensity in large cities of Korea. Theor. Appl. Climatol., 79, 151-164. https://doi.org/10.1007/s00704-004-0070-7
  14. Kusaka, H., H. Kondo, Y. Kikegawa, and F. Kimura, 2001: A simple single-layer urban canopy model for atmospheric models: Comparison with multi-layer and slab models. Bound.-Layer Meteor., 101, 329-358. https://doi.org/10.1023/A:1019207923078
  15. Lee, S.-H., and J.-J. Baik, 2010: Statistical and dynamical characteristics of the urban heat island intensity in Seoul. Theor. Appl. Climatol., 100, 227-237, doi:10.1007/s00704-009-0247-1.
  16. Lock, A. P., A. R. Brown, M. R. Bush, G. M. Martin, and R. N. B. Smith, 2000: A new boundary layer mixing scheme. Part I: Scheme description and single-column model tests. Mon. Wea. Rev., 128, 3187-3199. https://doi.org/10.1175/1520-0493(2000)128<3187:ANBLMS>2.0.CO;2
  17. Masson, V., 2000: A physically-based scheme for the urban energy budget in atmospheric models. Bound.-Layer Meteor., 94, 357-397. https://doi.org/10.1023/A:1002463829265
  18. Oke, T. R., 1982: The energetic basis of the urban heat island. Q. J. R. Meteorol. Soc., 108, 1-24. https://doi.org/10.1002/qj.49710845502
  19. Oke, T. R., G. Mills, A. Christen, and J. A. Voogt, 2017: Urban Climates. Cambridge University Press, 526 pp.
  20. Oleson, K. W., G. B. Bonan, J. Feddema, M. Vertenstein, and C. S. B. Grimmond, 2008: An urban parameterization for a global climate model. Part I: formulation and evaluation for Two cities. J. Appl. Meteor. Climatol., 47, 1038-1060. https://doi.org/10.1175/2007JAMC1597.1
  21. Park, J. J., C. Y. Ku, and B. S. Kim, 2007: Improvement of the level-2 land cover map with satellite image. J. GIS Assoc. Kor., 15, 67-80 (in Korean with English abstract).
  22. Porson, A., P. A. Clark, I. N. Harman, M. J. Best, and S. E. Belcher, 2010a: Implementation of a new urban energy budget scheme into MetUM. Part II: Validation against observations and model intercomparison. Q. J. R. Meteorol. Soc., 136, 1530-1542, doi:10.1002/qj.572.
  23. Porson, A., P. A. Clark, I. N. Harman, M. J. Best, and S. E. Belcher, 2010b: Implementation of a new urban energy budget scheme in the MetUM. Part I: Description and idealized simulations. Q. J. R. Meteorol. Soc., 136, 1514-1529, doi:10.1002/qj.668.
  24. Sailor, D. J., 2001: A review of methods for estimating anthropogenic heat and moisture emissions in the urban environment. Int. J. Climatol., 31, 189-199. https://doi.org/10.1002/joc.2106
  25. Skamarock, W. C., J. B. Klemp, J. Dudhia, D. O. Gill, D. M. Barker, M. G. Duda, X.-Y. Huang, W. Wang, and J. G. Powers, 2008: A description of the advanced research WRF version 3. NCAR Tech. Note NCAR/TN-475+STR, 125 pp.
  26. Tan, J., and Coauthors, 2010: The urban heat island and its impact on heat waves and human health in Shanghai. Int. J. Biometeorol., 54, 75-84, doi:10.1007/s00484-009-0256-x.
  27. UNDESA, 2014: World Urbanization Prospects: The 2014 revision. Methodology Working Paper No. ESA/P/WP.238, United Nations Department of Economic and Social Affairs, 27 pp.
  28. Unnikrishnan, C. K., B. Gharai, S. Mohandas, A. Mamgain, E. N. Rajagopal, G. R. Iyenger, and P. V. N. Rao, 2016: Recent changes on land use/land cover over Indian region and its impact on the weather prediction using Unified model. Atmos. Sci. Lett., 17, 294-300, doi:10.1002/asl.658.
  29. Webster, S., A. R. Brown, D. R. Cameron, and C. P. Jones, 2003: Improvements to the representation of orography in the Met Office Unified Model. Q. J. R. Meteorol. Soc., 129, 1989-2010. https://doi.org/10.1256/qj.02.133
  30. Wilson, D. R., and S. P. Ballard, 1999: A microphysically based precipitation scheme for the UK Meteorological Office Unified Model. Q. J. R. Meteorol. Soc., 125, 1607-1636. https://doi.org/10.1002/qj.49712555707