Comparing the diurnal and seasonal variabilities of atmospheric and surface urban heat islands based on the Beijing urban meteorological network

Abstract

This study compared the diurnal and seasonal cycles of atmospheric and surface urban heat islands (UHIs) based on hourly air temperatures (Ta) collected at 65 out of 262 stations in Beijing and land surface temperature (Ts) derived from Moderate Resolution Imaging Spectroradiometer in the years 2013–2014. We found that the nighttime atmospheric and surface UHIs referenced to rural cropland stations exhibited significant seasonal cycles, with the highest in winter. However, the seasonal variations in the nighttime UHIs referenced to mountainous forest stations were negligible, because mountainous forests have a higher nighttime Ts in winter and a lower nighttime Ta in summer than rural croplands. Daytime surface UHIs showed strong seasonal cycles, with the highest in summer. The daytime atmospheric UHIs exhibited a similar but less seasonal cycle under clear‐sky conditions, which was not apparent under cloudy‐sky conditions. Atmospheric UHIs in urban parks were higher in daytime. Nighttime atmospheric UHIs are influenced by energy stored in urban materials during daytime and released during nighttime. The stronger anthropogenic heat release in winter causes atmospheric UHIs to increase with time during winter nights, but decrease with time during summer nights. The percentage of impervious surfaces is responsible for 49%–54% of the nighttime atmospheric UHI variability and 31%–38% of the daytime surface UHI variability. However, the nighttime surface UHI was nearly uncorrelated with the percentage of impervious surfaces around the urban stations.

Amplified Urban Heat Islands during Heat Wave Periods

“…The locations and the numbers of selected AWSs are shown in Figure and Table , respectively. The percentage of impervious surfaces around the urban station (1 km 2 ) had a significant positive impact on UHIs, which had already been demonstrated in our previous study (Wang et al, ). Therefore, urban stations with a high percentage of impervious surfaces and rural stations with a low percentage of impervious surfaces were selected to reduce the impact of the station's surroundings.…”

“…Surface solar radiation observations were collected at one radiation station in each city. Land cover type data in 2013 at 30‐m resolution were used to calculate the percentages of different land cover types within a 1‐km 2 circle around each station (Wang et al, ).…”

“…To reduce the impact of the ambient environment background on the observations at the AWSs, the urban stations were selected according to the following considerations (Wang et al, ): (1) Stations had to be in central urban areas with more than 70% impervious surfaces. (2) The percentage of vegetation had to be less than 20% (i.e., stations in large urban parks were not selected).…”

“…In order to better compare UHIs between the three cities, cropland was selected as the rural reference in all three cities because cropland was the main rural type that the studied three cities shared. The rural stations were selected based on the following considerations (Wang et al, ): (1) The percentage of impervious surfaces around the stations had to be less than 30%, and the percentage of croplands had to be greater than 65%. (2) The differences in the surface elevations between rural stations and urban stations had to be less than 30 m. (3) The rural sites had to be outside major urban areas.…”

“…Other elements, that is, RH and wind speed, were processed in the same way. Also, the averages from 10:00 to 16:00 and from 22:00 to 4:00 were used to represent daytime and nighttime averages, respectively (Wang et al, ). The T max anomalies collected at ~220 stations during a heat wave period (8 to 12 July 2013) in Shanghai, and its neighboring cities were used in Figure .…”