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This contribution estimates the intensity of Urban Heat Island (UHI) during the period 2001 - 2020 for the city of Santos (CS), located in São Paulo, Brazil. The formation of the Surface Urban Heat Island (SUHI) was quantified from 2 methods: the first was Streutker’s method, which adjusts the surface soil temperature (LST) (urban and rural surface) to a Gaussian surface. The second, the quantile method proposed by Jose Flores, uses the difference between the 0.95 quantile of the LST of the urban area and the median of the LST of the rural area. Both methods use remote sensing data of LST at 0.05° resolution, obtained from the MODIS sensor on board the TERRA and AQUA satellites. In general, the quantile method can be used as a complementary analysis to the Streutker method for cities with high LST. The results of the CS analysis, during diurnal periods, indicate maximum values in May (5.09°C) and minimum values in August (3.87°C). During the night period, it presented maximum values in February (3.94°C) and minimum values in August (2.40°C) with the quantile method, and due to its proximity to the Small Ocean, the Streutker method presents interferences.

This study examined the impact of sea surface temperature (SST) on urban temperature across four cities located in three different countries (United States of America, Japan, and Morocco), all at nearly the same latitude, focusing on the summer season over the period from 2003 to 2020, because previously no one attempted to analyze the impact of SST on land surface temperature (LST). Data were acquired from the Moderate Resolution Imaging Spectroradiometer (MODIS) for LST and SST to evaluate the correlation between urban temperature and SST, the trends over time, and the relationship between urban areas and LST. The novelty of this study lies in its being the first to investigate the impact of SST on urban temperature based on a city’s proximity to warm and cold ocean currents. The findings revealed a positive correlation between LST and SST across all cities analyzed in this study (San Francisco, Tangier, Tokyo, and Atlantic City), and in some instances a significant positive relationship was observed at a 95% confidence level, but still the significance is in the range of weak to moderate. Specifically, the study found that during both daytime and nighttime, Tangier exhibited a decreasing trend in LST (99% confidence level) and SST. On the contrary, San Francisco displayed an increasing trend in both LST and SST during the daytime, but at nighttime, while SST continued to rise, LST showed a decreasing trend. Further analysis differentiated cities influenced by warm ocean currents (Tokyo and Atlantic City) from those affected by cold currents (San Francisco and Tangier). In Tokyo, influenced by a warm ocean current, there was a decreasing trend in LST despite increased SST. Conversely, Atlantic City, also influenced by warm ocean currents, showed an increasing trend in both LST and SST during the daytime. At nighttime, both Tokyo and Atlantic City exhibited increasing trends in LST and SST. Additionally, this study explored the correlation between urban areas and LST, finding that cities influenced by warm ocean currents (Tokyo and Atlantic City) showed a positive correlation between urban areas and LST. In contrast, cities influenced by cold ocean currents (San Francisco and Tangier) displayed a negative correlation between urban areas and LST. Overall, this research highlights the complex interplay between SST and urban temperatures, demonstrating how ocean currents and urbanization can influence temperature trends differently in cities at similar latitudes.

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.

The expansion of an urban tree canopy is a commonly proposed nature-based solution to combat excess urban heat. The influence trees have on urban climates via shading is driven by the morphological characteristics of trees, whereas tree transpiration is predominantly a physiological process dependent on environmental conditions and the built environment. The heterogeneous nature of urban landscapes, unique tree species assemblages, and land management decisions make it difficult to predict the magnitude and direction of cooling by transpiration. In the present article, we synthesize the emerging literature on the mechanistic controls on urban tree transpiration. We present a case study that illustrates the relationship between transpiration (using sap flow data) and urban temperatures. We examine the potential feedbacks among urban canopy, the built environment, and climate with a focus on extreme heat events. Finally, we present modeled data demonstrating the influence of transpiration on temperatures with shifts in canopy extent and irrigation during a heat wave.

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 temperature Green roofs do not decrease daily average temperature but have a daytime cooling effect Solar photovoltaic panels can reduce temperatures in London by capturing sensible heat flux and generate electrical power

Global climate change and rapid urbanization are transforming land use and thermal environments, particularly in developing megacities, impacting regional climate and sustainable development. In cities like Dhaka, Bangladesh, urbanization has significantly altered land use and land cover (LULC), directly affecting urban climate and land surface temperature (LST).This study investigates the impacts of rapid urbanization on LULC changes and LST in Dhaka, Bangladesh, using multi-temporal satellite imagery from Landsat 5, 7, and 8 from 2009 to 2023. The classification analysis was conducted using Support Vector Machine classification and Random Forest (RF) modeling in Google Earth Engine to predict future LST. The classification achieved high accuracy, with kappa values over 80%. Results found that, due to the Dhaka Metropolitan Development Plan (DMDP) urban settlements expanded by 139.52 km², and vegetation and water bodies declined by 16.71% and 51.71% respectively. The study also found a 4 °C increase in LST (from 34 °C in 2009 to 38 °C in 2023), with predictions indicating further increases up to 41 °C by 2030. Statistical analysis revealed strong correlations between LST and LULC indices, with R² values of 0.42 and − 0.68 for NDVI and NDWI (negative correlations), and 0.04 and 0.26 for NDBI (positive correlation). The RF model, with an R² of 0.953 between observed and predicted values, further predicts a 3 °C rise in LST over the next decade. Spatial analysis revealed the highest urban expansion occurred in the northeastern and southeastern regions of the city. This study demonstrates the utility of integrating multi-temporal satellite data, machine learning, and spatial modeling to quantify urban growth patterns, associated land cover changes, and thermal impacts. The findings highlight the need for climate-adaptive urban planning in rapidly developing megacities to mitigate rising urban temperatures and associated environmental and health risks. The modeling approach presented can support evidence-based policymaking for sustainable urban development and climate change adaptation in Dhaka and similar urban contexts globally.

Urban landscapes impact the lives of urban dwellers by influencing local weather conditions. However, weather forecasting down to the street and neighborhood scale has been beyond the capabilities of numerical weather prediction (NWP) despite the fact that observational systems are now able to monitor urban climate at these scales. In this study, weather forecasts at intra-urban scales were achieved by exploiting recent advances in topographic element mapping and aerial photography as well as looking at detailed mappings of soil characteristics and urban morphological properties, which were subsequently incorporated into a specifically adapted Weather Research and Forecasting (WRF) Model. The urban weather forecasting system (UFS) was applied to the Amsterdam, Netherlands, metropolitan area during the summer of 2015, where it produced forecasts for the city down to the neighborhood level (a few hundred meters). Comparing these forecasts to the dense network of urban weather station observations within the Amsterdam metropolitan region showed that the forecasting system successfully determined the impact of urban morphological characteristics and urban spatial structure on local temperatures, including the cooling effect of large water bodies on local urban temperatures. The forecasting system has important practical applications for end users such as public health agencies, local governments, and energy companies. It appears that the forecasting system enables forecasts of events on a neighborhood level where human thermal comfort indices exceeded risk thresholds during warm weather episodes. These results prove that worldwide urban weather forecasting is within reach of NWP, provided that appropriate data and computing resources become available to ensure timely and efficient forecasts.

Extreme temperatures pose a serious threat to human health, especially in urban areas where the majority of the world population is living. Temperature-related risks are exacerbated by urban-induced warming but existing exposure assessments rely on a static residential population, thus overlooking space-time changes in population density and their covariation with urban temperatures. Here we combine 1-km monthly daytime and nighttime population estimates for 80 European cities with existing high-resolution urban climate simulations to quantify the impact of daily and seasonal mobility on residents’ exposure to heat and cold. Using city-specific exposure-response curves and the respective minimum mortality temperatures as thresholds to define hazardous conditions we calculated that, on daily timescales, commuting towards city centers causes a 7.8% average increase (IQR:1.0-12.9%) in summer heat exposure but, during winter, it provides a slight protective effect against cold. On seasonal timescales, changes in total population are out of phase with the temperature cycle in most European cities, leading to a lower exposure to heat, with the exception of touristic destinations where exposure increases, on average, by 0.9% during the warmest months. These results highlight the key role of human mobility for heat risk assessment and adaptation and they reveal the existence of general exposure trends that hold across diverse cities and climates.

Increasing urban tree cover is a common strategy to lower urban temperatures and indirectly the building energy demand for air‐conditioning (AC). However, urban vegetation leads to increasing humidity with potential negative effects on the AC dehumidification loads in hot‐humid climates, an effect that has so far been unexplored. Here, we included a building energy model into the urban ecohydrological model Urban Tethys‐Chloris (UT&C‐BEM) to quantify the AC energy reduction effects of trees in seven hot cities with varying background humidity. A numerical experiment was performed simulating various urban densities and tree cover scenarios in the city‐climates of Riyadh, Phoenix, Dubai, New Delhi, Singapore, Lagos, and Tokyo. The relative contribution of tree shade, air temperature reduction, and humidity increase on the AC energy reduction was further quantified. We found that well‐watered trees provide the largest average summer AC energy reduction of −17% in the hot‐dry climate (Riyadh, Phoenix). As tree shade is the dominant factor leading to the AC energy reduction in all city‐climates, humid cities also show an average summer AC energy reduction ranging from −6% to −9%. However, increasing humidity is affecting AC dehumidification loads, especially under higher ventilation rates in humid climates and in these cities, AC energy reduction is most efficient with up to 40% tree cover. Additionally, we found that trees effectively reduce peak AC energy consumption due to higher shading effects in those hours. These results can inform urban planning strategies to maximize reduction in the AC energy demand using urban trees. Plain Language Summary Urban trees can provide multiple benefits, such as reducing temperature and potentially air‐conditioning (AC) energy consumption, but they might increase humidity. During AC operation, air is not only cooled but also dehumidified, which requires energy, to prevent indoor mold formation and health problems. However, a quantification of the humidity effects of urban trees on the AC energy consumption in hot‐humid cities has so far been lacking. Here, we quantify how urban trees influence the summer AC energy consumption in different climates (Riyadh, Phoenix, Dubai, New Delhi, Singapore, Lagos, and Tokyo). We found that well‐watered trees lead to the largest average AC energy reduction of −17% in hot‐dry cities. In all cities, tree shading is the dominant factor leading to reduced AC energy consumption. Because of this, we also simulated an average AC energy reduction in hot‐humid cities of −6% to −9%. However, increasing humidity leads to raised energy consumption for dehumidification, especially when indoor‐outdoor air exchange is high. In hot‐humid cities, AC energy reduction due to trees is the most efficient with up to 40% tree cover. Trees also provide larger energy reduction during AC peak hours. These findings can inform urban planning strategies to maximize the ecosystem services provided by trees. Key Points A building energy model was included into UT&C to quantify vegetation effects on energy demand for cooling, heating and dehumidification Tree shading was the dominant factor reducing air‐conditioning energy consumption in all analyzed hot‐dry to hot‐humid city climates Increasing humidity partially counteracts the shading effect in hot‐humid climates at high fresh air ventilation rates

This research is focused on identifying urban sprawl pattern and extent in two rapidly growing major Ghanaian cities (Accra and Kumasi) and how urban expansion affected heat island effect over the period of 2002–2017 using remote sensing imagery. The research employed remotely sensed images from Landsat 7 and 8 missions for mapping the urban sprawl. Land cover classification was done by using object-based image analysis, and for land surface temperature estimation single-channel algorithm was used. The intensity and magnitude of urban heat island were estimated. The results showed that urban expansion was more dominant process than densification in both cities. A significant area of bare soils and sparsely vegetated lands became built-up accompanied by total disappearance of forest belt of Kumasi. The intensity and magnitude values indicated the presence and expansion of urban heat island in both cities. However, there was a significant amount of bare lands and sparsely vegetated areas with relatively high surface temperature in and around these cities. From the results of this work, we note that bare or sparsely vegetated land cover types in urban areas located in tropical climates can escalate overall urban temperatures. The urban heat island magnitude values were relatively higher compared to values for European cities during the heat wave of 2016. Although urban configurations and climatic conditions may be the reason for the differences, this shows how alarming and dangerous urban heat islands could be in tropical cities.