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Korean Meteorological Society (한국기상학회)
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Sensitivity Analysis of Near Surface Air Temperature to Land Cover Change and Urban Parameterization Scheme Using Unified Model

통합모델을 이용한 토지피복변화와 도시 모수화 방안에 따른 지상 기온 모의성능 민감도 분석

  • Hong, Seon-Ok (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) ;
  • Lee, Young-Gon (Applied Meteorology Research Division, National Institute of Meteorological Sciences) ;
  • Kim, Baek-Jo (Observation and Forecast Research Division, National Institute of Meteorological Sciences) ;
  • Ha, Jong-Chul (Applied Meteorology Research Division, National Institute of Meteorological Sciences)
  • 홍선옥 (국립기상과학원 응용기상연구과) ;
  • 변재영 (국립기상과학원 응용기상연구과) ;
  • 박향숙 (국립기상과학원 응용기상연구과) ;
  • 이영곤 (국립기상과학원 응용기상연구과) ;
  • 김백조 (국립기상과학원 관측예보연구과) ;
  • 하종철 (국립기상과학원 응용기상연구과)
  • Received : 2018.10.22
  • Accepted : 2018.12.22
  • Published : 2018.12.31
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Abstract

This study examines the impact of the urban parameterization scheme and the land cover change on simulated near surface temperature using Unified Model (UM) over the Seoul metropolitan area. We perform four simulations by varying the land cover and the urban parameterization scheme, and then compare the model results with 46 AWS observation data from 2 to 9 August 2016. Four simulations were performed with different combination of two urban parameterization schemes and two land cover data. Two schemes are Best scheme and MORUSES (Met Office Reading Urban Surface Exchange Scheme) and two land cover data are IGBP (International Geosphere and Biosphere Programme) and EGIS (Environmental Geographic information service) land cover data. When land use data change from IGBP to EGIS, urban ratio over the study area increased by 15.9%. The results of the study showed that the higher change in urban fraction between IGBP and EGIS, the higher the improvement in temperature performance, and the higher the urban fraction, the higher the effect of improving temperature performance of the urban parameterization scheme. 1.5-m temperature increased rapidly during the early morning due to increase of sensible heat flux in EXP2 compared to CTL. The MORUSES with EGIS (EXP3) provided best agreement with observations and represents a reasonable option for simulating the near surface temperature of urban area.

Keywords

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Fig. 1. Domain for Unified Model simulation and study area.

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Fig. 2. Surface type fractions of (a, b) broad leaf trees, (c, d) needle leaf trees, (e, f) C3 grass, and (g, h) C4 grass. Left and right panels are derived from the IGBP and EGIS data, respectively (126.6~127.6°E, 37.1~37.8°N).

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Fig. 3. Surface type fractions of (a, b) shrubs, (c, d) urban, (c, f) inland water, and (g, h) bare soil. Left and right panels are derived from the IGBP and EGIS data, respectively (126.6~127.6°E, 37.1~37.8°N).

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Fig. 4. (a) Scatter plot of IGBP versus EGIS urban land-use fractions and (b) Spatial distribution of the difference between IGBP and EGIS urban land-use fractions with AWS station numbers.

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Fig. 6. Spatial distribution of (a) 1.5-m temperature RMSE for CTL, the difference of 1.5-m temperature RMSE between CTL and (b) EXP1, (c) EXP2, and (d) EXP3.

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Fig. 7. Spatial distribution of (a) 1.5-m temperature, (b) surface temperature, and (c) sensible heat flux for CTL (left), EXP1 (middle), and EXP1 minus CTL (right), respectively. The spatial distribution is averaged for the entire period of simulation. The thin black contour in CTL, EXP1, and EXP1 minus CTL means over 30% of urban fraction using EGIS data. The thick contour in CTL, and EXP1 means the administrative boundary of Seoul.

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Fig. 8. Mean diurnal variation of simulated 1.5-m temperature (℃) over observation site of (a) Category A (fu(EGIS) − fu(IGBP) < 0), (b) Category B (0 ≤ fu(EGIS) − fu(IGBP) < 0.3), and (c) Category C (0.3 ≤ fu(EGIS) − fu(IGBP) < 1) with observation for the entire period of simulation. ‘fu’ describes the fraction of urban land-use of IGBP and EGIS cover within the model grid box for each station.

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Fig. 9. Diurnal variation of (a) sensible heat flux, (b) latent heat flux, and (c) storage heat flux for CTL (solid line) and EXP1 (dashed line) for the entire period on the observation site of Category C (0.3 ≤ fu(EGIS) − fu(IGBP) < 1).

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Fig. 10. Mean diurnal variation of simulated 1.5-m temperature (℃) over observation site of (a) Category 1 (fu(IGBP) < 0.3 and (b) Category 2 (fu(IGBP) ≥ 0.3) with observation for the entire period of simulation. ‘fu’ describes the fraction of urban land-use of IGBP cover within the model grid box for each station.

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Fig. 11. Diurnal variation of (a) sensible heat flux, (b) latent heat flux, and (c) storage heat flux for CTL (solid line) and EXP2 (dashed line) for the entire period on the observation site of Category 2 (fu(IGBP) ≥ 0.3).

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Fig. 5. Diurnal variation of (a) mean and (b) RMSE of the 1.5-m temperature for CTL, EXP1, EXP2, and EXP3 simulation for the entire period.

Table 1. Summary of numerical experiments.

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Table 2. Area average fraction of surface types over Seoul metropolitan for IGBP and EGIS data and differences between IGBP and EGIS (i.e., EGIS minus IGBP).

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Table 3. Model evaluation statistics for 1.5 m temperature (℃) during 2-9 August 2016.

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Table 4. RMSE of 1.5 m temperature (℃) for CTL and EXP1 with different urban fraction change category between IGBP and EGIS.

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Table 5. RMSE of 1.5 m temperature (℃) for CTL and EXP2 with different urban fraction category of IGBP.

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