Search

Although the climate has admittedly always been hot and humid in cities like Karachi, increasingly hotter temperatures are exacerbating the impact of heat on informal, precariously employed outdoor workers such as street vendors, guards and rickshaw drivers, who must negotiate their right and access to shade at the everyday scale. Recalling Mike Davis' radical, political claim that shade is an inalienable human right, this paper proposes that few people working in the outdoor spaces of the South Asian city today understand or experience shade in these terms. Rather shade is something that must be claimed, alongside other rights and entitlements. Moreover, shade alone is insufficient as it cannot reduce the exposure of bodies to harmful ambient radiations and overall thermal discomfort. This paper makes three broad propositions for outlining a theory for the social study of shade in the South Asian city. By paying closer attention to the ways that outdoor workers negotiate shade in Karachi, this paper opens up for analysis a wider spectrum of claims-making activity in changing South Asian urban climates. It places workers' search for shade in the broader context of shade policing and urban management aimed at creating spatial as well as social order. Finally, this article emphasises key directions and questions for future research. [ABSTRACT FROM AUTHOR]

Until the 1990s and the spread of air-conditioning, cooling down during the hot, humid and windless summers in the city of Chongqing (Southwest China) was mainly practised outdoors: sleeping on the rooftops of multistorey buildings, playing mah-jongg in the streets, fanning oneself with a hand fan or installing bamboo beds in the compounds' leafy courtyards. With the availability of affordable electricity and the popularisation of mechanical cooling, refreshing oneself has been relocated to the indoors. The transforming practices in and around the house have led not only to an increasing dependency on electricity for cooling but also to a socio-economic stratification. This paper traces the history of heat mitigation in Chongqing since the 1950s. Based on five months of anthropological fieldwork, semi-structured interviews, and oral history, I analyse how Chongqing residents cope with heat in and around the built environment. Practices of cooling are closely intertwined with the architectural history of the city, for example, building design, construction materials, green spaces, or the arrangement of houses. Staying cool in the socialist era buildings from the 1960s meant something different compared to the high-rise buildings in the early-21st century. Theoretically, the paper engages with urban energy landscapes as 'connective tissue' where everyday heat mitigating practices are intertwined with the locally built environment including architecture, energy infrastructure and technologies. By focusing on the material culture involved in cooling, I shift our perspective from the large infrastructure to the small objects that co-constitute the energy landscape of urban heat mitigation. [ABSTRACT FROM AUTHOR]

Local wind, such as sea breezes, play a crucial role in cooling coastal cities. This study presents new insights about the thermo‐hygrometric influence of the Tagus and Atlantic Ocean breezes (sea and estuarine breezes [SEBs]) in Lisbon's urban climate (Portugal). SEB events were identified in the summer of 2022 according to a wind rotation criterion: the interruption of prevailing North and Northwest (Nortada) winds during the morning, the wind shift to Northeast/East/Southeast and, sometimes, to further South/Southwest/West (rotation between 22.6° and 292.5°) and the return of the regional flow at late afternoon. Additionally, air temperature and absolute humidity anomalies (ΔT/Haurb) were calculated according to the distance to the riverfront area. Results show that SEB occurred on 37 (31%) out of 120 days, mainly in July (43%) and August (32%), between, on average, 10:00 AM and 4:00 PM, and average wind speeds of 3.4 m/s. According to the daily thermo‐hygrometric cycle, the areas up to 4 km of the Tagus estuary were, on average, cooler than northern Lisbon during SEB events, especially the areas up to 500 m (average ΔTurb reached −1.7°C). Additionally, there was a significant increase in the moisture content during SEB hours across the city but especially close to the riverfront area: the areas up to 500 m registered, on average, ΔHaurb of 4.2 g/m3 on SEB events (12:00 PM) against 2.1 g/m3 during typical Nortada days. This research is a starting point for a future delimitation and preservation of SEB penetration zones in Lisbon to address outdoor thermal discomfort during summer. [ABSTRACT FROM AUTHOR]

Urban surfaces such as rooftops, facades, and infrastructure offer significant potential for solar energy integration, contributing to energy efficiency and sustainability in cities. This article introduces an advanced multi-criteria assessment (MCA) framework designed to evaluate the suitability of various urban surfaces for solar energy deployment. The framework extends beyond traditional economic, environmental, and technological factors to include social, political, legal, health and safety, cultural, and psychological dimensions, providing a comprehensive evaluation of photovoltaic (PV) applications in urban contexts. By synthesizing existing literature and applying this holistic MCA framework, this research offers valuable insights for urban planners, architects, and policymakers, enabling strategic optimization of solar energy integration in urban environments. The findings underscore the importance of sustainable urban development and climate resilience, highlighting key factors influencing solar technology deployment and proposing actionable recommendations to address existing challenges. [ABSTRACT FROM AUTHOR]

The urban heat island effect (UHI) is among the major challenges of urban climate, which is continuously intensifying its impact on urban life and functioning. Against the backdrop of increasingly prolonged heatwaves observed in recent years, practical questions about adaptation measures in cities are growing—questions that traditional meteorological monitoring can hardly answer adequately. On the other hand, UHI has long been the focus of research interest, but due to the technological complexity of providing accurate spatially referenced data at high spatial resolution and the requirement to survey at strictly defined parts of the day, information provision is becoming a major challenge. This is one of the main reasons why UHI research results are less often used directly in urban spatial planning. However, advances in geospatial technologies, including unmanned aerial systems (UASs), are providing more and more reliable tools that can be applied to achieve better and higher-quality information resources that adequately characterize the UHI phenomenon. This paper presents a developed and tested methodology for the rapid and efficient assessment and mapping of the effects of surface urban heat island (SUHI). It is entirely based on the integrated use of data from unmanned aerial systems (UAS)-based remote sensing methods, including thermal photogrammetry and GIS-based analysis methods. The study follows the understanding that correct SUHI research depends on a proper understanding of the urban geosystem, its spatial and structural heterogeneity, and its functional systems, which in turn can only be achieved by supporting the research process with accurate and reliable information resources. In this regard, the possibilities offered by the proposed methodological scheme for efficient geospatial registration of SUHI variations at the microscale, including the calculation of intra-urban SUHI intensity, are discussed in detail. The methodology builds on classical approaches for using local climate zoning (LCZ), adding capabilities for precise delineation of individual zone types and for geostatistical characterization of the urban surface heat island (SUHI). Finally, the proposed scheme is based on state-of-the-art technological tools that provide flexible and automated capabilities to investigate the phenomenon at microscales, including by enabling flexible observation of its dynamics in terms of heat wave manifestation and evolution. Results are presented from a series of sequential tests conducted on the largest residential area in Bulgaria's capital city, Sofia, in terms of area and population, over a relatively long period from 2021 to 2024. [ABSTRACT FROM AUTHOR]

Meteorological mesoscale models with different urban parametrization are used to predict the local urban climate at 250 m resolution. The authors propose a hybrid machine learning approach to improve the mesoscale prediction accuracy using measured air temperature data from a sensor network and remove simulation bias. The simulation of the urban climate of Zurich during a hot summer is used as case study showing the improvements of the simulation accuracy. Based on the hybrid model results, a cumulative heat exposure index is proposed to map local hotspots in the city and assess the difference of cooling loads between rural and urban environments. Furthermore, intra-urban microclimatic differences of a typical mid-latitude city are explored to highlight the benefits of detailed simulations for building physics purposes. [ABSTRACT FROM AUTHOR]

High-resolution urban climate projections are needed for local decision-making on climate change adaptation. Regional climate models have resolutions that are too coarse to simulate the urban climate at such resolutions. A novel statistical–dynamical downscaling (SDD) approach is used here to downscale the EURO-CORDEX ensemble to a resolution of 1 km while adding the effect of the city of Paris (France) on air temperature. The downscaled atmospheric fields are then used to drive the Town Energy Balance urban canopy model to produce high-resolution temperature maps over the period 1970–2099, while maintaining the city's land cover in its present state. The different steps of the SDD are evaluated for the summer season. The regional climate models simulate minimum (maximum) temperatures (TN/TX) that are too high (low). After correction and downscaling, the urban simulations inherit some of these biases but give satisfactory results for summer urban heat islands (UHIs), with average biases of −0.6 K at night and +0.3 K during the day. Changes in future summer temperatures are then studied for two greenhouse gas emission scenarios, RCP4.5 and RCP8.5. Outside the city, the simulations project average increases of 4.1 and 4.8 K for TN and TX for RCP8.5, respectively. In the city, warming is lower, resulting in a decrease in UHIs of −0.19 K at night (from 2.1 to 1.9 K) and −0.16 K during the day. The changes in UHIs are explained by higher rates of warming in rural temperatures due to lower summer precipitation and soil water content and are partially offset by increased ground heat storage in the city. [ABSTRACT FROM AUTHOR]

Accurate and reliable estimation of rainfall is crucial for scientific research and various applications. However, the observed rainfall data is often limited. With the advancements in technology, many global gridded rainfall products are now available, but their accuracy levels vary across the world. In this study, we comprehensively analyzed the reliability and effectiveness of 23 publicly available global rainfall datasets against the observed rainfall for Patna, representing a typical urban monsoon climate in India. Thirteen continuous and ten categorical statistical metrics were applied at daily, weekly, monthly, and annual intervals over 16 years (2000–2015). The results indicate that the reliability of all derived rainfall datasets varied on different temporal scales and reference datasets used. Overall, in continuous metrics, MERRA2 and MSWEP consistently outperformed in all the temporal scales whereas in categorical metrics for analyzing the rainfall detection ability, AIMERG, followed by MERRA2 demonstrated superior performance among others. Furthermore, IMD GRID, GSMAP, PCCS, AIMERG, and IMERG performed well in estimating different rainfall intensities. MERRA2 and MSWEP, which have not been widely considered for evaluation in a monsoon climate were found to be outstanding performers consistently. Therefore, we suggest broadening the selection of global rainfall products in the evaluation to fully utilize the potentiality of all available options. Furthermore, our approach offers a reliable framework to comprehensively assess the performance of different gridded rainfall products and assist in the selection of the best rainfall product for a particular region and purpose. [ABSTRACT FROM AUTHOR]

The Local Climate Zone (LCZ) scheme representing urban structure and land use pattern is essential for urban heat island (UHI) research. Fine-grained LCZ mapping considering spatial and temporal heterogeneity can provide a more precise characterization of surface thermal properties, thereby enabling a comprehensive analysis and understanding of spatiotemporal trends in climate change research. However, data-driven deep learning-based methods have limitations in coping with the complex urban landscapes of the real-world scenarios, including the spectral similarity of different LCZ and the geospatial heterogeneity of urban LCZ categories. In this study, we constructed a geographic knowledge base for enhanced LCZ characterization with the consideration of prior surface spatial information, including a set of spectral indices and urban morphological parameters (UMPs). Then, we integrated the explicable geographic knowledge base into a learnable deep learning framework in an end-to-end manner for accurate LCZ mapping by fusing multi-source heterogeneous data with a multi-level fusion strategy. The constructed Chinese Climate Zone Time Series LCZ (CClimate-TLCZ) dataset derived from Landsat-8 data with a 30 m spatial resolution, covering 18 representative cities in China, were used to evaluate the proposed framework. The experimental results demonstrate that the proposed framework achieved optimal outcomes across 18 cities, with an average overall accuracy exceeding 94 %, which is more than 20 % higher than that obtained by the standard WUDAPT method. Furthermore, the analysis of LCZ mapping-driven land surface temperature (LST) and surface UHI (SUHI) applications shows that cities within the same climate zone have similar LST distribution patterns, while significant heterogeneity exist between different zones. The annual consistency of LST patterns within each climate zone supports the validity of the LCZ classification scheme and accurate LCZ mapping for urban thermal environment studies. From 2016 to 2022, SUHI intensity initially increases and then decreases, indicating improvements in the urban thermal environment. These findings underscore the critical role of precise LCZ mapping in urban climate resilience and sustainable urban planning. [ABSTRACT FROM AUTHOR]

Local governments have taken an active role in climate change policymaking and have filled in gaps from the national and state levels in the USA. However, even with the growing scholarly interest in urban climate policymaking, there is still a lot that scholars do not understand about it. The current research project seeks to provide a deeper investigation into how municipalities interact with their citizens in sustainability and climate change policymaking. It further analyzes the specific ways that citizens are included in climate policymaking. To examine these questions, the research uses a mixed methods approach, combining survey and interview data. Findings reveal that municipal characteristics are related to citizen involvement around climate change, but that there are also specific motivations for how local governments interact with their citizens, particularly related to increasing citizen buy-in and diversity. The research provides implications for how local governments conceptualize citizen involvement for sustainability policymaking and how they might approach the issue in the future. [ABSTRACT FROM AUTHOR]

The complexities of urban climate and environmental challenges have garnered significant attention in the 21st century. Numerical simulations, offering high spatiotemporal resolution meteorological data, are essential tools in meteorological research and atmospheric science. Accurate representation of urban morphology parameters is crucial for enhancing the precision of these simulations in urban areas. Despite the availability of urban canopy parameter (UCP) data for 44 major cities in the United States and 60 in China for the weather research and forecasting (WRF) model, a comprehensive global dataset representing urban morphology remains absent. This study addresses this gap by leveraging existing global three-dimensional vector data of buildings, including footprints and heights, to compile a global 1 km spatially continuous UCP (GloUCP) dataset for the WRF model. Our findings indicate that GloUCP not only surpasses existing datasets in accuracy but also provides superior spatial coverage. In key urban agglomerations such as Beijing-Tianjin-Hebei, the Yangtze River Delta, and the Guangdong-Hong Kong-Macao Greater Bay Area in China, GloUCP offers detailed and reliable urban morphological information that closely aligns with reference datasets, outperforming other available sources. Similarly, in U.S. cities like Seattle, San Francisco, and Philadelphia, GloUCP consistently achieves lower RMSE values and higher correlation coefficients, demonstrating its robustness in modeling diverse urban environments. Furthermore, GloUCP's capability to effectively capture the vertical distribution of buildings, particularly in high-rise areas, highlights its utility in urban climate modeling and related applications. As UCPs are pivotal in regulating atmospheric responses to urbanization, the availability of this globally consistent urban description is a crucial prerequisite for advancing model development and informing climate-sensitive urban planning policies. The GloUCP dataset, converted to WRF binary file format, is available for download at https://doi.org/10.6084/m9.figshare.27011491 (Liao et al., 2024). [ABSTRACT FROM AUTHOR]

Improvements in high-resolution satellite remote sensing and computational advancements have sped up the development of global datasets that delineate urban land, crucial for understanding climate risks in our increasingly urbanizing world. Here, we analyze urban land cover patterns across spatiotemporal scales from several such current-generation products. While all the datasets show a rapidly urbanizing world, with global urban land nearly tripling between 1985 and 2015, there are substantial discrepancies in urban land area estimates among the products influenced by scale, differing urban definitions, and methodologies. We discuss the implications of these discrepancies for several use cases, including for monitoring urban climate hazards and for modeling urbanization-induced impacts on weather and climate from regional to global scales. Our results demonstrate the importance of choosing fit-for-purpose datasets for examining specific aspects of historical, present, and future urbanization with implications for sustainable development, resource allocation, and quantification of climate impacts. There has been a surge in global datasets of urban land recently. This paper shows large discrepancies in urban area across scales among multiple such datasets, which can influence the magnitude and direction of estimated urban climate impacts. [ABSTRACT FROM AUTHOR]

Remote sensing satellite data have been a crucial tool in understanding urban climates. The variety of sensors with different spatiotemporal characteristics and retrieval methodologies gave rise to a multitude of approaches when analyzing the surface urban heat island effect (SUHI). Although there are considerable advantages that arise from these different characteristics (spatiotemporal resolution, time of observation, etc.), it also means that there is a need for understanding the ability of sensors in capturing spatial and temporal SUHI patterns. For this, several land surface temperature products are compared for the cities of Madrid and Paris, retrieved from five sensors: the Spinning Enhanced Visible and InfraRed Imager onboard Meteosat Second Generation, the Advanced Very-High-Resolution Radiometer onboard Metop, the Moderate-resolution Imaging Spectroradiometer onboard both Aqua and Terra, and the Thermal Infrared Sensor onboard Landsat 8 and 9. These products span a wide range of LST algorithms, including split-window, single-channel, and temperature–emissivity separation methods. Results show that the diurnal amplitude of SUHI may not be well represented when considering daytime and nighttime polar orbiting platforms. Also, significant differences arise in SUHI intensity and spatial and temporal variability due to the different methods implemented for LST retrieval. [ABSTRACT FROM AUTHOR]

The connection between plant species and nest site preference for the Asian weaver ant Oecophylla smaragdina remains poorly understood despite their common occurrence in the urban areas of tropical regions. We predicted that plant species with specific leaf characteristics influence the nesting preference of Asian weaver ants, given that plant foliage is the main component of their nest structure. We studied the leaf characteristics of the tree species in which their nests were detected, including leaf shape, leaf apex shape, and leaf arrangement. Of the 206 total trees among the 20 tree species sampled, 185 trees from 18 tree species had at least one nest. Trees of the genus Lagerstroemia had the highest median nest abundance, whereas all Roystonea regia and Caryota no trees, and some Samanea saman trees, were not occupied by O. smaragdina. For the trees found to have O. smaragdina nests, the nest abundance in each tree ranged between 1 and 16 nests, with the majority having fewer than 10 nests per tree (4.38 ± 4.45). Based on nest abundance, the oblong and elliptic leaf shapes (LS), the cuspidate leaf apex shape (LX), and the opposite leaf arrangement (LR) were the most used for nest construction, consisting of more than 50% of the total in each respective category. A Kruskal-Wallis test revealed that O. smaragdina nest site preference is influenced by LX and LR but not by LS. Additionally, O. smaragdina nest abundance was negatively correlated to leaf surface ratio but insignificant for leaf surface area. These preferred qualitative and quantitative leaf characteristics determine the species of plants used as nesting sites to facilitate nest-building efficiency and the establishment of Asian weaver ant colonies. [ABSTRACT FROM AUTHOR]

Urban Land Surface Models (ULSMs) simulate energy and water exchanges between the urban surface and atmosphere. However, earlier systematic ULSM comparison projects assessed the energy balance but ignored the water balance, which is coupled to the energy balance. Here, we analyze the water balance representation in 19 ULSMs participating in the Urban‐PLUMBER project using results for 20 sites spread across a range of climates and urban form characteristics. As observations for most water fluxes are unavailable, we examine the water balance closure, flux timing, and magnitude with a score derived from seven indicators expecting better scoring models to capture the latent heat flux more accurately. We find that the water budget is only closed in 57% of the model‐site combinations assuming closure when annual total incoming fluxes (precipitation and irrigation) fluxes are within 3% of the outgoing (all other) fluxes. Results show the timing is better captured than magnitude. No ULSM has passed all water balance indicators for any site. Models passing more indicators do not capture the latent heat flux more accurately refuting our hypothesis. While output reporting inconsistencies may have negatively affected model performance, our results indicate models could be improved by explicitly verifying water balance closure and revising runoff parameterizations. By expanding ULSM evaluation to the water balance and related to latent heat flux performance, we demonstrate the benefits of evaluating processes with direct feedback mechanisms to the processes of interest. Plain Language Summary: Urban environments have their own local climates including typically higher nocturnal temperatures compared with rural areas. Ideally, modeling cities should capture their influences on the atmosphere above them. As the energy and water balances are linked by evaporation, a good water balance representation will support a good energy balance simulation. Focusing on the water balance, we find the water balance in models could be improved by paying attention to closure and runoff. Key Points: We evaluate the water balance in 19 urban land surface models (ULSM) from the Urban‐PLUMBER projectULSMs capture the timing of water fluxes more accurately than their magnitudeThe water balance appears unclosed in 43% of the model runs (19 models at 20 sites) [ABSTRACT FROM AUTHOR]

The Model for Prediction Across Scales–Atmosphere (MPAS-A) has been widely used for larger scale simulations, but its performance in mesoscale, particularly in tropical regions, is less evaluated. This study aimed to assess MPAS-A in simulating extreme surface air temperature in Jakarta during the hot spells of October 2023 with eight different simulation setups. Several validation metrics were applied to near-surface meteorological variables, land surface temperature (LST), and vertical atmospheric profile. From the eight simulations, MPAS-A captured diurnal patterns of the near-surface variables well, except for wind direction. The model also performed well in LST simulations. Moreover, the biases in the vertical profiles varied with height and were sensitive to the initial/boundary conditions used. Simulations with modified terrestrial datasets showed higher LST and air temperatures over the sprawling urban areas. MPAS-A successfully simulated the extreme event, showing higher air temperatures in southern Jakarta (over 36 °C) compared to the northern part. Negative temperature advection by sea breeze helped lower air temperature in the northern area. This study highlights the role of sea breezes as natural cooling mechanisms in coastal cities. Additionally, MPAS-A is feasible for several applications for urban climate studies and climate projection, although further development is needed. [ABSTRACT FROM AUTHOR]

Climate change is a major challenge for the global tourism sector, affecting destinations worldwide. Greece, known for its scenery and abundant cultural history, is particularly vulnerable to these impacts. Tourism is a key driver of Greece's economy, yet climate change threatens both natural environments and cultural sites. To evaluate the impacts of climate change on different types of Greek tourism (beach, sightseeing, winter tourism in mountainous areas), the widely used Holiday Climate Index (beach and urban versions) alongside three additional climatic indices customized for Greek climatic conditions, namely the Urban Climate Comfort Index, the Beach Utility Index, and the Mountainous Winter Climate Index were utilized for top tourist destinations of Greece. The results indicate that urban tourism may face challenges during peak summer months due to rising temperatures, but the shoulder seasons (April–May and September–October) will offer improved conditions, potentially extending the tourist season. For beach tourism, favorable conditions are expected to increase from April to October, with significant gains in June and September. Winter tourism in mountainous areas, especially snow-dependent activities like skiing, is at risk due to the declining snow availability. Overall, the study highlights both the challenges and opportunities posed by climate change for Greece's tourism sector. It emphasizes the importance of adaptation strategies, including infrastructural improvements and promoting alternative activities, to minimize negative impacts and enhance the future attractiveness of Greek tourism. [ABSTRACT FROM AUTHOR]

The urban heat island (UHI) effect is a widely recognized phenomenon consisting of heat accumulation by dense urban construction and human activities, resulting in higher temperatures across urban areas compared to their surroundings. This article aims to quantify the UHI effect on several areas throughout the city of Lisbon, Portugal, with the main goal of validating, evaluating, and reinforcing urban climate adaptation and resilience strategies proposed in the recent scientific literature. A set of nine quality-controlled weather stations from the "Lisboa Aberta" network that are compliant with the World Meteorological Organization (WMO) standards and installation requirements were used to characterize Lisbon's UHI, in comparison to a reference weather station from the Instituto Português do Mar e da Atmosfera (IPMA), located at Lisbon Airport. By applying a principal component analysis (PCA) in an innovative way to 10 urban indexes, it is shown that the thermal inertia in Lisbon's urban areas is positively correlated with the UHI intensity and urban density, regardless of the daily heating/cooling cycle. Furthermore, the results show that land use also has an impact on the UHI effect, with continuous, vertical building areas showing the greatest deviations in comparison to the reference, averaging +1.8 °C. Contrastingly, horizontal building areas reveal an average deviation of +1.3 °C, with sparse, discontinuously built areas representing an average UHI effect of +0.2 °C. Finally, through a climatope analysis, it is determined that, across Lisbon, high-density urban areas and ventilation corridors are responsible for inducing average UHI effects of +1.7 °C and +0.2 °C, respectively. [ABSTRACT FROM AUTHOR]

This paper introduces a methodology aimed at bridging the gap between building energy simulation and urban climate modeling. A coupling method was developed through the Building Control Virtual Test Bed (BCVTB) and applied to a case study in Taipei City, Taiwan, to address the microclimate factors of street trees crucial to cooling energy consumption. The use of the Urban Weather Generator for weather file modification revealed a 0.63 °C average air temperature disparity. The coupling method emphasized the importance of accurate wind speed and convective heat transfer coefficients (CHTCs) on building surfaces in determining cooling energy. The results indicated that elevated CHTC values amplify heat exchange, with higher wind velocities playing a crucial role in heat dissipation. The presence of street trees was found to significantly reduce heat flux penetration, leading to a reduction in building surface temperatures by as much as 9.5% during hot months. The cooling energy was lowered by 16.7% in the BCVTB simulations that included trees compared to those without trees. The EnergyPlus-only simulations underestimated the cooling energy needs by approximately 9.3% during summer months. This research offers valuable insights into the complex interactions between buildings and their environments. The results highlight the importance of trees and shading in mitigating the heat island effect and improving energy-efficient urban planning. [ABSTRACT FROM AUTHOR]

The current research on urban heat island (UHI) effect mostly focuses on the analysis of land use type changes and the surface UHI intensity. Few studies on the urban canopy heat island effect at a block scale of local climate zone (LCZ), although the canopy heat island effect is a key factor affecting human thermal comfort. Therefore, this study will combine the LCZ classification system and the urban weather generator (UWG) model to simulate and quantitatively analyse the urban canopy heat island effect in Beijing at the block scale. First, based on Sentinel-2 Multispectral remote sensing images, the residual neural network (ResNet) method was used to obtain the LCZ of Beijing, and the results of LCZs was validated based on the google earth engine (GEE). Then, according to the classification results of local climate zones, the input parameters of the UWG and their corresponding value ranges are calculated. Finally, the UWG model is used to simulate the canopy temperature in different local climate zones, and the urban canopy temperature is validated using the meteorological station dataset. We quantitatively analyse the temperature differences between different types of LCZs. The results shows that the canopy heat island effect in Beijing gradually weakened outward from the city centre. This is mainly due to the relatively dense distribution of compact local climate zones in the centre of Beijing, while the surrounding areas of Beijing have lower building density and better natural coverage. The canopy urban heat island intensity of built-up LCZs is significantly stronger than that of natural-covered LCZs. The heat island intensity of the compact LCZ is higher than that of the open LCZ with the same building height. However, for LCZs with comparable compactness, the heat island intensity of high-level LCZs is higher than that of low-level LCZs. [ABSTRACT FROM AUTHOR]

The numerical weather prediction model HARMONIE-AROME and a multiple linear regression model (referred to in this article as the TURCLIM model after the local climate observation network) were used to model surface air temperature for 25–31 July 2018 in the City of Turku, Finland, to study their performance in urban areas and surrounding rural areas. The 0200 LT (local standard time) temperatures modeled by the HARMONIE-AROME and TURCLIM models were compared to each other and against the observed temperatures to find the model best suited for modeling the urban heat island effect and other spatial temperature variabilities during heatwaves. Observed temperatures were collected from 74 sites, representing both rural and urban environments. Both models were able to reproduce the spatial nighttime temperature variation. However, HARMONIE-AROME modeled temperatures were systematically warmer than the observed temperatures in stable conditions. Spatial differences between the models were mostly related to the physiographic characteristics: for the urban areas, HARMONIE-AROME modeled on average 1.4°C higher temperatures than the TURCLIM model, while for other land-cover types, the average difference was 0.51°C at maximum. The TURCLIM model performed well when the explanatory variables were able to incorporate enough information on the surrounding physiography. Respectively, systematic cold or warm bias occurred in the areas in which the thermophysically relevant physiography was lacking or was only partly captured by the model. Significance Statement: As more and more people are living in an urban environment, the demand for accurate urban climate modeling is growing. This study aims to understand the differences between the numerical weather prediction and multiple linear regression modeling and their limitations in modeling surface air temperature in subkilometer scale. The case study shows that models are capable of predicting the spatial variation of 0200 LT nighttime temperature during a heatwave in a high-latitude coastal city. Both models are therefore valuable assets for city planners who need accurate information about the impacts of the physiography on the urban climate. The results indicate that to improve the performance of the models, more accurate physiographic description and higher spatial resolution of the models are needed. [ABSTRACT FROM AUTHOR]

High-resolution urban climate modeling has faced substantial challenges due to the absence of a globally consistent, spatially continuous, and accurate dataset to represent the spatial heterogeneity of urban surfaces and their biophysical properties. This deficiency has long obstructed the development of urban-resolving Earth System Models (ESMs) and ultra-high-resolution urban climate modeling, particularly at large scales. Here, we present a first-of-its-kind 1km-resolution present-day (circa-2020) global continuous urban surface parameter dataset – U-Surf. Using the urban canopy model (UCM) in the Community Earth System Model as a base model for developing dataset requirements, U-Surf leverages the latest advances in remote sensing, machine learning, and cloud computing to provide the most relevant urban surface biophysical parameters, including radiative, morphological, and thermal properties, for UCMs at the facet- and canopy-level. Our high-resolution U-Surf dataset significantly improves the representation of the urban land heterogeneity both within and across cities globally. U-Surf provides essential, high-fidelity surface biophysical constraints to urban-resolving ESMs, enables detailed city-to-city comparisons across the globe, and supports the next-generation kilometer-resolution Earth system modeling across scales. U-Surf parameters can be easily converted or adapted to various types of UCMs, such as those embedded in weather and regional climate models, as well as air quality models. The fundamental urban surface constraints provided by U-Surf are also relevant as features for machine learning models and can have other broad-scale applications for socioeconomic, public health, and urban planning contexts. We expect U-Surf to promote the research frontier on urban systems science, climate-sensitive urban design, and coupled human-Earth systems in the future. The dataset is publicly available at https://doi.org/10.5281/zenodo.11247599 (Cheng et al., 2024). [ABSTRACT FROM AUTHOR]

The urbanization and expansion of cities have raised sustainability concerns and impacted the overall quality of life. Numerous studies have explored sustainable cities with climate-adapted urban and architectural designs, particularly focusing on optimizing thermal comfort within different urban morphologies. This publication presents a meticulous systematic review analyzing how urban form influences microclimatic conditions through advanced computer simulations. The PRISMA methodology condenses key indicators to facilitate informed decision making. A robust dataset from reputable databases such as ProQuest, Web of Science, and Scopus was analyzed, revealing discernible climate patterns, with hot arid (Bwh) and humid subtropical (Csa) climates being the most studied. The Thermal Comfort Index predominantly relies on the PET metric, with ENVI-met software as a popular simulation tool. Uncontrolled urban sprawl, surface impermeability, and lack of greenery exacerbate the urban heat island effect, leading to multifaceted environmental, social, and energy-related implications. The study underscores the importance of exploring topics like urban forms and morphology while advocating for increased attention to specific climatic conditions and urban scales. The growing prevalence of computational simulations for climate analysis emerges as a pivotal area of interest for future research. [ABSTRACT FROM AUTHOR]

This study examines the temporal and spatial variability of near‐surface air temperature and the canopy layer urban heat island (UHICL) of Kuwait City. Observations collected at 12 locations across the country of Kuwait and for the period 2010–2022 are analysed on an hourly and 3‐hourly basis to provide monthly and diurnal insights of the city's UHICL characteristics. Research on Kuwait's UHICL was first conducted by Nasrallah et al. (International Journal of Climatology, 10, 401–405). Results presented here have been afforded the benefit of additional stations and more extensive data compared with the earlier study. Mean positive UHICL intensities, ranging from 1.1°C to 3.8°C at night, are observed consistently across all months, owing to the prevalence of clear skies from winter to summer. Negative UHICL intensities, indicating a typical daytime urban cool island (UCICL), are most prominent on summer days, exhibiting a mean hourly magnitude range between 0.6°C and 2.6°C that extends into the early parts of the evening. Heat and cool island effects are maintained up to wind speeds approaching 10 m s−1 at the urban periphery. A coastal site near the city demonstrates strong influences of the Arabian Gulf temperatures and associated sea and land breeze effects on UHICL development. The results can be used for comparison with other desert locales, in the evaluation of urban climate models, for urban planning policies and improving local weather forecasts. This study honours in memoriam Dr. Hassan Nasrallah, who produced the first UHICL study in the Arab World. [ABSTRACT FROM AUTHOR]

Populism is multilayered and involves two main dimensions – ideology and strategy – which are employed within and beyond political parties. These dimensions can result in sometimes overlapping but generally divergent backlashes, targeting specific climate and sustainability interventions in cities. This critical commentary presents episodic populist backlashes against urban climate actions by exploring how they create their own political landscapes across the political spectrum in cities against progressive urban climate agendas. Specifically, the article examines how episodic populist backlashes manifest on an urban scale and highlights the need for urban scholars to pay more attention to the phenomenon. The article proposes two complementary explanations for why populism precedes urban climate actions in episodic and thematic ways. These explanations include policy backlashes against diffused global climate norms in cities, and counter-movements and rhetoric against climate justice and what it entails, such as inclusion in decision-making and intersectionality. The article then concludes by offering a research agenda on the episodic populist backlashes against urban climate actions, which highlights the need for a better understanding of how episodic populist movements might emerge into global climate policy diffusion, and climate justice coupled with intersectionality in cities of the Global North and Global South. [ABSTRACT FROM AUTHOR]

This investigation builds upon and extends prior lightning research in the Bangkok Metropolitan Region (BMR) through the reconstruction of thunderstorm distribution, utilizing a novel lightning tracking algorithm. Five years (2016–2020) of lightning stroke data from the Global Lightning Dataset (GLD360) were used to identify 52 608 thunderstorms. Optimized hotspot analyses, track densities, and analyses of thunderstorms with respect to winds, landcover, and seasons were performed. Our findings suggest that significant modification of thunderstorm distribution within the region was due to urban landcover impacts on the local environment. Thunderstorm intensity, as measured by stroke counts and track length, also appeared to be sensitive to the urban environment. The thunderstorm distribution also highlighted areas prone to hazards such as flash flooding. By visualizing thunderstorms grouped by winds, thunderstorm initiation hotspots and track density corridors were identified. These corridors of augmented thunderstorm production tended to occur during specific months given the seasonal monsoon wind regime occurring across the BMR. As urbanization within the BMR continues, geospatial assessment of thunderstorms is important to inform forecast meteorologists, urban planners, government officials, and others who play a critical role in developing strategies, policies and insfrastructure that could mitigate thunderstorm impacts. [ABSTRACT FROM AUTHOR]

Global politics has shown increasing interest in cities, particularly in the field of climate policy and governance. Yet, we still have little understanding of which cities engage the most in global urban climate governance. Answering this question is a first step towards understanding who decides for whom in a system that has decisive influence on wider global policy processes. In this article, we seek to identify and analyse the characteristics and position of cities in global urban climate governance to reassess its composition. To do so, we conduct a social network analysis of 15 transnational city networks. Results emphasise that global and large cities are the most central, but small and middle‐size cities are the most numerous actors of the system. Global South cities are larger than their Northern counterparts in the system. Those less central and understudied actors likely have less influence over which norms are shared, yet they should not be seen as followers or imitators of climate policy. It is important to pay more attention to them to understand their multifaceted role in cities' collective efforts to address climate change. [ABSTRACT FROM AUTHOR]

Anthropogenic heat emissions, which are quantified as anthropogenic heat flux (AHF), have attracted significant attention due to their pronounced impacts on urban thermal environments and local climates. However, there remains a notable gap in research regarding the distinctions in the distribution of anthropogenic heat emissions (AHEs) along urban–rural gradients. To address this gap, the present study introduces a new concept—the anthropogenic urban heat island (ArUHI)—where the AHF within urban areas is higher than that in background areas. To quantitatively describe the magnitude and spatial extent of the ArUHI effect, two metrics—namely, ArUHI intensity (ArUHII) and ArUHI footprint (ArUHIFP)—are introduced. We conducted a comprehensive study across 208 cities in China to investigate the spatiotemporal patterns of AHF variations along urban–rural gradients during the period of 2000–2016. In addition, we explored how the complex interactions between land cover and building form components affect changes in the AHF along urban–rural gradients. Additionally, we analyzed how economic zones and city sizes alter the ArUHI intensity and ArUHI footprint. The results showed that 97% (201/208) of Chinese cities exhibited a significant ArUHI effect from 2000 to 2016. The modeled ArUHI intensity value exhibited a substantial increase of nearly fivefold, increasing from 5.55 ± 0.19 W/m2 to 26.84 ± 0.99 W/m2 over time. Regarding the spatial distribution of the ArUHI footprint, the analysis revealed that, for the majority of cities (86% or 179 out of 208), the ArUHI footprint ranged from 1.5 to 5.5 times that in urban areas. City sizes and economic zones yielded significant influences on the ArUHI intensity and ArUHI footprint values. Building forms were significantly positively correlated with AHF, with R2 values higher than 0.94. This study contributes to the understanding of ArUHI effects and their driving factors in China, providing valuable insights for urban climate studies and enhancing our understanding of surface urban heat island mechanisms. [ABSTRACT FROM AUTHOR]

Monitoring microclimate variables within cities with high resolution and accuracy is crucial for enhancing urban resilience to climate change. Assessing intra-urban characteristics is essential for ensuring satisfactory living standards. This paper presents a comprehensive methodology for studying urban heat islands (UHIs) on a university campus, emphasizing the importance of multi-modal and multi-temporal data collection. The methodology integrates mobile surveys, stationary sensor networks, and drone-based thermal imaging, providing a detailed analysis of temperature variations within urban microenvironments. The preliminary findings confirm the presence of a UHI on the campus and identify several hotspots. This comprehensive approach enhances the accuracy and reliability of UHI assessments, offering a cost-effective, fine-resolution approach that facilitates more effective urban planning and heat mitigation strategies. [ABSTRACT FROM AUTHOR]

Wind speed differences between weekdays and holidays at urban sites in Japan were investigated in search of the influence of urban anthropogenic heat on surface wind speed using data from the Automated Meteorological Data Acquisition System (AMeDAS) of the Japan Meteorological Agency (JMA) for 44 years. The wind speed was found to be lower on holidays than on weekdays, not only in large cities but also in areas with medium degrees of urbanization, which is interpreted to be due to the stronger stability of the surface boundary layer under lower temperatures with smaller amounts of anthropogenic heat. The rate of decrease is about −3% in central Tokyo, and about −0.5% for the average over stations with population densities between 1000 and 3000 km−2. Additionally, an analysis using the spatially dense data on the Air Pollution Monitoring System of Tokyo Metropolis for 28 years showed that negative anomalies in wind speed on holidays were detected at many stations in the Tokyo Wards Area, although negative temperature anomalies were limited to a few stations in the central area or near big roads, suggesting different spatial scales in the response of temperature and wind speed to anthropogenic heat. [ABSTRACT FROM AUTHOR]

In the face of climate change and population growth, Local Climate Zone (LCZ) maps have emerged as crucial tools for urban planners and policymakers to address Urban Heat Island (UHI) effects, thereby playing a significant role in mitigating climate change. This study presents a methodology for classifying major Swiss cities into LCZs, offering an efficient, cost-effective, and uniform tool for supporting climate action plans across municipalities and cantons. Initial results show that Sentinel-2, Landsat 8, and Landsat 5 imagery perform well in LCZ classification with an overall accuracy usually exceeding 80%, and Sentinel-2 displays marginally superior performance. Temporal analysis reveals that the built-up classes of Open low-rise and Open mid-rise have increased by ~3%, while Large low-rise and Bare rock or paved have decreased, and Compact mid-rise remains stable. For the natural classes, Water and Dense trees remain stable, but Low plants have declined (~4%). A general decline in overall accuracy over time is noted, attributed to landscape changes. This preliminary effort emphasizes the need to enhance and automate the methodology, integrate it into the Swiss Data Cube, and potentially extend analyses with climate data to better study UHI effects. Future work will include developing visualization and tracking services for urban planners and authorities. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Planning Education & Research is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Evapotranspiration (ET) $(ET)$ is a key process in the hydrological cycle that can help mitigate urban heat. ET $ET$ depends on the surface cover, as the surface affects the partitioning of precipitation between runoff and evapotranspiration. In urban neighborhoods, this surface cover is highly heterogeneous. The resulting neighborhood‐scale ET $ET$ can be observed with eddy‐covariance systems. However, these observations represent the signal from wind‐ and stability‐dependent footprints resulting in a continuously changing contribution of surface cover types to the observation. This continuous change prevents quantifying the contribution of the surface cover types to neighborhood ET $ET$ and their hourly dynamics. Here, we disentangle this neighborhood‐scale ET $ET$ at two sites in Berlin attributing the patch‐scale ET $ET$ dynamics to the four major surface cover types in the footprint: impervious surfaces, low vegetation, high vegetation, and open water. From the bottom‐up, we reconstruct neighborhood ET $ET$ based on patch‐scale observations and conceptual models. Alternatively, we start top‐down and attribute neighborhood ET $ET$ to the surface cover types solving a system of equations for three eddy‐covariance systems. Although data requirements for the bottom‐up approach are met more frequently, both approaches indicate that vegetation is responsible for more ET $ET$ than proportional to its surface fraction in the footprint related to the large evaporating surface compared to the ground surface. Evaporation from impervious surfaces cannot be neglected, although it is less than from vegetation due to limited water availability. The limited water availability causes impervious surfaces to cease evaporation hours after rainfall, while vegetation and open water sustain ET $ET$ for extended periods. Plain Language Summary: Different types of surfaces, like grass, trees, pavement, and open water, affect how rainwater is divided between evaporation and runoff. In cities with lots of pavement and buildings, more water runs off than in natural areas leaving less water for evaporation. Measurement towers have been observing the evaporation from whole neighborhoods, but separating the effects of different surfaces is hard. In our study, we figure out how much each surface type contributes to evaporation with two methods: one starting from the separate surfaces and rebuilding the neighborhood evaporation, and the other starting with the neighborhood evaporation and breaking it down into evaporation from each surface. Both ways showed that plants evaporate more than proportionally to their surface area, but even built surfaces like pavement evaporate. Our findings confirm that more plants lead to more evaporation, but built surfaces cannot be ignored. This information can help urban planners create cities that manage water better, making cities nicer places to live. Key Points: Neighborhood ET is reconstructed from patch‐scale data (bottom‐up) and disentangled attributing ET to four surface types (top‐down)The neighborhood and patch scale are connected through half‐hourly‐varying eddy‐covariance footprintsET dynamics after rainfall reveal that water limitation drives differences between surface cover types [ABSTRACT FROM AUTHOR]

Copyright of Romanian Journal of Family Medicine / Revista Română de Medicina Familiei is the property of Media DOM Express and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Understanding how cities develop climate plans is crucial to capture their potential to achieve ambitious climate goals. Previous literature has highlighted the role of external pressures or heroic actors in driving local changes. By highlighting the everyday practices of actors in urban climate planning, we reveal new sets of contradictions in climate governance. Drawing from social practice theory, this paper examines how contradictions were managed in the process of developing a new climate plan in Bergen, Norway. Through a variety of empirical sources, we explore the strategic value these offer, and the organisational work accomplished by the navigation of contradictions. We highlight three strategic benefits of negotiating contradictions: the legitimisation, expansion and signalling of climate work. In conclusion, the paper argues that considering practices of climate planning reveals novel forms of agency, namely the potential of mundane organisational processes and the pivotal role of civil servants in this work. [ABSTRACT FROM AUTHOR]

City size is a primary determinant of the urban heat island (UHI) intensity, with its effects further nuanced by the urban form. But how to factor in the urban form into the UHI assessment remains unresolved. We propose an every‐pair‐interaction model that meaningfully incorporates urban size and fractal dimension to characterize the UHI intensity. Regression on the summertime surface UHI intensity of 5,000 European cities shows that the model outperforms the simple linear combination of logarithmic size and fractal dimension. Subject to the interplay between the range of the every‐pair interaction and the urban fractal shape, the model also represents a generalization as it includes power‐law, logarithmic, and saturating size dependence of UHI—all three possibilities have been reported empirically in the literature. Our theoretical framework indicates that the surface UHI intensity saturates with urban size, opening up new research perspectives around UHI intensity. Plain Language Summary: City size is an important determinant of the urban heat island (UHI) intensity. While most studies report a logarithmic dependence of UHI intensity on city size, other functions like power‐law and logistic functions have also been reported. In addition, the urban form plays an important role and, intuitively, increased UHI intensity is expected for compact cities. However, how to incorporate urban size and form for modeling UHI is less clear. Based on the perception that every urban site interacts with every other one, whereas the intensity of interaction decreases with the distance, we propose an every‐pair‐interaction model to characterize the UHI intensity. The model combines urban size and fractal dimension non‐linearly and outperforms the simple linear model. It also represents a generalization of the three functional possibilities reported in the literature. Our model indicates that the surface UHI intensity saturates with urban size. Whether the UHI saturates with the expansion of the urban area or follows a continuously increasing trend is relevant for sustainable urban development. Our approach paves the way for fresh research avenues into UHI intensity and urban climate as a whole. Key Points: We propose an every‐pair interaction model that unifies urban size and fractal dimension to model surface urban heat island (UHI) intensityThe model outperforms multi‐linear models and is a generalization of three functional shapes of the size dependence of surface UHI intensityThe empirical results in combination with our theoretical framework indicate that the surface UHI intensity saturates with urban size [ABSTRACT FROM AUTHOR]

Local climate zones (LCZs) and urban functional zones (UFZs) can intricately depict the multidimensional spatial elements of cities, offering a comprehensive perspective for understanding the surface urban heat island (SUHI) effect. In this study, we retrieved two types of land surface temperature (LST) data and constructed 12 SUHI scenarios over the Guangdong–Hong Kong–Macao Greater Bay Area Central region using six SUHI identification methods. It compared the SUHI sensitivity differences among different types of LCZ and UFZ to analyze the global and local sensitivity differences of influencing factors in the 12 SUHI scenarios by utilizing the spatial gradient boosting trees, geographically weighted regression, and the coefficient of variation model. Results showed the following: (1) The sensitivity of different LCZ and UFZ types to multi-scenario SUHI was significantly affected by differences in SUHI identification methods and non-urban references. (2) In the morning, the shading effect of building clusters reduced the surface urban heat island intensity (SUHII) of some built environment types (such as LCZ 1 (compact high-rise zone) to LCZ 5 (open midrise zone)). The SUHIIs of LCZ E (bare rock or paved zone) and LCZ 10 (industry zone) were 4.22 °C and 3.87 °C, respectively, and both are classified as highly sensitive to SUHI. (3) The sensitivity of SUHI influencing factors exhibited regional variability, with importance differences in the sensitivity of importance for factors such as the impervious surface ratio, elevation, average building height, vegetation coverage, and average building volume between LCZs and UFZs. Amongst the 12 SUHI scenarios, an average of 87.43% and 89.97% of areas in LCZs and UFZs, respectively, were found to have low spatial sensitivity types. Overall, this study helps urban planners and managers gain a more comprehensive understanding of the complexity of the SUHI effect in high-density cities, providing a scientific basis for future urban climate adaptability planning. [ABSTRACT FROM AUTHOR]

The widespread availability of Land Surface Temperature (LST) data from various sources presents a contemporary challenge for urban climate studies: how to efficiently compare these data with the results of traditional methods of temperature monitoring, which typically assume measurements at 2 m under sheltered conditions. In this line, the current study is based primarily on data extracted from a network of 31 points of hourly temperature monitoring at the 2 m level (Tair2m), in use between 2019 and 2021, in the city of Suceava in north-eastern Romania. These data allowed a detailed mapping for each hourly time step through multiple regression, adjusted by IDW, which was identified as the best interpolation method of Tair2m. These data were analyzed in parallel with LST data derived from Landsat imagery available in the analyzed period for 35 summer days with no or low cloud cover. The mapping results of both the Tair2m and LST data describe the main characteristics of the Suceava urban agglomeration (SvMA) heat island, which presents polynuclear features with intensities—as expressed by the temperature difference between the cores of the heat island and the surrounding rural areas—spanning during the summer noontime between 3.0 °C based on Tair2m and 7.1 °C on LST, respectively. The values of the Tair2m–LST differences were 0.68 °C on average, ranging from 5.33 to −19.17 °C, directly proportional to the imperviousness ratio (IMD) values, reaching the highest values in the local climate zones (LCZs) with a high built-up ratio (up to −19.17 °C) and the lowest (0.5 ÷ −0.5 °C) for those with bare soils, with isolated bushes and trees, with few or no buildings. The study results could serve as a tool to downscale the LST data to the level of Tair2m, which is useful for interpretation of the data derived from these commonly used tools in urban climate monitoring. [ABSTRACT FROM AUTHOR]

The exponential growth in population and physical infrastructure since 1980 has imposed immense pressure on Dhaka Metropolitan City (DMC), triggering extensive urbanization and a surge in built-up areas. This urban expansion has intensified the urban heat island (UHI) effect (the phenomenon where urban areas experience significantly higher temperatures compared to surrounding areas), resulting in adverse global urban climate changes such as temperature spikes, erratic rainfall, and compromised air quality. This study aims to discern the heat wave trends in DMC from 1972 to 2020 and elucidate their impact on public health and livelihoods. Employing a blend of quantitative and qualitative methodologies, the research analysis of land surface temperatures (LST) and land cover indices reveals a significant increase in built-up areas, particularly in western and central parts of the city, correlating with higher LST. Satellite imagery (Landsat 5 and 8) with 30-m resolution and meteorological data indicate a rise in surface temperatures. Analysis reveals April, May, and June as the peak months for heat wave occurrences, with DMC enduring a total of 328 heat wave days according to heat index (a measurement of perceived temperature in human body indicating how hot it feels when relative temperature is added to the actual air temperature) classifications. Notably, the density of built-up areas, particularly in central and western regions, exacerbates the UHI effect, evident in rising surface temperatures reaching 40.1 °C in 2014. The study underscores the dire health hazards posed by heat waves, with vulnerable populations, such as slum dwellers and outdoor laborers, facing heightened risks. Despite widespread impacts, public awareness about heat waves remains deficient, as evidenced by respondents' limited understanding of the term. These outcomes and prepared maps of the current research will be supportive for the local inhabitant, urban planner, and environmental resources management manager, national and international policy advisor, government and non-movement stakeholder, and researcher to observe the urban climate, trend of heat wave, and its impact on exposed people. [ABSTRACT FROM AUTHOR]

Embracing a "more global" urban comparison in scientific assessments of climate actions by cities is essential to drive greater and more inclusive participation in global efforts to curb climate change. This comparison needs to engage cities irrespective of their size and status: when we do so, distinctive patterns of urban climate mitigation actions across a diverse range of cities emerge. Employing K-means clustering as a pattern recognition method, this study compares cities based on selected aspects of their reported mitigation actions to the Carbon Disclosure Project. It explores whether the identified clusters facilitate the comparison of a socio-spatially diverse range of cities. The study identifies five clusters within two themes, namely the nature and finance–implementation of actions, shedding light on shared and distinct governance aspects of mitigation actions by cities. Notably, the study underscores how governance patterns transcend city size and global status. These findings offer valuable insights for broadening the comparative imagination of cities and inter-city networking opportunities. [ABSTRACT FROM AUTHOR]

Understanding the characteristics of minute‐scale short‐duration intense rainfall and its possible link with urbanization is valuable for addressing urban climate change and evaluating biases in high‐resolution historical precipitation data series. This study analyses the long‐term trends of rainy‐season minute‐scale intense rainfall events at different continuous time periods based on precipitation data from 20 national meteorological stations in the Beijing area covering the period from 1961 to 2021. This study shows that during the whole period analysed, the number of events of intense rainfall lasting 15 min or longer decreased, while the number of shorter‐duration events and the accumulated precipitation increased. The frequency and amount of minute‐scale intense rainfall recorded by urban stations are generally higher than those recorded by rural stations in terms of climatological means. The signal of the urbanization effect in minute‐scale precipitation changes is detectable, with the contribution of urbanization to the trends in average precipitation of 30‐min intense rainfall being 68.8%, whereas the contributions with respect to the frequency of 20‐ and 30‐min intense rainfall and the maximum precipitation of intense rainfall with a duration of 45 min reach 100%. The results of the analysis can provide reference for understanding long‐term changes in short‐duration rainfall and the possibility that they are driven by local anthropogenic activity, as well as bias in the minute‐scale precipitation data series of urban observation sites when they are used in studies of large‐scale intense precipitation changes. [ABSTRACT FROM AUTHOR]

Rapid urbanization worldwide has significantly altered urban climates, creating a need to balance urban growth with thermal environmental quality for sustainable development. This study examines the relationship between land surface temperature (LST) and urban characteristics, particularly focusing on how green cover can mitigate urban heat and how air pollution can increase temperatures. Recognizing the predictive value of LST for urban heat island (UHI) intensity, we analyzed three distinct U.S. cities—Chicago, San Francisco, and Phoenix—each characterized by unique climate and urban planning features. This study investigates the relationship between atmospheric pollutants (SO2, NO2, CO, O3) and the Normalized Difference Vegetation Index (NDVI) with land surface temperature (LST) using regression and correlation analyses. The analysis aims to elucidate how changes in atmospheric pollutants and NDVI affect variations in land surface temperature. Regression analysis is employed to estimate the coefficients of independent variables and quantify their impact on LST. Correlation analysis assesses the linear relationships between variables, providing insights into their pairwise associations. The study also examines multicollinearity between independent variables to identify potential confounding factors. The results reveal significant associations between atmospheric pollutants, NDVI, and land surface temperature, contributing to our understanding of the environmental factors influencing LST dynamics and informing climate change mitigation strategies. The observed inconsistencies in correlations across cities highlight the importance of the local context in environmental studies. Understanding these variations can aid in developing tailored urban planning policies that consider unique city characteristics for more effective climate resilience. Furthermore, a positive association was consistently obtained between pollutants and LST, indicating that increased pollution levels contribute to higher surface temperatures across different urban settings. [ABSTRACT FROM AUTHOR]

Green urban squares are essential in densely built neighborhoods and enhance their quality of life. Investment in the greening of urban areas will have a beneficial impact, particularly regarding human thermal comfort. Smaller than parks, squares can be easily spread over the cities and should be part of any neighborhood. While the cooling effect of green squares during hot summer days is increasingly well established, microclimatic assessments during all seasons are still missing. This study aimed to determine whether it is possible to identify an optimal greenery design that maximizes human thermal comfort, as indexed by physiological equivalent temperature (PET), in temperate climates across all seasons. This study employed a "research by design" methodology, utilizing the micrometeorological simulation model ENVI-met to analyze the impact of greenery on PET improvement across different seasons. The objective was to identify the most effective combination of greenery for PET improvement. To achieve these objectives, two urban squares in Munich, Germany were selected. This selection was based on the assumption that typical greening practices, exemplified by the presence of trees, shrubs, and grass, would significantly impact urban squares and their microclimatic effects on human thermal comfort. The small square with a grass surface underneath trees, Alpenplatz, is highly influenced by the surrounding buildings, affecting the sky view factor (SVF), a crucial aspect of the urban environment. Marstallplatz, an open, large square that is not highly affected by urban morphology, was analyzed through simulation scenarios combining grass, shrubs, and trees. The results demonstrate that hot summer days are of primary concern for climate-sensitive urban square design in order to avoid health risks and thus need to be prioritized without compromising comfort for cold days. To attend to both needs, increasing the number of deciduous trees for shading during the day and the amount of grass to enhance air cooling at night are particularly effective. Nevertheless, microclimate design for the spring and autumn periods must also be considered, with the provision of adaptable opportunities for sheltered and sun-exposed spaces. [ABSTRACT FROM AUTHOR]

Future projections indicate a global increase in heat waves, especially in urban areas due to climate change and the heat island effect, leading to adverse human, economic, and environmental outcomes. Cities worldwide respond by integrating climate mitigation and adaptation measures. While long-term strategies dominate research, this paper explores the increasing interest in short-term solutions, examining the Barcelona Climate Shelter Network. Investigating climate shelter initiatives, it highlights the use of public or private spaces for respite during extreme heat. This model offers insights for other European cities facing escalating heat waves, encouraging reflections on reshaping public services amidst climate crises. [ABSTRACT FROM AUTHOR]

Background Vulnerable populations across the United States are frequently exposed to extreme heat, which is becoming more intense due to a combination of climate change and urban-induced warming. Extreme heat can be particularly detrimental to the health and well-being of older citizens when it is combined with ozone. Although population-based studies have demonstrated associations between ozone, extreme heat, and human health, few studies focused on the role of social and behavioral factors that increase indoor risk and exposure among older adults. Methods We conducted a household survey that aimed to understand how older adults are affected by extreme heat and ozone pollution inside and outside of their homes across Houston, Phoenix, and Los Angeles. We examine contributing factors to the risk of self-reported health effects using a generalized linear mixed-effects regression model of telephone survey data of 909 older adults in 2017. Results We found an increased occurrence of self-reported symptoms for extreme heat with preexisting respiratory health conditions and a lack of air conditioning access; self-reported ozone symptoms were more likely with preexisting respiratory health conditions. The risk of heat-related symptoms was slightly higher in Los Angeles than Houston and Phoenix. We found several demographic, housing, and behavioral characteristics that influenced the risk of heat- and ozone-related symptoms. Conclusions The increased risk among older adults based on specific social and behavioral factors identified in this study can inform public health policy and help cities tailor their heat and ozone response plans to the specific needs of this vulnerable population. [ABSTRACT FROM AUTHOR]

Urban green space (UGS) is a fundamental infrastructure in modern urban settings, crucial for regulating the urban climate and improving public health. Accessibility to UGS represents a significant environmental justice issue, influencing the sustainable development of local communities. In this work, we comprehensively evaluated the temporal dynamics of UGS accessibility and the disparity in exposure for all 31 metropolitan divisions in the United States from 2013 to 2022. Our findings indicate that there have been no significant changes in both population-weighted UGS exposure and within-city inequality of UGS exposure for all racial/ethnic groups across the study period. Furthermore, a consistent racial/ethnic disparity in UGS exposure was noted throughout the study period, with the White group tending to have higher-than-average UGS accessibility, and all other racial/ethnic groups, including Asian, Black, Hispanic, Native American, and Pacific Islander, experiencing lower-than-average UGS accessibility. These findings underscore the urgency for urban planning and policy-making strategies to address these inequities, promoting healthy and sustainable urban development. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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 (∼−0.5°C) and street level vegetation (∼−0.3°C). Application of air conditioning across London (United Kingdom) increases air temperatures by ∼+0.15°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. Plain Language Summary: Multiple common city scale passive and active interventions exist to reduce urban population's exposure to extreme heat during hot spells. Nonetheless, a proper comparison of the effect that each of these interventions may have on the temperatures experienced within large cities is missing. Additionally, the radiative and thermal mechanisms that lead to outdoor temperature changes are often not detailed and could lead to detrimental effects for local populations, such as indirect increase of water vapor or reflection of solar radiation. Our study, focusing over London, compares several common interventions through a modeling experiment and finds that cool roofs largely outperform other interventions during the two hottest days of the summer 2018. We also find that green roofs are ineffective on average and that solar panels and tree vegetation would only marginally change temperature exposures. Large scale deployment of air conditioning would lead to increased temperature in the core of London. Solar panels could potentially provide sufficient energy for running air conditioning all over London, creating comfortable indoor environments, and green roofs could reduce temperatures during the day. We argue that such inter‐comparisons should guide future decision making. Key Points: City scale deployment of cool roofs leads to the greatest reduction in 2 m air temperatureGreen roofs do not decrease daily average temperature but have a daytime cooling effectSolar photovoltaic panels can reduce temperatures in London by capturing sensible heat flux and generate electrical power [ABSTRACT FROM AUTHOR]

Researchers have recently focused on the interplay of the urban heat island (UHI) effect and heat waves (HWs). However, the synergies of these two phenomena remains inconclusive at present. To address this gap, this study investigated UHIs and HWs synergies during the last 30 years in the Tokyo metropolitan area, through a unique and novel approach named Land‐Surface‐Physics‐Based Downscaling (LSP‐DS). LSP‐DS integrates the widely used Noah‐Multiparameterization (Noah‐MP) land‐surface model coupled with urban canopy‐process physics, aiming to conduct high‐resolution, long‐term urban‐specific simulations with much less computational resources. Our comprehensive analysis combining observation data and numerous LSP‐DS simulations confirms exacerbated UHIs during HWs. Specifically, HWs amplify the temperature differences between urban and rural environments, which is quantified by UHI intensity (UHII). During HWs, UHII increased more at night in inland areas and more during daytime in coastal areas. HWs present especially a heightened threat to coastal regions where daytime UHII increased by approximately 1°C during HWs. The Bowen ratio can explain the increase in the daytime UHII, and the daytime accumulated storage heat increase during HWs can explain the increase in nighttime UHII. Based on future projections of the increasing frequency of high temperatures, our findings highlight the impending heat‐related health challenges faced by urban residents. Plain Language Summary: In the past 30 years in Tokyo, we studied how heat waves influence the urban heat island (UHI) effect using a unique approach called Land‐Surface‐Physics‐Based Downscaling. This method helps simulate urban‐specific conditions in high resolution over a long period using fewer computational resources. Our analysis, combining observations and simulations, confirms that heat waves worsen UHI effects. During heat waves, temperature differences between urban and rural areas (UHI intensity (UHII)) increase more at night in inland regions and more during the day in coastal areas. Coastal areas, especially, face increased daytime UHII by around 1°C during heat waves. Less vegetation in urban areas explains the increase of UHII in the daytime during heat waves, and the higher heat capacity of urban land surface explains the nighttime increase of UHII during heat waves. As heat waves become more frequent and urban populations grow, our findings emphasize the health challenges urban residents may face due to extreme heat. Key Points: Development of Land‐Surface‐Physics‐Based Downscaling approach for urban climate predictionReveals the Added Heat Load (AHL), that is, the enhancement of the urban heat island effect during heat waves (HWs) in the Tokyo regionPositive daytime AHL due to higher increase of urban Bowen ratio during HWs; positive nighttime AHL due to urban's higher heat capacity [ABSTRACT FROM AUTHOR]

As recent geopolitical conflicts and climate change escalate, the effects of war on the atmosphere remain uncertain, in particular in the context of the recent large-scale war between Russia and Ukraine. We use satellite remote sensing techniques to establish the effects that reduced human activities in urban centers of Ukraine (Kharkiv, Donetsk, and Mariupol) have on Land Surface Temperatures (LST), Urban Heat Islands (UHI), emissions, and nighttime light. A variety of climate indicators, such as hot spots, changes in the intensity and area of the UHI, and changes in LST thresholds during 2022, are differentiated with pre-war conditions as a reference period (i.e., 2012–2022). Findings show that nighttime hot spots in 2022 for all three cities cover a smaller area than during the reference period, with a maximum decrease of 3.9% recorded for Donetsk. The largest areal decrease of nighttime UHI is recorded for Kharkiv (− 12.86%). Our results for air quality changes show a significant decrease in carbon monoxide (− 2.7%, based on the average for the three cities investigated) and an increase in Absorbing Aerosol Index (27.2%, based on the average for the three cities investigated) during the war (2022), compared to the years before the war (2019–2021). The 27.2% reduction in nighttime urban light during the first year of the war, compared to the years before the war, provides another measure of conflict-impact in the socio-economic urban environment. This study demonstrates the innovative application of satellite remote sensing to provide unique insights into the local-scale atmospheric consequences of human-related disasters, such as war. The use of high-resolution satellite data allows for the detection of subtle changes in urban climates and air quality, which are crucial for understanding the broader environmental impacts of geopolitical conflicts. Our approach not only enhances the understanding of war-related impacts on urban environments but also underscores the importance of continuous monitoring and assessment to inform policy and mitigation strategies. [ABSTRACT FROM AUTHOR]