Effects of urbanization on winter wind chill conditions over China

Combining computational fluid dynamics and neural networks to characterize microclimate extremes: Learning the complex interactions between meso-climate and urban morphology

The urban form and extreme microclimate events can have an important impact on the energy performance of buildings, urban comfort and human health. State-of-the-art building energy simulations require information on the urban microclimate, but typically rely on ad-hoc numerical simulations, expensive in-situ measurements, or data from nearby weather stations. As such, they do not account for the full range of possible urban microclimate variability and findings cannot be generalized across urban morphologies. To bridge this knowledge gap, this study proposes two data-driven models to downscale climate variables from the meso to the micro scale in arbitrary urban morphologies, with a focus on extreme climate conditions. The models are based on a feedforward and a deep neural network (NN) architecture, and are trained using results from computational fluid dynamics (CFD) simulations of flow over a series of idealized but representative urban environments, spanning a realistic range of urban morphologies. Both models feature a relatively good agreement with corresponding CFD training data, with a coefficient of determination R² = 0.91 (R² = 0.89) and R² = 0.94 (R² = 0.92) for spatially-distributed wind magnitude and air temperature for the deep NN (feedforward NN). The models generalize well for unseen urban morphologies and mesoscale input data that are within the training bounds in the parameter space, with a R² = 0.74 (R² = 0.69) and R² = 0.81 (R² = 0.74) for wind magnitude and air temperature for the deep NN (feedforward NN). The accuracy and efficiency of the proposed CFD-NN models makes them well suited for the design of climate-resilient buildings at the early design stage.

The Temporal Variation of the Microclimate and Human Thermal Comfort in Urban Wetland Parks: A Case Study of Xixi National Wetland Park, China

As an important part of the ecological infrastructure in urban areas, urban wetland parks have the significant ecological function of relieving the discomfort of people during their outdoor activities. In recent years, the specific structures and ecosystem services of urban wetland parks have been investigated from different perspectives. However, the microclimate and human thermal comfort (HTC) of urban wetland parks have rarely been discussed. In particular, the changing trends of HTC in different seasons and times have not been effectively presented. Accordingly, in this research, a monitoring platform was established in Xixi National Wetland Park, China, to continually monitor its microclimate in the long term. Via a comparison with a control site in the downtown area of Hangzhou, China, the temporal variations of the microclimate and HTC in the urban wetland park are quantified, and suggestions for clothing are also provided. The results of this study demonstrate that urban wetland parks can mitigate the heat island effect and dry island effect in summer. In addition, urban wetland parks can provide ecological services at midday during winter to mitigate the cold island effect. More importantly, urban wetland parks are found to exhibit their best performance in improving HTC during the daytime of the hot season and the midday period of the cold season. Finally, the findings of this study suggest that citizens should take protective measures and enjoy their activities in the morning, evening, or at night, not at midday in hot weather. Moreover, extra layers are suggested to be worn before going to urban wetland parks at night in cold weather, and recreational activities involving accommodation are not recommended. These findings provide not only basic scientific data for the assessment of the management and ecological health value of Xixi National Wetland Park and other urban wetland parks with subtropical monsoon climates, but also a reference for visitor timing and clothing suggestions for recreational activities.

Contribution of urbanization to the changes in extreme climate events in urban agglomerations across China

Unprecedented urbanization in China facilitates the rapid development of urban agglomerations (UAs) and may exert prominent effects on regional climate and environment change. By analyzing a set of 27 extreme temperature and precipitation indices, this study examines the changes in extreme climate events in 20 UAs in China and evaluates the urbanization effects using a dynamic classification of urban and rural stations by time-varying land use/cover maps. The regional differences of the urbanization effects on extreme climate events are also investigated by a k-means clustering. It is found that, for both temperature and precipitation extremes, the urban and rural areas exhibit remarkably distinct changes and demonstrate significant urbanization effect, which also varies across different climate backgrounds. Urbanization profoundly contributes to increasing hot extremes and reducing cold extremes in most UAs, while it seems to pose the opposite effects in several UAs of arid and high-latitude regions. On average, the urbanization effect accounts for around 30% of the total change in extreme temperature events over the urban core areas of 20 UAs. On the other hand, the urbanization effects on extreme precipitation indices display stronger regional discrepancies than temperature extremes. Urbanization tends to have weakening effects on extreme precipitation events in UAs over coastal regions and intensifying influences on those in central/west China. It causes more (less) frequent and more (less) intense precipitation in UAs of inland central/west (coastal) areas. Our findings provide a systematic understanding of the urbanization effects on extreme climate and may have important implications for the mitigation of urban disasters.

Cool Roof and Green Roof Adoption in a Metropolitan Area: Climate Impacts during Summer and Winter

This study, for the first time, estimates the climate impacts of adopting green roofs and cool roofs on the seasonal urban climate of 16 cities that comprise the Yangtze River Delta metropolitan. We use a suite of regional climate simulation to compare the local climate impacts of the implementation of different roof strategies in summer and winter. The results indicate that in summer, the 2 m surface temperature reduced significantly when these two roof strategies are adopted, with peak reductions of 0.74 and 1.19 K for green roofs and cool roofs, respectively. The cooling impact of cool roofs is more effective than that of green roofs under the scenarios assumed in this study. Besides, rooted in the different mechanisms influencing urban heat flux, significant indirect effects were also observed: adopting cool roofs leads to a decreased precipitation in summer and an apparent reduction in wintertime temperatures in the urban area. Although cool roofs can be an effective way to reduce high temperatures during the summer, green roofs have fewer adverse impacts on other climate conditions. These results underline the need for comprehensive climate change policies that incorporate place-based solutions and extend beyond the nearly exclusive focus on summertime cooling.