Anthropogenic modification of natural landscapes to urban environments impacts land-atmosphere interactions in the boundary layer. Ample research has demonstrated the effect of such landscape transitions on development of the near-surface urban heat island (UHI), while considerably less attention has been given to impacts on regional wind flow. Here we use a set of high-resolution (1 km grid-spacing) regional climate modeling simulations with the Weather Research and Forecasting (WRF) model coupled to a multi-layer urban canopy scheme to investigate the dynamical interaction between the urban boundary layer (UBL) of the Phoenix Metro (U.S.) area and the thermal circulation of the complex terrain it resides within. We conduct paired simulations for the extremely hot and dry summer of 2020, using a contemporary urban representation and a pre-settlement landscape representation to examine the effect of the built environment on local to regional scale wind flow. Analysis of our simulation results shows that, for a majority of the diurnal cycle, 1) the thermo-topographical circulation dominates, 2) the built environment obstructs wind flow in the inertial sublayer during the nighttime, and (3) the built environment of Phoenix Metro produces an UHI circulation of limited vertical extent that interacts with the background flow to modulate its intensity. Such interaction is modulated by greater daytime urban sensible heat flux and dampens the urban roughness induced drag effect by promoting a deeper UBL through vigorous mixing. Our results highlight the need for future research – both observational and simulation based - into urbanizing regions where multi-scale flows are dominant.

Urban climate model evaluation often remains limited by a lack of trusted urban weather observations. The increasing density of personal weather stations (PWS) make them a potential rich source of data for urban climate studies that address the lack of representative urban weather observations. In our study, we demonstrate that PWS data not only improve urban climate models’ evaluation, but can also serve for bias-correcting their output prior to any urban climate impact studies. After simulating near-surface air temperatures over London and south-east England during the hot summer of 2018 with the Weather Research Forecast (WRF) model and its Building Effect Parameterization with the Building Energy Model (BEP-BEM) activated, we evaluated the modelled temperatures against 402 urban PWS and showcased a heterogeneous spatial distribution of the model’s cool bias that was not captured using official weather stations only. This finding indicated a need for spatially-explicit urban bias corrections of air temperatures, which we performed using an innovative method using machine learning to predict the models’ biases in each urban grid cell. Our technique is the first to consider that urban temperatures are heterogeneously accurate in space and that this accuracy is not linearly correlated to the urban fraction. Our results showed that the bias-correction was beneficial to bias-correct daily-minimum, -mean, and -maximum temperatures in the cities. We recommend that urban climate modellers further investigate the use of PWS for model evaluation and derive a framework for bias-correction of urban climate simulations that can serve urban climate impact studies.

Given that more than half of the world’s population currently resides in cities, further understanding of the potential impact of future climate change on urban areas is needed. In this regard, we project recent heatwave (HW) episodes in the Metropolitan Area of Barcelona (AMB) with future climate conditions until 2100 using the pseudo global warming (PGW) method. First, we determine all the HWs that occurred in the AMB during the last climatological period of 30 years (1991-2020) and simulate each individual event using the Weather and Research Forecasting (WRF) model at high-resolution. Then, these historical HW events are re-simulated with the modified atmospheric conditions of the mid-century (2041-2070) and the end-of-the-century (2071-2100) according to the scenario SSP370, in which CO2 emissions are projected to almost double from current levels by 2100 following a low emission reduction scenario. HW intensity is expected to increase by 2.5 °C and 4.2 °C in the mid- and end-of-the-century periods, respectively. Higher temperatures are related to stationary and stable synoptic patterns, which are projected to experience the greatest intensification in the future. The geopotential height at 500 hPa could increase up to 100 geopotential meters (gpm) by the end of the century, leading to values up to 6050 gpm, which indicates changes in thermodynamic and dynamic effects resulting in potentially warmer HW episodes. The results obtained can aid in understanding the expected changes for this century, which could facilitate the formulation of heat mitigation and adaptation strategies, particularly for the most exposed areas.

Comprehensive studies comparing impacts of building and street levels interventions on air temperature at metropolitan scales are still lacking despite increased urban heat-related mortality and morbidity. We therefore model the impact of 9 interventions on air temperatures at 2 m during 2 hot days from the summer 2018 in the Greater London Authority area using the WRF BEP-BEM climate model. We find that on average cool roofs most effectively reduce temperatures ($\sim$~-1.2~$^\circ$C), outperforming green roofs ($\sim$~0~$^\circ$C), solar panels ($\sim$~-0.5~$^\circ$C) and street level vegetation ($\sim$~-0.3~$^\circ$C). Application of air conditioning across London (United Kingdom) increases air temperatures by $\sim$~+0.15~$^\circ$C. A practicable deployment of solar panels could cover its related energetic consumption. Current practicable deployments of green roofs and solar panels are ineffective at large scale reduction of temperatures. We provide a detailed decomposition of the surface energy balance to explain changes in air temperature and guide future decision-making.

Comprehensive studies comparing impacts of building and street levels interventions on air temperature at metropolitan scales are still lacking despite increased urban heat-related mortality and morbidity. We therefore model the impact of 9 interventions on air temperatures at 2 m during 2 hot days from the summer 2018 in the Greater London Authority area using the WRF BEP-BEM climate model. We find that on average cool roofs most effectively reduce temperatures (~ -1.2°C), outperforming green roofs (~ 0°C), solar panels (~ -0.3°C) and street level vegetation (~ -0.3°C). Application of air conditioning across London increase air temperatures by ~ +0.15°C but related energetic consumption could be covered by energy production from solar panels. Current realistic deployments of green roofs and solar panels are ineffective at large scale reduction of temperatures. We provide a detailed decomposition of the surface energy balance to explain changes in air temperature and guide future decision-making.

Urban overheating, driven by global climate change and urban development, is a major contemporary challenge which substantially impacts urban livability and sustainability. Overheating represents a multi-faceted threat to well-being, performance, and health of individuals as well as the energy efficiency and economy of cities, and it is influenced by complex interactions between building, city, and global scale climates. In recent decades, extensive discipline-specific research has characterized urban heat and assessed its implications on human life, including ongoing efforts to bridge neighboring disciplines. The research horizon now encompasses complex problems involving a wide range of disciplines, and therefore comprehensive and integrated assessments are needed that address such interdisciplinarity. Here, the objective is to go beyond a review of existing literature and provide a broad overview and future outlook for integrated assessments of urban overheating, defining holistic pathways for addressing the impacts on human life. We (i) detail the characterization of heat exposure across different scales and in various disciplines, (ii) identify individual sensitivities to urban overheating that increase vulnerability and cause adverse impacts in different populations, (iii) elaborate on adaptive capacities that individuals and cities can adopt, (iv) document the impacts of urban overheating on health and energy, and (v) discuss frontiers of theoretical and applied urban climatology, built environment design, and governance toward reduction of heat exposure and vulnerability at various scales. The most critical challenges in future research and application are identified, targeting both the gaps and the need for greater integration in overheating assessments.

In the present work, the sensitivity of near-surface air temperature and building energy consumption to different rooftop mitigation strategies in the urban environment is evaluated by means of numerical simulations in idealized urban areas, covering a large spectra of possible urban structures, for typical summer and winter conditions. Rooftop mitigation stategies considered include cool roofs, green roofs and rooftop photovoltaic panels. In particular, the latter two rooftop technologies are simulated using two novel parameterization schemes, incorporated in the mesoscale model Weather Research and Fore-5 casting (WRF), coupled with a multilayer urban canopy parameterization and a building energy model (BEP+BEM). Results indicate that near-surface air temperature within the city is reduced by all the RMSs during the summer period: cool roofs are the most efficient in decreasing air temperature (up to 1°C on average), followed by irrigated green roofs with grass vegetation and photovoltaic panels. Green roofs reveal to be the most efficient strategy in reducing the energy consumption by air conditioning systems, up to 45%, because of their waterproof insulating layer, while electricity produced by photovoltaic 10 panels overcomes energy demand by air conditioning systems. During wintertime, green roofs maintain a higher near-surface air temperature than standard roofs, because of their higher thermal capacity and the consequent release of sensible heat during nighttime. On the other hand, photovoltaic panels (during nighttime) and cool roofs (during daytime) reduce near-surface air temperature, resulting in a reduced thermal comfort. Green roofs are the most efficient rooftop mitigation strategy in reducing energy consumption by heating, and are able to reduce the energy demand up to 40% for low rise buildings, while cool roofs 15 always increase consumption due to the decreased temperature. The results presented here show that the novel parameterization schemes implemented in the WRF model can be a valuable tool to evaluate the effects of mitigation strategies in the urban environment. Moreover, this study demonstrates that all rooftop technologies present multiple benefits for the urban environment , showing that green roofs are the most efficient in increasing thermal comfort and diminish energy consumption, while photovoltaic panels can reduce the dependence on fossil fuel consumption through electricity generation.

This paper describes and evaluates novel parameterizations for accounting for the effect of rooftop mitigation strategies on the urban environment, in the context of the mesoscale model Weather Research and Forecasting (WRF), coupled with a urban canopy parameterization and a building energy model (BEP+BEM). Through the new implementation, the sensitivity of near-surface air temperature and building energy consumption to different rooftop mitigation strategies is evaluated by means of numerical simulations in idealized urban areas, for typical summer and winter conditions. Rooftop mitigation strategies considered include cool roofs, green roofs and rooftop photovoltaic panels. Results indicate that near-surface air temperature is reduced by all the RMSs during the summer period: cool roofs are the most efficient in decreasing air temperature (up to 1 °C on average), followed by green roofs and photovoltaic panels. Green roofs reveal to be the most efficient strategy in reducing the energy consumption by air conditioning systems, up to 45%, while electricity produced by photovoltaic panels overcomes energy demand by air conditioning systems. During wintertime, green roofs maintain a higher near-surface air temperature than standard roofs. On the other hand, photovoltaic panels and cool roofs reduce near-surface air temperature, resulting in a reduced thermal comfort. The results presented here show that the novel parameterization schemes implemented in the WRF model can be a valuable tool to evaluate the effects of mitigation strategies in the urban environment. The new model is available as part of the public release of WRF in version 4.3.