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BackgroundExtreme heat events (EHEs) are increasing in frequency in large U.S. cities and are responsible for a greater annual number of climate-related fatalities, on average, than any other form of extreme weather. In addition, low-density, sprawling patterns of urban development have been associated with enhanced surface temperatures in urbanized areas.ObjectivesIn this study. we examined the association between urban form at the level of the metropolitan region and the frequency of EHEs over a five-decade period.MethodsWe employed a widely published sprawl index to measure the association between urban form in 2000 and the mean annual rate of change in EHEs between 1956 and 2005.ResultsWe found that the rate of increase in the annual number of EHEs between 1956 and 2005 in the most sprawling metropolitan regions was more than double the rate of increase observed in the most compact metropolitan regions.ConclusionsThe design and management of land use in metropolitan regions may offer an important tool for adapting to the heat-related health effects associated with ongoing climate change.

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Abstract. Dynamical downscaling has been extensively used to study regional climate forced by large-scale global climate models. During the downscaling process, however, the simulation of regional climate models (RCMs) tends to drift away from the driving fields. Developing a solution that addresses this issue, by retaining the large scale features (from the large-scale fields) and the small-scale features (from the RCMs) has led to the development of "nudging" techniques. Here, we examine the performance of two nudging techniques, grid and spectral nudging, in the downscaling of NCEP/NCAR data with the Weather Research and Forecasting (WRF) Model. The simulations are compared against the results with North America Regional Reanalysis (NARR) data set at different scales of interest using the concept of similarity. We show that with the appropriate choice of wave numbers, spectral nudging outperforms grid nudging in the capacity of balancing the performance of simulation at the large and small scales.