Urbanization Impact on Regional Climate and Extreme Weather: Current Understanding, Uncertainties, and Future Research Directions.

Urban environments lie at the confluence of social, cultural, and economic activities and have unique biophysical characteristics due to continued infrastructure development that generally replaces natural landscapes with built-up structures. The vast majority of studies on urban perturbation of local weather and climate have been centered on the urban heat island (UHI) effect, referring to the higher temperature in cities compared to their natural surroundings. Besides the UHI effect and heat waves, urbanization also impacts atmospheric moisture, wind, boundary layer structure, cloud formation, dispersion of air pollutants, precipitation, and storms. In this review article, we first introduce the datasets and methods used in studying urban areas and their impacts through both observation and modeling and then summarize the scientific insights on the impact of urbanization on various aspects of regional climate and extreme weather based on more than 500 studies. We also highlight the major research gaps and challenges in our understanding of the impacts of urbanization and provide our perspective and recommendations for future research priorities and directions.

Identification of surface urban heat versus cool islands for arid cities depends on the choice of urban and rural definitions.

The urban heat island (UHI) effect in arid cities can be small or even negative, the latter known as the urban cool island (UCI) effect. Differences in defining urban and rural areas can introduce uncertainties in detecting UHI or UCI, especially when the UHI signal is small. Here, we compared the surface UHI intensity (SUHII) estimated by a dozen different methods (with multiple urban and/or rural definitions) across 104 arid cities globally, providing a comprehensive evaluation of the uncertainty in SUHII estimates. Results show that the absolute difference in annual average SUHII (∆SUHII) among methods exceeded 1 °C in about half of the arid cities during both daytime and nighttime. The overall annual mean ∆SUHII for all arid cities was 1.35 °C during daytime and 1.03 °C at night. The uncertainty arising from simultaneous variations in urban and rural definitions was generally higher than that resulting from their individual changes. It was observed that, with varying definitions of urban and rural areas, nearly 50 % of arid cities experienced a sign reversal in daytime SUHII estimates, while approximately 15 % exhibited a sign reversal in nighttime SUHII. Variations in urban-rural differences in surface properties, such as vegetation index and albedo, due to differing urban and rural definitions, contributed strongly to the observed SUHII uncertainties. Overall, our results offer new insights into the ongoing debate on heat and cold islands in arid cities, emphasizing a critical need to standardize SUHII estimation frameworks.

Impact of different roofing mitigation strategies on near-surface temperature and energy consumption over the Chicago metropolitan area during a heatwave event.

This study examined the impact of cool roofs, green roofs, and solar panel roofs on near-surface temperature and cooling energy demand through regional modeling in the Chicago metropolitan area (CMA). The new parameterization of green roofs and solar panel roofs based on model physics has recently been developed, updated, and coupled to a multilayer building energy model that is fully integrated with the Weather Research and Forecasting model. We evaluate the model performance against with observation measurements to show that our model is capable of being a suited tool to simulate the heatwave event. Next, we examine the impact by characterizing the near-surface air temperature and its diurnal cycle from experiments with and without the different rooftops. We also estimate the impact of the rooftop on the urban island intensity (UHII), surface heat flux, and the boundary layer. Finally, we measure the impact of the different rooftops on citywide air-conditioning consumption. Results show that the deployment of the cool roof can reduce the near-surface temperature most over urban areas, followed by green roof and solar panel roof. The cool roof experiment was the only one where the near-surface temperature trended down as the urban fraction increased, indicating the cool roof is the most effective mitigation strategy among these three rooftop options. For cooling energy consumption, it can be reduced by 16.6 %, 14.0 %, and 7.6 %, when cool roofs, green roofs, and solar panel roofs are deployed, respectively. Although solar panel roofs show the smallest reduction in energy consumption, if we assume that all electricity production can be applied to cooling demand, we can expect almost a savings of almost half (46.7 %) on cooling energy demand.

Urban Versus Lake Impacts on Heat Stress and Its Disparities in a Shoreline City.

Shoreline cities are influenced by both urban-scale processes and land-water interactions, with consequences on heat exposure and its disparities. Heat exposure studies over these cities have focused on air and skin temperature, even though moisture advection from water bodies can also modulate heat stress. Here, using an ensemble of model simulations covering Chicago, we find that Lake Michigan strongly reduces heat exposure (2.75°C reduction in maximum average air temperature in Chicago) and heat stress (maximum average wet bulb globe temperature reduced by 0.86°C) during the day, while urbanization enhances them at night (2.75 and 1.57°C increases in minimum average air and wet bulb globe temperature, respectively). We also demonstrate that urban and lake impacts on temperature (particularly skin temperature), including their extremes, and lake-to-land gradients, are stronger than the corresponding impacts on heat stress, partly due to humidity-related feedback. Likewise, environmental disparities across community areas in Chicago seen for skin temperature are much higher (1.29°C increase for maximum average values per $10,000 higher median income per capita) than disparities in air temperature (0.50°C increase) and wet bulb globe temperature (0.23°C increase). The results call for consistent use of physiologically relevant heat exposure metrics to accurately capture the public health implications of urbanization.

Global mapping of urban thermal anisotropy reveals substantial potential biases for remotely sensed urban climates.

Urban thermal anisotropy (UTA) drastically impacts satellite-derived urban surface temperatures and fluxes, and consequently makes it difficult to gain a more comprehensive understanding of global urban climates. However, UTA patterns and associated biases in observed urban climate variables have not been investigated across an adequate number of global cities with diverse contexts; nor is it known whether there are globally measurable factors that are closely related to the UTA intensity (UTAI, quantified as the maximum difference between the nadir and off-nadir urban thermal radiation). Here we investigate the UTAI over more than 5500 cities worldwide using multi-angle land surface temperature (LST) observations from 2003 to 2021 provided by Moderate Resolution Imaging Spectroradiometer (MODIS). The results show that the global mean UTAI can reach 5.1, 2.7, 2.4, and 1.7 K during summer daytime, winter daytime, summer nighttime, and winter nighttime, respectively. Using nadir LST observations as a reference, our analysis reveals that UTA can lead to an underestimation of satellite-based urban surface sensible heat fluxes (H) by 45.4% and surface urban heat island intensity (Is) by 43.0% when using LST observations obtained from sensor viewing zenith angles (VZAs) of ±60°. Practitioners can limit the biases of H and Is within ±10% by using LSTs from sensor VZAs within ±30°. We also find that UTAI is closely related to urban impervious surface percentage and surface air temperature across global cities. These findings have implications for angular normalization of satellite-retrieved instantaneous LST observations across cities worldwide.

Using supervised learning to develop BaRAD, a 40-year monthly bias-adjusted global gridded radiation dataset.

Diffuse solar radiation is an important, but understudied, component of the Earth's surface radiation budget, with most global climate models not archiving this variable and a dearth of ground-based observations. Here, we describe the development of a global 40-year (1980-2019) monthly database of total shortwave radiation, including its diffuse and direct beam components, called BaRAD (Bias-adjusted RADiation dataset). The dataset is based on a random forest algorithm trained using Global Energy Balance Archive (GEBA) observations and applied to the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) dataset at the native MERRA-2 resolution (0.5° by 0.625°). The dataset preserves seasonal, latitudinal, and long-term trends in the MERRA-2 data, but with reduced biases than MERRA-2. The mean bias error is close to 0 (root mean square error = 10.1 W m-2) for diffuse radiation and -0.2 W m-2 (root mean square error = 19.2 W m-2) for the total incoming shortwave radiation at the surface. Studies on atmosphere-biosphere interactions, especially those on the diffuse radiation fertilization effect, can benefit from this dataset.

Ocean surface energy balance allows a constraint on the sensitivity of precipitation to global warming.

Climate models generally predict higher precipitation in a future warmer climate. Whether the precipitation intensification occurred in response to historical warming continues to be a subject of debate. Here, using observations of the ocean surface energy balance as a hydrological constraint, we find that historical warming intensified precipitation at a rate of 0.68 ± 0.51% K-1, which is slightly higher than the multi-model mean calculation for the historical climate (0.38 ± 1.18% K-1). The reduction in ocean surface albedo associated with melting of sea ice is a positive contributor to the precipitation temperature sensitivity. On the other hand, the observed increase in ocean heat storage weakens the historical precipitation. In this surface energy balance framework, the incident shortwave radiation at the ocean surface and the ocean heat storage exert a dominant control on the precipitation temperature sensitivity, explaining 91% of the inter-model spread and the spread across climate scenarios in the Intergovernmental Panel on Climate Change Fifth Assessment Report.