Identifying and separating the signal of urbanization effect in current temperature data series are essential for accurately detecting, attributing and projecting mean and extreme temperature change on varied spatial scales. This paper proposes a new method based on machine learning to classify the observational stations into rural stations and urban stations. Based the classification of rural and urban stations, the global and regional land annual mean and extreme temperature indices series over 1951-2018 for all stations and rural stations were calculated, and the urbanization effects and the urbanization contribution of global land annual mean and extreme temperature indices series are quantitatively evaluated using the difference series between the all stations and the rural stations. The results showed that the global land annual mean time series for mean temperature and most extreme temperature indices experienced statistically significant urbanization effects. The urbanization effects in the ETI series generally occurred after the mid-1980s, and there were significant differences of the magnitudes of urbanization effects among different regions. The urbanization effect on the trends of annual temperature indices series in East Asia is generally the strongest, which is consistent with the rapidly urbanization process in the region over the past decades, but it is generally small in Europe during the recent decades.
Quantifying the urbanization effect on station and regional surface air temperature (SAT) trends is a prerequisite for monitoring and detecting long‐term climate change. Based on the data set of satellite visible spectral remote sensing, a new method is developed to determine the urbanization level around observational sites on varied spatial scales and to classify the sites into different categories of stations (U1, U2, …, U6) with U1 the least and U6 the largest affected by urbanization. Urbanization effect on SAT anomaly series of urban and national stations are then evaluated for the periods of 1980–2015 and 1960–2015. Results show that the percentage of built‐up area in different circumferences of the observational sites can be considered as a good indicator of comprehensive urbanization level of station and can be used to classify stations and to determine reference stations; the largest increase in annual mean SAT (Tmean) during 1980–2015 occurred at U6 stations, and U1 stations registered the weakest annual mean warming. The urbanization level is significantly positively correlated to the linear trends of annual mean Tmean and minimum SAT (Tmin) and significantly negatively correlated to the diurnal temperature range (DTR) change. The data sets of the national reference climate station network and basic meteorological station network show large urbanization effect and contribution, with the annual mean urbanization contributions reaching 28.7% and 25.8% for the periods 1960–2015 and 1980–2015, respectively. For all the national stations (2,286 in total), the urbanization contributions are 17.1% and 14.6% for the two same periods, respectively.
Decrease in light precipitation (LP) frequency has been reported in many regions. However, reason for the decrease remains poorly understood. Here, we quantify urbanization effect on LP (< 3.0 mm day−1) trend in China over the period 1960–2018. We show that urbanization has significantly affected the decreasing LP trend. The urbanization effect becomes more significant as the definition of LP becomes stricter, with the largest effect appearing in trace precipitation change (< 0.3 mm day−1) (LP0.3) during summer and autumn. We estimate that at least 25% of the decreases in LP0.3 days and amount are due to urbanization near the observational stations. Our analysis thus confirms that urbanization has largely contributed to the observed downward trend in LP, and the large-scale change in LP is less than previously believed.
Whether the urban heat island (UHI) is affected by air pollution in urban areas has attracted much attention. By analyzing the observation data of automatic weather stations and environmental monitoring stations in Beijing from 2016 to 2018, we found a seasonally dependent interlink of the UHI intensity (UHII) and PM2.5 concentration in urban areas. PM2.5 pollution weakens the UHII in summer and winter night, but strengthens it during winter daytime. The correlation between the UHI and PM2.5 concentration has been regulated by the interaction of aerosol with radiation, evaporation and planetary boundary layer (PBL) height. The former two change the surface energy balance via sensible and latent heat fluxes, while the latter affects atmospheric stability and energy exchange. In summer daytime, aerosol‐radiation interaction plays an important role, and the energy balance in urban areas is more sensitive to PM2.5 concentration than in rural areas, thereby weakening UHII. In winter daytime, aerosol‐PBL interaction is dominant, because aerosols lower the PBL height and stabilize atmosphere, weaken the heat exchange with the surrounding, with more heat accumulated in the urban areas and the increased UHII. Changes in evaporation and radiation strengthen the relationship. At night, the change of UHII more depends on the energy stored in the urban canopy. Aerosols effectively reduce the incident energy during daytime, and the long‐wave radiation from the buildings of urban canopy at night becomes less, leading to a weakened UHII. Our analysis results can improve the understanding of climate‐aerosols interaction in megacities like Beijing.
The causes of the pan-evaporation decline have been debated, and few researches have been carried out on the possible effect of local land use and land cover change on the regional pan-observation data series. In this paper, the urbanization effect on the estimate of pan-evaporation trends over 1961–2017 was examined for the data series of 331 urban stations, applying a previously developed dataset of the reference stations, in seven large river basins of the China mainland. The trends of pan-evaporation difference series (transformed to anomaly percentage) between urban stations and reference stations were negative and statistically significant in all of the basins, indicating that urbanization significantly reduced the pan-evaporation. The urbanization-induced trend in the whole study region was −2.54%/decade for the urban stations. Except for the Yellow River Basin and the upper Yangtze River Basin, the urbanization effects in the other five large river basins of the country are all significant, with the mid and low reaches of the Yangtze River and the Songhua River registering the largest urbanization effects of −4.08%/decade and −4.06%/decade, respectively. Since the trends of regional average series for reference stations across half of the river basins are not statistically significant, the urbanization effect is a dominant factor for the observed decline in pan-evaporation. This finding would deepen our understanding of the regional and basin-wide change in pan-evaporation observed over the last decades.
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