Heat waves and urban heat islands (UHIs) may interact together, but the dependence of their interaction on background climate is unclear. Hourly meteorological observations in June to August from 2013 to 2015 collected in the megacities of Beijing (temperate semihumid monsoon climate), Shanghai (subtropical humid monsoon climate), and Guangzhou (marine subtropical monsoon climate) in China were used to study the interaction. At each megacity, eight rural stations and eight urban stations, respectively, were selected to study the UHI. Although under different background climates, UHIs in Beijing and Guangzhou shared a similar diurnal variability, that is, higher in the nighttime. However, the diurnal cycle is opposite for Shanghai if rural coastal stations were selected as rural reference stations. During heat wave periods, daytime (10:00–16:00) UHIs were intensified by 0.9 ± 0.13 (mean ± 1 standard deviation) °C in Shanghai, nighttime (22:00–4:00) UHIs were intensified by 0.9 ± 0.36 and 0.8 ± 0.20 °C in Beijing and Guangzhou, respectively. The surface solar radiation during the heat wave period was approximately 1.5 times to that under normal conditions in each city. The enhanced solar radiation during heat waves, which was absorbed by the urban canopy in the daytime and released at night, was closely related to nighttime UHIs in Beijing and Guangzhou and daytime UHIs in Shanghai. Additionally, changes in wind direction were observed in Shanghai under heat waves, that is, with more than 63% (wind direction) of the wind originating from neighboring hot cities in the southwest instead of the cool sea breeze from the southeast, which led to a significant increase in daytime UHIs during heat wave periods.
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.
Diurnal cycle of surface air temperature T is an important metric indicating the feedback of land–atmospheric interaction to global warming, whereas the ability of current reanalyses to reproduce its variation had not been assessed adequately. Here, we evaluate the daily maximum temperature Tmax, daily minimum temperature Tmin, and diurnal temperature range (DTR) in five reanalyses based on observations collected at 2253 weather stations over China. Our results show that the reanalyses reproduce Tmin very well; however, except for Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2), they substantially underestimate Tmax and DTR by 1.21°–6.84°C over China during the period of 1980–2014. MERRA-2 overestimates Tmax and DTR by 0.35° and 0.81°C, which are closest with observation. The reanalyses are skillful in reproducing the interannual variability of Tmax and Tmin but relatively poor for DTR. All reanalyses underestimate the warming trend of Tmin by 0.13°–0.17°C (10 yr)−1 throughout China during 1980–2014, and underestimate the warming trend of Tmax by 0.24°–0.40°C (10 yr)−1 in northwestern China while overestimating this quantity by 0.18°–0.33°C (10 yr)−1 in southeastern China. These trend biases in Tmax and Tmin introduce a positive trend bias in DTR of 0.01°–0.26°C (10 yr)−1 within China, especially in the north China plain and southeastern China. In the five reanalyses, owing to the sensitivity discrepancies and trend biases, the surface solar radiation Rs and precipitation frequency (PF) are notable deviation sources of the diurnal cycle of air temperature, which explain 31.0%–38.7% (31.9%–37.8%) and 9.8%–22.2% (7.4%–15.3%) of the trend bias in Tmax (DTR) over China, respectively.
Land surface temperature (Ts) and near surface air temperature (Ta) are two main metrics that reflect climate change. Recently, based on in situ observations, several studies found that Ts warmed much faster than Ta in China, especially after 2000. However, we found abnormal jumps in the Ts time series during 2003-2005, were mainly caused by the transformation from manual to automatic measurements due to snow cover. We explore the physical mechanism of the differences between automatic and manual observations and develop a model to correct the automatic observations on snowy days in the observed records of Ts. Furthermore, the nonclimatic shifts in the observed Ts were detected and corrected using the RHtest method. After corrections, the warming rates for Ts-max, Ts-min, and Ts-mean were 0.21, 0.34, and 0.25 °C/10 yr during the 1960-2014 period. The abnormal jump in the difference between Ts and Ta over China after 2003, which was mentioned in existing studies, was mainly caused by inhomogeneities rather than climate change. Through a combined analysis using reanalyses and CMIP5 models, we found that Ts was consistent with Ta both in terms of interannual variability and long-term trends over China during 1960-2014. The Ts minus Ta (Ts-Ta) trend is -0.004 to 0.009 °C/10 yr, accounting for -3.19%∼5.93% (-3.09∼6.39%) of the absolute warming trend of Ts (Ta).
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