Cities globally are at the epicenter of socioeconomic and technological advances but are also disproportionately impacted by weather extremes. Interestingly, while cities are affected by the large-scale weather patterns, cities themselves create thermodynamical feedback which affects local and regional meteorology. The changes in temperature due to urban growth are likely most recognizable as 'urban heat islands' (UHIs). These UHIs are indicators of the systemic changes that are dynamically underway- with the temperature changes affecting the local humidity, local scale convergence, and potential for cloud-convection processes, and in some instances changes in the precipitation characteristics. These changes in the meteorological parameters have now been well documented and a consistent signature and understanding emerging that emphatically highlights that cities change local weather and climate.
Given this understanding of the state of the science, the next question that comes up is how can we then design cities that can create regionally "desired" weather and climate for the cities- or at the very least reduce the exposure to the climatic extremes and the vulnerability. This invited presentation will discuss the evolution of the community's understanding of urban science and the efforts underway to translate this synthesis from being useful to usable and actionable for city operations, decision-making, and planning activities that can help build resilience. The talk will also discuss how the information from upcoming assessments such as the IPCC Special Report on Cities and Climate and related activities will need to be leveraged by cities globally, through local municipal- academia - community colabs, urban digital twins, and community and consensus building regarding data governance, ethics, experimental testbed, and experiential outcomes that can be scaled, adapted, and engaged for cities globally.
How to cite: Niyogi, D.: Urban Climate Science to Engineering Future Cities - Evolution of Understanding and Opportunities , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11222, https://doi.org/10.5194/egusphere-egu25-11222, 2025.
Outdoor shade, as a vital environmental asset of heat stress reduction, has long been evading systematic treatment in theory and practice. Yet, to become an effective climatic tool to address urban heat, outdoor shade has to be systematically quantified, mapped, and managed. The spatial Shade Index (SI) we have developed in the past allows us to describe on a simple scale from 0 to 1 the extent to which the cumulative effect of solar radiation at ground level is blocked during a typical spring or summer day. By calculating an average SI for each street segment, open space, or any other spatial unit of interest in a city, it is possible to produce maps that present the shade hierarchy of the city’s main pedestrian spaces, allowing for a quick and easy comparison of their shading quality.
Recently, we have managed to develop a computationally efficient method for producing high-resolution urban shade maps in a relatively short time by processing digital surface models, tree canopy contour maps, and official spatial land use layers. With experience gained from generating shade maps for 15 cities, it is now possible to conclude that shade mapping can only be the first step in a more complex procedure of prioritising shading actions across a city. In each of the mapped cities, the high number of street segments or open spaces with poor shading conditions made it impossible to allocate enough municipal resources to bring each and every one of them to a reasonable level of spatial shading. A shade map and the SI it assigns to each spatial unit thus become only the starting point of evidence-based policy negotiations that bring to the table other climate-related considerations, including public health, social equity, promotion of public transport travel, and increasing the appeal of commercial streets.
The presentation will provide examples of the application of shade maps in real-life planning tasks in different cities, including as the basis for developing strategic urban forestry plans. It will also expand on the underlying differences and discrepancies in historical urban planning concepts and resource allocation preferences that shade maps expose, given that outdoor shade in its entirety is always, even inadvertently, the product of urban planning and design decisions.
How to cite: Aleksandrowicz, O.: Shade maps, theory and practice: the basis of efficient and effective heat adaptation actions in cities, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5187, https://doi.org/10.5194/egusphere-egu25-5187, 2025.
Increased vegetation and the removal of sealed surfaces have been major efforts to reduce urban heat. Nevertheless, a lot of green facades and roofs are not irrigated, which causes drought stress in the plants during future, longer, or more intense summer heat waves without precipitation, leading to lower actual evapotranspiration. As a result, the anticipated cooling effect may not be achieved by many non-irrigated green roofs.
In our study, four green roof sites in different local climate zones and elevations regarding average roof level (compact-midrise above rooftop, compact-midrise below rooftop, open mid-rise, and large low-rise) within Vienna, Austria were investigated, over a two-year period. The impact of microclimatic site conditions on typically applied extensive and intensive green roof set ups, respectively, on actual evapotranspiration were measured under rainfed conditions by mini-lysimeters combined with meteorological in-situ measurements. The extensive green-roof set-up represented a soil substrate with a depth of 10 cm covered with drought stress tolerant plant species, whereas the intensive set-up represented a soil substrate with a depth of 25 cm covered with plant species of higher biomass growth potential, but more sensitive to drought stress. The measured actual evapotranspiration rates on a daily base were further used to calibrate the FAO-approach (Allen et al., 1998) for calculating actual evapotranspiration for the applied green roof set-ups and applied for different scenarios (irrigation and climate scenarios).
The results revealed that the green roof site conditions (especially wind speed) as well as the type of substrate and vegetation influence the temporal dynamics of actual evapotranspiration significantly. Also, it was found that actual evapotranspiration rates (and related cooling potential) were strongly limited under rainfed conditions during hot and dry summer conditions. Support irrigation to enhance actual evapotranspiration and avoid plant damages during drought periods and heat waves may need considerable amounts of water over the city of Vienna, showing a need for effective irrigation systems and irrigation water management.
Reference: Allen R, Pereira L, Raes D, Smith M (1998) Crop Evapotranspiration – Guidelines for computing crop water requirements. Irrigation and Drainage Paper Nr. 56. Rome, Italy.
How to cite: Thaler, S., Eitzinger, J., Gützer, C., Hörbinger, S., Rauch, J. P., Trimmel, H., and Weihs, P.: The potential for evaporative cooling from Vienna's green roofs, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12805, https://doi.org/10.5194/egusphere-egu25-12805, 2025.
The thermal performance of vertical greenery systems (VGSs) in indoor/outdoor environments has been extensively studied; nevertheless, spatiotemporal observational experiments of VGSs at block-scale are scarce, with the evapotranspiration and cooling potential of VGSs in response to urban morphology remaining unclear. Therefore, scaled outdoor experiments were conducted to thoroughly investigate the effects of VGSs on the wind, radiation, and thermal parameters in urban blocks with plan area index (λp) of 11% and 25% in the temperate region of Xingtai, China. Additionally, the evapotranspiration effect of VGSs in urban blocks was further quantified. VGSs reduce wind speed in the crossroads by 26% in the block with λp=25%, but no significant effect for λp=11%. Compared with non-VGS cases, VGS cases absorb more shortwave radiation and emit less longwave radiation, resulting in more net radiation capture and lower albedo. VGS cases experience significant temperature reductions in wall (Tw), indoor, canyon air (Ta), and ground (Tg), as well as mean radiant temperature (Tmrt) and physiological equivalent temperature (PET). The south walls in blocks with λp=11% and 25% show the best cooling effect, with maximum reductions of 22.2 and 18.4 °C at 0.1 m height, respectively, while the north walls show weaker cooling. The east and south streets experience better air and ground cooing than the crossroads. In the south street of blocks with λp=11% and 25%, the maximum reductions of Ta are 1.5 and 3.9 °C, and of Tg being 7.4°C and 9.0 °C, respectively. VGSs in urban block with λp=11% have a greater evapotranspiration rate than that with λp=25%. Thus, block with λp=11% achieve more pronounced cooling effects on walls and indoor air, whereas block with λp=25% exhibit better air and ground cooling due to lower wind speed. Moreover, the reductions in Tmrt and PET in block with λp=25% are 36.7 and 20.2 °C, respectively, significantly higher than those with λp=11%.
Keywords: Vertical greenery systems; Scaled outdoor experiments; Plan area index; Cooling potential; Evapotranspiration rate
How to cite: Xiaona, Z. and Jian, H.: Effects of vertical greenery systems on microclimate in urban blocks with different plan area indices: Scaled outdoor experiments, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19887, https://doi.org/10.5194/egusphere-egu25-19887, 2025.
Urban trees contribute to summer cooling by offering solar shading and evapotranspiration. But their relative significance is still unclear due to the challenges in directly measuring tree evapotranspiration rate or latent heat flux (QE) in actual urban environment. Concurrently, high-quality experimental data are still required for validation of urban canopy model simulations. For this purpose and as a novelty, this study directly measures tree evapotranspiration (or QE) and shading effects in street canyons (building height/street width, H/W=1 or 2, H=1.2m), using scaled outdoor experiment in subtropical region, i.e. suburban Guangzhou, China, from August to October 2022. Results show that urban trees (leaf area index: 3.5) can effectively deliver surface temperature reductions beneath tree canopies up to 5.1℃ as H/W=1 and 8.2℃ as H/W=2, while slightly raise air temperature by less than 2.0℃ above tree canopies. Energy flux comparison indicates tree shading as the primary cooling mechanism, with up to 97% of incoming solar radiation (800Wm-2) intercepted, while evapotranspiration, with a rate of less than 1.6g/min and a latent heat flux below 90Wm-2, plays a secondary role. Additionally, trees decrease street air velocity by up to 63.6%, and increase of water vapor pressure by up to 2.77hPa.
How to cite: Wu, Z., Shi, Y., Ren, L., and Hang, J.: Scaled outdoor experiments to assess impacts of tree evapotranspiration and shading on microclimates and energy fluxes in 2D street canyons , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5456, https://doi.org/10.5194/egusphere-egu25-5456, 2025.
In areas with a long summer season, especially where heat stress prevails throughout the day, such as Mediterranean coastal cities, Urban Heat Island (UHI) causes further thermal discomfort and an increase in energy consumption for air conditioning. The intense warming of the area, which is greater at night compared to the day, significantly enhances energy consumption from air conditioning during these hours. Previous studies have indicated a pronounced UHI in stable winter nights, whereas most studies on the UHI in the warm season have focused on individual case studies rather than examining hot days and the warm season.
The study identifies the summer UHI characteristics of Tel Aviv under different weather conditions and develops a statistical downscaling model that predicts the UHI intensity, based on synoptic- and meso-scale variables. It focuses on extreme summer nights, exhibiting elevated temperatures and heat stress. Meteorological data was collected over five summer seasons (2020-2024). Detailed (10-minute) urban and rural meteorological measurements for the mid summer months (July and August), including temperature, relative humidity and wind velocity, together with radiosonde data from a nearby rural station.
We show that the Tel Aviv nocturnal UHI is quite variable during summer days, depending on meso-scale variations under the semi-permanent synoptic seasonal conditions. During daylight hours, the UHI is weak (under 2°C) and occasionally negative, i.e., temperatures at the urban stations are lower than at the rural location. The nighttime UHI ranges from negligible (below 1°C) to extreme (9°C), and is typically between 4-7°C.
Our study shows that the intensity of the UHI is significantly dependent on the westerly wind component (the sea breeze, enhanced by the synoptic-scale Etesian winds), the height of the persistent seasonal marine inversion and the existence and intensity of a surface inversion, observed near the city. A prediction equation for the UHI, based on these predictors, yields 0.85 correlation with the observed values. A surprising finding is an inverse relationship between the nocturnal temperature and the UHI intensity.
To investigate the finding mentioned above, several cases of excessive nocturnal heat events, with minimum temperatures exceeding 26°C, with high humidity were examined. In some of them the nocturnal UHI disappeared or remained very weak (0-2⁰C), whereas during others it was distinct and occasionally extreme (5-9⁰C). We found that extreme hot nights with minimal UHI are often characterized by the absence of the night land breeze and increased nocturnal westerly winds from the warm Mediterranean Sea. These events are characterized by a relatively high base of the marine inversion accompanied by clouds. Conversely, clearer nights with light land breeze and a pronounced surface inversion, or a low marine inversion base, result in a distinct and strong UHI.
How to cite: Zohar, M., Ziv, B., and Saaroni, H.: Contrasting meteorological factors affecting the summer urban heat island - the case of the Mediterranean coastal city of Tel Aviv, Israel, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2902, https://doi.org/10.5194/egusphere-egu25-2902, 2025.
Climate change is projected to raise the frequency of heat events, triggering also enhanced indoor heat loads for urban dwellers. However, understanding of the climatology of indoor environments in existing residences remains limited. Establishing and maintaining long-term and systematic networks recording indoor temperatures is both challenging and costly, making such networks scarce. This study uses a unique dataset collected through weather stations that we placed in 93 residences in Amsterdam, the Netherlands, since 2022 as part of a citizen science project. We aim to observe and analyse indoor air temperature, humidity and CO2 concentrations through indoor placed Netatmo weather stations in bed and living rooms. From these observations, we also estimate the thermal comfort indices PMV (predicted mean vote) and the Dutch GTO (weighted temperature threshold exceedance hours). We report on the climatology, variability and exceedance of limit values for these observed and estimated variables during the summer seasons May-September of 2023 and 2024.
During a warm period from 1 to 15 September 2023, which included a regional heatwave, the median and 95th percentile (P95) of the daily maximum indoor air temperatures observed in both the bedrooms and living rooms within this professional network were 25.4 °C and 27.8 °C, respectively. For comparison, the WHO recommends a comfortable indoor air temperature range of 18-24 °C. The corresponding median and P95 of the daily maximum CO2 concentrations observed in the bedrooms were 882.9 ppm and 1223.6 ppm, respectively. Ideally, indoor CO2 concentrations should remain close to the outdoor CO2 concentration of 420 ppm. Regarding thermal comfort, the corresponding median and P95 of the estimated PMV in the living room were 1.10 and 1.30, respectively, indicating a slightly warm thermal sensation. Similarly, the corresponding median and P95 of the estimated GTO in the living room were 2.3 hours and 879.1 hours, respectively. That means that 5% of the residences exceeded the annual 900-hour threshold for GTO within just 2 weeks.
Additionally, we will also present preliminary findings on how house characteristics (e.g., energy label, window orientation, room volume, etc.) may explain indoor air temperature characteristics. This study contributes to understanding the health risks and cooling demands faced by residents of Amsterdam in their homes.
How to cite: Peerlings, E. and Steeneveld, G.-J.: Exploring indoor thermal comfort and CO₂ concentrations observations in Amsterdam, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19081, https://doi.org/10.5194/egusphere-egu25-19081, 2025.
Air pollution and inadequate or poorly equipped health infrastructure are drivers in determining the health risks of urban populations globally. The spatial impacts of these drivers on the health risks of densely populated areas remain unexplored, particularly in the global south. Air pollutants such as fine particulate matter (PM2.5), sulfur dioxide (SO2), carbon dioxide (CO2), carbon monoxide (CO), tropospheric ozone (O3), and nitrogen dioxide (NO2) vary spatially along gradients in maximum temperature (Tmax), relative humidity (RH), and wind speed (WS). The current study spatially predicts the combined impact of air pollutants within varying climatic conditions (Tmax, RH and WS) and the distribution of healthcare facilities on Ghana’s urban population health risk in the Greater Accra Metropolitan Area (GAMA). The study employed spatial correlation and regression kriging using district-level population density per km2 as the primary variate (dependent variable) with the air pollutants, climate, and distribution of health facilities as covariates (drivers) of the population health risk. Preliminary results revealed specific spots (areas) of high and low health risks to communities in GAMA. The Accra and Tema Metropolitan Areas and Ablekuma Central, Ablekuma North, Korle Klottey, and Ashaiman District Assemblies had high population health risk spots (>25,000 persons/km2) with a mean dry season PM2.5 of 75.0 µg m-3, five times higher than the World Health Organization (WHO) recommendation of 15 µg m-3, as the main significant driver. Other determining risk factors were monthly mean PM2.5 (36.5 µg m-3), NO (68.4 µg m-3), NO2 (70.5 µg m-3), O3 (16 ppb), SO2 (3 ppb), CO (327 ppb) and Tmax (26℃), HR (80%) and WS (9 km h-1). The concentration and spatial autocorrelation of the pollutants diminished towards peri-urban areas such as Kpone-Katamanso, Ga East, and Ga West. These results underscore the critical role of applying geospatial tools to monitoring, understanding and managing population health risks induced by air pollution and adverse climate of densely populated areas. The results highlight the need to manage and address the combined effects of air pollutants and the role of climate and inadequate health facilities in the health risks of the GAMA population with spatial precision and district-level policies. It also contributes to global efforts toward achieving spatial equity in universal healthcare coverage, aligning with the United Nations Sustainable Development Goal 3.8 and strengthening policy and practical relevance of geospatial approaches for sustainable interventions.
Keywords: Urban climate condition and informatics; Environment and social challenges; Spatial dependency; Urbanization; spatial regression
How to cite: Doe, E. K., Koubodana Houteta, D., Amekugbe, M. A. S., Kong’ani, L. N. S., Bbuye, M., Egyir, S., Schandorf, P. N. O., Lartey, D. L., Dordaa, S. D. D. P., Boahen, E., Fosu-Mensah, B. Y., Jack, C., and Gordon, C.: Predicting the Geospatial Distribution of Urban Population Health Risks Based on Air Pollutants, Climate, and Health Facilities in African Cities, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9363, https://doi.org/10.5194/egusphere-egu25-9363, 2025.
In a warming world, accurately modeling urban climate is essential for sustainable urban planning. Bulk urban canopy models, such as TERRA_URB coupled to the icosahedral non-hydrostatic weather and climate model (ICON), rely on urban canopy parameters (UCPs) to capture the surface–atmosphere interaction in cities. Consequently, model performance strongly depends on the quality of these UCP inputs to realistically represent urban boundary layer processes - such as boundary layer height, wind drag, heat island effects, and greenhouse gas concentrations. UCPs can be prescribed uniformly across all urban grid cells or vary according to local climate zone (LCZ) classification or real-world datasets. While LCZ-based approaches provide a globally consistent categorization of urban environments, they may overlook the fine-scale heterogeneity within individual cities. In this study, we examine whether using high-resolution urban characteristics, derived from 3D building geometries, satellite data, and other local information, improves urban climate simulations in the state-of-the-art ICON TERRA_URB model compared to LCZ-based UCPs. Focusing on the Swiss cities of Zurich and Basel, we refine UCPs by incorporating local datasets that capture actual building heights, densities, and solar-thermal properties, then compare simulations from the summer of 2023 against observational data. Our results highlight that activating TERRA_URB significantly enhances model accuracy for nighttime temperatures in regions characterized by high artificial surface fractions (ASF). Among the tested configurations, employing realistic UCP data provides a slight advantage over uniform and LCZ-based alternatives. However, these benefits observed in temperature do not extend to wind speed, where no single scenario demonstrates a clear overall advantage. Moreover, neither LCZ-based TERRA_URB nor realistic TERRA_URB proves sufficient in less densely urbanized contexts (low ASF), suggesting that disabling TERRA_URB - or opting for simpler model approaches - may be more suitable where urban influences are small.
How to cite: Dönmez, K., Emmenegger, L., and Brunner, D.: How Critical Are Urban Canopy Parameters in a State-of-the-Art Coupled Bulk Urban Canopy Model?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6626, https://doi.org/10.5194/egusphere-egu25-6626, 2025.
Rapid urbanization and climate change have intensified the need for accurate urban microclimate modelling tools to support sustainable urban planning and mitigate adverse environmental impacts. Models capable of simulating the complex interactions between urban surfaces, buildings, and vegetation are essential for assessing the effects of climate change, urban overheating, and energy consumption. The MLUCM BEP+BEM model introduces advancements in urban microclimate modelling by integrating enhanced turbulent diffusion schemes with the Building Effect Parameterization (BEP) and the Building Energy Model (BEM). The model incorporates updated turbulent length scales and eddy diffusivity coefficients that account for atmospheric stability, as well as a representation of urban vegetation, including green spaces and street trees. Designed for offline operation, it offers low computational cost, making it suitable for standalone use, coupling with climate projections, and conducting long-term simulations to assess the effects of different emission scenarios on urban environments. Validation against observational data from the Urban-PLUMBER project, conducted at a suburban site in Preston (Melbourne, Australia), demonstrates reliable performance in simulating upward shortwave (SWup) and longwave (LWup) radiation. Sensible heat flux (Qh) and momentum flux (Qtau) are also accurately reproduced, highlighting the model’s robustness in complex urban environments. An underestimation of latent heat flux (Qle) suggests that further investigation and refinement of the representation of moisture-related processes in the model would be beneficial. The adaptability of the MLUCM BEP+BEM model enables its application across various climatic contexts to evaluate the impacts of climate change on urban heat stress, energy demand, and the effectiveness of adaptation strategies. Potential applications include analysing green roofs, cool roofs, photovoltaic systems, and other mitigation measures to support sustainable urban development.
This work is supported by ICSC – Centro Nazionale di Ricerca in High Performance Computing, Big Data and Quantum Computing, funded by European Union – NextGenerationEU (CUP F83C22000740001).
How to cite: Pappaccogli, G., Zonato, A., Martilli, A., Buccolieri, R., and Lionello, P.: Validation of an Offline One-Dimensional Multi-Layer Urban Canopy Model Using the BEP+BEM Scheme: A Case Study in Melbourne, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11970, https://doi.org/10.5194/egusphere-egu25-11970, 2025.
Pavement watering has emerged as a potential strategy for mitigating urban heat and adapting cities to climate change. However, modelling tools to support the large-scale planning of such interventions remain limited. This study introduces the integration of pavement watering dynamics into an established fast urban climate model. The proposed new model was validated through comparisons with measurements and existing modelling data, demonstrating good agreement. To ensure robustness and reliability, the approach was tested using diverse input information, showing that wetting impervious pavements can reduce surface temperatures by up to 15 °C and air temperature by as much as 2 °C. The results also provide valuable insights into effective pavement watering practices for optimising surface and air temperature reductions. Additionally, a city-scale simulation illustrated the broader potential of expanding the application of pavement watering strategies. Our proposed model offers new approaches for advancing understanding the cooling effects and water resource needs for pavement watering practices, facilitating smarter planning of heat mitigation measures for more liveable urban environments.
How to cite: Chen, J., Bach, P. M., and Leitão, J. P.: Using a fast urban climate model to simulate the effects of pavement watering on urban heat mitigation , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17620, https://doi.org/10.5194/egusphere-egu25-17620, 2025.
Urbanization substantially modifies surface water and energy cycles. Compared to natural vegetation, paved urban surfaces produce more runoff, trap more heat, and lower evapotranspiration. At the same time, increased heatwaves and rainfall due to climate change are amplified in urban areas due to feedbacks between cities and meteorological processes. Land surface models, the part of atmospheric models tasked with modeling the earth’s surface and hydrology, lack the fine-scale, ecohydrologic process representation in cities to capture important feedbacks between urbanization, hydrology, and near surface energy partitioning. For example, tree cover that shades pavement and enhances evapotranspiration is ubiquitous amongst many cities worldwide, but contemporary land surface models cannot allow for tree canopy to extend over pavements. Further, lateral transfers of surface water from impervious to permeable surfaces are critical for runoff reduction, like routing of rainfall to natural vegetation, but are similarly not represented. Lack of ecohydrologic processes is problematic because we are unable to predict the impact of increasingly common greening initiatives that feature both nature-based solutions, like increased tree cover, and green infrastructure practices, like permeable pavements and green roofs. These practices are targeted to reduce runoff and urban heat, but will likely modify other urban atmospheric processes like rainfall in unknown ways. Unfortunately, potential connections between urban greening initiatives and resulting changes to the urban climate have not been explored rigorously at city scale.
In this project, we use Noah-MP for Heterogenous Urban Environments (HUE), a new land surface model capable of resolving fine-scale ecohydrologic processes like urban tree cover shading pavements and routing of surface water to permeable surfaces with multiple landcover types per grid cell (e.g. a mosaicking scheme) in urban spaces. We use HUE to examine the impact of widespread climate adaptation policy in multi-year WRF regional climate simulations centered on the coastal city of Milwaukee, Wisconsin, USA at convective permitting scales. Different landcover configurations that represent cases of city-wide greening are interpreted from an ambitious real-world regional urban greening “master plan.” We show that more greening leads to a reduction of runoff throughout the warm season, although partitioning of runoff reduction between evapotranspiration and deep drainage varies year to year. We also examine how changes in sensible and latent heat fluxes affect near surface meteorology within the city, generally increasing humidity and decreasing air temperatures. These differences are especially apparent during days of strong lake-breeze coupling between Milwaukee and nearby Lake Michigan. We further show that urban greening leads changes in rainfall event totals, peak intensities, and seasonal averages. While only for a single city, our results highlight that widespread urban greening changes not only urban hydrology but also urban hydrometeorology. This highlights that the evaluation of urban greening initiatives worldwide is critical for climate change adaptation and mitigation.
How to cite: Alexander, A., Wright, D., Voter, C., and Loheide, S.: Quantifying the City-Scale Benefits of Urban Vegetation and Green Infrastructure using Novel Land-Atmosphere Simulations , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4597, https://doi.org/10.5194/egusphere-egu25-4597, 2025.
Urban areas modify natural land surfaces through the artificialization and sealing of surfaces, which has an impact on their exchanges of energy, water and momentum with the atmosphere. These modifications create specific meteorological conditions in cities, generally referred to as “urban climate”. The most notable – and most studied – climate effect of urbanisation is the increase in nighttime near surface air temperature, known as the Urban Heat Island (UHI).
Future climate change projections are most often derived from Global Climate Models (GCMs), which are downscaled to the regional scale using statistical tools, or by limited-area regional climate models (RCMs). Because of their horizontal resolution, which often remains too coarse, RCMs cannot represent most urban areas adequately and, as a result, little is known about projected changes in UHIs in the future. A few studies have examined the projected evolution of UHIs in the context of climate change using different approaches ranging from GCMs and RCMs to high-resolution land surface models, but with little agreement between studies and great sensitivity to the city analyzed, its climate and, more particularly, the downscaling approach used.
The latest generation of RCMs, known as Convection Permitting Regional Climate Models (CPMs), now reach horizontal resolutions of the order of a few kilometers, and can be coupled with various urban parameterizations to improve the representation of the urban climate in climate change projections.
Here we use transient (i.e. GCM-driven) climate simulations from the EURO-CORDEX initiative (12.5 km RCM) and the CORDEX Flagship Pilot Study on Convection (3 km CPM). We study the future UHIs of several European cities at the end of the century (2090-2099) under a scenario of very high greenhouse gas emissions (RCP8.5) and in which the cities are fixed in their historical states.
In particular, our analysis focuses on:
- The concordance between the previous generation of climate projections from RCMs and the most recent one using CPMs
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The potential differences due to the multiple urban parameterizations used between RCMs and CPMs, and within the CPM ensemble
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The expected changes in UHIs under projected regional climate conditions at the end of the 21st century, and their potential effects on certain impact indicators
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The possibility of highlighting the physical drivers of potential future UHI changes
Acknowledgment: This work was conducted in the CIRCE project (City-oriented Impacts of Regional Climate for Europe) funded by the European Commission under the Marie Skłodowska-Curie Actions (MSCA) (Grant agreement ID: 101067769).
How to cite: Le Roy, B. and Rechid, D.: Investigating the future urban climates of European cities using an ensemble of convection permitting regional climate models, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1307, https://doi.org/10.5194/egusphere-egu25-1307, 2025.
With over half of the world's population living in cities and urbanization expected to increase, understanding how urban environments affect heavy rainfall is crucial due to its implications for flood risk. Urban areas have been shown to intensify convective heavy rainfall; however, the extent of this effect varies across cities worldwide, and the specific influence of urban form on rainfall modification remains unclear. We use the Weather Research and Forecasting (WRF) model to simulate 11 convective events that cross the city of Indianapolis, Indiana. The land cover of Indianapolis is then replaced with that of eight other cities to assess how different urban forms affect rainfall. We find differences in rainfall intensity when comparing simulations with and without a city present, and these differences are related to the size and structure of the city, specifically the proportion of buildings arranged in an open configuration. Half of the simulated rainfall events intensified over the urban areas. In these cases, convection was enhanced due to low background wind speeds and a strong urban heat island effect. The latter half of the storms were suppressed over the cities, when background wind speeds were high, and the urban heat island effect was weak. Here, convection was inhibited due to reduced boundary layer moisture and strong deceleration effects at the surface caused by increased urban surface roughness. Given the expected growth of cities, our results point towards further enhancements in rainfall implying that future flood risk may increase in growing cities.
How to cite: Torelló-Sentelles, H., Villarini, G., Koukoula, M., and Peleg, N.: Urban dynamic and thermodynamic influences on short-duration heavy rainfall across different urban structures, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8012, https://doi.org/10.5194/egusphere-egu25-8012, 2025.
Microclimate modeling serves as an indispensable tool for fostering sustainable urban development by quantifying the benefits of green and blue infrastructures. Urban green spaces (UGS) are integral to urban sustainability, acting as ecological refuges and providing essential regulating and cultural ecosystem services. This study presents a novel methodology for microclimate modeling by integrating an extensive tree database of 4,264 public and private urban trees in Augsburg, Germany, into ENVI-met simulations. The database allowed for unprecedented accuracy in representing tree species, heights, and distributions. The research, funded by the German Research Foundation under contract 471909988, focused on an urban park and a nearby residential district to evaluate microclimatic conditions and human thermal comfort under varying scenarios.
A total of sixteen ENVI-met simulation scenarios were developed, incorporating variations in vegetation modeling techniques, forcing methods, topography, spatial resolution, and seasonality. The baseline scenario was validated against in-situ measurements of air temperature (T) and relative humidity (RH) collected at nine representative locations. For a hot summer day in August 2024, scenario performance was analyzed through time series of T and mean radiant temperature (MRT) and spatial distributions of the Universal Thermal Climate Index (UTCI) at the pedestrian level.
The analysis revealed a consistent overestimation of T (Bias: from +0.66°C to +1.85°C) and underestimation of RH (Bias: from -0.89% to -4.73%), especially during daytime hours. Daytime T differences between residential and park sites averaged 1.88°C in the reference data but were underestimated by the baseline scenario (1.21°C) and almost entirely overlooked in the "Simple Plants" approach (0.05°C). Scenarios incorporating finer spatial resolution (1.37°C) and a digital elevation model (1.27°C) provided better approximations of these gradients. Greater variability was observed in MRT and UTCI results, with tree height, species, and vegetation models exerting considerable influence. At 14:00 UTC+1, the largest UTCI reductions (median: -1.51°C) were achieved in the L-tree scenario, which includes only one tree species (Acer platanoides), while the "Simple Plants" approach (+0.91°C) offered minimal thermal comfort improvement compared to the "No Trees" scenario (+1.40°C). Despite domain-wide neutral UTCI effects (-0.05°C) in the medium-high L-tree scenario, areas with the tallest trees experienced significant overestimations exceeding +7°C.
This study highlights the complexities and challenges in simulating urban green infrastructure impacts on microclimates and thermal comfort. It underscores the critical importance of detailed, accurate tree data – including species, heights, and leaf-area density profiles – to produce reliable and actionable microclimate modeling outputs. The findings provide valuable insights not only for climate modellers, but also for urban planners seeking to enhance climate resilience through evidence-based UGS design and management.
How to cite: Simon, J., Oster, J., and Beck, C.: Exploring the Role of Urban Green Spaces in Microclimate Modeling: Insights from ENVI-met Simulations in Augsburg, Germany, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17996, https://doi.org/10.5194/egusphere-egu25-17996, 2025.
This study presents a comparison of two leading urban climate models - the Single Layer Urban Canopy Model (SLUCM) and ENVI-met - across six Local Climate Zones (LCZs) in Hong Kong. While previous validations have focused primarily on individual LCZ types, this research evaluates model performance across diverse urban morphologies, incorporating seasonal variations and vegetation effects. The study analyzed model performance using high-resolution pedestrian-level observational data.
Both models demonstrated comparable accuracy in simulating air temperature (Ta) and relative humidity (RH), with SLUCM showing slightly superior performance. The largest Ta prediction errors were observed in the most and least dense LCZ types, while the presence of vegetation increased RH prediction errors. The impact of SLUCM's Building Energy Model (BEM) had a significant impact on summer simulations, particularly affecting waste heat predictions and thermal comfort calculations.
Seasonal analysis revealed an average Ta decrease of 11.94°C and RH reduction of 7.94% between summer and winter conditions across sites. All studied locations exhibited strong heat stress according to both Universal Thermal Climate Index (UTCI) and Physiological Equivalent Temperature (PET) metrics during summer months, with the compact high-rise zone (LCZ 1) showing the highest thermal stress levels. This suggests the need for city-wide heat mitigation strategies rather than targeted localized interventions.
The research highlights how methodological choices in urban climate modeling influence the interpretation of results. While ENVI-met's computational fluid dynamics (CFD) approach produced warmer, more homogeneous conditions due to resolved flow fields, SLUCM's surface energy balance method resulted in cooler, more stratified conditions owing to its simplified treatment of turbulent mixing. The study recommends incorporating HVAC calculations into ENVI-met simulations and using both PET and UTCI indices for a more balanced assessment of thermal comfort conditions.
These findings contribute to our understanding of urban microclimate modeling capabilities and limitations, providing valuable insights for researchers and urban planners in subtropical climates. The research emphasizes the importance of considering model selection, seasonal variations, and multiple thermal comfort indices in urban climate analysis and design decision-making.
How to cite: Lieber, J. and Yang, J.: Assessing Urban Microclimate Modelling Variability: A Comparative Analysis Of ENVI-met And SLUCM Across Multiple LCZs In Hong Kong, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2363, https://doi.org/10.5194/egusphere-egu25-2363, 2025.
City development has significantly transformed local climate conditions, modified wind patterns, and variations in altered air quality. These transformations have important consequences for human health, energy usage, and environmental sustainability. Improvements in Computational Fluid Dynamics (CFD) modeling have offered essential tools for examining the intricate nature of urban microscale processes. These allow researchers to replicate airflow around structures, winds at pedestrian levels, and the spread of pollutants with high spatial detail, considering turbulence movements. In the Large-eddy simulation (LES) model, the significant turbulent eddies are explicitly resolved and modeled. Since a proper definition of urban morphology has been recognized as vital to improve model efficiency, reliable numerical simulations at the local level necessitate a suitable fine-scale depiction of the urban fabric.
This study employs LES simulations to characterize the daily cycle of key micro-meteorological parameters at high spatio-temporal resolution over the Bolognina district during summertime under high-pressure conditions. Situated within the municipality of Bologna, Bolognina exemplifies many urbanisation features typical of Italian cities. For the first time, PALM-4U was implemented using off-line nesting within the GLOBO-BOLAM-MOLOCH modeling system. It is important to highlight that the MOLOCH meteorological model, specifically developed for the Italian peninsula, provides highly accurate mesoscale predictions compared to other state-of-the-art models such as COSMO and WRF. This accuracy is particularly critical when using MOLOCH as a meteorological driver for LES urban simulations, where precise mesoscale input as well as high-quality urban morphological data significantly influence the results.
To develop the static driver for Bolognina, diverse data sources—such as remote sensing, municipal datasets, and open-access data—were utilized. Additionally, a dedicated census was conducted for privately-owned trees. In total, over 5,000 trees, both public and private, were cataloged within a 1 km² area.
The experiment was conducted over a three-day period, from August 23 to 25, 2023, under weather conditions characterized by clear skies, calm winds, and strong daytime insolation - ‘ideal’ for the development of the Urban Heat Island (UHI). This case study primarily serves to assess the numerical stability of the novel meteorological dynamic driver. Additionally, the impact of different pavement types on micrometeorological profiles and on the partitioning of available energy at the surface was analysed to investigate how materials with varying heat capacities and urban vegetation can enhance or mitigate the UHI effect and the thermal comfort.
How to cite: Landi, T. C., Mortarini, L., Drofa, O., Fiorillo, E., de Vargas Brondani, D., and Cava, D.: A multi-scale approach combining MOLOCH and PALM-4U for simulating urban micro-meteorology in an italian neighborhood, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18592, https://doi.org/10.5194/egusphere-egu25-18592, 2025.
Understanding and modelling the urban climate and the impacts of climate change on the urban environment is essential to underpin the development of adequate adaptation and mitigation measures and policies. City-scale climate projections require very high-resolution physically-based models which are commonly time-intensive and computationally expensive to run. To overcome this problem, cost and time effective alternatives, such as Deep Learning, are often sought.
Here, we present an application of two lightweight 3-layer Convolutional Neural Network (CNN) architectures to downscale 7 Global Climate Models (GCMs) from the Coupled Model Intercomparison Project Phase 6, for the 2015-2100 period using four socioeconomic pathways (SSP1-2.5, 2-4.5, 3-7.0, 5-8.5), for the city of Paris, France. The CNNs generate projections of 2-meter temperature (T2m) at point-level (using data from 7 in-situ observational stations) and Land Surface Temperature (LST) at a spatial resolution of ~5 km. The resulting dataset is used to analyse the CNNs representation of air temperatures, LST, the urban heat island (UHI) and temperature extremes, including heatwave frequency, in future climate. The CNN downscaled projections replicate the Parisian UHI effect, described in previous studies and in the observational data used to train the CNNs. The GCMs, on the other hand, due to their coarse resolution, are unable to capture the UHI effect. Moreover, the CNNs projections are consistent with the GCMs overall warming mean trend for maximum and minimum T2m and LST, 90th percentile maximum T2m, and an increase in tropical nights throughout the 21st century (for the warmest scenarios). However, CNNs underestimate the increase in heatwave frequency present in the GCMs under the warmest scenarios. Although further research is required to understand the shortcomings in heatwave DL projections, this work supports the potential of DL as a downscaling method for urban climate studies.
Acknowledgements: This work is supported by the Portuguese Fundação para a Ciência e Tecnologia, FCT, I.P./MCTES through national funds (PIDDAC):
UID/50019/2025 and LA/P/0068/2020 (https://doi.org/10.54499/LA/P/0068/2020).
Frederico Johannsen was supported by FCT, I.P with the doctoral grant with the reference UI/BD/151498/2021 and DOI identifier 10.54499/UI/BD/151498/2021.
How to cite: Johannsen, F., M. M. Soares, P., and S. Langendijk, G.: Using Deep Learning to generate future projections of temperature extremes and the urban heat island in Paris, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9424, https://doi.org/10.5194/egusphere-egu25-9424, 2025.
Cities are considered local “hotspots” of climate change. Urban areas concentrate a large fraction of global population, wealth, and emissions, exposing their inhabitants to climate change impacts. Therefore, the improvement of urban present climate description and future projections are paramount for designing adaptation and mitigation strategies. Global Climate Models are state-of-the-art tools for projecting future climate. However, most of the simulations have coarse resolutions and do not have physical urban parametrisations to adequately represent the physical properties and processes at the urban scale.
The advantage of applying a machine learning approach – Extreme Gradient Boosting (XGBoost) – is explored for better describing Madrid’s urban present and future climates, namely, the ability to reproduce the 2-m air temperature (Tmax, Tmin), land surface temperature (LST), urban heat island (UHI) and surface urban heat island (SUHI) effects. The XGBoost is evaluated at daily scales for local ground temperatures and, at both daily and hourly scales, to represent the spatial structure of LST w.r.t. remote sensing data. Firstly, for present climate, XGBoost is trained with a set of ERA5 predictors (at 0.25°), ground stations, and LST observations. Secondly, a number of sensitivity cases are performed to assess the results dependency to predictors and their resolution. Thirdly, the learned relationship between the set of predictors and predictands is applied to 4 Earth System Global Climate Models (ESGCM) predictors, providing historical and future climate projections for the 21st century under four emission scenarios.
Overall, XGBoost results reveal a good performance and significant added value against ERA5 and the ESGCMs. XGBoost greatly improves the reproduction of the present climate Tmax, Tmin, LST, and more importantly, the UHI (-0.5°C and +3°C for Tmax and Tmin, respectively), and the SUHI (+1°C and +2°C for Tmax and Tmin, respectively). For future climate, XGBoost significantly corrects the ESGCM UHI misrepresentation but seems to underestimate the expected Madrid’s local warming (3.5°C anomaly under the SSP5-8.5 scenario).
Acknowledgments:
DCAL are supported by the Portuguese Foundation for Science and Technology (FCT) financed by national funds from the MCTES through grant https://doi.org/10.54499/2022.03183.CEECIND/CP1715/CT0004.
How to cite: Bushenkova, A., M.M. Soares, P., Johannsen, F., and C.A. Lima, D.: A Machine Learning application towards a better representation of Madrid’s urban future climate, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9395, https://doi.org/10.5194/egusphere-egu25-9395, 2025.
This study examines the heat and stability patterns of the urban boundary layer in cities. It emphasises the impact of urbanisation and anthropogenic activities on atmospheric characteristics and provides insights into stability implications for the urban environment. Using a high-resolution, sub-kilometre scale city-wide numerical modelling framework, part of the Digital Urban Climate Twin (DUCT), examines the variability of thermal regimes within the boundary layer. The findings reveal distinct thermal regimes influenced by diurnal local circulations, urban heat island effects, and moisture availability. During the daytime, convective turbulence dominates due to strong surface heating, while at night, stable stratification may develop, but local heating sources (e.g., anthropogenic heat) disrupt stability. The study highlights that stability profiles are crucial in determining outdoor comfort and air ventilation under the influence of the transilient nature of wind and thermo-moisture fields at a city scale. These findings are essential for urban climate adaptation strategies and for improving local weather forecasting.
How to cite: Benny Chalakkal, J., Wong, M. L., Zozaya, A., and Orehounig, K.: Exploring Thermal Regimes in Urban Heating using DUCT City-scale Modeling, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8844, https://doi.org/10.5194/egusphere-egu25-8844, 2025.
The Imp_DroP project deals with the effects of climate change-related dry periods on ground temperatures and water requirements for irrigation in Vienna. The focus of the study is to examine the effects of cooling on the climate of the following components:
- a) Evaporative cooling by irrigated agricultural regions
- b) Evaporative cooling in urban Vienna (especially green roofs)
- c) Anthropogenic heat reduction
To quantify the effects of these mitigation measures, part of the project consisted of experimental studies: evaporation and precipitation, runoff, growthrate were recorded for two seasons with 10 lysimeters on four green roofs with different urban climates. The in-situ measurement data served as input for the modeling tasks. In a second step, anthropogenic heat emissions were quantified. For this purpose, data from E-Control was used, the energy consumption was broken down in time and space for selected periods and the anthropogenic heat flow was calculated for various scenarios.
Based on all these results, SURFEXv9/TEB was used to examine the local effects of building envelopes, green roof construction, irrigation, tree shade and vegetation in the canyon, traffic heat reduction and other possible climate mitigation measures on indoor, roof and canyon thermal conditions. Anthropogenic heat can lead to local increases in air temperature of up to 2 °C. Reducing anthropogenic heat by reducing traffic, increasing the contribution of renewable local energy production and improving building insulation can reduce air temperature to this extent. Tree shade can reduce the thermal comfort index UTCI by 4 ° during noon. Full irrigated low vegetation can cool between 1-3 °UTCI throughout the day.
The coupled urban climate model WRF-TEB was used to simulate the current and future climate in the greater Vienna area. Climate scenarios suggest that climate change could lead to a temperature increase of around 3.8 °C by mid-century if no significant action is taken. Remedial measures related to irrigation of the agricultural areas east of Vienna (Marchfeld) can lead to a reduction in the maximum temperature, especially close to the irrigated area. A maximum reduction of only 0.2 °C can be achieved for the entire city, while in the districts of Vienna closer to Marchfeld the reduction can even be up to 0.4 °C. However, if all possible cooling measures are implemented to the maximum, a maximum cooling of 1.5 °C can be achieved. However, exhausting all adaptation measures cannot compensate for the warming induced by climate change while seriously challenging the water supply of Greater Vienna.
How to cite: Weihs, P., Trimmel, H., Thaler, S., Eitzinger, J., Hörbinger, S., Rauch, H.-P., Preiss, J., Wöss, D., Pröll, T., Formayer, H., Nadeem, I., Wittkowsky, M., Schoetter, R., Masson, V., Mirebeau, A., and Lemonsu, A.: Impact of longer Drought Periods on Climate in Greater Vienna, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18008, https://doi.org/10.5194/egusphere-egu25-18008, 2025.
Climate change is driving higher global temperatures and more frequent heatwaves. Urban populations are exposed to high temperatures and heat stress because of the urban heat island. For effective adaptation planning, quantifying the additional heat burden for urban populations during heat waves and finding possible heat mitigation strategies is key. Our study focusses on Brussels during the record-breaking heat wave of 2019 in Belgium, where temperatures approached 40°C. We use high-resolution models to characterise spatial variations in heat exposure across the city, and quantify how certain adaptation strategies like urban greening can reduce heat exposure and related health impacts.
To quantify the influence of the urban presence as well as detect spatial differences in heat exposure, we compare urban and non-urban scenarios using three urban climate models: UrbClim at 100m, WRF with BEP/BEM at 1km and COSMO-CLM with TERRA_URB at 2.8km. Additionally, we link the temperature output from the simulations to mortality data for Brussels using existing temperature-health relationships to quantify the mortality attributable to the heatwave. For each of the models in the urban mini-ensemble, the same adaptation strategy is implemented at hotspot locations. Three different scenarios are tested: increasing the permeable surface, implementing cool roofs and a combination of both. The mini-ensemble allows for a better understanding of the uncertainties related to implementing such strategies, particularly the effects of different model parameterizations. These results help us understand the potential of mitigation measures to reduce impacts from heat stress, and thereby serve as an important basis for urban adaptation and planning policy.
How to cite: Serras, F., Vanderkelen, I., Brousse, O., Simpson, C., Lauwaet, D., Demoury, C., van Lipzig, N. P. M., and Heaviside, C.: Quantifying the impacts of urban adaptation measures to reduce heat stress in Belgium, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17430, https://doi.org/10.5194/egusphere-egu25-17430, 2025.
Heat extremes significantly impact human health, causing heat stroke, reduced productivity, and heat-related mortality. In Europe, the increasing frequency of intense heat waves results from a combination of natural climate variability and anthropogenic climate change. Urbanization exacerbates these extremes through the urban heat island (UHI) effect, intensifying warming in cities and their surroundings. Since the 2000s, France has experienced severe heat waves leading to substantial loss of life, with Lyon experiencing a significant mortality increase after Paris, as reported by the EM-DAT database.
Heat stress exhibits high spatiotemporal variability influenced by morphological and climatic conditions. While heat stress classifications based on climate and urban development provide a general overview of population impacts, they lack the detailed resolution needed to understand intra-urban temperature intensification.
This study investigates the complex dynamics of heat stress at a micro-scale by analyzing three heat stress indices over the Lyon region from 2000 to 2022 during summer (June-August): 1) a temperature-based heat index, 2) the Universal Thermal Climate Index (UTCI), and 3) UHI intensity. A comparative spatiotemporal analysis was conducted across these datasets.
Maximum and minimum air temperature and relative humidity data were obtained from Météo-France's ground observation network. Hourly data were converted to daily values for heat stress index (HSI) calculation. High-resolution (0.25° x 0.25°) daily UTCI data were extracted from the ERA5 reanalysis dataset. Landsat 5, 7, and 8 satellite images covering Lyon were acquired and processed using a single-channel method, including radiometric and geometric corrections, and NDVI-based emissivity corrections, to derive land surface temperature (LST). Image fusion techniques were applied to combine the multi-temporal satellite data into a single dataset.
Cubic interpolation was used to standardize the temporal resolution of the LST, ground observation, and reanalysis data and to address data gaps. The HSI was calculated using Steadman’s index, using daily air temperature and relative humidity. Spatial and temporal analysis of surface temperatures was performed over urban and rural areas of Lyon to calculate UHI intensity. Using the HSI derived from direct temperature data as a benchmark, bias correction, root mean square error, and correlation analyses were conducted to validate the UTCI and UHI. Spatial mapping of the derived HSI was performed using QGIS, and temporal analysis was conducted to compare seasonal, annual, and decadal HSI patterns.
Results revealed significant discrepancies between air temperature-based and thermal data-derived metrics, particularly in urbanized areas where land surface characteristics and anthropogenic activities enhance heat retention. Urban areas exhibited significantly higher temperatures and increased heat stress compared to rural areas due to the UHI effect. Remote sensing data provided more localized and detailed information on heat stress than traditional temperature-based indices. Despite some disparities, the datasets complemented each other by enabling necessary spatial and temporal adjustments. This research highlights the need for multi-dimensional approaches to heat stress assessment, integrating both meteorological and remotely sensed data. These findings have crucial implications for urban planning and climate adaptation strategies in Lyon and other European cities facing increasing heat stress risks.
How to cite: Geesupalli, P. D. and Pothier, C.: A Multi-Scale Comparison of Heat Stress Metrics in Lyon using meteorological, reanalysis and remote sensing datasets, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12651, https://doi.org/10.5194/egusphere-egu25-12651, 2025.
Urban green has the potential to reduce urban heat stress, with shading and evapotranspiration being both important contributors. We here investigate street green space (SGS), which comprises street-level vegetation in the form of trees, bushes or green facades, i.e. that is “visible to a person in the street”. We measure SGS using the green view index (GVI).
We first analyze the relationship between climate zone, urban form and SGS. This is based on published research on current SGS in 181 global cities. We find that observed attainable GVI values vary by urban form, but more importantly, by climate zone at large. Cities in temperate and tropical climates showing much larger values than in dry climate zones. In the next step, we develop three scenarios of future street green space. These are based on the observed greenness from 2016-2023, and on a careful trend analysis. We extend the current values until 2050, using optimistic assumptions that we deem plausible and that have been informed by the trends. In two “optimistic outlook” scenarios, we assume positive trends that are however constrained by maximum observation. This is complemented by a more pessimistic “baseline scenario”, for which we assume current street green space conditions, but negatively impacted by climate change.
In a second step we translate these scenarios into a quantifiable perspective of future urban heat mitigation potential for 143 cities using high-resolution climate model data from the URBCLIM project. The cooling potential of SGS by urban form and climate zone has been determined using a random-effects regression analysis with place-specific confounding factors in a related project (EGU presentation EGU25-5169).
This work results in a dataset of urban cooling potential of greening scenarios in 143 cities. The scenarios also take into account different climate change scenarios from the RCP-SSP framework.
How to cite: Lohrey, S. and Falchetta, G.: Scenarios of street green space to inform future heat adaptation in cities, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11496, https://doi.org/10.5194/egusphere-egu25-11496, 2025.
The increasing frequency of extreme heat events due to climate change calls for a deeper understanding of the factors contributing to heat stress-related morbidity and mortality. Urban areas, with their high population densities, are especially vulnerable due to the urban heat island (UHI) effect, which intensifies temperatures compared to rural surroundings. Traditional heat stress assessments often focus solely on air temperature (Ta), overlooking other factors such as humidity, wind speed, and solar radiation important for the thermal comfort. Spatial data on heat stress is often lacking at fine spatial resolutions, making the use of LST a common. However the relationship between Ta, LST and thermal comfort metrics such as Universal Thermal Climate Index (UTCI) or Humidex are not yet well understood.
To address this gap, we conducted a spatio-temporal assessment of thermal comfort metrics, LST and Ta across Hesse, Germany, using 1 km scale data. By examining temperature anomalies (ΔT), the difference between local and comparable rural background temperatures, we quantify the urban impact on heat stress while minimizing broader climatic influences. Diurnal and spatial patterns of temperature variations and thermal conditions are examined across different land use types and urban forms. Satellite-derived parameters were also incorporated to assess regional heat risk in areas lacking local measurement data.
Our findings reveal a stronger UHI effect in ΔHumidex (max. 4.3°C) compared to ΔTa (max. 2.9°C) and ΔLST (max. 3.4°C), indicating that reliance on LST or Ta alone might underestimates the full extent of heat stress of the urban population. This is particularly significant as nearly one-third of the population in Hesse (30.4% for Ta, 25.6% for Humidex, and 34.7% for LST) lives in areas where temperatures exceed baseline levels both during the day and at night. The duration of temperature exceedance is consistently longer in urban areas, with average values of 3.8 hours and 2.8 hours of the diurnal cycle for ΔHumidex and ΔTa, respectively, compared to 0.7 hours and 0.4 hours in rural areas. Densely built areas, where nighttime cooling deficits persist longer, are particularly vulnerable, while inhabitants of open urban areas experience more moderate heat stress. These results emphasize the need to incorporate thermal comfort metrics that account for multiple parameters when evaluating the UHI effect. Although LST is commonly used as a substitute in UHI studies, its correlation with Ta and Humidex varies both spatially and temporally, warranting cautious application.
How to cite: Krikau, S. and Benz, S.: Spatial-temporal insights into heat stress metrics for regional heat hazard and comfort assessment , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4411, https://doi.org/10.5194/egusphere-egu25-4411, 2025.
Cities and municipalities are particularly affected by climate change and its impacts. Considering climate mainstreaming, the planning sector is called upon to provide appropriate adaptation services. Many German and Austrian cities have already prepared urban climatic maps (UCM) and are partially integrating them in their spatial planning policies. These UCM usually contain two components: an urban climatic analysis map (UC-AnMap) and an urban climatic planning recommendation map (UC-ReMap). In German speaking countries the compilation is often based on the VDI (Association of German Engineers) directive 3787 (Climate and Pollution Maps for Cities and Regions), which provides a conceptual guideline, but is open to design and interpretation and leaves the actual implementation to engineering firms and consulting agencies.
The research project “OSCAR - Objectifying and Standardizing Urban Climate Analyses for Climate-Resilient Urban Planning” (funded by the Climate and Energy Fund and carried out under the program "Austrian Climate Research Programme (ACRP)") identified three major shortcomings in the development process of UCM: (1) Lack of standards and comparability, (2) high development costs, as the methods and recommendations must be developed and elaborated individually in each case, and (3) insufficient trust in provided data and maps for planning decisions due to poor documentation or missing understanding to applied methodologies and results.
OSCAR aims to provide solutions to close identified gaps by preparing the basis for a good practice and objectification opportunities. A comprehensive literature review and stakeholder-engagement process based on expert interviews and workshops with urban climate modeling experts, consulting agencies and city representatives provides the theoretical and practical groundwork for the research and methodological advancements. Within the project, one central objective is the development of an objectified and reproducible model for UC-AnMaps, based on the VDI directive to (i) accelerate the assessment of urban climate conditions, (ii) provide a basis and method to make climate adaptation measures numerically and rapidly tangible on a city scale level, (iii) enable comparability of urban climate conditions of a city over time and (iv) provide secured planning recommendations through reproducible and well documented methods. Aligning with the recently update draft of the “Climate and Planning” guideline (VDI 11/2024) to develop UC-AnMaps, a reproducible calculation method for spatial designation of climatopes (areas of similar climatic characteristics), and partial integration of specific climate phenomena is under ongoing development. To use the developed method without data constraints, publicly available data and open-source software is a prerequisite.
The envisioned approach is based on weighting of static input data (e.g. imperviousness density), a rudimentary cold air flow algorithm, logical combinations of spatial data sources, and presupposing factors (e.g. area sizes) to define climatopes. In-depth documentation, strengths and limitations of the suggested method, along with a conceptual model and case studies provide promising results for scaling and accelerating the development of UC-AnMaps. Regarding derived planning recommendations, findings of the stakeholder-engagement formats suggest, that a close exchange with urban stakeholders for developing the UC-ReMaps is crucial and shall not be replaced by a reproducible and quantified information provision about urban climate conditions.
How to cite: Schneider, M., Formanek, S., Hochebner, A., Pfattner, S., Reinwald, F., Thiel, S., Tötzer, T., and Wentz, J.: Objectifying Urban Climate Mapping: A Scalable Approach to Enhancing Climate-Resilient Spatial Planning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3053, https://doi.org/10.5194/egusphere-egu25-3053, 2025.
In the city of Bern, Switzerland, two urban measurement campaigns were operated by the University of Bern in the periods 1972 to 1974 and 2018 to 2024. In the first campaign, air temperature was measured at six locations with a resolution of two hours, in the second campaign at 55 locations with a resolution of 10 minutes. The summer (June to August) air temperature data of both measurement campaigns was homogenized and corrected for three variables: daily minimum temperature (Tmin), daily mean temperature (Tmean), and daily maximum temperature (Tmax). The data from the second measurement campaign was subsequently used to simulate urban temperatures in Bern during the currently used reference period (1991 to 2020) and for three global warming scenarios (Global warming level (GWL) 1.5 °C, 2.0 °C and 3.0 °C). To do so, a two-step quantile mapping approach (which includes a transfer function from rural to urban stations) was applied, since the original urban dataset is too short for a direct quantile mapping. Finally, a dataset of three temperature variables for six to 55 locations in and around the city of Bern during six different periods was created.
In this poster, we illustrate the calculation of this dataset and highlight the results regarding calculated heat indices (tropical nights and official heat warning levels). We show that the heat warning thresholds, which were never hit in the first measurement campaign, are reached at almost all locations in the second campaign. We furthermore discuss the differences between the GWL scenarios and the importance of the urban heat island effect during past, present and future (urban) climates of Bern.
How to cite: Burger, M., Senoner, A., and Brönnimann, S.: Past, present and future (urban) climates of Bern, Switzerland, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3156, https://doi.org/10.5194/egusphere-egu25-3156, 2025.
In a warming climate, heatwaves are becoming more intense and more frequent. Their effects have already proven to be one of the highest contributors to climate hazard related mortality. Additionally, due to the urban heat island (UHI) effect, heat is intensified in the most densely populated areas and since urbanization is expected to continue increasing, more and more people are facing enhanced risks.
In this project, we investigate the severity of hot spells in cities with the help of health-relevant heat indices, namely wet bulb temperature, the universal thermal climate index (UTCI) and the amount of tropical nights, among others. We process reanalysis data from ERA5-Land from 1950 onwards and simulation data from the downscaled EURO-CORDEX simulations for various climate scenarios until 2100 to generate these indicators for Europe at a resolution of 0.1°. After this step, we plan to train a shallow machine learning model (XGBoost) to downscale reanalysis and simulation data to the city level at a resolution of 100m for selected European cities. For model validation, temperature series from 12 European cities’ urban measurement networks will be used. Finally, the indicators will be applied to four pilot cities (Oslo, Bern, Lyon and Naples), as part of the EU project healthRiskADAPT under the framework of Horizon Europe. In a subsequent phase, advanced modeling techniques such as Weather Research and Forecasting (WRF) models or computational fluid dynamics (CFD) may be applied to better understand the compound effects of heat and pollution in cities.
How to cite: Pierce, C.: Health-relevant Heat Indices for Urban Areas: A Machine Learning Approach with Downscaled Climate Data and City Measurement Networks, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3184, https://doi.org/10.5194/egusphere-egu25-3184, 2025.
Urban areas, with their dense populations and numerous socio-economic activities, are increasingly vulnerable to floods, droughts, and heat stress due to land use changes and climate change. Traditionally, the urban thermal environment and water resources management have been studied separately using urban land surface models (ULSMs) and urban hydrological models (UHMs). However, as our understanding deepens and the urgency to address future climate disasters grows, it becomes evident that hydroclimatological disasters—such as floods, droughts, severe urban thermal environments, and more frequent heat waves—are not isolated events but compound events. This highlights the close interaction between the water cycle and the energy balance. Consequently, the existing separation between ULSMs and UHMs creates significant obstacles in better understanding urban hydrological and meteorological processes, which is crucial for addressing the high risks posed by climate change. Defining the future direction of process-based models for hydro-meteorological predictions and assessments is essential for better managing climate disasters and evaluating response measures in densely populated urban areas. Our review focuses on three critical aspects of urban hydro-meteorological simulation: similarities, differences, and gaps among different models; existing gaps in physical process implementations; and efforts, challenges, and potential for model coupling and integration. We find that ULSMs inadequately represent water surfaces and hydraulic systems, while UHMs lack explicit surface energy balance solutions and detailed building representations. Coupled models show potential for simulating urban hydro-meteorological environments but face challenges at regional and neighborhood scales. Our review highlights the need for interdisciplinary communication between the urban climatology and urban water management communities to enhance urban hydro-meteorological simulation models.
How to cite: Chen, X., van der Werf, J. A., Droste, A., Coenders-Gerrits, M., and Uijlenhoet, R.: Overcoming challenges in urban hydro-meteorological simulation: Where is our first step?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3539, https://doi.org/10.5194/egusphere-egu25-3539, 2025.
Since September 2022, South Korea has mandated climate change impact assessments (CCIA) for urban development projects exceeding 1 million square meters. To achieve its 2050 carbon neutrality goal, the government encourages these projects to establish concrete greenhouse gas (GHG) reduction measures through CCIA. This study examines the evaluation and reduction strategies for GHG emissions in urban development projects using case analyses and reviews of the latest technologies, proposing effective approaches to enhance their feasibility. First, minimizing GHG emissions during the urban development planning stage is crucial. This involves strategic land use planning, such as securing green spaces and optimizing park locations. Second, promoting energy-efficient building designs and the use of renewable energy technologies, such as solar power and geothermal systems, is essential. This applies not only to public buildings but also to residential and commercial structures. Third, establishing a systematic framework to monitor and verify the implementation of GHG reduction measures proposed during CCIA is necessary. This study contributes to the development of effective GHG reduction strategies for large-scale urban projects and provides actionable frameworks to strengthen climate change mitigation efforts during urban development processes.
How to cite: Kim, Y., Cho, H., Cho, Y., and Heo, N.: Implementation Strategies for Greenhouse Gas Reduction in Urban Development Projects through Climate Change Impact Assessment, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4031, https://doi.org/10.5194/egusphere-egu25-4031, 2025.
In the city of Berlin, the Institute for Meteorology (Freie Universiät Berlin) operates a citywide meteorological network for measurements of temperature and other meteorological variables.
During the project Urban climate under Chance (BMBF funded), additional stations were installed in the city, shortly after the project start in 2016, even to evaluate the urban model PALM-4U (Maronga et al. 2020). The model evaluation shows that there are uncertainties in the daily fluctuations: The model is too cold at midday, while it is too warm at night. The city stations measure 2m-temperature and humidity and are still in operation. The locations of the stations were chosen such that various local climate zones (LCZ, Steward & Oke 2012) of Berlin were covered. Eight LCZs exist in the city, these are the LCZs 2 (compact midrise), 4 (open highise), 6 (open lowrise), 8 (large lowrise), A (dense trees), B (scattered trees), D (low plants) and G (water). On average 3 Stations exist in each LCZ, except in LCZ 4, where there exists only one station, with an availability of 85% of measured data.
With this network, we are now in a position to carry out an analysis of urban climate with a focus on these existing eight LCZs.
The results between the different LCZs during the summer months show us the urban heat island effect, which is about 4 K (LCZ 2 minus LCZ A), as well as the different timing of the occurrence of maximum and minimum temperatures. This analysis could help both stakeholders and landscape planners to redesign districts in such a way that residents benefit from a better urban climate. All station data are available in the Refubium of the Freie Universität Berlin under the keywords MEVIS and FUMINET and can be downloaded.
Maronga, B. et al. (202): Overview of the PALM model system 6.0. Geosci. Model Dev., 13, 1335–1372, 2020
https://doi.org/10.5194/gmd-13-1335-2020
How to cite: langer, I., Rust, H., and Ulbrich, U.: City network of Berlin under the aspect of temperature measurements in different Local Climate zone's, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4515, https://doi.org/10.5194/egusphere-egu25-4515, 2025.
Densely built urban areas often experience overheating, contributing to the urban heat island effect and increasing heat-related risks. These phenomena directly impact various aspects of city life, including public health, biodiversity, urbanization, traffic, and green spaces. They are also the focus of numerous scientific disciplines and local/regional institutions. Despite this attention, many questions remain regarding the causes, timing, and locations of heat-related risks within urban environments.
To address these questions and support interdisciplinary collaboration, detailed spatial and temporal meteorological data are essential. The creation and collection of micrometeorological measurements and datasets can enhance interdisciplinary research, facilitate comprehensive assessments, and inform climate change adaptation strategies.
The experience of the Novi Sad Urban Climate Lab (NSUCL) at the University of Novi Sad, Faculty of Sciences (Serbia), has shown that an interdisciplinary research approach yields valuable outcomes for mitigating heat-related and urbanization challenges. Collaborative research with architecture experts has revealed that strategically planting additional trees can effectively mitigate the urban heat island effect and improve outdoor thermal comfort (OTC) in densely built-up areas. For example, the placement of additional trees has been shown to reduce OTC values at specific manikin locations by up to 6.11°C (UTCI), highlighting the importance of carefully determining their locations to enhance thermal comfort during hot summer days.
These findings are based on micrometeorological monitoring conducted in the field during the summers of 2022 and 2023, utilizing Mobile Micrometeorological Carts (MMCs) developed by the NSUCL. The impact of additional trees on OTC conditions was assessed at three selected locations: Catholic Porta Square, Gymnasium Street (with a north-south orientation), and Laze Teleckog Street (with a southeast-northwest orientation). These locations represent densely built urban morphologies characterized as intensive pedestrian zones in downtown Novi Sad.
Acknowledgement: The research was supported by the project no. 003026234 2024 09418 003 000 000 001, funded by the Autonomous Province of Vojvodina (regional government).
How to cite: Savić, S., Bajšanski, I., Dunjić, J., Vasić, M., and Milošević, D.: Mobile Microclimate Monitoring: Enhancing Climate Change Mitigation and Adaptation in Temperate Cities, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4539, https://doi.org/10.5194/egusphere-egu25-4539, 2025.
Development of cities and reduction of green spaces is one of the important and influential factors in the formation of heat islands, a phenomenon that has become one of the important problems in cities. The urban heat island (UHI) is a concept that refers to the increase in temperature in urban areas compared to rural areas or urban outskirts. This temperature increase is due to the abundance of buildings, roads, parking lots, and other human structures in cities, as well as the reduction of green spaces and water, and also the increase in human activities such as traffic and industries. This study conducts an analysis on the complex relationships between topography, surface biophysical properties, and the UHI effect in Mashhad city (NE Iran), serving as a local case study for understanding climate change impacts. For this purpose, land use was extracted from Sentinel 2A images using the object-oriented processing method of satellite images. In the next step, using the thermal bands of Landsat 9 images and the MODIS sensor, urban heat islands were calculated for the city of Mashhad during the day and night. Finally, the correlation between temperature distribution during the day and night with land use, topography, vegetation, etc. was calculated. According to the obtained results, the significant loss of vegetation within and around the city has contributed to notable changes in surface characteristics. The transition from a relatively cool temperature layer (25-29 °C) to an average temperature class (33-37 °C) indicates the impact of vegetation loss on local dynamics. Moreover, the manipulation of slopes around the city has led to temperature increase, resulting in the emergence of hot (41-44 °C) and very hot (45-47 °C) temperature classes. The redistribution of surface clusters, particularly in the northwest, south, and southwest of Mashhad, is linked to specific land use changes. Additionally, the temporal variability of surface temperatures was examined, revealing the highest temperatures in July and August and the lowest in Azar. The spatial range of surface temperature exhibited seasonal variations, with the highest range observed in April and spring and the lowest in October and autumn. Moreover, the observed changes in surface temperature patterns highlight the significant impact of anthropogenic land use modifications on local climate dynamics. The expansion of the urban heat island phenomenon in Mashhad is evident through the overall increase in surface temperature and the reduction in temperature differentials between urban areas and their surroundings. Overall, this research provides important insights into the complex interplay between topography, land use changes, and surface dynamics in Mashhad, emphasizing the critical role of land use changes in shaping local climate patterns. It underscores the need to consider environmental impacts, urban development decisions, and land use planning to address the challenges posed by the expanding UHI effect and promote sustainable and climate-conscious development.
Keywords: Urban Heat Island, Surface temperature, Land use changes, Topography, Mashhad, Environmental management, Urban planning, Spatial analysis.
How to cite: Jananeh, K., Samadi, M., Avatefi Akmaland, F., and Mohammadzadeh Alajujeh, K.: Assessing Urban Heat Island Effect and its Correlation with Topography and Surface Vegetation Characteristics in Mashhad City (NE Iran): Case Study on a Local Climate Change, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4872, https://doi.org/10.5194/egusphere-egu25-4872, 2025.
Temperature patterns in urban environments are shaped by the complex pattern of urban design. Accordingly, urban temperature extremes are not distributed homogenously over entire cities, but rather shaped by local land use. Resulting disparities in heat exposure can have severe consequences for the health and general well-being of individuals.
This study examines the relationship between urban heat and demographic patterns for each district in Germany. Population subgroups, such as foreigners and elderly residents, are analysed to determine whether certain groups are disproportionately affected by summertime urban heat. The analysis is conducted in a 1 km grid focusing on daytime and night-time separately. Demographic data is derived from the 2011 Population and Housing Census.
Instead of absolute temperatures, we focus on urban temperature anomalies, which are defined as temperature differences between urban pixels and their rural surroundings. This approach allows us to focus on the aspects of climate that are shaped by urban planning decisions, while disregarding large scale climate-patterns.
First results focusing on satellite derived LST reveal significant disparities. E.g., in over 66 % of German districts, foreign residents experience substantially higher heat exposure compared to nonforeigners. This analysis highlights the unequal distribution of urban heat stress within Germany and suggests avenues for further research. We now focus on disparities in air temperature and simple heat stress indices like the Humidex to provide insights into the perceived heat stress experienced by residents. Furthermore, the recent release of the 2022 Housing and Population Census offers more expanded demographic information for more detailed analysis.
How to cite: Keller, P. and Benz, S. A.: Inequalities in exposure to summer urban heat extremes in Germany, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5839, https://doi.org/10.5194/egusphere-egu25-5839, 2025.
Domestic gardens comprise up to 30% of urban area in the UK, providing many ecosystem services (ES), such as flood risk mitigation and temperature regulation, through vegetation present. Current estimates of ES provisioning using urban land surface models often focus on green space in an entire town/city, rather than specific greenspace types (Zawadzka et al., 2021), and often omit domestic gardens entirely, which may lead to unreliable recommendations. We chose the Urban Tethys-Chloris model (UT&C; Meili et al., 2020) to estimate ES delivery by domestic gardens because it considers both the energy and water balance, at the local scale, and allows for configuration and simulation of both vegetated and man-made surfaces. UT&C is a fully coupled energy and water balance model, that calculates 2m air temperature and skin temperatures of urban areas, accounting for biophysical and ecophysiological characteristics of ground vegetation and urban trees. Input meteorological data over the course of 2024 was obtained from the University of Reading Atmospheric Observatory (Reading, UK). Model garden plant species were specified as Lolium perenne (Perennial ryegrass; for ground vegetation) and Pyrus calleryana (Callery pear; for urban trees), and urban geometry values (such as house height and width) were specified as UK averages. We have found that UT&C realistically estimates seasonal and diurnal urban surface energy fluxes within a typical UK garden. Specifically, in summer, a garden made up of 100% vegetation (short lawn and 2 trees) had a peak surface temperature 13°C cooler, and a 2m air temperature 1°C cooler, than a garden made entirely of concrete. This is largely because vegetated ground loses heat through latent heat flux throughout the growing period, while impermeable surfaces can only do so after heavy rainfall (when water ponds on the surface). Gardens with 100% granite, concrete and slate surfaces had a surface temperature up to 6°C lower and a 2m air temperature 0.5°C lower than asphalt and wood decking as a result of their high thermal conductivity and heat capacity, suggesting these materials would be marginally better at maintaining a lower air temperature within an entirely impermeable garden, particularly in urban summers. Air and surface temperatures over semi-permeable materials, such as artificial turf and wood chips, were often higher than those found for impermeable surfaces, suggesting that they may not be an appropriate method of reducing air temperatures in gardens. Further work will focus on modelling the role of vegetation and garden surface choices on the surface water balance, and on translating the mechanistic model outputs into human comfort and flood mitigation indices. Additional models, such as SUEWS (Järvi et al., 2011), will also be used to estimate ES delivery, and to allow for model intercomparison. Following these simulations, we hope to provide recommendations to UK gardeners about the best hard landscaping materials, plant species, and garden configuration (e.g. proportion of trees versus lawn and bedding plants) to help reduce air temperatures and flooding within their neighbourhoods.
How to cite: Sherlock, M., Verhoef, A., and Blanusa, T.: Using the Urban Tethys-Chloris (UT&C) model to estimate the surface energy balance of different garden materials and configurations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6247, https://doi.org/10.5194/egusphere-egu25-6247, 2025.
The Urban Heat Island (UHI) effect, where urban areas experience higher temperatures than surrounding rural regions, presents a significant challenge in urban climatology due to global warming and rapid urbanization. This study investigated the fundamental mechanisms of UHI by combining theoretical modeling with observational data from South Korea, focusing on urban heat storage, anthropogenic heat, and climatic factors. Using a simple day-night model based on the Surface Energy Flux Balance (SEFB) framework, we demonstrated that UHI primarily results from two mechanisms: reduced diurnal temperature range (DTR) due to high heat capacity of urban materials, and increased mean temperature from additional energy fluxes like anthropogenic heat. These findings emphasize stronger UHI intensity during nighttime compared to daytime. Validation using observational data showed qualitative agreement between theoretical predictions and actual phenomena. Comparison between Seoul (urban) and Boeun (rural) revealed higher nighttime temperatures in Seoul despite its higher latitude, highlighting the role of urban heat storage. Analysis of metropolitan and new cities showed strengthening UHI effects with population growth, consistent with model predictions of decreased DTR and increased mean temperature. This research provides foundational data for understanding urbanization's impact on climate change and sustainable urban planning.
How to cite: Jeon, M. and Moon, W.: Exploring Urban Heat Islands with a simple thermodynamic model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7856, https://doi.org/10.5194/egusphere-egu25-7856, 2025.
As the intensity and duration of heat waves increase with climate change, the importance of the cooling effect of cold air drainages from surrounding elevated terrain on the urban heat island increases. In the urban boundary layer, air temperatures throughout the diel cycle are commonly higher than in rural areas due to modifications in the radiation and energy transfers which depend on the microscale urban surface structure. This well-known urban heat island phenomenon also applies to mid-sized (< 100 000 citizens) cities, but has received less attention. Spatial variability of nighttime air temperatures showed that flow obstacles including dams can impede the nocturnal cold air drainage in urban areas. During the daylight period, parks can form cool islands in the heated city body because of evapotranspirational cooling from leaf surfaces and interception of a significant fraction of the sunlight leading to lower surface air temperatures. The aim of this study was to quantify the impacts of flow obstacles on nocturnal cold air drainages and of heterogeneity in the urban surface structure including city parks on the daytime thermal variability in a mid-sized city (Bayreuth, Germany, about 75 000 inhabitants) in Southern Germany, Europe. Observations were collected using a city-wide meteorological microclimate station network and mobile fast-response air temperature measurements by bicycle during 6 cloud-free nights and days in August 2023. The results showed that an 8 to 10 m high dam blocked the cold air drainage originating from surrounding topography elevated by about 150 m approaching the obstacle at an average wind gust speed of 0.9 m s-1 and depth of several meters. The blocking resulted in the formation of a cold air pool on the upwind side and a distinct microclimatic difference on both sides. While the direct cold-air drainage was blocked, some cold-air drainage was able to circumflow the dam mostly alleviating the air temperature difference between both sides. On average, the nocturnal cold air drainages reduce the ground-level air temperatures on the urban surrounding of a medium-sized city on average by 1.5 K, while instantaneous cooling effects were quantified up to 3.2 K. Additionally, areas with a high proportion of vegetation had a cooling effect on the surroundings in the afternoon, but also at night, as less energy was stored in the ground. The research highlights the importance of considering cold air flow drainage paths in urban planning. The reconstruction of barriers can contribute to the reduction of urban heat island at night.
How to cite: Schappacher, O. and Thomas, C. K.: Quantifying the effects of microscale heterogeneity in urban surface structure on the urban heat and park cool islands in a mid-sized city in Central Europe, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8686, https://doi.org/10.5194/egusphere-egu25-8686, 2025.
The present work aims at estimating the Urban Heat Island effect in the capital cities of European Union (EU) countries. The main objectives of the research were: a) the estimation of the magnitude of UHI in the EU capitals and b) the evaluation of possible relationships with latitude and / or population density. To address these objectives two independent datasets were used that provide Land Surface Temperature estimates over the past eleven years (2013 to 2024), namely the MODIS Terra Land Surface Temperature product and the Landsat 8 Surface Temperature. These two LST products have differences in their spatial and temporal resolution, but were selected due to small difference in daytime acquisition time. The methodology focused on the construction of surface temperature time series from the above data sources for three distinct land cover classes, i.e. urban, forest, and cropland areas, retrieved from the CORINE 2018 land cover dataset. The time series from both MODIS and Landsat 8 were evaluated for their difference in their mean values in the three land cover types. All examined EU capitals were found to have statistically significant higher surface temperatures compared to their surrounding forest and cropland areas. MODIS and Landsat yielded comparable results, whereas the spatial pattern of UHI of EU capitals and its correlation with population density were evaluated highlighting the characteristics of increasing temperatures in large EU cities.
How to cite: Gemitzi, A., Chalkias, G., Stathopoulos, S., and Kourtidis, K.: Assessing Urban Heat Island in the European Union capitals by means of MODIS and Landsat data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11758, https://doi.org/10.5194/egusphere-egu25-11758, 2025.
Over the past decades, urbanization has surged globally, with more than 50% of the population now residing in cities. Projections indicate a continued increase in urban populations, accompanied by significant changes in land use and land cover. These alterations are expected to affect the temporal and spatial distribution of precipitation. Combined with the effects of climate change, which is likely to increase the frequency and intensity of short-duration heavy rainfall, the risk of flooding in urban environments is expected to rise. Understanding the combined impacts of urbanization and climate change on rainfall dynamics is therefore critical for effective flood risk management and urban planning.
In this study, we investigate the influence of urbanization and climate change on short-duration rainfall events in Milan. We used the Weather Research and Forecasting (WRF) model to simulate 8 rainfall events in the current urban setup and climate, and to assess how these events will alter in different scenarios of urbanization and climate change. Results reveal that the impact of global warming on rainfall space-time characteristics is stronger than that of urbanization. However, urbanization significantly contributes to the urban heat island effect, which, when combined with global warming, amplifies its influence on rainfall patterns. These findings underscore the importance of accounting for the combined impacts of climate change and urbanization in studies of future rainfall patterns, particularly for flood risk assessments and urban resilience planning.
How to cite: Koukoula, M., Torelló-Sentelles, H., and Peleg, N.: Urbanization and Climate Change Impacts on Rainfall: A Numerical Sensitivity Study over Milan, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13280, https://doi.org/10.5194/egusphere-egu25-13280, 2025.
The third-party-funded research project "Climate and Health Effects of Urban Forest Structures" (German Research Foundation under contract 471909988) aims to evaluate and compare different urban forest structures with regard to their microclimatic and health-related properties. In order to assess the microclimatic differentiations, extensive stationary and mobile measurements of various meteorological parameters have been carried out in the urban area of Augsburg since 2022. Thereby, four differently structured forest areas in the Augsburg “city forest,” an inner-city park, and an urban comparison area are taken into account.
In all areas, between 5 and 7 Onset HOBO MX2300 loggers were installed to continuously (four-minute measurement intervals) record air temperature and relative humidity. In addition, measurement and survey campaigns were carried out in the study areas in all seasons and under different weather conditions. The campaigns took place in the early afternoon over a period of about 30 minutes along predefined paths that touched different structure types within the study areas.
For mobile microclimate measurements Kestrel 5400 WBGT Heat Stress Trackers and optional additional sensors have been used. In addition, physiological data (heart rate, cortisol level) of study participants have been collected, and surveys on subjective well-being have been conducted.
The microclimate measurements reveal not only climatic differences between the urban comparison area and park and forest areas in general, but also between the different urban forest structures and as well within the structure types.
In addition to the microclimatic differentiation, further analysis of the measurement results will provide information on the health relevance of different urban forest structures. Based on this, recommendations for the use and development of urban forests will be derived.
How to cite: Beck, C., Simon, J., André, E., Brandl, S., Falkenrodt, L.-M., Mahesh, B., Rathmann, J., Said Can, Y., Stocker, M., and Zwolsky, P.: Assessment of forest microclimates in different urban forest structures in Augsburg, Germany, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13872, https://doi.org/10.5194/egusphere-egu25-13872, 2025.
Urban heat island (UHI) is a significant anthropogenic climate impact in cities, with construction and human activities altering the radiative and thermodynamic properties of urban surfaces, leading to higher temperatures relative to rural areas. Mitigation strategies such as green roofs (GR) and cool roofs (CR) have been implemented to counteract UHI, reducing adverse effects like exacerbated heat waves and increased cooling energy demand. However, limited research has addressed the influence of background climate on the efficacy of these mitigation measures. Given that background climate factors—such as regional temperature, humidity, and precipitation—can significantly impact processes like evapotranspiration and albedo, it is crucial to assess how these factors interact with mitigation strategies.
This study uses the Weather Research and Forecasting (WRF) model to evaluate the mitigation effects of CR and GR across six populous mega-cities in China, which represent a range of background climates from temperate semi-arid to subtropical humid. To accurately simulate urban heat environments, various urban parameterization schemes (e.g., LCZ, UCP) and urban climate models (e.g., SLUCM, BEP, BEM+BEP) were tested, and the best combination was selected for simulation. Sensitivity tests were conducted across multiple scenarios, ranging from practical to idealized conditions, to assess the impact of background climate on mitigation effectiveness. The results indicate that background climate and regional weather patterns significantly influence the success of UHI mitigation strategies. These findings offer valuable insights for urban planning and the design of context-specific UHI mitigation measures.
How to cite: Yin, H., Zhao, X., Wang, L., and Wang, R.: The efficacy of roof mitigation strategies for urban heat island in various background climate, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14553, https://doi.org/10.5194/egusphere-egu25-14553, 2025.
India's rapid economic growth and unprecedented urbanization have led to a significant rise in energy consumption and greenhouse gas (GHG) emissions from residential buildings. Currently, the residential sector accounts for approximately 20% of India's total GHG emissions, with a few major cities contributing up to 42.8% of total urban emissions. Projections indicate an 8-fold increase in energy demand by 2050, driven by rising urban expansion, household incomes, and greater ownership of energy-intensive appliances, such as air conditioners and refrigerators. With India’s urban population expected to reach 50.3% by 2050 and over 70% of the country’s building stock yet to be constructed, cumulative emissions from the buildings sector between 2020 and 2070 could surpass 90 gigatonnes of CO2e, exceeding the nation’s allocated carbon budget.
This study aims to create high-resolution CO2e emissions datasets for 100 Indian cities to address the lack of reliable data necessary for effective urban GHG mitigation planning. We employed a semi-supervised learning approach to classify building types and estimate heights using an XGBoost model trained on Microsoft Building Footprint data, satellite imagery, OpenStreetMap features, and other urban datasets. These outputs were then integrated into a building-climate-energy model, which combines household survey data, climate variables, and derived building features to estimate residential energy consumption. The household survey provides detailed insights into appliance usage, energy consumption patterns, and variations across income classes.
The building characteristic prediction model achieved good performance, with an average F1-macro score exceeding 0.8 for type and height predictions on the testing set. Similarly, the energy prediction model demonstrated robust accuracy, with an R2 > 0.6 on the testing set. Using explainable machine learning techniques, such as SHAP, we identified air humidity and income class as the most critical factors influencing residential energy consumption, highlighting the interconnected roles of climate and socioeconomic conditions in shaping residential energy demand. Finally, gridded emission map time series were developed for each city using the city population, building characteristics, and a simplified energy model that incorporates climate data and regional income classifications.
This work is part of the CHETNA project (City-wise High-resolution carbon Emissions Tracking and Nationwide Analysis), which leverages artificial intelligence and advanced datasets to provide high-resolution, near real-time CO2e and air pollutant emissions data for over 100 Indian cities. By integrating building-climate-energy modelling, this study delivers spatially and temporally granular emissions datasets for the residential sector. These results empower the CHETNA project to support localized mitigation strategies, promote energy-efficient building practices, and inform sustainable urban planning tailored to India’s unique urban landscape.
How to cite: De Sarkar, K., Sarkar, A., Zhou, C., Ciais, P., Phuleria, H., and Jana, A.: CHETNA-Residential Sector: High-Resolution GHG Emissions Analysis for Indian Cities using Building-Level Climate-Energy Modelling, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15001, https://doi.org/10.5194/egusphere-egu25-15001, 2025.
The ability to accurately describe urban climate and the role of urban environments in determining heat conditions and hotspots is key to informing risk assessment, planning climate change adaptation measures, and developing mitigation strategies for cities. However, the characterization of Urban Heat Islands (UHI) still presents unique challenges from both observational and modeling perspectives, especially in complex mountainous terrain. This is due to the different scales of the features involved in the redistribution of the temperature field and the significant amount of data required to correctly capture the local specificities of both the city and its valley environment.
This study presents a novel approach integrating multi-source meteorological information to investigate the UHI in Bolzano, a city located in the south-eastern Alps and one of the Italian cities most exposed to high temperatures during summer months, especially during heatwave episodes. Specifically, we combine an extensive mobile measurement network with fixed observations, remote-sensing data, and high-resolution climate modeling. A unique distributed mobile measurement network, consisting of up to 25 meteorological sensors (MeteoTracker) installed on public buses, provides continuous spatial and temporal coverage of meteorological parameters (temperature, humidity, and pressure) across the urban area. The buses' fixed routes, including transitions between urban and rural areas, enable systematic quantification of UHI intensity across different temporal scales. This mobile network is complemented by fixed observations from quality-controlled official weather stations managed by the provincial meteorological office and crowdsourced sensors (NetAtmo), with the latter undergoing strict quality assessment based on spatial consistency.
To bridge observational gaps and provide continuous spatial coverage, we employ two modeling approaches: (1) 200-m resolution urban climate simulations provided by the UrbClim model, resolving mesoscale features – such as thermally driven winds – for the recent period, the mid-term and the far future, and (2) weather predictions from the Weather Research and Forecasting (WRF) model at 1-km resolution covering the past 6 years. Temperature patterns described by the two model datasets, run over an extended area centered on the city, are compared and evaluated against observations, aggregated spatially and temporally to match model grid points.
In addition to in-situ observations and model simulations, the relationship between land surface temperature derived from remote sensing data and near-surface temperature is analyzed across different urban climate zones to further assess the effects of the urban environment on heating conditions and identify urban hotspots. The multi-source approach is first tested by considering recent heatwave episodes recorded in Bolzano, including the summers of 2022 and 2023. Preliminary results demonstrate the effectiveness of combining multiple observation types with high-resolution modeling to characterize UHI patterns in complex terrain.
The work is conducted within the framework of the RETURN Extended Partnership (European Union Next-Generation EU, National Recovery and Resilience Plan – NRRP, Mission 4, Component 2, Investment 1.3 – D.D. 1243 2/8/2022, PE0000005).
How to cite: Simonet, G., Crespi, A., Pittore, M., Lorenzo, G., and Zandonella Callegher, C.: Multi-Source Observations and High-Resolution Modeling to Investigate the Urban Heat Island in the City of Bolzano (Italy), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16253, https://doi.org/10.5194/egusphere-egu25-16253, 2025.
In urban climatology green infrastructure is considered as useful measure to mitigate extreme temperatures during hot summer days. The presented research investigates the effect of green infrastructure on the air temperature and on the thermal stress of humans, quantified as Universal Thermal Climate Index (UTCI). The main study site is within the premises of the Botanical Garden of the university in Dresden, Germany. It comprises a mix of different types of tall and low vegetation, and non-vegetated surfaces covered with gravel and buildings. The influence of green infrastructure on ambient meteorological conditions was measured using a mobile and a stationary system. The stationary system measured the energy balance based on the eddy-covariance method on a lawn area. An instrumented backpack measured global radiation, air temperature and humidity and the radiative surface temperature on a predefined route, which was sampled several times over the course of a cloudless day with high global radiation. The route crosses sections with different vegetation types and densities, impervious surfaces and shaded areas. Background data of urban and rural meteorological stations is used to assess the heat mitigation potential of the urban green infrastructure within the Botanical Garden. During the day (7:00 to 18:00 UTC) a mean UTCI of 26.6 °C is measured at the stationary system in the Botanical Garden. At the background stations, the mean UTCI is 3 °C lower for rural surroundings and 3.8 °C higher within the urban environment of Dresden. Inside the Botanical Garden, the low and medium-tall vegetation reduces the UTCI by in average 0.08 to 0.7 °C compared to the stationary measurements. This small reduction is probably due to the radiation emitted by the warm gravel path, which was heated by occasional patches of sunshine. The maximum heat mitigation is observed in the shade of dense and tall vegetation, where the UTCI is reduced by 9.8 °C. Based on the mobile measurements, data of the various green infrastructure arrangements with different micrometeorological characteristics are compared with standardized measurements of a meteorological station located along the measurement route. The comparison with background stations exemplifies the urban heat mitigation potential of parks such as the Botanical Garden in Dresden.
How to cite: Biehl, J. L., Ziemann, A., Queck, R., and Mauder, M.: Influence of urban green infrastructure on thermal stress for humans, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17695, https://doi.org/10.5194/egusphere-egu25-17695, 2025.
Climate change presents significant challenges for urban areas, with heat waves posing a critical threat to urban resilience and liveability. Urban stakeholders must adopt data-driven approaches to ensure equitable transformation of urban spaces while addressing environmental justice challenges. This study examines the distribution and quality of green spaces in Dortmund, Germany, through the lens of environmental justice, focusing on their societal value, accessibility, and role in mitigating heat exposure for vulnerable populations.
The study further assesses the contribution of the Data2Resilience (D2R) biometeorological observation network, designed to provide high-resolution, near-surface climate data, to support equitable climate resilience efforts in Dortmund. Building on prior analyses of green space availability in Dortmund, we integrate demographic and vulnerability data to identify deficiencies in green space distribution and quality, where we include demand and supply criteria and quality characteristics, such as recreational features and noise pollution. The findings are synthesized into comprehensive maps, offering insights into environmental inequalities across Dortmund’s districts.
We found disparities in green space distribution and quality, with socioeconomically disadvantaged districts often underserved. The findings underscore the need for targeted interventions to enhance green space accessibility and functionality, emphasizing their role in fostering environmental justice and climate resilience. Further the D2R network’s spatial distribution contributes to the increased representation and visibility of vulnerable and undersupplied hot spots in Dortmund and therefore builds a foundation for the application of data-driven actions and measures towards an environmental just urban space.
How to cite: Hüser, C., Reinhart, V., Sismanidis, P., Weickhmann, L., Kittner, J., and Bechtel, B.: Design of a biometeorological observation network following environmental justice guidelines for urban spaces - a case study in Dortmund, Germany, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21872, https://doi.org/10.5194/egusphere-egu25-21872, 2025.
With approximately fifty percent of the world's population currently living in towns, urbanisation seems to be the distinct consequence across nations. The most prominent effects of Urbanization are the land use and land cover change from a natural landscape into a built-up landscape. Vegetation and open land are generally replaced with increasing impervious surfaces, which increases the Land Surface Temperature (LST), especially in dense urban areas like Varanasi.
Land surface temperature is an important parameter in understanding the Urban Heat Island (UHI) effect, which is the occurrence of warmer temperatures in urban areas compared to neighbouring suburban and rural areas. LST provides information on the physical features, such as climate and soil surface, as well as modifications in land use and human activities that impact the climate. While the presence of water-permeable surfaces and a surplus of vegetation lowers the level of land surface temperature (LST), human-induced heat discharges brought on by energy consumption and the expansion of land surface coverage by materials with high heat conductivity and capacities are the primary root causes of an increase in LST. Warmer temperatures produce greater transpiration and the reduction in soil moisture. It is therefore crucial for micro-climatic change studies to monitor and understand the spatial distribution of LSTs for comprehending LST landscape patterns across the globe.
The major components of studies in LST include Land cover, the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Built-up index (NDBI). The correlation between the impacts of changes in vegetation and built-up on LST has been examined in this study by applying GIS statistical techniques on LANDSAT satellite data on a spatio-temporal scale. The study reveals that LST exhibits a positive correlation with built-up but a negative correlation with vegetation, and this relationship varies with variations in the nature of built-up and vegetation in the study area.
Keywords: Urbansiation, Land Surface Temperature (LST), Urban Heat Island (UHI), NDVI, NDBI, LULC, Climate, Micro-climate.
How to cite: Rawat, R.: Investigating the impact of vegetation and built-up as determinants of land surface temperature change in the Varanasi Metropolitan Area of India, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-558, https://doi.org/10.5194/egusphere-egu25-558, 2025.
In the context of climate change, trees are increasingly used as tools to create healthier and more comfortable urban environments. However, the extent of their impact on urban settings is intricately tied to their physiological health, growth, and vitality. This study evaluates urban trees' physiological response to the urban climate's primary stressors, high temperatures, low precipitations, traffic emissions and environmental pollutants. We investigated tree growth and δ13C, δ18O, δ15N, radiocarbon (F14C) levels and heavy metals in tree rings by comparing periurban parks, urban parks, busy streets, and airport zones in two Italian cities, Firenze and Pisa with a focus on Pinus pinea.
Our preliminary findings indicate that climatic conditions did not directly affect tree growth in urban parks. However, high temperatures and reduced precipitations influenced tree physiology more than pollution. In detail, carbon (δ13C) and oxygen (δ18O) stable isotopes revealed sensitivity to high temperatures and drought in urban parks, whereas the indicators of pollution investigated in this study (δ15N and F14C) did not exhibit pronounced differences between urban and periurban parks.
The general hypothesis is that the other urban sites (busy streets and airport zones), characterized by environmental constraints such as water deficit and high temperatures, show a higher δ13C and a lower δ18O than the periurban area. Regarding the characterization of the 15N and 14C and environmental pollutant concentrations in the tree rings, we assume they are more evident in the urban neighbours than in periurban contexts.
These findings underscore the importance of selecting tree species adapted to urban conditions to maximise the ecosystem services provided by trees. In addition, it is essential to study the effects of the urban environment on plant growth and physiology, as the urban environment—characterized by higher temperatures and lower precipitations—represents a model of future climate conditions. This setting provides an opportunity to investigate tree responses to climate change, offering insights that may inform urban forestry and resilience strategies. Ultimately, our data demonstrate the utility of tree rings as an effective tool for assessing air and environmental quality in urban compared to periurban sites.
How to cite: Mondanelli, L., Cherubini, P., Salbitano, F., Saurer, M., Wacker, L., and Cocozza, C.: Assessing Urban Tree Responses to Climate and Pollution: Implications for Environmental Monitoring and Management, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-850, https://doi.org/10.5194/egusphere-egu25-850, 2025.
Urban heat island intensity (UHII) is a critical metric for assessing urban thermal environments, yet traditional approaches often lack a human-centric perspective, resulting in limited insights into human thermal stress and global variability in UHII responses to influencing factors. Addressing this gap is critical for mitigating UHII through factor regulation (regulating the scale of factors influencing UHII), thereby contributing to the development of sustainable and livable cities. In this study, we employ the Universal Thermal Climate Index (UTCI) to calculate UHII (UHII-UTCI) based on a human-centric delineation of urban and rural areas. We further evaluate UHII-UTCI responses to changes in influencing factors at a fine spatial scale. The differences in these responses are captured through three key elements: (1) dominant influencing factors, (2) response patterns—classified as monotonic increasing (Type-I), monotonic decreasing (Type-D), and downward parabola (Type-P)—and (3) regulation thresholds of the response patterns. Additionally, the spatial heterogeneity of these key elements is notable, with distinct clusters observed in eastern North America, East Asia, and Europe. These findings emphasize the urgency of adopting human-centric approach to address the growing threat of urban heat, and underscores the necessity of tailored, context-sensitive strategies to mitigate UHII in diverse global settings.
How to cite: Yang, Z., Peng, J., Jiang, S., Yu, X., Dong, J., and Corcoran, J.: Towards sustainable cities: A human-centric approach to evaluating urban heat island intensity responses, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6286, https://doi.org/10.5194/egusphere-egu25-6286, 2025.
Urban-induced changes in local microclimate, such as the urban heat island effect and air pollution, are known to vary with city size, leading to power law or logarithmic relations between average climate variables and city-scale quantities (e.g., total population or area). However, these approaches suffer from biases related to the choice of city boundaries and they neglect intra-urban variations of city properties. In this study we use high-resolution data of urban temperatures and annual concentrations of particulate matter together with population density and street network properties and show that their marginal and joint probability distributions follow universal finite-size scaling functions. These results extend previous findings on city-scale relations, offering a novel description of intra-urban fluctuations of climate characteristics.
How to cite: Manoli, G., Duran Sala, M., and Hendrick, M.: Towards a statistical description of intra-urban climate variations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6944, https://doi.org/10.5194/egusphere-egu25-6944, 2025.
The Eastern Mediterranean and Middle East (EMME) region is a recognized as a climate change hotspot, characterized by rising temperatures, declining precipitation, and increasing extreme weather events. These challenges are particularly pronounced in urban areas, highlighting the need for high-resolution data to capture localized climate impacts and support effective mitigation strategies. This study employs the Weather Research and Forecasting (WRF) model, coupled with the Single-Layer Urban Canopy Model (SLUCM), to investigate the urban thermal environment of Nicosia, Cyprus, at a 1 km spatial resolution for the period 2008–2012. To analyze intra-urban variability, the study utilizes the CGLC-MODIS-LCZ dataset, which integrates the Copernicus Global Land Service Land Cover (CGLC) product, resampled to MODIS IGBP classes (CGLC-MODIS), and combined with Local Climate Zones (LCZ). A comprehensive evaluation is conducted across different LCZs for key variables, including 2-m air temperature (T2), 2-m relative humidity (RH), and land surface temperature (LST). Model output is evaluated against station-based observations for T2 and RH, while LST is evaluated using data from the MODIS Terra and MODIS Aqua satellites, with assessments performed at diurnal, monthly, seasonal, and annual scales. Results demonstrate the variability of T2, RH and LST amongst the LCZs and highlight the importance of localized modelling in addressing climate change impacts in this city.
How to cite: Koutroumanou-Kontosi, K., Cartalis, C., Hadjinicolaou, P., Constantinidou, K., and Agathangelidis, I.: Investigating the Urban Thermal Environment of a Climate Change Hotspot through High-Resolution Modelling: Case Study for Nicosia, Cyprus, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9245, https://doi.org/10.5194/egusphere-egu25-9245, 2025.
Accurate prediction of ground-level air temperature is crucial for developing heat hazard maps that help anticipate the impacts of heat waves on vulnerable populations. Numerical weather prediction models typically operate at the mesoscale resolution (1-2 km) (Garbero et al., 2021), and their application at high spatial resolution within large urban areas faces significant challenges, such as the complexities of urban parameterization and the high computational costs. In this context, machine learning has emerged as a promising alternative or complementary approach to these traditional methods (Zumwald et al., 2021).
This study presents a machine learning-based model designed to reconstruct high-resolution temperature maps and predict hourly temperatures for the city of Turin during a heat wave in June 2022. The model leverages nine predictor variables related to urban morphology, including building density, building height, sky view factor, and vegetation density, combined with temperature data from citizen weather stations (CWS). The CWS data, sourced from the Netatmo meteorological network, support the model's potential for generalization to other cities. Furthermore, this study evaluates the impact of integrating the outputs of the COSMO meteorological model into the predictor set.
This study compares the performance of two modelling approaches, trained for each nighttime hour, to reconstruct temperature maps: (i) a baseline multi-linear regression (MLR) model and (ii) a convolutional neural network (CNN). The MLR model was trained at two spatial resolutions - 50 and 100 m. Results indicate that the 100 m resolution yields lower RMSE values, with a maximum error of 1.36°C (reduced to 1.23°C when COSMO outputs are included as additional predictors). This finding highlights the importance of averaging predictors over sufficiently large spatial areas around sensor locations. The CNN model outperforms the MLR, achieving a maximum RMSE of 1.21°C (further reduced to 1.17°C). Both models demonstrate substantial improvement over the COSMO model, which exhibits a notably higher RMSE exceeding 2.5°C when predicting Netatmo temperatures.
A sensitivity analysis highlights the slightly greater influence of specific predictors, such as the Sky View Factor or the altitude. However, the relatively low magnitude of sensitivity suggests an excessive number of predictors, leading to compensatory effects when individual predictors are excluded from the model.
This study demonstrates the effectiveness of machine learning techniques in reconstructing temperature maps in Turin. Future work should focus on reducing predictor redundancy, improving data cleaning processes to mitigate the impact of outliers, and assessing the generalizability of this methodology to other cities.
References :
Garbero, V., Milelli, M., Bucchignani, E., Mercogliano, P., Varentsov, M., Rozinkina, I., Rivin, G., Blinov, D., Wouters, H., Schulz, J.-P., Schättler, U., Bassani, F., Demuzere, M., Repola, F., 2021. Evaluating the Urban Canopy Scheme TERRA\_URB in the COSMO Model for Selected European Cities. Atmosphere 12, 237. https://doi.org/10.3390/atmos12020237
Zumwald, M., Knüsel, B., Bresch, D.N., Knutti, R., 2021. Mapping urban temperature using crowd-sensing data and machine learning. Urban Climate 35, 100739. https://doi.org/10.1016/j.uclim.2020.100739
How to cite: Houget, T., Garbero, V., Piras, M., Dellandrea, E., and Salizzoni, P.: Enhancing Urban Heat Island Mapping in Turin During a Heat Wave: A Machine Learning Approach with Citizen Science Data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16861, https://doi.org/10.5194/egusphere-egu25-16861, 2025.
Posters virtual: Thu, 1 May, 14:00–15:45 | vPoster spot 5
EGU25-1411 | Posters virtual | VPS6
Spatial downscaling of urban temperatures: evaluation of an approach using satellite and reanalysis dataThu, 01 May, 14:00–15:45 (CEST) | vP5.3
Climate change is expected to exacerbate heat stress, particularly in urban areas where the urban heat island (UHI) effect tends to amplify warming compared to surrounding rural regions. Due to the heterogeneity of urban environments, heat stress can vary significantly within cities. Heat vulnerability maps, which combine data on heat sensitivity, heat exposure and adaptive capacity, are valuable tools for identifying areas that should be prioritized for heat stress mitigation measures. One important component of such heat vulnerability maps is data on the spatial distribution of heat. The present study explores the use of satellite data to generate high-resolution temperature maps, addressing two key challenges in the process.
The first challenge arises from the fact that satellites measure land surface temperature (LST) rather than air temperature (AT), whereas the latter is needed as input for most heat stress indicators. While linear models calibrated with weather station data are frequently used to estimate AT from LST, there are cities where the availability of weather stations is insufficient for calibrating models with multiple control variables. Additionally, the LST-AT relationship depends on the prevailing atmospheric conditions. The second challenge of using satellite data is that satellite images are usually not available on an hourly or daily basis due to factors such as satellite scheduling or excessive cloud cover.
To address the first challenge, we adopt a technique introduced by the ECOSTRESS mission, which leverages reanalysis data (GEOS-5) to estimate AT using LST, the normalized difference vegetation index (NDVI), and albedo. We apply this method to spatially downscaled LST data (100m) from the VIIRS instrument aboard the Suomi NPP satellite, AT reanalysis data from ERA5-Land (9km), as well as NDVI and albedo derived from Harmonized Landsat Sentinel (HLS) data (aggregated to 100m). Applying the method to individual satellite images enables day-specific adjustments for varying atmospheric conditions. To overcome the second challenge, we utilize high-resolution AT maps derived from LST images to calculate spatial patterns of air temperature distribution, which are then used to downscale ERA5-Land AT data for those times without satellite images available.
To evaluate the approach described, it is exemplary applied to various cities, whereby the downscaled temperature estimates are validated against (i) temperature estimates based on alternative methods than the ECOSTRESS technique to derive AT from LST, (ii) weather station data, and (iii) existing results from urban climate models.
How to cite: Kortschak, D., Gallaun, H., Kernitzkyi, M., Köberl, J., Miletich, P., and Strohmaier, M.: Spatial downscaling of urban temperatures: evaluation of an approach using satellite and reanalysis data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1411, https://doi.org/10.5194/egusphere-egu25-1411, 2025.
