Explore the vast range of titles available.

Cities are generally warmer than their adjacent rural land, a phenomenon known as the urban heat island (UHI). Often accompanying the UHI effect is another phenomenon called the urban dry island (UDI), whereby the humidity of urban land is lower than that of the surrounding rural land 1 – 3 . The UHI exacerbates heat stress on urban residents 4 , 5 , whereas the UDI may instead provide relief because the human body can cope with hot conditions better at lower humidity through perspiration 6 , 7 . The relative balance between the UHI and the UDI—as measured by changes in the wet-bulb temperature ( T w )—is a key yet largely unknown determinant of human heat stress in urban climates. Here we show that T w is reduced in cities in dry and moderately wet climates, where the UDI more than offsets the UHI, but increased in wet climates (summer precipitation of more than 570 millimetres). Our results arise from analysis of urban and rural weather station data across the world and calculations with an urban climate model. In wet climates, the urban daytime T w is 0.17 ± 0.14 degrees Celsius (mean ± 1 standard deviation) higher than rural T w in the summer, primarily because of a weaker dynamic mixing in urban air. This T w increment is small, but because of the high background T w in wet climates, it is enough to cause two to six extra dangerous heat-stress days per summer for urban residents under current climate conditions. The risk of extreme humid heat is projected to increase in the future, and these urban effects may further amplify the risk. An analysis of data from urban and rural areas shows that in wet climates the net effect of temperature and humidity in urban areas is an increase in heat stress.

Increasing urbanization is likely to intensify the urban heat island effect, decrease outdoor thermal comfort, and enhance runoff generation in cities. Urban green spaces are often proposed as a mitigation strategy to counteract these adverse effects, and many recent developments of urban climate models focus on the inclusion of green and blue infrastructure to inform urban planning. However, many models still lack the ability to account for different plant types and oversimplify the interactions between the built environment, vegetation, and hydrology. In this study, we present an urban ecohydrological model, Urban Tethys-Chloris (UT&C), that combines principles of ecosystem modelling with an urban canopy scheme accounting for the biophysical and ecophysiological characteristics of roof vegetation, ground vegetation, and urban trees. UT&C is a fully coupled energy and water balance model that calculates 2 m air temperature, 2 m humidity, and surface temperatures based on the infinite urban canyon approach. It further calculates the urban hydrological fluxes in the absence of snow, including transpiration as a function of plant photosynthesis. Hence, UT&C accounts for the effects of different plant types on the urban climate and hydrology, as well as the effects of the urban environment on plant well-being and performance. UT&C performs well when compared against energy flux measurements of eddy-covariance towers located in three cities in different climates (Singapore, Melbourne, and Phoenix). A sensitivity analysis, performed as a proof of concept for the city of Singapore, shows a mean decrease in 2 m air temperature of 1.1 ∘C for fully grass-covered ground, 0.2 ∘C for high values of leaf area index (LAI), and 0.3 ∘C for high values of Vc,max (an expression of photosynthetic capacity). These reductions in temperature were combined with a simultaneous increase in relative humidity by 6.5 %, 2.1 %, and 1.6 %, for fully grass-covered ground, high values of LAI, and high values of Vc,max, respectively. Furthermore, the increase of pervious vegetated ground is able to significantly reduce surface runoff.

A comprehensive comparison of the trends and drivers of global surface and canopy urban heat islands (termed Is and Ic trends, respectively) is critical for better designing urban heat mitigation strategies. However, such a global comparison remains largely absent. Using spatially continuous land surface temperatures and surface air temperatures (2003–2020), here we find that the magnitude of the global mean Is trend (0.19 ± 0.006°C/decade, mean ± SE) for 5,643 cities worldwide is nearly six‐times the corresponding Ic trend (0.03 ± 0.002°C/decade) during the day, while the former (0.06 ± 0.004°C/decade) is double the latter (0.03 ± 0.002°C/decade) at night. Variable importance scores indicate that global daytime Is trend is slightly more controlled by surface property, while background climate plays a more dominant role in regulating global daytime Ic trend. At night, both global Is and Ic trends are mainly controlled by background climate. Plain Language Summary Surface and canopy urban heat islands (surface and canopy UHIs, termed Is and Ic) are two major UHI types. These two counterparts are both related to urban population heat exposure and have long been a focus of urban climate research. However, the differences in the trends and major determinants of Is and Ic over global cities remain largely unclear. Based on spatially continuous land surface temperature and surface air temperature observations from 2003 to 2020, we find that the global mean Is trends are about 6.3 times and 2 times the Ic trends during the day and at night, respectively. During the day, the global Is trend is more regulated by surface property than by background climate, and vice versa for global Ic trend. At night, both the global Is and Ic trends are mainly regulated by background climate. These findings are important for better understanding global urban climate change and informing heat mitigation strategies. Key Points The global Is trend is six‐fold and twofold larger than the Ic trend during the day and at night, respectively During the day, global Is trend is slightly more controlled by surface property, yet background climate plays a dominant role in Ic trend At night, both global Is and Ic trends are more regulated by background climate

Rapid and uncontrolled urbanization in tropical Africa is increasingly leading to unprecedented socio-economical and environmental challenges in cities, particularly urban heat and climate change. The latter calls for a better representation of tropical African cities’ properties relevant for urban climate studies. Here, we demonstrate the possibility of collecting urban canopy parameters during a field campaign in the boreal summer months of 2018 for deriving a Local Climate Zone (LCZ) map and for improving the physical representation of climate-relevant urban morphological, thermal and radiative characteristics. The comparison of the resulting field-derived LCZ map with an existing map obtained from the World Urban Data and Access Portal Tool framework shows large differences. In particular, our map results in more vegetated open low-rise classes. In addition, site-specific fieldwork-derived urban characteristics are compared against the LCZ universal parameters. The latter shows that our fieldwork adds important information to the universal parameters by more specifically considering the presence of corrugated metal in the city of Kampala. This material is a typical roofing material found in densely built environments and informal settlements. It leads to lower thermal emissivity but higher thermal conductivity and capacity of buildings. To illustrate the importance of site-specific urban parameters, the newly derived site-specific urban characteristics are used as input fields to an urban parametrization scheme embedded in the regional climate model COSMO-CLM. This implementations decreases the surface temperature bias from 5.34 to 3.97 K. Based on our results, we recommend future research on tropical African cities to focus on a detailed representation of cities, with particular attention to impervious surface fraction and building materials.

Urban climate model evaluation often remains limited by a lack of trusted urban weather observations. The increasing density of personal weather sensors (PWSs) 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 carefully quality-checked 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 southeast England during the hot summer of 2018 with the Weather Research and Forecasting (WRF) Model and its building Effect parameterization with the building energy model (BEP–BEM) activated, we evaluated the modeled temperatures against 402 urban PWSs 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. This bias-correction technique is the first to consider that modeled urban temperatures follow a nonlinear spatially heterogeneous bias that is decorrelated from urban fraction. Our results showed that the bias correction was beneficial to bias correct daily minimum, daily mean, and daily maximum temperatures in the cities. We recommend that urban climate modelers further investigate the use of quality-checked PWSs for model evaluation and derive a framework for bias correction of urban climate simulations that can serve urban climate impact studies.

Improved representation of urban processes in Earth System Models (ESMs) is a pressing need for climate modeling and climate‐driven urban energy studies. Despite recent improvements to its fully coupled Building Energy Model (BEM), the current Community Land Model Urban (CLMU) in the Community Earth System Model (CESM) lacks the infrastructure to model air‐conditioning (AC) adoption explicitly. This undermines CESM's fidelity in modeling urban climate and energy use, and limits its use in climate and energy risk assessments. Here, we establish a new parameterization scheme in CESM that represents AC adoption explicitly through an AC adoption rate parameter in the BEM of CLMU, and build a present‐day, global, survey‐based, and spatially explicit AC adoption rate data set at country and sub‐country level that is integrated within CESM. The new data set can be leveraged for other ESMs or global‐scale models and analyses. The explicit AC adoption scheme and the AC adoption rate data set significantly improve the accuracy of anthropogenic heat modeling due to AC in CESM. The new parameterization scheme makes it possible to evaluate the effects of changing AC adoption on global urban energy and climate using CESM. These developments enhance CESM in its use for climate impact assessments under future climate and socioeconomic development scenarios, and represent continued efforts in better representing urban processes and coupled human‐urban‐Earth dynamics in ESMs. Plain Language Summary Human activities in cities, such as building energy use, need to be better represented in models designed to simulate urban climate around the world. The Community Land Model Urban is one such model that has been continuously improved, but still cannot effectively model varying air conditioning (AC) adoption rate across countries or regions. This limitation hinders the model's ability in simulating urban climate and building energy use. Here, we improve the model by developing a new explicit‐AC‐adoption parameterization that represents the proportions of buildings with AC systems, and constructing a global AC adoption rate data set at present‐day for all countries and regions in the world. These improvements help the model simulate the air‐conditioning energy use more accurately, and provide opportunities to evaluate the combined effects of climate change, population growth, and economic development on building energy use and climates for cities around the world. Key Points An explicit air‐conditioning adoption scheme is developed for the building energy model in the Community Land Model Urban A global air‐conditioning adoption rate data set is built for Community Earth System Model, with potential for use in other global‐scale models and analyses The new scheme and data set greatly improve model performance and enable more comprehensive climate and energy risk assessments

Cities in the USA and around the world have begun to take an active role in responding to climate change. A central requirement for effective urban climate strategies is the capacity to understand and measure how the climate is changing, the physical, environmental, and social impacts of the changes, and whether adaptation and resiliency policies and programs put in place in response are working. The objective of this paper is to review and assess how urban climate change and resiliency efforts can be measured and to define what might serve as meaningful indicator and monitoring protocols. The New York City Panel on Climate Change (NPCC) is used as a case study along with a reviews of the emerging literature of urban climate change indicators to analyze the requirements and processes needed for a successful urban climate resiliency indicator and monitoring (I and M) system. In the paper, the basic requirements of a proposed Urban Climate Resilience Indicators and Monitoring System are presented. A specific illustration of an I and M system for tracking the urban heat island highlights challenges as well as potential solutions embedded within such systems. Discussions how these protocols can be translated to other locales and settings, as well as the relationship to the US National Climate Assessment indicator process, are presented.

Transformative capacity (TC) is key for addressing climate change impacts. It refers to urban areas’ ability for profound and intentional change to address current challenges and move towards a more desirable and resilient state. However, its varied applications across disciplines can lead to misunderstandings and implementation challenges. Thus, this Semi-Systematic Literature Review (SSLR) on TC within urban studies from 2016 to 2022 aims to overview and synthesise TC literature and its gaps to inform ongoing debates, intersecting it with climate-related research. The results show an increasing interest in TC within two fields of knowledge: resilience studies and transformative research. The review found TC as a catalyst for transformative actions, promoting sustainable pathways, enhancing resilience, and driving fundamental changes in urban climate adaptation. Finally, the prevailing literature gaps concern the TC concept’s fragmentation, excessive research on governance features, and lack of joint research about TC and innovation.

Increasing the albedo of urban surfaces, through strategies like white roof installations, has emerged as a promising approach for urban climate adaptation. Yet, modeling these strategies on a large scale is limited by the use of static urban surface albedo representations in the Earth system models. In this study, we developed a new transient urban surface albedo scheme in the Community Earth System Model and evaluated evolving adaptation strategies under varying urban surface albedo configurations. Our simulations model a gradual increase in the urban surface albedo of roofs, impervious roads, and walls from 2015 to 2099 under the SSP3‐7.0 scenario. Results highlight the cooling effects of roof albedo modifications, which reduce the annual‐mean canopy urban heat island intensity from 0.8°C in 2015 to 0.2°C by 2099. Compared to high‐density and medium‐density urban areas, higher albedo configurations are more effective in cooling environments within tall building districts. Additionally, urban surface albedo changes lead to changes in building energy consumption, where high albedo results in more indoor heating usage in urban areas located beyond 30°N and 25°S. This scheme offers potential applications like simulating natural albedo variations across urban surfaces and enables the inclusion of other urban parameters, such as surface emissivity. Plain Language Summary Higher albedo surfaces reflect more sunlight, which helps cool down cities. Yet, research into how altering the albedo of urban surfaces on a global scale can aid climate adaptation is limited. It either relies on empirical analysis, oversimplifying urban physical processes, or assumes that urban surface albedo remains constant over time. These limitations hinder our understanding of how changes in urban surfaces can impact the urban thermal environment. In this study, we developed a new option that allows urban surface albedo to vary over time within a global climate model. By gradually increasing global urban surface albedo, we quantified the cooling effects of implementing high urban albedo in a more realistic way. This new option sets the stage for future exploration of scenarios like painting roofs white or how materials age, shedding light on effective urban climate adaptation strategies. Key Points We developed a new representation scheme of transient urban surface albedo in Community Earth System Model (CESM) to improve urban climate adaptation modeling The new scheme enables CESM to assess evolving adaptation strategies for roofs, impervious roads, and walls over time Simulations show increasing roof albedo cools cities more effectively than increasing wall or impervious road albedo

As the frequency and intensity of heatwaves will continue to increase in the future, accurate and high-resolution mapping and forecasting of human outdoor thermal comfort in urban environments are of great importance. This study presents a machine-learning-based outdoor thermal comfort model with a good trade-off between computational cost, complexity, and accuracy compared to common numerical urban climate models. The machine learning approach is basically an emulation of different numerical urban climate models. The final model consists of four submodels that predict air temperature, relative humidity, wind speed, and mean radiant temperature based on meteorological forcing and geospatial data on building forms, land cover, and vegetation. These variables are then combined into a thermal index (universal thermal climate index – UTCI). All four submodel predictions and the final model output are evaluated using street-level measurements from a dense urban sensor network in Freiburg, Germany. The final model has a mean absolute error of 2.3 K. Based on a city-wide simulation for Freiburg, we demonstrate that the model is fast and versatile enough to simulate multiple years at hourly time steps to predict street-level UTCI at 1 m spatial resolution for an entire city. Simulations indicate that neighbourhood-averaged thermal comfort conditions vary widely between neighbourhoods, even if they are attributed to the same local climate zones, for example, due to differences in age and degree of urban vegetation. Simulations also show contrasting differences in the location of hotspots during the day and at night.