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Open AccessArticle

Modelling Daily Mean Surface Air Temperature Calculated from Different Methods and Its Impact on Urban-Related Warming Evaluations over Guangzhou and Shenzhen Using the WRF Model

by 1,* and 2
1
CAS Key Laboratory of Regional Climate-Environment for Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
2
Department of Atmospheric Science, Yunnan University, Kunming 650091, China
*
Author to whom correspondence should be addressed.
Atmosphere 2019, 10(2), 48; https://doi.org/10.3390/atmos10020048
Received: 26 December 2018 / Revised: 19 January 2019 / Accepted: 21 January 2019 / Published: 27 January 2019
(This article belongs to the Special Issue Urban Climate)
The impacts of urban surface expansion, based on satellite-derived data displaying urban surface expansion in China at different spatial scales from 1980 to 2016, were investigated using nested dynamical downscaling methods with the Weather Research and Forecasting (WRF) regional climate model at a 3.3-km resolution over a city and city cluster scale. Urban-related warming, based on daily mean surface air temperature at 2 m (SAT), calculated from the averages of four time records each day (00, 06, 12, and 18 h UTC, T4) and averages of SAT maximum (Tmax) and minimum (Tmin) (Txn), was evaluated. Differences in urban-related warming contributions calculated using T4 and Txn were small, whereas annual mean SAT and trends calculated using Txn were respectively and significantly larger and smaller than those calculated using T4 over Guangzhou and Shenzhen, excluding the trends over middle-northern Shenzhen. The differences in annual mean SAT calculated using T4 and Txn are attributed to nonlinear or asymmetric variations with time for the diurnal cycle of SAT. Meanwhile, differences in trends between T4 and Txn are interpreted as a strong trend for Tmin and a weak one for Tmax, which mitigated the trend for Txn. The impacts on the evaluations of urban-related warming contributions calculated from different methods were the largest over the areas classified as urban surfaces in both time periods (U2U), especially during intense urban-surface-expansion periods between 2000 and 2016. The subregional performances in the changes in annual mean SAT, trends, and urban-related warming are attributed to urban-surface-expansion, which induced varied changes in the diurnal cycle due to asymmetric warming during the daytime and nighttime over different subregions. View Full-Text
Keywords: dynamical downscaling; surface air temperature; urban-related warming; trend; asymmetric warming dynamical downscaling; surface air temperature; urban-related warming; trend; asymmetric warming
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MDPI and ACS Style

Zhao, D.; Wu, J. Modelling Daily Mean Surface Air Temperature Calculated from Different Methods and Its Impact on Urban-Related Warming Evaluations over Guangzhou and Shenzhen Using the WRF Model. Atmosphere 2019, 10, 48. https://doi.org/10.3390/atmos10020048

AMA Style

Zhao D, Wu J. Modelling Daily Mean Surface Air Temperature Calculated from Different Methods and Its Impact on Urban-Related Warming Evaluations over Guangzhou and Shenzhen Using the WRF Model. Atmosphere. 2019; 10(2):48. https://doi.org/10.3390/atmos10020048

Chicago/Turabian Style

Zhao, Deming; Wu, Jian. 2019. "Modelling Daily Mean Surface Air Temperature Calculated from Different Methods and Its Impact on Urban-Related Warming Evaluations over Guangzhou and Shenzhen Using the WRF Model" Atmosphere 10, no. 2: 48. https://doi.org/10.3390/atmos10020048

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