Qi Li

Previous studies have noted that cities enhance cloud cover, but the mechanisms of urban morphological types on cloud formation remain elusive. Observations of cloud climatology from 44 major U.S. cities show that cloud enhancement increases with the street-canyon aspect ratio and decreases with building density. Here, to explain these observations, we conducted numerical experiments using urban morphology-resolving large-eddy simulations. In these simulations, urban and rural surfaces retain their respective heat-flux differences, while the moisture sources and background atmospheric water vapor are prescribed to be identical, allowing us to isolate the morphological controls on moist convection. Results show that urban morphology influences cloud formation through two mechanisms: taller buildings intensify urban-breeze circulations at the urban-rural interface, while denser buildings, acting as momentum sinks, reduce vertical turbulent transport at the urban core. These vertical motions modify the transport of moisture in the urban atmospheric boundary layer, causing different cloud amounts across different urban morphology. This study highlights the mechanistic link between urban form, vertical motions, and cloud enhancement, thus providing a basis for city-specific boundary-layer convective parameterizations in large-scale weather and climate models.

Anthropogenic heat (AH) emissions in urban environments alter the surface energy budget and significantly influence urban climates. However, these emissions vary spatiotemporally, leading to considerable uncertainty in their estimation. As remote sensing in the urban environment advances, the remotely sensed urban surface temperatures are becoming increasingly available. Yet, assimilating these observations into surface energy modelling for AH estimation has not been fully explored. In this study, a model for AH estimation based on the Kalman filter-surface energy balance (KF-SEB) is developed. Urban meteorological data, including air temperature and building surface temperature, are assimilated into the Kalman filter (KF), yielding sensible heat flux, building heat storage and estimated AH using the surface energy balance (SEB) equation. The KF-SEB model is evaluated using two forward models with predefined AH emissions. The first model is a simple slab model, and the second one is a more complex single-layer urban canopy model (UCM). The results show that the KF-SEB model accurately captures the magnitude and temporal variation of AH, with reduced uncertainties compared to previous studies. This study offers a novel approach to AH estimation based on urban meteorological data and provides important insights into the feedback between urban microclimates and anthropogenic energy use. This article is part of the theme issue ‘Urban heat spreading above and below ground’.

Recent advances in urban climate modeling resolution have improved the representation of complex urban environments, with large‐eddy simulation (LES) as a key approach, capturing not only building effects but also urban vegetation and other critical urban processes. Coupling these ultrafine‐resolution (hectometric and finer) approaches with larger‐scale regional and global models provides a promising pathway for cross‐scale urban climate simulations. However, several challenges remain, including the high computational cost that limits most urban LES applications to short‐term, small‐domain simulations, uncertainties in physical parameterizations, and gaps in representing additional urban processes. Addressing these limitations requires advances in computational techniques, numerical schemes, and the integration of diverse observational data. Machine learning presents new opportunities by emulating certain computationally expensive processes, enhancing data assimilation, and improving model accessibility for decision‐making. Future ultrafine‐resolution urban climate modeling should be more end‐user oriented, ensuring that model advancements translate into effective strategies for heat mitigation, disaster risk reduction, and sustainable urban planning.

Previous studies have noted that cities enhance cloud cover, but the mechanisms of urban morphological types on cloud formation remain elusive. Observations of cloud climatology from 44 major U.S. cities show that cloud enhancement increases with the street-canyon aspect ratio and decreases with building density. To explain these observations, we conducted numerical experiments using urban morphology-resolving large-eddy simulations. Results show that urban morphology influences cloud formation through two mechanisms: taller buildings intensify urban-breeze circulations at the urban-rural interface, while denser buildings, acting as momentum sinks, reduce vertical turbulent transport at the urban core. These vertical motions modify the transport of moisture in the urban atmospheric boundary layer, causing different cloud amounts across different urban morphology. This study for the first time establishes a mechanistic link between urban form and cloud enhancement. The results also provide a basis for city-specific boundary-layer convective parameterizations in large-scale weather and climate models.

Climate change has amplified the effects of extreme heat events (EHEs), exacerbating heat-related morbidity and mortality, particularly in cities due to the urban heat island effect. While a long-term solution to mitigating heat exists via urban greening, less is known about how to implement an urban greening plan co-designed by community members that addresses heat-related health outcomes. To examine the current state of urban greening interventions focused on communities and heat mitigation, we conducted a scoping review of papers at the nexus of urban greening/forestry, heat-related health outcomes, and community engagement. We then evaluated 46 eligible papers using a conceptual framework informed by the literature with the following criteria: (1) identification of urban green space/trees for climate change-amplified heat mitigation with quantifiable benefits; (2) association between objective heat-related health outcomes/health equity and urban greening intervention design; and (3) sustainable and ongoing community engagement and/or community co-creation. We found multiple differences between study methods. Most papers lacked objective heat-related health outcomes data and instead focused on subjective thermal comfort/heat stress measures. Additionally, almost all papers utilized one-time community engagement methods such as surveys or interviews to inform urban greening recommendations or study findings. Our findings have useful implications for urban greening decision-making, further emphasizing the importance of transdisciplinary cooperation and long-term community engagement, so an equitable, context-dependent urban greening and health intervention can be effectively co-produced by city planners, public health officials, and community members.

In inland water covering lakes, reservoirs, and ponds, the gas exchange of slightly soluble gases such as carbon dioxide, dimethyl sulfide, methane, or oxygen across a clean and nearly flat air‐water interface is routinely described using a water‐side mean gas transfer velocity , where overline indicates time or ensemble averaging. The micro‐eddy surface renewal model predicts , where is the molecular Schmidt number, is the water kinematic viscosity, and is the waterside mean turbulent kinetic energy dissipation rate at or near the interface. While has been reported across a number of data sets, others report large scatter or variability around this value range. It is shown here that this scatter can be partly explained by high temporal variability in instantaneous around , a mechanism that was not previously considered. As the coefficient of variation in increases, must be adjusted by a multiplier that was derived from a log‐normal model for the probability density function of . Reported variations in with a macro‐scale Reynolds number can also be partly attributed to intermittency effects in . Such intermittency is characterized by the long‐range (i.e., power‐law decay) spatial auto‐correlation function of . That varies with a macro‐scale Reynolds number does not necessarily violate the micro‐eddy model. Instead, it points to a coordination between the macro‐ and micro‐scales arising from the transfer of energy across scales in the energy cascade.