Abstract

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.

Highlights

  • Surface air temperature at 2 m (SAT) plays a primary role in the climatic systems, and has been widely regarded as an important climatic indictor in daily human life

  • The time series of differences in urban-related warming and the corresponding trends over the subregions of Guangzhou and Shenzhen calculated using T4 and Txn (Figure 7) display the Notably, the time series of differences in urban-related warming and the corresponding trends over the subregions of Guangzhou and Shenzhen calculated using T4 and Txn (Figure 7) display the largest trend (−0.0457 ◦ C/decade) over the U2U areas of Guangzhou, whereas there are smaller values over subregions of Shenzhen and other subregions of Guangzhou. These results show that the differences in urban-related warming contributions over Guangzhou and Shenzhen with daily mean

  • Two numerical experiments were performed based on the fixed-in-time land use data in the 1980 (EX1) and reconstructed annual land use data displaying urban surface expansion during 1980 and 2016 (EX2), respectively

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Summary

Introduction

Surface air temperature at 2 m (SAT) plays a primary role in the climatic systems, and has been widely regarded as an important climatic indictor in daily human life. Simulated SAT values were used to study the differences in urban-surface-expansion during the past several decades, were chosen to compare the daily mean the long-term time series of daily mean SAT calculated using T4, Txn, and Tc. the impacts of SAT calculated using different methods. Simulated SAT values were used to study the differences differing SAT records on evaluating urban-related warming over Guangzhou and Shenzhen were in the long-term time series of daily mean SAT calculated using T4 , Txn , and Tc. the impacts investigated. The differences in annual mean SAT, trends, and urban-related warming contributions of differing SAT records on evaluating urban-related warming over Guangzhou and Shenzhen were calculated using T4, Txn, and Tc, both in spatial and long-term times series, are discussed. The differences in annual mean SAT, trends, and urban-related warming contributions calculated using T4 , Txn , and Tc , both in spatial and long-term times series, are discussed

Experiments
Experimental Design
Design
Urban Surface Expansion over Guangzhou and Shenzhen
Spatial Differences and Trends in Annual Mean SAT
Annual Mean SAT
Spatial
Spatial distributions
Trends in Annual Mean SAT
Trends in T
Time Series Differences in Annual Mean SAT and Trends
Comparing Trends for Tmax and Tmin
Tmax Trends
Tmin Trends
Discussions
Conclusions
Full Text
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