This study explores the sensitivity of high‐resolution mesoscale simulations of urban heat island (UHI) in the Chicago metropolitan area (CMA) and its environs to urban physical parameterizations, with emphasis on the role of lake breeze. A series of climate downscaling experiments were conducted using the urban‐Weather Research and Forecasting (uWRF) model at 1‐km horizontal resolution for a relatively warm period with a strong lake breeze. The study employed best available morphological data sets, selection of appropriate urban parameters, and estimates of anthropogenic heating sources for the CMA. Several urban parameterization schemes were then evaluated using these parameter values. The study also examined (1) the impacts of land data assimilation for initialization of the mesoscale model, (2) the role of urbanization on UHI and lake breeze, and (3) the effects of sub‐grid scale land‐cover variability on urban meteorological predictions. Comparisons of temperature and wind simulations with station observations and Moderate Resolution Imaging Spectroradiometer satellite data in the CMA showed that uWRF, with appropriate selection of urban parameter values, was able to reproduce the measured near‐surface temperature and wind speeds reasonably well. In particular, the model was able to capture the observed spatial variation of 2‐m near‐surface temperatures at night, when the UHI effect was pronounced. Results showed that inclusion of sub‐grid scale variability of land‐use and initializing models with more accurate land surface data can yield improved simulations of near‐surface temperatures and wind speeds, particularly in the context of simulating the extent and spatial heterogeneity of UHI effects.

The interaction of global climate change and urban heat islands (UHI) is expected to have far-reaching impacts on the sustainability of the world’s rapidly growing urban population centers. Given that a wide range of spatiotemporal scales contributed by meteorological forcing and complex surface heterogeneity complicates UHI, a multimodel nested approach is used in this paper to study climate-change impacts on the Chicago, Illinois, UHI, covering a range of relevant scales. One-way dynamical downscaling is used with a model chain consisting of global climate (Community Atmosphere Model), regional climate (Weather Research and Forecasting Model), and microscale (“ENVI-met”) models. Nested mesoscale and microscale models are evaluated against the present-day observations (including a dedicated urban miniature field study), and the results favorably demonstrate the fidelity of the downscaling techniques that were used. A simple building-energy model is developed and used in conjunction with microscale-model output to calculate future energy demands for a building, and a substantial increase (as much as 26% during daytime) is noted for future (~2080) climate. Although winds and lake-breeze circulation for future climate are favorable for reducing energy usage by 7%, the benefits are outweighed by such factors as exacerbated UHI and air temperature. An adverse change in human-comfort indicators is also noted in the future climate, with 92% of the population experiencing thermal discomfort. The model chain that was used has general applicability for evaluating climate-change impacts on city centers and, hence, for urban-sustainability studies.

Heatwaves lead to catastrophic consequences on public health and the economy. Accurate and timely predictions of regional heatwaves can improve climate preparedness and foster decision‐making to alleviate the burdens due to climate change. In this paper, we propose a heatwave prediction algorithm based on a novel deep learning model, that is, Graph Neural Network (GNN). This new GNN framework can provide real time warnings of the sudden occurrence of regional heatwaves with high accuracy at lower costs of computation and data collection. In addition, its interpretable structure unravels the spatiotemporal patterns of regional heatwaves and helps to enrich our understanding of the general climate dynamics and the causal influences between locations. The proposed GNN framework can be applied for the detection and prediction of other extreme or compound climate events, which calls for future studies.