Abstract
Land-use and land cover changes may have important local, regional, and global climatic impacts by modifying the underlying land surface conditions, which in turn influence the exchange of energy and moisture between the land surface and atmosphere. Many studies have shown that urbanization has contributed to climate warming, and the amount of warming has varied. As the capital of China and one of the world’s megacities, Beijing has experienced rapid urbanization over the past 30 years. In this study, we quantitatively investigated the impacts of urbanization on regional temperatures based on observations from meteorological stations and National Centers for Environmental Prediction (NCEP) reanalysis data and overestimating of the impacts were found. Comparing the temperature trends of land-use types, forest showed stronger inhibitory effects on temperature increase (−0.085°C/10a). Cropland also had a negative effect on climate warming yearly and seasonally, especially in winter (−1.133°C/10a) and spring (−0.299°C/10a). Conversely, the urban area showed strong warming effects (0.438°C/10a). The conversion of cropland to urban land appeared to show the highest warming trend (0.548°C/10a). However, the cooling effect of forest and grassland with high vegetation coverage inhibited climatic warming attributed to rapid urbanization. In addition, planting trees or grass along roadsides and increasing green parks and green roofs can also suppress surface warming. Therefore, the actual warming effects of urbanization on temperatures were overestimated in megacities or urban agglomeration regions. The results showed that the green space and landscape configuration should be considered in urban planning to increase green space and reduce the influence of urban heat island effect.
Highlights
Increased greenhouse gases represent one of the primary factors underlying global climate change
Stations in the suburban area showed a larger increase in temperature, and the nearest suburban stations to the city center, such as Daxing, Tongzhou, and Shunyi exhibited the most significant warming trends, with values of 0.95°C/10a, 0.93°C/10a, and 0.85°C/10a, respectively. ese trends were higher than those of stations in the urban area, such as Shijingshan, Haidian, and Beijing. e warming trends for the Shangdianzi, Zhaitang, Xiayunling, and Tanghekou stations, which were located in rural areas, were not remarkable; Tanghekou station showed the lowest trend of 0.004°C/10a
Precipitation was higher than that in other seasons, and soil moisture would be higher; most of the radiative energy would be absorbed at the ground surface and used for physical evaporation and transpiration of water. us, the partitioned latent heat would be higher while the sensible heat would be lower, which could explain why the temperature trend was lower in the wet season
Summary
Increased greenhouse gases represent one of the primary factors underlying global climate change. Fully separating the climatic impacts of LULC changes and global warming is difficult. Climate models such as the community land model (CLM), the community climate system model (CCSM), the dynamic global vegetation model (DGVM), the Lund–Potsdam–Jena model for managed land (LPJmL), the general circulation model (GCM), and the simplified parameterizations primitive equation dynamics model (SPEEDY) were used to simulate, couple, or compare for estimating the effects of LULC on climate change [6,7,8]. E simulations were based on land cover or vegetation change experiments and comparisons of climatic parameters, such as temperature and precipitation, simulated from actual and potential land-use scenarios Climate models such as the community land model (CLM), the community climate system model (CCSM), the dynamic global vegetation model (DGVM), the Lund–Potsdam–Jena model for managed land (LPJmL), the general circulation model (GCM), and the simplified parameterizations primitive equation dynamics model (SPEEDY) were used to simulate, couple, or compare for estimating the effects of LULC on climate change [6,7,8]. e simulations were based on land cover or vegetation change experiments and comparisons of climatic parameters, such as temperature and precipitation, simulated from actual and potential land-use scenarios.
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