This session solicits submissions from the observational, modelling, and science-based tool development communities. We particularly welcome contributions that bridge natural and social sciences to address urban climate challenges in an integrated way. Submissions may cover urban atmospheric and landscape dynamics, urban-climate conditions under global to regional climate change including uncertainty propagation, processes and impacts due to urban-induced climate change, and the efficacy of various strategies to reduce such impacts. Studies linking urban climate dynamics with human health and well-being under extreme and compound events are especially encouraged. We also welcome techniques highlighting how cities use novel science data products and tools, including urban climate services, that facilitate planning and policies on adaptation and mitigation. Emerging approaches such as digital twins, citizen science, crowdsourcing, AI-based modelling, frugal informatics, and other innovations for climate resilience are highly encouraged.
As extreme heat events are becoming more frequent and more intense in the context of climate change, it is a major objective to mitigate urban overheating through strategic urban planning and design. For example, the introduction and expansion of vegetation in urban settings is often considered a very promising means to reduce heat stress, and even more generally improve quality of life in cities as it is widely associated with better human health and well-being, flood risk management and biodiversity. However, also unwanted effects may occur, e.g. with respect to water demand, or social injustice. The specific design, placement, and management of greening solutions in the context of the complex urban environment highly determine the “success” of a given intervention.
In practice, it is still challenging to implement solutions that fundamentally mitigate heat risk in urban settings while enhancing a city’s resilience. This is in part explained by the complexity and variability of natural and anthropogenic processes in the urban environment, but also by the insufficient integration of urban climate sciences at multiple levels – be it
- within the discipline itself (e.g. linking near-surface micro-climate conditions with synoptic-scale atmosphere dynamics)
- with other natural science disciplines (e.g. soil sciences, plant ecophysiology, …)
- with social and political sciences, ...
- or with those who are in fact responsible for implementing solutions on the ground (e.g. urban planners, architects, services of local authorities, etc).
To ensure urban climate science will play a more active role in informing the rapid urban transitions that take place around the globe, all these components need to be connected more effectively through improved knowledge exchange and careful co-construction.
To address this need, interdisciplinary initiatives are developing in many cities and regions. Here we present first experiences and results from the interdisciplinary project inteGREEN (funded through the French Priority Research programme for Sustainable Cities -- PEPR VDBI) and the recent ANR project H2C. inteGREEN is developing a more integrated view on the topic of vegetation in urban settings. Firstly, we describe how extreme heat hazards form in Paris and which influence can be attributed to the urban environment and the larger-scale weather circulations, respectively. Then we discuss how different types of vegetation can be used to reduce heat hazards under certain conditions (e.g. at night, during day). Finally, these ecosystem services are put into the larger context by e.g. incorporating considerations of soil and plant health in urban settings. We conclude with some experiences regarding the aspects of knowledge exchange and co-construction with diverse stakeholders in the Paris region.
How to cite: Kotthaus, S., Haeffelin, M., Bastin, S., Céspedes, J., Delarue, F., Drouin, M.-A., Dupont, J.-C., Faber, M., Fathalli, M., Faucheux, A., Gros, V., Hersent, M., Lemonsu, A., Leymarie, J., Luu, K., Martinet, P., Nagel, T., Ribaud, J.-F., Segura Barrero, R., and Van Hove, M.: Integrated urban climate studies – first experiences and results from the interdisciplinary project inteGREEN, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11576, https://doi.org/10.5194/egusphere-egu26-11576, 2026.
Urban heat is a major climate-related hazard in cities, with direct impacts on public health, energy demand, and social vulnerability. A robust and spatially detailed observational basis is essential to characterize the spatio-temporal variability of urban thermal environments and to support climate services for heat risk management and urban adaptation. In this contribution, we present a newly deployed high-density thermo-hygrometric monitoring network designed to observe urban heat and thermal exposure at high spatial and temporal resolution in Valencia (eastern Spain), a Mediterranean metropolitan area identified as a climate change hotspot.
The network comprises more than 70 fixed monitoring stations distributed across the metropolitan area at an average density of approximately one station per 2 km². Near-surface air temperature and relative humidity are measured at 3 m above ground level across a wide range of urban morphologies, including compact city centers, residential neighbourhoods, industrial areas, urban green spaces, and peri-urban zones. Sensors are installed following standardized exposure criteria to ensure data quality and inter-site comparability, with hourly sampling that allows the characterization of diurnal cycles, heatwave conditions, and intra-urban thermal heterogeneity. The system includes centralized data acquisition, quality control, and long-term data storage, enabling both operational applications and climatological analyses.
This observational framework is conceived as a multi-purpose tool for urban climate science and services. It enables a detailed assessment of urban heat patterns, including urban heat island (UHI) intensity, nocturnal heat retention, and thermal contrasts associated with different urban fabrics and land uses. Furthermore, the dataset provides an empirical basis for the evaluation and calibration of urban climate models and downscaled reanalysis products, improving the representation of local-scale thermal processes. The network also supports the development of climate services focused on heat exposure and risk assessment, contributing to the identification of priority areas for targeted adaptation measures.
Preliminary analyses reveal pronounced intra-urban thermal contrasts, with daytime near-surface air temperature differences of several kelvin between densely built or industrial areas and nearby vegetated or less urbanized locations. Results further indicate that industrial areas tend to cool more rapidly during nighttime compared to compact urban fabrics, leading to sustained nocturnal heat exposure in specific neighbourhoods. These patterns are particularly evident under clear-sky and heatwave conditions. A comparison with satellite-derived land surface temperature (LST) shows amplified spatial contrasts during daytime, linked to surface radiative heterogeneity, while nighttime LST provides a closer proxy for near-surface thermal patterns. Overall, the network provides a robust observational foundation to advance urban heat research and to inform evidence-based strategies for climate-resilient cities.
How to cite: Gavila-Lloret, S., Valiente-Pardo, J. A., and Khodayar, S.: High-density thermo-hygrometric observations for urban heat and thermal exposure assessment in Valencia (Spain), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18981, https://doi.org/10.5194/egusphere-egu26-18981, 2026.
Global warming has been an ongoing phenomenon since the mid-20th century, with the Mediterranean basin identified as one of the major climate change hotspots.
To identify climate variability at the local, regional and global scales the scientific community has prompted to intensify the analysis of long, reliable and high quality historical meteorological data.
Here we present a comprehensive climatic analysis of the 190-year-long thermo-pluviometric dataset (1833–2022) recorded at the Historical Meteorological Observatory of the University of Genoa (NW Italy), one of the longest continuous series in the country and part of the global network of 388 weather stations recognized by the World Meteorological Organization (WMO), of which only 22 have a greater or equally long series.
Genoa is a very interesting urban study site which exhibits a peculiar climatic setting within the Italian context, due to its geographic position within the Mediterranean basin and the complex morphology of the surrounding terrain, consisting of slopes degrading steeply towards the sea.
From a meteorological point of view, this sector of Italy is often affected by the convergence between southern warm air masses coming from the Ligurian Sea and colder air masses coming from the northern Po basin. This, combined with exposure to moist southerly air flows carrying important sensible and latent heat fluxes, can trigger the development of convective systems and storm supercells, whose magnitude can be enhanced by orography.
The combined action of atmospheric circulation, geomorphological and orographic factors along with urbanization (e.g., culverted streams and channels) plays a crucial role in causing the occurrence of severe urban floods and mass-wasting processes along slopes that have significant effects on the population, the territory, and the infrastructures.
The aim of the study is to analyse the thermo-pluviometric dataset of this historic station and to explore the temporal variability of its climate extremes, identifying possible statistically significant trends (Mann-Kendall test, 95% confidence-level).
To evaluate variations in extreme events, a set of 8 ETCCDI-defined climatic indices was selected. Annual anomaly values were calculated with respect to the 1991-2020 climatological average, while the analysis of extreme precipitation at the daily and 5-days scales using GEV distribution was performed. Results reveal a clear and consistent warming pattern, particularly in minimum temperatures during the cold season (November–March), with rates ranging from 0.005 to 0.010 °C/year. The warming trend has intensified since the mid-1980s, testified by 49% of positive thermal annual anomaly for minimum temperatures occurring since 1988, confirming local alignment with broader Mediterranean and global-scale temperature accelerations. Conversely, precipitation analysis indicates a general decrease in rainfall (~200 mm in annual precipitation lost) and rainy days, suggesting a tendency toward longer dry periods and shorter wet spells. Three 1-day extreme rainfall events with return periods exceeding 200 years were identified but no significant trend was observed. Overall, the findings provide robust evidence of significant local climatic change in Genoa, consistent with Mediterranean trends, and offer valuable insights for developing future adaptation and resilience strategies at regional and urban scales.
How to cite: Bondanza, M., Pepe, G., Ferretti, G., and Scafidi, D.: Analysis of thermo-pluviometric long-period trends for an ultra-centenary urban data series , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9757, https://doi.org/10.5194/egusphere-egu26-9757, 2026.
As urbanization accelerates worldwide, understanding how urban areas influence cloud patterns is increasingly important because clouds directly affect local climate and related impacts, including heat stress, precipitation patterns, solar energy resources, and air quality. While urban impacts on temperature, humidity, and rainfall have been widely studied, urban effects on clouds remain comparatively less explored. Existing evidence suggests that cities often act to enhance cloud cover; however, less is known about urban effects on other cloud properties or how these effects vary across different cloud types and background atmospheric conditions. Here, we investigate how urbanization modifies cloud patterns across Europe using high-resolution satellite observations from 2002 to 2025. We use Moderate Resolution Imaging Spectroradiometer (MODIS) Level-2 products at ~1 km spatial resolution (nadir) from the Terra and Aqua satellites, with approximately four daily overpasses (~10:30 a.m./p.m. and ~1:30 a.m./p.m.). Cloud cover fractions are compared between urban areas and their adjacent rural surroundings. Urban–rural differences in cloud cover are evaluated as a function of season, time of day, and cloud type. Cloud types are classified using MODIS-derived cloud-top properties, following the International Satellite Cloud Climatology Project (ISCCP) framework. Finally, we relate urban cloud modification effects to background atmospheric conditions, as well as to urban characteristics such as urban form, regional climate, topography, and proximity to the coast. Overall, our results indicate a general enhancement of cloud cover over urban areas relative to their rural surroundings, with the strongest differences occurring at night and during summer. The urban influence is most pronounced for low-level clouds. We discuss potential drivers of these patterns, including atmospheric stability, moisture availability and urban characteristics, and highlight potential implications for urban radiative forcing and urban heat islands. This research is part of the UrbanAIR project under the Horizon Europe research and innovation programme.
How to cite: Torelló-Sentelles, H. and Meyer, A.: Urban influences on cloud patterns over Europe from satellite observations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20780, https://doi.org/10.5194/egusphere-egu26-20780, 2026.
The impact of urban areas on precipitation is widely accepted, with increased precipitation often observed over and downwind of city centers. However, this urban impact on precipitation is still not fully understood, and uncertainties remain regarding the underlying mechanisms as well as the role of background climate and city morphology (e.g. city size, density, and surface characteristics). Since most existing studies focus on large, isolated cities, the impact of fragmented urban areas, characterized by midsize cities and urban sprawl, remains uncertain.
This study investigates the impact of fragmented urban areas on downwind precipitation using a dense hourly to daily frequency rain-gauge network in Belgium. To account for the fragmented urban morphology, an innovative method was applied that assigns each observation an urban or rural label based on the fraction of urban land cover in the upwind area. The results indicate a decrease in precipitation frequency downwind of urban areas, while a significant increase in precipitation intensity is observed during summer. Furthermore, the dependence of the urban impact on season, time of day, and wind direction suggests that convection plays an important role. The study also highlights the added value of hourly observations in revealing the characteristics of the urban precipitation impact.
How to cite: Jacobs, A., Van Weverberg, K., and Caluwaerts, S.: Observed impact of fragmented urban areas on downwind precipitation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10983, https://doi.org/10.5194/egusphere-egu26-10983, 2026.
Urban areas in the Mediterranean basin are increasingly exposed to thermal stress as a result of climate change and ongoing urbanization, creating an urgent need for urban climate information that supports heat-risk assessment and adaptation strategies at city scale. This study presents an integrated multiscale assessment of the urban microclimate in Bari (southern Italy), a mid-sized Mediterranean coastal city, with the aim of disentangling the relative contributions of sea–land breeze dynamics and urban morphological characteristics to intra-urban thermal variability. The analysis combines three complementary approaches. First, in situ observations of air temperature and relative humidity were collected during summer 2023 using a dense network of eight canyon-level sensors distributed across neighborhoods characterized by different distances from the coastline, building density, vegetation cover, and land use. Second, satellite-derived land surface temperature (LST) from ECOSTRESS was employed to provide a spatially continuous view of surface thermal patterns at different times of the day. Third, the recently developed offline MLUCM BEP+BEM urban canopy model (Pappaccogli et al., 2025) was applied and evaluated against observations as a science-based tool for representing intra-urban thermal variability under realistic mesoscale forcing. Observations reveal a pronounced coastal–inland gradient in both air temperature and humidity, particularly during daytime, driven by the onset and persistence of sea-breeze circulations. Coastal locations experience moderated warming and higher humidity, whereas inland districts exhibit stronger heating and daytime drying, amplifying thermal stress. Satellite LST confirms these patterns, highlighting persistent hotspots in dense urban fabrics and large impervious areas, while also capturing the diurnal evolution of surface thermal contrasts. Model results demonstrate that MLUCM BEP+BEM improves the representation of intra-urban variability compared to reanalysis data alone, particularly in reproducing canopy-level temperature differences across neighborhoods. While mesoscale forcing largely controls the background climate signal, microscale processes associated with urban geometry, surface properties, vegetation, and anthropogenic heat contribute substantially to spatial variability and are effectively captured by the model. The relative importance of these contributions varies with distance from the coastline and the choice of boundary forcing. Overall, this work highlights the necessity of integrating observations, remote sensing, and urban canopy modeling to accurately characterize thermal environments in Mediterranean coastal cities. The proposed framework is transferable to other coastal contexts and provides a robust basis for assessing urban heat exposure and for the development of urban climate services that support climate-sensitive planning and the evaluation of mitigation and adaptation strategies under current and future climate conditions.
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).
Reference
Pappaccogli, G., Zonato, A., Martilli, A., Buccolieri, R., and Lionello, P.: MLUCM BEP + BEM: an offline one-dimensional multi-layer urban canopy model based on the BEP + BEM scheme, Geosci. Model Dev., 18, 7129–7145, https://doi.org/10.5194/gmd-18-7129-2025, 2025.
How to cite: Pappaccogli, G., Zonato, A., Martilli, A., Buccolieri, R., Esposito, A., and Lionello, P.: Multi-scale observations and urban canopy modelling of heat exposure in a Mediterranean coastal city, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13649, https://doi.org/10.5194/egusphere-egu26-13649, 2026.
It is well established that ongoing climate change is leading to more heat stress events, which have a significant negative impact on public health. This is especially the case for urban environments, where the largest portion of the population lives and, depending on the metric, increased levels of heat stress can be found. Depaving cities and implementing green- and blue infrastructures (GBI) results in less heat accumulation, lowering the urban heat island (UHI) effect, and the shade of tree canopies act as a buffer for extreme temperatures during the day.
The implementation of these solutions is, however, less evident in practice, due to the complexity of the urban redevelopment process. A plethora of aspects play an important role in (re)design, planning and construction, and more traditional concerns - such as mobility, accessibility, and maintenance - are often prioritized over human thermal comfort. Tools and evidence-based knowledge are needed to emphasize the value of climate adaptive design and nature based solutions such as GBI. The value of these tools for urban redevelopment professionals lies, for example, in their ability to visualize and quantify the impacts of climate-related hazards, both with and without GBI, thereby demonstrating GBI’s potential to mitigate these hazards while preserving priority functions.
In the urban development field, many tools are available for professionals active in different aspects of the urban (re)development process. The most applicable type of tools to directly assess the future heat mitigation impact of a design are microclimate models, such as ENVI-Met and UMEP. However, when these tools are used to compare current and future urban environments, there is a lack of observations to validate the outcome of these models. The main reason for this lack is the complexity of such a measurement campaign. Besides common technical issues regarding sensors, data logging and long-lasting measurements in public spaces, multiple other practical difficulties arise, mainly related to practical feasibility, variability, comparability, and, most notably, time.
This study presents an attempt to undertake this kind of measurement campaign. Three urban areas in three different Belgian cities were redeveloped for the benefit of climate adaptation. Four microclimate stations were placed in each location, both before and after redevelopment, with the goal of observing changes in microclimate and, more specifically, investigating the role that the newly implemented GBI play in these changes. Three stations were placed to measure a representative microclimate both before and after redevelopment, while a fourth was placed outside yet close to the project area where no changes occurred, to serve as a reference.
This presentation will focus on the practical and technical implementation of these measurement campaigns. Difficulties and limiting factors will be highlighted. Moreover, based on the conducted measurements, it will be shown how urban redevelopment and GBI can be evaluated based on their impact on thermal comfort.
How to cite: Puynen, S., Top, S., Caluwaerts, S., and Wauters, G.: Microclimate measurements for quantifying climate adaptation impact of urban redevelopment, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6767, https://doi.org/10.5194/egusphere-egu26-6767, 2026.
Water bodies have been recognized as an effective strategy for mitigating the urban heat island effect. However, the underlying cooling mechanisms remain insufficiently explored, particularly regarding the magnitude of evaporative cooling and cooling potential at different heights within street canyons under different water coverage ratios. Therefore, this study conducted scaled outdoor experiments from July to September 2025 in a temperate region (suburban Xingtai, China), aiming to examine the effects of different water coverage ratios (0%, 50%, and 100%) on microclimate parameters of wind speed, radiation flux, and temperature within street canyons.
The results indicate that water bodies absorb more shortwave radiation and emit less longwave radiation, resulting in increased net radiation capture and lower albedo. Additionally, water bodies attenuate the wind speed ratio (U0.25H/U2H) within street canyons by 16% as water coverage increases from 0% to 100%. With higher water coverage, temperatures of the south-facing wall (TS-w), canyon air (Ta), ground (Tg), mean radiant temperature (Tmrt), and Physiological Equivalent Temperature (PET) all decrease significantly, with the cooling effect intensifying closer to the water surface. Specifically, the maximum temperature reduction on the south-facing walls is observed at a height of 0.1 m, reaching 4.9°C for 50% water coverage and 5.5°C for 100% coverage in street canyons. In contrast, the cooling effect on the north-facing walls is relatively weaker and shows little difference between the two coverage scenarios. The maximum reductions in Ta at 0.1m height are 0.9°C and 1.3°C in street canyons with 50% and 100% water coverage. Water bodies significantly improve daytime pedestrian-level thermal comfort, with maximum PET reductions of 4.6°C (50% coverage) and 10.0°C (100% coverage), respectively, while their influence on nighttime thermal comfort is negligible. Moreover, the evaporation fluxes of the water bodies in street canyons with 50% and 100% water coverage were quantified, with maximum values of up to 92 Wm-2 and 155Wm-2 at 14:00, respectively.
Figure 1. (a) Schematic illustration of the thermal effects of water bodies; (b) Schematic illustration of experimental design; (c) Photographs of the street canyons for each experimental cases.
How to cite: Yan, H., Zheng, X., and Hang, J.: Effects of Water Bodies with Different Coverage Ratios on Urban Microclimate in Street Canyons: Scaled outdoor experiments, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18925, https://doi.org/10.5194/egusphere-egu26-18925, 2026.
Exposure to urban heat stress and associated vulnerability vary with topography, climate, and socio-economic conditions, creating a need for both locally tailored and scalable heat-mitigation strategies. A key open question is which land-cover and urban-form drivers of near-surface air temperature are transferable across cities, and which are context-specific. Here, we assess the transferability of land-cover and urban-form effects on near-surface air temperature across four European cities spanning oceanic, temperate, and Mediterranean climates.
We combine quality-controlled citizen weather station air-temperature observations with standardized land-cover and urban-morphology datasets, and apply explainable machine-learning models to quantify the direction and magnitude of feature effects. Transferability is evaluated by testing models across cities and accounting for diurnal variability. Vegetation emerges as a robust and transferable cooling driver across all cities, confirming its role as a scalable urban heat-mitigation strategy. Impervious-surface metrics, including building height and footprint, act as broadly transferable warming drivers, with effect magnitudes modulated by urban geometry (e.g. sky-view characteristics) and city form. In contrast, water-related predictors show no consistent effect across cities, reflecting limited spatial coverage, configuration and scale effects, and variable proximity between monitoring sites and water bodies. Altitude-related cooling is transferable only where distinct elevation gradients and airflow patterns are present. Overall, transferability is higher at night, when feature–temperature relationships are more stable.
Our results demonstrate a systematic framework for cross-city comparison that integrates crowdsourced observations with explainable machine learning, and identifies which urban climate drivers can support generalizable planning guidance. The findings provide actionable insights for urban climate science and services, highlighting greening and reduced imperviousness as broadly effective strategies, while emphasizing that water- and geometry-based interventions require context-sensitive design.
How to cite: Zekar, A.: Transferability of land-cover and urban-form effects on near-surface air temperature across European cities, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2782, https://doi.org/10.5194/egusphere-egu26-2782, 2026.
Crowd-Grid is an innovative new 12-year gridded daily temperature dataset released by the Met Office in December 2025. It leverages quality-controlled crowdsourced and third-party temperature observations, interpolated to a 1km grid, to provide a snapshot of recent UK climate (2013-2024) that enhances understanding of local-scale variability. Crowdsourced data has often been assessed at city scale on individual days; using it to build a decadal-length gridded dataset at national scale comparable to established products (HadUK-Grid) is new. This presents an opportunity to use temperature information from the built urban environment, rather than standard observing sites alone, to give a better representation of the day-to-day temperatures experienced by citizens. This data can thus inform adaptation decisions aimed at reducing the impact of future climate changes on the UK population, particularly regarding extreme heat risk.
The Recent Heat Packs form a prototype climate service that presents curated information from Crowd-Grid for use at local level to support decisions on climate adaptation. A 2-page factsheet and a set of supporting .csv files are provided for each of 393 local government areas in the UK. These complement the climate information that is currently available, addressing an oft-recognised need for recent climate data to bridge the gap between historical records and future projections.
This paper discusses the development and delivery of these data and services. It compares Crowd-Grid with existing climatological baselines and highlights its value using case studies from extreme events over the past 12 years of UK climate. In addition, the insights that are gained through the incorporation of crowdsourced observations into an urban climate service are discussed, along with an examination of the past and future impacts of transient sensing on such baselines. Finally, potential future enhancements to the dataset and its delivery are explored.
How to cite: Fry, M., Mitchell, T., Chapman, L., Pope, F., and Farrar, L.: Crowd-Grid & Recent Heat Packs: From Crowdsourced Observations to a Prototype Climate Service, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5694, https://doi.org/10.5194/egusphere-egu26-5694, 2026.
Remotely sensed spectral and thermal measurements are used to address how the mix of built-up and green urban surfaces influence microclimates: thermal infrared sensors measure the emission of longwave radiation from surfaces, which is closely related to their temperature and can be used to explore how urban surfaces store and release heat. Visible to shortwave infrared sensors on the other hand capture how surfaces absorb and reflect incoming solar radiation at different wavelengths, which helps identify materials (like vegetation, asphalt, metal roofs) based on their reflectance signatures. Currently, issues like spatial variation and spectral mixing reduce accuracy in urban heat studies: when multiple surface types (e.g., vegetation, concrete, soil) coexist in satellite pixels, temperature assessments become more complex.
In this study measurements were obtained using a ground-based experimental layout consisting of a spectrometer and a thermal camera mounted on a portable crane. This layout was deployed to study the physical system that consisted of the tree canopy of containerised Acer platanoides trees placed on a paved surface and the background area. Numerical experiments involving the 4SAIL and PROSAIL optical radiative transfer models were used in an inverse mode to disentangle the contribution of the thermal signatures of the tree canopy and the underlying urban surface to the spectral reflectance variation. The sensitivity of the physical system was explored using the delta global sensitivity analysis metric. Strong correlations between the canopy-background temperatures and the fractional vegetation cover indicate that synergies between thermal and spectral measurements in the fine scale is a promising method for disentangling the combined signal components.
How to cite: Halios, C., Branthwaite, O., Lo Piano, S., Smith, S., Pickles, B., and Shao, L.: A study on optical and thermal signatures of the tree canopy-urban surface systems, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20283, https://doi.org/10.5194/egusphere-egu26-20283, 2026.
Human populations are increasingly concentrated in cities, creating some of Earth’s most modified ecosystems. Yet, spatially explicit, observation-based assessments of urban climates and especially soils remain scarce. This limits evidence-based planning for climate adaptation and urban resilience. Here, we leverage over 80 million crowd-sensed plant observations from 326 European cities as “living sensors” to map high-resolution patterns of urban climate and soil properties. This approach builds on consolidated knowledge of plant ecological preferences, integrated through three new pan-European systems of ecological indicator values.
Beyond the urban heat island, we identify additional consistent contrasts between built-up and green areas in moisture, light, soil pH, disturbance, and salinity. The magnitude of these within-city environmental gradients rivals those observed between cities thousands of kilometers apart across Europe. Environmental conditions in built-up areas are remarkably similar across cities, highlighting urban environmental homogenization. In contrast, urban forests maintain natural environmental diversity, contributing to cooling, moisture retention, and key ecosystem functions.
Our new sensing approach, called mobile crowd sensing of environments (MCSE), supports participatory assessment of nature-based solutions and provides actionable insights for planners, policymakers, and local communities. It enables evidence-based decision-making for climate adaptation, sustainable urban development, and the promotion of human health and well-being under rapid urbanization and climate change.
How to cite: Tautenhahn, S., Jung, M., Rzanny, M., Mäder, P., Reichstein, M., Ahrens, B., Bebber, A., Boho, D., Chytrý, M., Dengler, J., Jansen, F., Katal, N., Midolo, G., Tichý, L., Walther, S., Weber, U., Wittich, H. C., and Wäldchen, J.: Crowd-sensed plants as living sensors of urban climate and soils: High-resolution insights from 326 European cities, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20079, https://doi.org/10.5194/egusphere-egu26-20079, 2026.
Climate change poses a critical global challenge, threatening human well-being, ecosystems, economies, and societies. While mitigation efforts remain essential, the increasing severity and immediacy of climate impacts demand timely and effective adaptation measures. In the context of urban climate services (Baklanov et al., 2018, 2020), effective adaptation requires advanced modeling tools that provide higher spatial and temporal resolution, integrate urban structure, ecosystem processes, and social dynamics (Yang et al., 2025), and enable the assessment of diverse adaptation scenarios.
Seamless multi-scale and local-scale models—operating at the scales of streets, cities, administrative regions, countries, or specific domains—are particularly valuable, as they allow for the explicit representation of targeted adaptation measures and the generation of precise, context-specific information (Mahura et al., 2024; Ouyang et al., 2025; Esau et al., 2024). Such models play a crucial role in the development of tailored climate adaptation strategies and actionable planning frameworks.
This overview highlights the significance of seamless multi-scale modeling approaches and discusses the key scientific and practical challenges associated with their development and implementation. We emphasize the urgent need to accelerate progress in this area and call upon the scientific community and policymakers to prioritize the advancement of tailored local-scale modeling tools and integrated services. Strengthening these capabilities is essential to enhance urban resilience and to better support adaptive responses to the complex and rapidly evolving challenges of climate change and urbanization at the local level.
References:
Baklanov, A., C.S.B. Grimmond, D. Carlson, D. Terblanche, X. Tang, V. Bouchet, B. Lee, G. Langendijk, R.K. Kolli, A. Hovsepyan, 2018: From urban meteorology, climate and environment research to integrated city services. Urban Climate, 23 330–341, https://doi.org/10.1016/j.uclim.2017.05.004
Baklanov, A., B. Cárdenas, T.C. Lee, S. Leroyer, V. Masson, L.T. Molina, T. Müller, C. Ren, F.R. Vogel, J.A. Voogt, 2020: Integrated urban services: Experience from four cities on different continents, Urban Climate, 32, 2020, 100610, https://doi.org/10.1016/j.uclim.2020.100610
Esau, i., M. Belda, V. Miles, J. Geletič, J. Resler, P. Krč, P. Bauerová, M. Bureš, K. Eben, V. Fuka, R. Jareš, J. Karel, J. Keder, W. Patiño, L.H. Pettersson, J. Radović, H. Řezníček, A. Šindelářová, O. Vlček (2024) A city-scale turbulence-resolving model as an essential element of integrated urban services, Urban Climate, 56, 102059, https://doi.org/10.1016/j.uclim.2024.102059
Mahura, A., Baklanov, A., Makkonen, R., Boy, M., Petäjä, T., Lappalainen, H. K., … Kulmala, M. (2024). Towards seamless environmental prediction – development of Pan-Eurasian EXperiment (PEEX) modelling platform. Big Earth Data, 8(2), 189–230. https://doi.org/10.1080/20964471.2024.2325019
Ouyang H., A. Baklanov, X. Tang, P. Wang, R. Zhang (2025) Urgency and Importance of Local-scale Modeling Tools to Support Climate Adaptation and Sustainable Development. Frontiers of Environmental Science & Engineering, 2025, 19(12): 171 https://doi.org/10.1007/s11783-025-2091-7
Yang J., Yu W., Baklanov A., He B., Ge Q. (2025) Mainstreaming the local climate zone framework for climate-resilient cities. Nature Commun 16, 5705. https://doi.org/10.1038/s41467-025-61394-w
How to cite: Baklanov, A., Ouyang, H., Tang, X., Wang, P., Zhang, R., Mahura, A., and Esau, I.: Towards Urban Climate Services: Urgency and Importance of Seamless Multi-Scale Modeling Tools to Support Climate Adaptation and Sustainable City Development, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5452, https://doi.org/10.5194/egusphere-egu26-5452, 2026.
The Community Land Model Urban (CLMU) is a process-based numerical urban climate model that simulates the interactions between the atmosphere and urban surfaces, serving as a powerful tool for the convergence of urban and climate science research. However, CLMU presents significant challenges due to the complexities of model installation, environment and case configuration, and generating model inputs. To address these challenges, we developed an open-source framework, including a Python toolkit and a cloud-based platform, for accessible urban climate modelling. The Python toolkit streamlines the generation of model inputs and simplifies the configuration and execution for CLMU simulations. This toolkit also supports code extensibility, allowing users to develop and test new parameterizations easily. Further, by integrating the fifth generation ECMWF reanalysis (ERA5) atmospheric forcing, local climate zone (LCZ), and the 1 km urban surface data, the cloud-based platform enable on-demand simulations for any global location without requiring any local installation. This framework empowers users to rapidly explore urban climate responses under various morphological and climatic conditions and thus provides an accessible tool for urban climate research and design. (Python toolkit: https://envdes.github.io/pyclmuapp/; Cloud-based platform: http://app.open-urbanclimate.com/)
How to cite: Yu, J., Li, X., Oleson, K., and Zheng, Z.: An Open-Source Framework for Accessible Community Land Model Urban Simulations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10141, https://doi.org/10.5194/egusphere-egu26-10141, 2026.
Urban areas exhibit complex and spatially heterogeneous climate conditions driven by urban fabrics, surface materials, land cover, and anthropogenic heat emissions, requiring tailored approaches to analyze climate information at spatial scales relevant for cities. Convection-permitting regional climate models (CPRCMs), with horizontal resolutions of around 3 km, offer new opportunities to investigate urban climate dynamics over climatological timescales and are therefore well-suited for urban climate change impact assessments. To fully exploit this potential, dedicated approaches are required to localize CPRCM output and extract relevant climate change information. This contribution aims to explore such a methodological framework, focusing on classifying urban and rural areas in CPRCMs according to their individual land cover representations including built-up and impervious surfaces as well as green spaces and water bodies. We develop a new weighting method to integrate the CPRCM output data suitable for localized urban climate impact assessments. The localized datasets can further provide a foundation for exploring climate indicators across different urban-rural classification schemes. In addition to near‑surface air temperature, composite thermal metrics such as Globe Temperature (GT) as presented in Puri et al. (2025, preprint) can be considered to illustrate the added value of localized CPRCM output. GT integrates the effects of air temperature, radiative fluxes, and wind, and thus captures environmental thermal loads relevant for sun‑ and wind-exposed urban surfaces and infrastructure more comprehensively than air temperature alone does. Overall, this work, part of the Urban Climate Future Lab (UCFL), contributes to the development of a transferable and reproducible framework for generating urban‑scale climate information from high‑resolution RCMs, supporting climate projections, future analyses, climate services, and climate‑aware adaptation planning for cities.
How to cite: Puri, R., Teichmann, C., Rechid, D., Böhner, J., Nam, C., and Bouwer, L.: Localizing Convection‑Permitting Regional Climate Model Output for Urban Climate Impact Assessment, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12239, https://doi.org/10.5194/egusphere-egu26-12239, 2026.
Urban sprawl intensifies the urban heat island (UHI) effect, leading to elevated temperatures in densely populated areas. This phenomenon, combined with the adverse impacts of soil sealing, highlights the urgent need for targeted investigation and mitigation strategies.
The project INTERFERE examines suburban development, the consequences of urban sprawl, and densification strategies in the suburban areas surrounding Graz. Using local and regional expertise, future spatial development concepts were defined and simulated to assess the impacts of urbanization on the local climate.
Two regional climate modeling systems were applied to simulate three development scenarios: Current, Business as Usual, and Adapted. The simulations were conducted using the ICON (ICOsahedral Nonhydrostatic) regional climate model with the TERRA_URB urban canopy module, and the Weather Research and Forecasting (WRF) model coupled with the Town Energy Balance (TEB) scheme. ICON is widely used for numerical weather prediction and climate simulations, including the assessment of urban climate effects such as the UHI. The TERRA_URB module enables the two-dimensional representation of urban land-surface processes and their interactions with the atmosphere, while WRF-TEB provides a detailed three-dimensional description of urban energy exchanges by accounting for radiative, convective, and conductive heat fluxes and explicitly representing urban structures and their influence on local climate conditions.
A comparative analysis of ICON–TERRA_URB and WRF-TEB simulations is conducted for the suburban areas surrounding Graz, with a focus on how each modeling system represents regional development patterns and the resulting urban heat island effects during heatwave conditions. Differences in model outputs across the development scenarios are analyzed, alongside challenges related to computational efficiency and model calibration. The results emphasize the importance of model choice in relation to specific research objectives and urban-climate assessment needs.
How to cite: Formayer, H., Hasel, K., Nadeem, I., Becsi, N., Hochebner, A., Tötzer, T., Freiburghaus, J., and Leitner, J.: Assessing Urban Sprawl and Densification Impacts on Local Climate Around Graz Using ICON and WRF, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21866, https://doi.org/10.5194/egusphere-egu26-21866, 2026.
Madrid is located in a topographically complex environment, with the Sierra de Guadarrama being the most relevant mountain system in the area, where thermally-driven flows (TDFs), such as mountain and valley breezes, interact with the urban heat island (UHI) and modulate local meteorological conditions. Over recent decades (1970–2020), the population of Madrid has doubled while the urbanized area has expanded by a factor of five. Future projections indicate a further urban expansion of 1.15 to 2.14 times the 2010 extent, accompanied by an approximate 15% population increase by 2037 (Gao & Pesaresi, 2021; INE, 2022). In this context, understanding how urbanization modifies wind regimes through changes in surface properties and terrain roughness, as well as its interaction with the UHI, is essential.
The main objective of this study is to characterize the TDFs affecting Madrid and to analyze their interaction with the UHI, assessing their spatial and temporal variability and their influence on the thermal and dynamical structure of the urban atmospheric boundary layer. The study is based on long-term observational and statistical analysis of meteorological datasets from urban and rural stations, complemented by field campaigns. These observations allow for the assessment of diurnal, seasonal, and annual variations in wind patterns, with a particular focus on detecting and characterizing breeze events, as well as quantifying differences in their intensity, direction, frequency, and duration between the urban environment and the surrounding mountainous areas.
In addition, numerical simulations are performed using the mesoscale Weather Research and Forecasting (WRF) model with advanced urban schemes, such as BEP-BEM (Martilli et al., 2002; Salamanca et al., 2010; Carbone et al., 2024), to further explore the underlying physical processes and to assess the impact of urbanization and thermally-driven flows on thermal comfort and air quality.
This research is part of the MULTIURBAN-II and AIRTEC2-CM projects. The results are expected to advance the understanding of urban atmospheric processes in topographically complex settings and provide critical information for urban planning and climate adaptation strategies.
How to cite: Carbone, J., Sánchez, B., Román-Cascón, C., Martilli, A., Santiago, J. L., Ortiz-Corral, P., Cicuéndez, V., Inclán, R. M., Royé, D., Viana, S., Sastre, M., and Yagüe, C.: Characterization of thermally-driven flows and their interaction with the urban heat island in Madrid, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12371, https://doi.org/10.5194/egusphere-egu26-12371, 2026.
Urban heat stress poses a growing challenge for rapidly expanding cities in hyper-arid regions, where extreme summer temperatures, limited vegetation, and strong land–sea interactions amplify thermal discomfort and energy demand. While urban greening is widely promoted as a heat mitigation strategy, its effectiveness in desert coastal environments remains insufficiently quantified, particularly at neighborhood scales relevant for urban planning. In this study, we assess the thermal impacts of hypothetical urban greening scenarios in Doha, Qatar, using the Weather Research and Forecasting (WRF) model configured with Local Climate Zones (LCZs) and a coupled Building Energy Model (BEM). Simulations are performed at high spatial resolution (400 m) over the metropolitan area of Doha using a nested WRF configuration for a representative summer period in July 2024. Urban morphology is explicitly represented through LCZ-based land-use classes, enabling a realistic description of spatial heterogeneity in building density and surface properties. Three greening scenarios are designed by systematically increasing vegetated cover within urban LCZs by 25%, 50%, and 75%, and are evaluated relative to a baseline configuration. Model performance is assessed using surface meteorological observations from both urban-core and near-coastal stations, showing satisfactory agreement for 2-meter temperature and wind speed. Results indicate that urban greening leads to spatially heterogeneous but consistent reductions in near-surface air temperature, with the strongest cooling occurring during nighttime hours and in densely built LCZs. Nighttime temperature reductions of more than five degrees Celsius are simulated under the most aggressive, transformative scenario. Daytime responses are weaker but remain non-negligible in selected urban zones. Analysis of Cooling Degree Hours (CDH) further reveals substantial reductions in cumulative thermal exposure, highlighting the potential of greening to alleviate heat stress and cooling energy demand in desert cities. Overall, this study demonstrates the added value of combining LCZ-based urban classification with high-resolution urban climate modeling to evaluate nature-based heat mitigation strategies in hyper-arid coastal environments. The findings provide quantitative guidance for climate-resilient urban planning in Doha and other rapidly urbanizing desert cities.
How to cite: Fountoukis, C., Jayarajan Roshini, R., Abedrabboh, O., Moosakutty, S., Siddique, A., and Alfarra, R. M.: Quantifying the Cooling Potential of Urban Greening in an Arid Coastal City Using High-Resolution LCZ–WRF Simulations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10670, https://doi.org/10.5194/egusphere-egu26-10670, 2026.
Typical natural ventilation modeling involves coupling outdoor airflow CFD simulations with indoor airflow network models using CONTAM. However, directly scaling this strategy to the neighborhood or district level—which can include tens to hundreds of buildings—is computationally inefficient. At this larger scale, the primary concern often shifts from precise calculations to reliable estimations and performance comparisons.
This work presents a preliminary exploration that uses aerodynamic drag (F) as an indicator of natural ventilation potential. Extending previous work—which established that a single building's cross-ventilation rate (Q, m3/s) is proportional to the square root of its drag force (√F, N)—this study applies the concept to the neighborhood scale. A RANS numerical simulation campaign is performed for a Nanjing district involving 20 residential neighborhoods (comprising 252 buildings up to 57 m high) located in a heterogeneous context that includes high-rise towers up to 330 m.
Results reveal that F is closely related to wind direction (θ), building typology, orientation, and the shelter effect within the heterogeneous urban fabric. Using stepwise regression analysis, a nonlinear correlation formula is established between aerodynamic drag and key morphological parameters, specifically directional frontal area density (λf,θ) and flow tortuosity.
Specifically, northern neighborhoods (#01 to #04) exert higher F due to less shelter from adjacent medium-height facility blocks. This high-drag condition can be leveraged for cross-ventilation in residential units with north-south layouts when internal doors are open. Conversely, neighborhoods #07 and #08 experience the lowest F and thus the lowest ventilation potential. For neighborhoods #01, #10, and #14, wind along the long axis of slab-shaped buildings creates high drag on shorter façades but a minimal pressure difference across main façades, thereby hindering effective cross-ventilation.
The present study provides a cost-effective approach for exploring spatial variations in natural ventilation potential at the neighborhood and district scales. The resulting dataset offers a valuable reference for aerodynamic parameterization and ventilation estimation within urban building energy simulations, supporting the development of more resilient and energy-efficient urban environments.
How to cite: Zhang, M., Li, J., Buccolieri, R., and Guo, X.: Linking Aerodynamic Drag to Neighborhood-Scale Building Natural Ventilation Potential in Heterogeneous Urban Area, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19317, https://doi.org/10.5194/egusphere-egu26-19317, 2026.
Urban climate adaptation strategies such as including greening, surface unsealing, urban forestry, and street tree expansion are increasingly recognized as effective measures to mitigate urban heat stress. While state-of-the-art microclimate models can assess the thermal impacts of such interventions for individual heat days or short heatwave episodes, extrapolating these findings to long-term, temperature-based climate indices remains methodologically challenging. The cuboid method, originally developed to downscale regional climate model outputs using a limited set of urban climate simulations with MUKLIMO_3, offers a promising framework. Recent advances in the PALM model system now enable the application of this approach within its large-eddy simulation environment. This study within the research project “HeatProtect” presents a proof-of-concept implementation of a temperature-only cuboid method within the PALM model system to downscale regional climate data and generate high-resolution (32 m) urban climate indices. While PALM simulations with typical spatial resolutions of 1 – 10 m require higher computational efforts, the recently developed SLUrb module (single-layer urban canopy model) uses parameterized land-use and building datasets, allowing for coarser modeling tasks.
For the present use case, we reduced the original three-dimensional cuboid framework (temperature, wind speed, humidity) to a temperature-only approach with the potential to extend it to other variables in the future. Two PALM reference states, representing a typical temperate day and an extreme heat event, were conducted for Vienna, Austria. Daily mean temperatures from regional WRF model outputs are used to interpolate between these reference states, enabling spatial downscaling of daily minimum and maximum temperatures across multi-decadal periods. The SLUrb module initially derives 2 m air temperature by weighting outputs from canyon and land-surface models (LSM) according to urban fraction. With this initial representation of vegetation canopy and model setup, some local features of the urban climate, like cold air channels, could not be resolved. Sensitivity tests with thermal roughness length of forests and parallel simulations with the plant canopy model (PCM) replacing LSM, led to more realistic representation of cold air streams and forest areas during nighttime.
Static modifications of urban fraction permit assessment of greening and unsealing scenarios without additional simulations, although spatial propagation of thermal effects to adjacent areas is not captured. To evaluate tree impacts, we performed a separate PALM simulation with complete forest coverage. Using a high-resolution (2 m) vegetation dataset, the weighting scheme was extended to incorporate for high-vegetated areas. While this approach enables the theoretical consideration of adaptation measures based on high vegetation, the rather simplified method has significant limitations, including (1) missing lateral interactions and advection within canopy, (2) non-linear mixing effects, or (3) decoupled radiation interactions.
Model verification was conducted by comparing simulated climate indices (tropical nights (Tmin ≥ 20°C), summer days (Tmax ≥ 25°C), and heat days (Tmax ≥ 30°C)) against 10-30 years of observational data from meteorological stations across Vienna. Final results, validation statistics, and detailed performance assessments will be completed by early 2026 and presented at the conference.
How to cite: Schneider, M., Hochebner, A., Gazzaneo, P., Karttunen, S., and Bügelmayer-Blaschek, M.: Unlocking long-term insights from short-term PALM simulations: A simplified downscaling strategy using the SLUrb module, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6498, https://doi.org/10.5194/egusphere-egu26-6498, 2026.
Rapid population growth and urban expansion are altering land surface characteristics in cities worldwide. At the same time, climate change is increasing the frequency and intensity of heatwaves, making urban areas more vulnerable to thermal stress, particularly where green and permeable surfaces are reduced or modified. In recent years, urban climate models have increasingly been used to examine these processes and to support climate adaptation at the local scale. In this study, the PALM-4U model system is applied to investigate the microclimatic effects of changes in green space within a residential district of Šiauliai (Lithuania). A scenario-based approach is used to compare current land-use conditions with alternative configurations in which existing green areas around residential buildings are replaced by hard surface materials, such as asphalt. Simulations are conducted for a representative heatwave period, and differences between scenarios are evaluated using PALM-4U model outputs, focusing on near-surface air temperature and surface thermal characteristics. The results illustrate the sensitivity of neighborhood-scale urban climate conditions to modifications in green space distribution and demonstrate the value of high-resolution urban climate modeling for assessing land-use scenarios relevant to climate adaptation and planning under increasing heat stress, and provide a consistent reference for the interpretation and validation of Earth Observation derived land surface temperature data in subsequent modeling applications.
How to cite: Cetin, S. and Sieck, K.: Scenario-Based PALM-4U Simulations of Green Space Reduction in a Residential District of Šiauliai, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19087, https://doi.org/10.5194/egusphere-egu26-19087, 2026.
Urban climate is shaped not only by processes within city centres but also by land-use changes and development dynamics in surrounding suburban areas. High-resolution urban climate models such as the PALM model system enable the explicit representation of urban morphology and surface characteristics and are therefore well suited to investigate how urban densification and soil sealing modify local microclimatic conditions and potentially amplify urban heat island (UHI) effects. While microclimatic impacts of specific urban development projects within city borders already received wide attention in different studies, the microclimatic interrelations of large-scale suburban developments based on different scenarios still require more detailed evaluation in the research community.
This contribution presents first results from the INTERFERE project, where the PALM model system is applied to investigate how business-as-usual (BAU) and future regional development pathways influence suburban climate conditions around the city of Graz (Austria) by addressing suburban growth, infrastructure expansion, and associated land-use changes. Two spatial development scenarios are examined: 1) A BAU scenario assumes continuation of current planning practices, including full utilisation of designated building land that has not yet been developed, limited targeted densification, expansion of transport infrastructure, and no reduction of existing development reserves. 2) In contrast, a climate-sensitive planning scenario (Best Practice - BP) follows similar development constraints but emphasises compact urban development and targeted densification—particularly around public transport corridors—enhanced greening measures, and reduced land take through more efficient use of existing reserves. The spatial development scenarios are derived from official zoning and regional planning instruments and are developed by a local spatial planner, who is actively involved in supra-regional and regional planning processes in Styria to ensure realistic scenarios and policy-relevant future pathways. The resulting land-use configurations are than translated into the PALM model system.
The PALM simulations are driven by boundary conditions from high-resolution mesoscale modelling (WRF coupled with the Town Energy Budget model, TEB; Trimmel et al., 2021) at 300 m resolution, provided within the project. Since the mesoscale forcing explicitly accounts for urban structures and urban energy exchange processes, it provides a substantially more realistic representation than forcings from large-scale reanalyses products. For each scenario, a 30-hour heatwave episode, including a spin-up phase, is simulated based on a historical extreme summer event.
By explicitly linking regional spatial planning scenarios with high-resolution microclimate modelling, this study provides new insights into how suburban development patterns influence heat exposure and thermal comfort. In a next step, the results will be discussed with local mayors and key stakeholders to identify and derive appropriate counteracting measures, which will subsequently be assessed through additional simulations. The findings, to be presented at this conference, aim to support evidence-based spatial planning and climate adaptation strategies under increasing heat stress.
How to cite: Hochebner, A., Hasel, K., Tötzer, T., Schneider, M., Becsi, N., Formayer, H., Nadeem, I., Freiburghaus, J., and Leitner, J.: How regional spatial planning shapes suburban heat stress: Scenario-based PALM simulations around Graz, Austria, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11316, https://doi.org/10.5194/egusphere-egu26-11316, 2026.
Ongoing climate change, insufficient urban resilience, and the ever-increasing exposure of city dwellers to environmental hazards such as urban heat stress require carefully tailored mitigation strategies supported by high-fidelity urban climate modelling tools. Urban boundary layer processes are strongly affected by urban morphology, vegetation, and the dynamic nature of human activities. Moreover, the complex structure of cities and their properties exacerbate an already complex process of modelling urban areas and climate; as spatial resolution increases, the complexity of radiative processes also increases.
Radiative transfer modelling in microscale urban environments remains challenging due to the highly complex interactions between buildings, vegetation, and dense urban morphology. These interactions include multiple reflections, scattering, shading, and thermal emission. Key factors, such as the sky view factor, the fractions of sunlit and shaded surfaces, and the spatial variability of surface properties, are also considered bottlenecks for numerical models. Despite considerable progress, uncertainties related to radiative transfer parameterisations and input data persist in street-scale simulations.
This study evaluates the Parallelized Large Eddy Simulation Model (PALM) and its Radiative Transfer Model (RTM) in representing shortwave radiation processes within a realistic urban environment. PALM simulations were conducted in spin-up mode at 1 m spatial resolution for a densely built and vegetated area in Prague, Czech Republic, and validated against measurements collected during observational campaigns in 2017 and 2018. Incoming and outgoing shortwave radiation were assessed across a series of selected episodes at four urban locations characterised by contrasting surface properties and urban morphology.
The results demonstrate that PALM reproduces the incoming shortwave radiation with high fidelity, particularly when driven by carefully selected mesoscale forcing. Larger discrepancies are observed for outgoing shortwave radiation, highlighting its sensitivity to surface representation, vegetation structure, and urban geometry. The analysis identifies inaccuracies in static urban input data, such as building geometry and vegetation parameters, as a dominant source of error. Overall, the findings emphasise the high importance of accurate mesoscale forcing and high-quality urban static datasets for reliable street-scale radiative transfer modelling. Furthermore, this study provides a comprehensive validation of PALM’s shortwave radiation modelling, advancing the understanding of uncertainties in microscale urban radiative transfer simulations and supporting the improved modelling of urban heat exposure and mitigation strategies development.
How to cite: Radovic, J., Belda, M., Bureš, M., Eben, K., Geletič, J., Jura, J., Krč, P., Řezníček, H., and Resler, J.: Meter-scale radiative transfer process modelling in complex urban environments: a PALM model validation study, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13311, https://doi.org/10.5194/egusphere-egu26-13311, 2026.
In light of increasingly intense heat waves threatening various regions around the globe, densely built urban environments are vulnerable to elevated heat stress values. Large-eddy simulations like the obstacle-resolving model PALM-4U act as suitable tools to quantify urban heat stress on the neighborhood- to street-level due to their ability to resolve small-scale variability of atmospheric variables. PALM-4U simulations must be forced with vertical profiles, either as initial profiles or as dynamic profiles adapted from mesoscale models. However, the use of the latter can potentially introduce some model biases into the urban climate model, affecting the representation of the boundary layer. Furthermore, mesoscale model simulations might not always be available at a certain location at a specific time. This favors the use of observation-informed data products as driving data. This study presents the performance of a set of PALM-4U simulations forced with various initial vertical atmospheric and soil profiles in simulating 2m-air temperature at different grid spacings for a densely populated city quarter within the Hamburg-Altona district in northern Germany. Furthermore, the ability to simulate the variability of heat stress indices and other thermal comfort-related measures is investigated. Three different atmospheric forcing profiles are employed: (i) an observation-based profile constructed by merging data from the Hamburg Weathermast with CERRA and ERA5 reanalysis data, (ii) a reanalysis-based profile constructed by merging data from CERRA and ERA5 reanalysis, and (iii) a profile based on ERA5 reanalysis data only. Soil parameters were taken from CERRA-Land reanalysis. Simulations were conducted at horizontal grid spacings of 8m, 4m, and 2m for a heatwave day that stands out against other summer days regarding the near-surface air temperature. The evaluation was performed against measurements of three weather stations, which had been set up during a field campaign in the months June-August 2020 in Hamburg, Germany, and are located within the modelling domain. This study answers the question whether reanalysis data provide a suitable base for forcing PALM-4U simulations. This would allow realistic forcing profiles to be constructed over domains with little observational or mesoscale model data availability.
How to cite: Loprieno, L. L., Hoffmann, P., Fritz, S., and Sillmann, J.: Can measurement and reanalysis data act as suitable forcing data for neighborhood-level PALM-4U simulations?, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11487, https://doi.org/10.5194/egusphere-egu26-11487, 2026.
Urban populations are disproportionately affected by heat stress and heat-related health risks caused by climate change. To assess outdoor thermal comfort of humans using indices such as the Universal Thermal Climate Index (UTCI), air temperature, humidity, wind speed and the three-dimensional radiative environment (Tmrt) must be accurately represented. However, the influence of these variables on thermal comfort depends on the spatial and temporal scale and varies significantly within urban areas. While mesoscale variability dominates thermodynamic conditions, urban morphology and surface conditions control wind and radiant fluxes, as well as their temporal variance at local- and microscale.
The multiscale nature of the urban environment poses a significant challenge in modelling urban thermal comfort on a meaningful scale. Although physics-based numerical weather prediction (NWP) models with urban parameterisations can accurately depict urban–atmosphere interactions and local-scale processes across large model domains and over long time periods, they are limited to hectometer-scale resolutions and idealised urban geometries. Conversely, high-resolution urban climate models can resolve street-level microclimatic processes; however, they have limited spatial extent and temporal coverage due to computational constraints. While recent deep learning approaches have shown promising results in emulating microscale urban climate models, these approaches are typically applied offline and lack dynamic urban–atmosphere interactions. This restricts their ability to capture urban-induced feedback at meso- to local-scales.
In this study, we present a hybrid meso-to-microscale modelling chain that couples the physics-based ICON model, using the TERRA_URB urban parameterisation scheme, with a deep-learning-based microscale model that resolves buildings for the entire Greater Paris agglomeration. The ICON model was run at a spatial resolution of 1 km over a six-month period in summer 2023 to estimate local-scale air temperature and humidity while accounting for urban–atmosphere interactions such as downwind effects of the urban plume. We then trained, evaluated, and coupled a deep learning model to estimate Tmrt at the microscale (1 m) and coupled a diagnostic wind speed model to estimate wind speed at a resolution of 2 m. We subsequently computed the UTCI across the entire urban agglomeration of Greater Paris at a resolution of 1 m. The model domain encompasses Greater Paris (35 km x 37 km). It was chosen as a test case due to its size, which introduces large-scale urban–atmosphere interactions and variability in urban morphology types. This enables the deep learning model to be trained holistically and improves the generalisation capability.
We demonstrate that coupling physically consistent mesoscale dynamics with data-driven microscale diagnostics leverages the strengths of numerical and deep learning-based models. The proposed model chain is scalable and computationally efficient. It presents a method for assessing human-scale thermal comfort across spatial and temporal scales, thereby supporting urban heat risk analysis and climate adaptation planning.
How to cite: Briegel, F., Bohmann, M., Ludwig, P., Christen, A., and Pinto, J. G.: Coupling physics-based mesoscale weather models and deep-learning microscale models for outdoor thermal comfort assessments., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9347, https://doi.org/10.5194/egusphere-egu26-9347, 2026.
Heat stress is an emerging hazard in temperate regions, driven by rising temperatures and increasing urbanisation. Understanding its spatial distribution and future evolution is critical for informing effective climate adaptation strategies. This study investigates human thermal comfort across Ireland using the Universal Thermal Climate Index (UTCI), with particular emphasis on urban–rural contrasts and Local Climate Zone (LCZ) classifications. High-resolution (~4 km) regional climate projections are generated through Regional Climate Models (RCMs) that dynamically downscale Coupled Model Intercomparison Project Phase 6 (CMIP6) outputs. Both standard atmosphere-only and fully coupled atmosphere–ocean–wave RCM configurations are employed, providing a detailed representation of regional climate processes and extremes through to 2100. Human thermal response is simulated using the UTCI-Fiala multi-node thermoregulation model, integrated with an adaptive clothing model to account for physiological responses to thermal stress.
The study addresses four primary objectives. First, UTCI distributions are mapped nationwide to identify regions and periods most susceptible to heat stress. Second, variations in UTCI between LCZs are analysed to evaluate how urban form and land cover modulate thermal stress patterns. Third, projected changes in UTCI under multiple Shared Socioeconomic Pathway (SSP) scenarios are assessed to quantify future shifts in the intensity, frequency, and spatial extent of heat stress. Fourth, intra LCZ variation in UTCI is examined across varying spatial and temporal scales to capture differential sensitivities to climate change.
Preliminary results indicate strong spatial heterogeneity in thermal stress, with urban cores and densely built LCZs experiencing higher UTCI values than surrounding rural areas. Future projections suggest a marked increase in the duration and intensity of heat stress events, particularly under high-emission SSP scenarios, with implications for public health, urban planning, and climate adaptation policy. By integrating high-resolution climate projections with physiologically based thermal indices and urban morphology, this study provides a comprehensive assessment of both current and future human thermal comfort across Ireland. The findings offer an evidence base for guiding mitigation strategies, designing climate-resilient urban environments, and informing health-focused interventions in a warming climate.
How to cite: Rahul, A., Clarke, J., Nolan, P., King, M., and Heaphy, L.: High-resolution modelling of heat stress across Ireland using the Universal Thermal Climate Index, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12443, https://doi.org/10.5194/egusphere-egu26-12443, 2026.
Quantitative descriptions of urban morphology enhance our understanding of urban systems' operation and evolution. In recent years, with the rapid development of the AI, the application of machine learning in urban research has become increasingly widespread. Current applications can be broadly categorized into two main types:
The first category utilizes machine learning to reveal nonlinear relationships between urban morphology and ecosystem services. For example, research examines how spatial morphological indicators of urban green spaces or blue-green infrastructure affect vegetation's cooling effect, carbon sequestration, flood mitigation and other ecosystem services (Sun et al., 2019; Wang et al., 2023). This type of research breaks through the limitations of traditional linear analysis and can capture complex urban environmental interactions.
The second category employs deep learning-based representation-learning methods (e.g., contrastive self-supervised encoders, graph auto-encoders, Vision Transformers) for urban morphology clustering (de-Miguel-Rodriguez et al., 2025; Dong et al., 2019; Kempinska & Murcio, 2019). Traditional methods of urban classification, based on morphological indicators, often suffer from information loss, spatial mismatches, and lack of robustness. Deep learning techniques for high-dimensional feature extraction and latent variable representation have been developed, improving the robustness of urban classification. These advanced methods significantly enhance the accuracy and reliability of urban classification.
In this presentation, I will share empirical research findings in both areas, including specific cases in which I have participated, and discuss future development directions and application potential of this field in urban climate research.
Reference:
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Dong, J., Li, L., & Han, D. (2019). New Quantitative Approach for the Morphological Similarity Analysis of Urban Fabrics Based on a Convolutional Autoencoder. IEEE Access, 7, 138162–138174. https://doi.org/10.1109/ACCESS.2019.2931958
Kempinska, K., & Murcio, R. (2019). Modelling urban networks using Variational Autoencoders. Applied Network Science, 4(1), 114. https://doi.org/10.1007/s41109-019-0234-0
Sun, Y., Gao, C., Li, J., Wang, R., & Liu, J. (2019). Quantifying the Effects of Urban Form on Land Surface Temperature in Subtropical High-Density Urban Areas Using Machine Learning. Remote Sensing, 11(8), Article 8. https://doi.org/10.3390/rs11080959
Wang, M., Li, Y., Yuan, H., Zhou, S., Wang, Y., Adnan Ikram, R. M., & Li, J. (2023). An XGBoost-SHAP approach to quantifying morphological impact on urban flooding susceptibility. Ecological Indicators, 156, 111137. https://doi.org/10.1016/j.ecolind.2023.111137
How to cite: Yu, M.: Machine Learning in Urban Morphology and Urban Climate: Prospects and Applications, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11968, https://doi.org/10.5194/egusphere-egu26-11968, 2026.
Urban areas are vulnerable to extreme heat events, a vulnerability that is intensified by climate change. Although many studies have examined heat mitigation strategies using observational or reanalysis data, there have been few explicit assessments of future climate conditions at an urban scale. From a climate modelling perspective, the storyline approach provides a consistent physical framework for evaluating event-specific responses to warming. Its application, however, in urban contexts has been constrained by the coarse resolution of regional climate models at this scale. This study addresses this gap by analyzing the June 2019 European summer heatwave in Karlsruhe, Germany, using an urban climate model. A 3 km resolution ICON regional climate model, which is driven by global storylines from the AWI-CM1 model, provides input for high-resolution urban climate simulations. Two scenarios are considered: (i) the present-day heatwave, and (ii) a future warming storyline. Each includes a baseline and an adaptation configuration featuring enhanced urban green infrastructure (e.g. green roofs). Urban-scale simulations are performed using the Parallelized Large-Eddy Simulation Model with urban parametrization (PALM-4U). This employs a 20 m parent domain and a nested 5 m child domain in order to explicitly resolve urban morphology, land–atmosphere interactions and vegetation processes. Thermal comfort indices, including the Universal Thermal Climate Index (UTCI) and Physiological Equivalent Temperature (PET), are calculated to evaluate the impact of the heatwave on thermal comfort and the effectiveness of green infrastructure interventions in current and future conditions. Our findings aim to demonstrate the added value of combining warming frameworks based on storylines with urban-scale modelling for event attribution and climate-resilient urban planning. The results provide actionable insights into the potential of increased green spaces to mitigate heat stress during extreme heatwaves in the present and future.
These simulations are part of the research project “climate Adaptation sCenarios To redUce the impActs of exTreme Events” (ACTUATE).
How to cite: Isik-Cetin, I. and Sieck, K.: Integrating Storyline-Based Warming Framework with Urban Climate Modeling: Assessing Green Infrastructure Cooling During Extreme Heatwaves, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16921, https://doi.org/10.5194/egusphere-egu26-16921, 2026.
The deterioration of outdoor thermal comfort under recurrent heatwaves has become a critical constraint on pedestrian activity. Campus walkways function not only as circulation routes but also as everyday spaces for rest and social interaction, which can prolong pedestrian exposure to thermal stress. Due to the combined configuration of pavements, tree canopies, building façades, and adjacent open spaces, campus walkways exhibit spatially heterogeneous radiative environments that cannot be adequately explained by single-factor thermal analyses.
This study investigates how combinations of pavement albedo and tree canopy leaf transmittance affect pedestrian thermal comfort under different adjacent spatial conditions, using a university campus as a case study. A continuous pedestrian axis within the University of Seoul campus was classified into two spatial types: a plaza-type walkway with high openness and a large proportion of artificial pavement, and a continuous façade-type walkway characterized by aligned building façades and continuous rows of street trees.
Urban microclimate simulations were conducted using ENVI-met (v5.6.1) and the BioMet module. The Universal Thermal Climate Index (UTCI) was calculated at pedestrian height (1.4 m) based on air temperature, humidity, wind speed, and mean radiant temperature. Simulations were performed for a representative summer hot day (27 July 2025) under three wind speed conditions (1.5, 2.5, and 3.5 m/s). Pavement albedo (A = 0.12–0.55) and canopy leaf transmittance (τ = 0.15–0.45) were systematically combined into twelve scenarios, with existing site conditions defined as a baseline.
Results indicate that increasing wind speed generally reduced UTCI across both spatial types, while the relative effects of pavement albedo and canopy transmittance remained consistent. However, the timing of peak thermal stress differed by spatial type, occurring mainly around midday in the plaza-type walkway and in the late afternoon in the continuous façade-type walkway. Higher pavement albedo consistently increased maximum UTCI, particularly in the more open environment, whereas lower canopy transmittance reduced thermal stress. Combined modifications of pavement and canopy properties produced non-additive UTCI responses, indicating complex radiative interactions.
This case study demonstrates that thermal comfort responses along campus walkways are highly sensitive to spatial configuration and the combined properties of pavements and tree canopies, highlighting the need for context-specific thermal mitigation strategies in pedestrian environments.
How to cite: Han, Y. and Park, C.: Effects of Pavement Albedo and Tree Canopy Transmittance on Pedestrian Thermal Comfort along Campus Walkways: University of Seoul Case Study, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19896, https://doi.org/10.5194/egusphere-egu26-19896, 2026.
With current temperature increase likely to miss Paris Agreement targets and rapid urbanization, cities worldwide face increasing vulnerability to unprecedented extreme climate events like heat waves and extreme precipitation. We will present a new project Urban Extreme Climate Adaptation Digital Twin (UrbExt DT) and the preliminary results. UrbExt DT aims to equip decision-makers with hectometer-scale climate data and new insights to respond effectively to unprecedented climate extremes, fostering resilience and sustainability in cities. This is achieved by a Digital Twin of urban climate systems that integrates an advanced convection-permitting regional climate model, machine learning-based climate emulators, and a contextual visual analysis interface. We will provide probable first occurrence time, characteristics and prevailing meteorological conditions for record-breaking climate extremes using unprecedented 100-metre scale climate data over urban areas. We will also explore the physical processes linking urbanization to future extremes, addressing several unresolved questions. The interactive interface allows users to adjust urban development scenarios to test various adaptation strategies. UrbExt DT focuses on Sweden and Africa but adopts a global perspective. By developing tools that work in both settings, the project addresses vulnerabilities in small, medium and large cities alike and lays the groundwork for global scalability.
How to cite: Wang, F., Vrotsou, K., Mirjalili, S., Lennard, C., Nilsson Keskitalo, E., Vinuesa, R., Nikulin, G., Aldama Campino, A., Ribeiro, I., Amorim, J., Döscher, R., Lind, P., Wang, Y.-C., Petersson, R., Reski, N., Navarra, C., Brown, N., and Nilsson, L.: Urban Extreme Climate Adaptation Digital Twin, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19945, https://doi.org/10.5194/egusphere-egu26-19945, 2026.
We present an operational data-fusion framework for high-resolution urban heat island analysis, producing hourly near-surface air temperature fields at 20 × 20 m² spatial resolution over complex urban environments. The framework is demonstrated over the Thessaloniki metropolitan area, a dense Mediterranean city characterized by warm summers and mild winters. It integrates heterogeneous data streams, including meteorological forecasts, land-use and land-cover indicators, building height and urban morphology descriptors, and low-cost temperature sensor measurements. Inputs are harmonized in space and time, while automated gap-filling procedures ensure spatial completeness under real-world data availability constraints.
Τhe framework employs an uncertainty-aware ensemble combining Universal Kriging and Gaussian Process regression models. Both methods are executed in parallel to generate temperature estimates along with their predictive uncertainty fields. These uncertainty maps are explicitly used to construct a weighted ensemble, where model contributions are modulated according to local and temporal uncertainty, allowing the final temperature field to reflect the predictor expected to be more reliable at each location and time. This approach preserves fine-scale thermal gradients associated with land use and built structure while maintaining spatial coherence.
Results from the operational deployment, running continuously over several months, demonstrate the system’s ability to deliver stable and spatially consistent high-resolution temperature fields in a fully automated manner (Figure 1). The produced maps capture persistent intra-urban thermal contrasts linked to urban morphology and land-use patterns. Importantly, these results are derived from a live operational pipeline rather than post-processed reanalysis, highlighting robustness under real-time data availability and sensor sparsity constraints. Although the current evaluation period corresponds primarily to winter conditions, the outputs already provide valuable insight into urban thermal variability and establish a reliable baseline for forthcoming warm-season heat assessments.
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Figure 1. Example hourly realization of the operationally produced near-surface air temperature field at 20 × 20 m² resolution over the Greater Thessaloniki Area.
The resulting temperature fields are designed to act as a core thermal layer within a city-scale digital twin, supporting integrated analysis of urban heat patterns, micro-climatic variability, and heat exposure, and providing a scalable backbone for urban heat studies and climate-adaptation planning.
How to cite: Kassandros, T., Bagkis, E., Adamopoulou, L., Stergiou, I., Kontos, S., Melas, D., and Karatzas, K.: An Operational Uncertainty-Aware Framework for Urban Heat Mapping in City-Scale Digital Twins, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21184, https://doi.org/10.5194/egusphere-egu26-21184, 2026.
Growing climate change impacts in cities, where people, assets, and infrastructure are densely concentrated, call for adaptation strategies that are effective, equitable, and financially sustainable. Despite rapid growth in quantitative urban climate-risk research, most studies operate either at coarse spatial scales or rely on single-city case studies, limiting systematic comparison across urban areas. This constrains the ability of decision-makers to evaluate alternative infrastructure-based adaptation pathways, particularly at the public–private interface and under explicit equity objectives.
Here we develop a scalable, automated framework for the data-driven assessment of urban adaptation infrastructure options at sub-city scale. The framework is designed to (i) operate at high spatial resolution within cities, (ii) explicitly represent infrastructure-based adaptation measures together with their benefit and cost streams, and (iii) be transferable across cities and climate hazards. It is built around a modular geospatial pipeline that maps present and future climate hazards, overlays exposure and socio-demographic determinants of vulnerability, and represents adaptation-relevant infrastructure options on a common spatial grid. The framework includes both public adaptation options, such as street trees, cooling centres, and heat-health action plans, and private options, such as air conditioning and building-level retrofits. For each option, alternative rollout strategies can be tested, including uniform deployment, hotspot targeting, and prioritisation of vulnerable populations.
For each adaptation scenario, empirically calibrated impact functions implemented within the CLIMADA risk-modelling framework are combined with cost modules to estimate avoided impacts, side effects, and costs. Outputs include reductions in climate-related impacts, such as heat-related mortality and extreme heat exposure, as well as additional effects, such as changes in electricity demand and air-conditioning waste-heat feedbacks that can locally raise outdoor temperatures. Capital expenditures and operation and maintenance costs are tracked separately, enabling consistent city-level cost–benefit assessments of individual and combined adaptation pathways.
We illustrate the framework with an application to urban heat adaptation in Rome, using harmonised climate, population, income, and infrastructure data to compare tree-based cooling and expanded air-conditioning coverage under different rollout patterns. We simulate a needs-based tree-planting policy that raises all municipi to at least the third quartile of the pre-policy distribution of street-level green space. Implemented as a linear rollout over 25 years with empirically estimated costs and maintenance scaling with tree maturity, this policy entails a present-value public cost of about €0.45 billion and avoids an estimated 86 heat-related deaths over 2030–2054. An air-conditioning expansion targeting lower-income areas, adding roughly 190,000 new users, entails a present-value private cost of about €1.24 billion and avoids approximately 1,021 deaths. Implementing both policies jointly costs about €1.70 billion and avoids roughly 1,098 deaths, with tree expansion on top of air-conditioning still preventing additional mortality at higher marginal cost.
Ultimately, the framework is intended for application across a pool of European cities and extension beyond heat to other climate hazards and adaptation infrastructures. It provides a flexible basis for designing, comparing, and optimising city-scale adaptation pathways under explicit efficiency, equity, and policy constraints.
How to cite: Falchetta, G. and Aboudrar-Méda, A.: A scalable geospatial framework for city-level public-private adaptation infrastructure cost-benefit analysis, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2308, https://doi.org/10.5194/egusphere-egu26-2308, 2026.
Urban regions increasingly face culminating challenges of climate change impact, such as heat stress, altered precipitation patterns and biodiversity loss. This imposes serious risks to human well-being and urban ecosystems. Effective responses require integrated strategies that include environmental monitoring, adaptation planning, and community involvement. Therefore, we started SciWalK (Science Walk Climate Adaptation and Biodiversity) as an interdisciplinary real-world learning laboratory at the University of Stuttgart. We aim to bridge education with hands-on experience, urban microclimate science and future-oriented spatial adaptation measures in urban environments.
SciWalK is a two-semester Master’s module involving students from architecture and urban planning, civil and environmental engineering as well as electrical engineering. During the project students construct portable microclimate measurement systems (“sensor backpacks”) and conduct measurement walks in the urban context of Stuttgart, Germany. These sensor systems capture physical key parameters including air temperature, relative humidity, radiation and wind speed, enabling high resolution characterization of urban microclimate variability on street level. The student teams benefit from the prior knowledge and perspectives of peers from different fields of study, whom they would not normally encounter in their own discipline; and by this preparing them for post‑graduation collaboration on sustainable adaptation strategies across fields. The final public “Science Walk” presents the results to an interested public raising awareness within the community.
At present, a fully functional prototype has already been developed. Based on a Raspberry Pi Zero 2W, the system is capable of recording air temperature and relative humidity (SHT-31D), mean radiant temperature (light grey globe, 40 mm, MAX31685), and wind speed (Modern Device - Rev. C). On this basis, the Universal Thermal Climate Index (UTCI) is calculated automatically. Spatial positioning is captured via a custom-developed smartphone application programmed using MIT App Inventor, which records GPS coordinates at the same temporal resolution as the microclimate measurements. The resulting datasets are subsequently visualized as georeferenced outputs in QGIS. Physically, the measurement system is integrated into a custom-designed 3D-printed housing.
By the time of the conference, two additional measurement-tracker designs will have been developed by the student teams, and further sensors will have been integrated to enable improved assessment of wind speed as well as air quality and illuminance. In addition, extended data analysis and complementary evaluation campaigns will have been completed. Consequently, the presentation will not only introduce the developed prototype but also report key lessons learned from the first pilot semester of student involvement.
Overall SciWalK highlights how interdisciplinary education and participatory observation contribute to resilient cities in the face of global climate change. This integrated approach aligns with the goals of EGU26’s Urban Climate Science and Services session, demonstrating how observational tools and innovative educational formats can lead to adaptive urban climate strategies.
How to cite: Lüling, C., Bhattacharya, M., and Schuderer, F.: SciWalK: An interdisciplinary Urban Climate and Biodiversity Learning Lab integrating Microclimate Measurement and Adaptation Strategies, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8231, https://doi.org/10.5194/egusphere-egu26-8231, 2026.
Cities are generally warmer than their surroundings due to the Urban Heat Island (UHI) effect, which can be intensified during heat waves and lead to reduced labor capacity and increased mortality. Urban vegetation is known to mitigate UHI intensity, but quantifying the cooling impact of different green infrastructures, such as parks or street trees, on urbanized areas remains challenging. The time required for trees to grow is often long enough for substantial urban changes to occur around them, making it difficult to isolate vegetation-driven temperature effects from other factors. Moreover, air temperature (Ta) and surface temperature (TS) play different roles in the energy balance and exhibit distinct spatial patterns, although they are thermodynamically linked through heat exchange.
In this study, we assess the impact of a newly implemented linear park in São Paulo, Brazil, on surrounding TS using clear-sky Landsat-derived TS and Normalized Difference Vegetation Index (NDVI) at 10 a.m. (30 m resolution). TS and NDVI were averaged in two decades (1985-1995 and 2015-2025), where trees were planted at the beginning of the first decade and became substantially denser on the latter (NDVI increase up to 0.25), while the adjacent southern urbanized area remained unchanged.
A cross-section perpendicular to the park axis was used to quantify the cooling reach, defined as the distance between the last pixel showing a statistically significant NDVI increase and the last pixel with a significant TS decrease. The observed cooling reach was 30 m (one Landsat pixel). This result is compared with three previous case studies in São Paulo that investigated dense vegetation removal, where NDVI changes were stronger (up to 0.50) and warming reaches ranged from 64 to 168 m. Interpreted inversely, as a conceptual restoration of dense vegetation, these values provide an upper benchmark for the potential cooling reach of parks (and the present case study lies within it).
Our results indicate that even under favorable conditions, the cooling influence of parks on surrounding urban areas is spatially limited. This suggests that distributed strategies such as street trees and other forms of urban greening may be more effective for reducing overall city temperatures.
How to cite: Lustosa, R. and da Rocha, H.: Quantifying the Cooling Reach of Urban Vegetation: A Linear Park Case Study in São Paulo, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19983, https://doi.org/10.5194/egusphere-egu26-19983, 2026.
Integrating climate knowledge into urban planning has become increasingly critical amid accelerating climate change. While research on urban climate adaptation gaps has expanded considerably, comparatively little attention has been paid to long-established instruments such as urban climatic maps (German: Stadtklimakarten), which have been developed since the late 1970s (VDI 3787, Part 1). These maps are internationally recognised tools designed to translate urban climate knowledge into spatially explicit planning recommendations by depicting climate functions relevant to land-use planning and decision-making. This study provides a content analysis of the availability and characteristics of urban climatic maps in German cities. We analysed the ten largest cities in each of Germany’s 13 federal states, as well as all 3 city-states, using internet-based research and publicly available sources. The results show that nearly 90% of cities with more than 100,000 inhabitants (n = 63) provide an urban climatic map, whereas such maps are available in only about 20% of smaller cities. This indicates substantial disparities in the data basis for climate-adaptive planning, which are only partially compensated by state-wide climatic maps that generally offer lower spatial resolution. Further analysis reveals considerable heterogeneity in map characteristics, including grid resolutions ranging from 5 to 50 meters and diverse methodological approaches, with FITNAH-based modelling clearly dominating. Overall, more than half of the analysed cities (n = 134) make use of urban climatic maps; however, the wide variation in methods, spatial resolution, and accessibility significantly limits comparability and transferability. Consequently, the potential of urban climatic maps to support harmonised climate risk assessments and the development of coherent adaptation action plans remains constrained.
How to cite: Jänicke, B., Lümkemann, J., and Schönewald, A.: From Climate Knowledge to Planning Practice: Urban Climatic Maps in German Cities, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11586, https://doi.org/10.5194/egusphere-egu26-11586, 2026.
Urban heat adaptation policies often rely on hotspot analyses to prioritize areas of concern, yet such maps provide limited guidance on why specific locations are vulnerable and which actions are most appropriate at the neighborhood scale. To support local decision-making, fine-scale diagnostic information on strengths and deficits is required before local goals can be assigned. And once goals are defined, suitable mitigation measures must be selected to achieve them, often under a regional policy and funding framework that requires transparent prioritization.
We present a stepwise assessment framework that connects these levels from strategic prioritization to actionable planning. Building on an existing multi-criterion hot and cold spot prioritization, we derive diagnostic profiles for each urban 100-m grid cell across the German state of Hesse. These profiles summarize local strengths and deficits in goal-relevant dimensions such as vegetation condition, tree presence, and available open space, and are communicated for each grid cell through compact spider plots that enable rapid interpretation and comparison between locations.
In a subsequent step, diagnosed deficits and locally assigned goals are linked to measure-specific suitability maps for five common urban heat mitigation options: unsealing, tree-based shading, vegetation improvement, green facades, and green roofs. Suitability is derived from combinations of land-cover characteristics, vegetation metrics, and urban structural indicators, explicitly accounting for local constraints such as built-up density, parcel structure, and urban typology. The resulting scores allow a consistent comparison of mitigation options within and between neighborhoods. By mapping these suitabilities across the study region, the framework supports both neighborhood-scale decision-making and region-wide analyses of where specific measures are most feasible, providing an evidence base for targeted investment and long-term climate resilience strategies.
How to cite: Benz, S. A., Jehling, M., and Keller, S.: Diagnosing local deficits and mitigation suitability for neighborhood-scale urban heat adaptation in Hesse, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13273, https://doi.org/10.5194/egusphere-egu26-13273, 2026.
As cities continue to warm, where should cooling investments be deployed to maximise public health benefits? Here we address this question for cities like Los Angeles, where intensifying heat, entrenched socio-economic inequality, and uneven adaptive capacity intersect. To identify priority “hotspots” where cooling interventions would most effectively reduce temperatures and save lives, we map the intersection of three key variables: (a) high heat exposure, (b) socio-economic vulnerability, and (c) feasibility of intervention strategies. We take an inventory of the urban built form and map the current albedo to evaluate where reflective coating can be deployed. We then formulate a city-specific temperature–mortality relationship to optimize city-wide public health benefits of the potential reduction in temperature. Applying this framework to Los Angeles shows that reflective surface treatments can produce substantial local air temperature reductions in select high-risk areas and yield large mortality benefits relative to the treated area. The resulting benefit distribution is sharply skewed as treating only 9% the city generates half of the total potential reduction in heat-related deaths, highlighting a strong opportunity for targeted, high-return investment. We have also developed an interactive web-based tool that would allow practitioners to explore the cooling potential in every neighborhood, visualize the life years that could be saved, and identify priority neighborhoods for heat adaptation. The utility of this work extends beyond Los Angeles by offering a scalable framework for other cities seeking to deploy equitable and life-saving heat adaptation strategies.
How to cite: Shreevastava, A., Druckenmiller, H., Dehaene, Q., Halsey, A., Prasanth, S., Yoo, C., Hulley, G., Frankenburg, C., and Yin, Y.: Leveraging temperature–mortality risk relationship to identify most effective urban heat adaptation sites, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15726, https://doi.org/10.5194/egusphere-egu26-15726, 2026.
Urban areas are becoming increasingly affected by the impacts of climate change, particularly through intensifying heat extremes that constitute a threat to public health and economic productivity. A key driver of urban heat stress is the Urban Heat Island (UHI) effect, a phenomenon in which nighttime cooling is reduced due to heat accumulation within the city structure.
The UHI is usually defined as the temperature contrast between urban and rural areas; however, thermal discomfort is not solely determined by temperature: humidity, wind and radiation strongly modulate heat perception. Therefore, a purely temperature-based definition may underestimate its actual impact on the urban population. This study proposes a human-centered characterization of the UHI effect, introducing the concept of Urban Heat Stress Island (UHSI), by integrating several heat stress indices and assessing their complementarity.
We analyze observations from ten meteorological stations in Paris and its surroundings for 1980-2017, within the CORDEX URB-RCC Flagship Pilot Study (Langendijk et al. 2024). The dataset includes subdaily records of air temperature, relative humidity, wind speed and radiation, from which widely used multivariable heat stress indices are computed. The UHSI effect is consequently defined as the urban-rural degree difference for each index, calculated at 3-hourly resolution and restricted to summer nights (from 21:00 to 06:00), when the phenomenon is strongest. Daily mean and maximum differences are considered to capture both average and extreme contrasts.
We examine the UHI (for air temperature) and UHSI for each index independently, assessing complementarity and redundancy through correlation analysis and Kolmogorov-Smirnov distance, which quantify temporal co-variability and distributional similarity, respectively. We also evaluate the sensitivity of UHSI to key drivers, specifically temperature and humidity.
Results show that the highest UHSI contrasts systematically occur for relatively cool rural nights, typically below the 20th percentile of rural temperature. Two highly redundant subgroups of heat stress indices emerge: one formed by air temperature and the Heat Index, and another dominated by humidity-sensitive indices such as the simplified WBGT, humidex and WBGT in the shade. In contrast, UTCI and Effective Temperature exhibit consistent independence from the rest, highlighting the added value of a multivariable UHSI approach over a solely temperature-based UHI definition by capturing complementary dimensions of urban thermal stress. Hence, the UHSI reframes the traditional UHI definitions offering a novel framework to quantify urban thermal risk beyond temperature, with implications for urban climate adaptation and public health.
This work is part of Grant PID2023-149997OA-I00 (PROTECT) funded by MICIU/AEI/10.13039/501100011033 and by ERDF/EU. C.R.R. acknowledges support from Grant PREP2023-001919 funded by MICIU/AEI/10.13039/501100011033 and by ESF+.
Langendijk, G. S., et al. (2024). Towards better understanding the urban environment and its interactions with regional climate change—The WCRP CORDEX Flagship Pilot Study URB-RCC. Urban Climate, 58, 102165. https://doi.org/10.1016/j.uclim.2024.102165
How to cite: Rodríguez-Rumayor, C., Casanueva, A., Quintana, Y., Diez-Sierra, J., and Bedia, J.: Defining the Urban Heat Stress Island: A novel characterization of human discomfort for urban environments, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21121, https://doi.org/10.5194/egusphere-egu26-21121, 2026.
This research investigates the occurrence of meteorological hazards and evaluates associated risks in Ulaanbaatar between 1990 and 2025. Over the study period, 564 hazardous weather incidents were identified, with rain-induced events representing the largest share (33.9%), followed by episodes of strong winds (22.0%). Using observations from four local meteorological stations together with ERA5-Land reanalysis data, we analyzed the temporal and spatial patterns of heavy precipitation and produced a detailed precipitation hazard map. The findings reveal a clear spatial gradient: the western and northwestern districts of Ulaanbaatar are more prone to intense precipitation, whereas the eastern and southern areas experience comparatively fewer such events. The outcomes of this study offer valuable insights for improving disaster preparedness, strengthening urban resilience, and informing future development and adaptation strategies in Mongolia’s capital city.
How to cite: Nyamjantsan, B. and Dashkhuu, D.: Risk Assessment of Extreme Precipitation in Ulaanbaatar, Mongolia, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-898, https://doi.org/10.5194/egusphere-egu26-898, 2026.
We are developing a model-to-model temperature downscaler for urban climate applications, the goal is extracting information in cities at hectometric scales. For this purpose, we use the Met Office global ensemble model MOGREPS-G (20 km resolution) with an intermediate grid length nest of 2.2 km over France, as the low-resolution model. This model has been dynamically downscaled to 300 m over the Paris area. A dynamical downscaler is excessive computational expensive for 300 m climate downscaling, for this reason we decided to develop a ML model using available weather data. The dataset was created for the Paris 2024 Olympics Research Demonstration Project (RDP).
The machine learning (ML) model developed in this project, will try to emulate the 300m dynamical downscaler by using low-resolution (2.2km) data as predictors and the 300m high-resolution downscaled data as target. We started with a limited set of hourly data, covering the period from 17th July 2024 to 10th September 2024. We are using all the data (80% of data was used for training and 20% for testing). We explored two different approaches: sequential (training from 17th July to 30th of August, the rest of the days for testing) and random which uses a random splitting. We are evaluating different models on the same dataset, using various predictors. The predictors include model variables from the low-resolution model reconfigured at 300m as well as fixed values used in the model, which influence temperature (such has surface altitude and urban fraction). Early results indicate that increasing the number of predictors does not significantly improve the ML model’s performance. Additionally, using random days for training and testing the model is necessary to provide a more statistically robust basis for the method.
The best ‘Paris trained ML model’ (optimum configuration in regard to ML approach and predictors), is being tested over the UK, using UKCP18-local climate predictions, to evaluate spatial transferability to cities not included in the training. We will present results on the ability of the approach to spatially transfer to cities not included in the training data set.
How to cite: Milan, M., Dawson, M., Athanassiadou, M., and Blunn, L.: Model-to-model Machine Learning downscaler for urban scales, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3773, https://doi.org/10.5194/egusphere-egu26-3773, 2026.
Urban vegetation helps reduce heat stress through evapotranspiration (ET) and shading. However, we still do not fully understand how atmospheric energy demand and soil water availability influence ET in an urban environment. Specifically, it is unclear when urban ET shifts from being limited by energy to being limited by water. In this study, we examine this transition at an urban tree-dominated site in Berlin, Germany. We use eddy-covariance (EC) flux measurements combined with soil moisture observations at various depths.
We analyze two years (2019-2020) of EC data, which includes latent heat flux, net radiation, and weather variables, as well as soil moisture observations at six depths ranging from 5 cm to 1 m. After applying quality control, turbulence filtering, and selecting condensation-free daytime data, we aggreage the ET data to daily daytime averages. We then relate ET to net radiation, vapor pressure deficit (VPD), and soil moisture. We represent near‑surface soil moisture as the average of the 5-20 cm layers, which reflects the most dynamically active portion of the root zone under the constrained soil conditions typical of this urban environment.
We hypothesize that ET is mainly energy-limited when the soil is wet. However, as the topsoil dries, it becomes water-limited, even when the atmospheric demand is high. Our hypothesis is supported by several exploratory analyses. Scatter plots showing ET against net radiation, classified by soil moisture levels (wet, medium, dry), reveal three consistent trends: (A) in wet conditions, ET rises sharply with radiation, showing an energy-limited state; (B) in dry conditions, the ET-radiation relationship weakens and levels off, indicating water limitation despite high radiation; and (C) in intermediate soil moisture conditions, we see a transitional response.
Linear regression models demonstrate that the slope of the ET-radiation relationship significantly declines from wet to dry soil states. Adding VPD enhances the performance of the linear regression model (R² ≈ 0.75), highlighting the influence of atmospheric demand. Meanwhile, the interaction terms between soil moisture and radiation remain significantly important. A linear mixed-effects model, which includes year as a random factor, produces similar results, indicating that these patterns hold steady across different years.
Segmented regression of ET against topsoil moisture identified a statistically significant breakpoint at approximately 8-9% volumetric soil moisture, marking a transition from water-limited conditions at low soil moisture to weak ET sensitivity at higher soil moisture. Below this threshold, ET responds strongly to changes in soil moisture, indicating a water-limited regime. Above the threshold, ET shows little additional sensitivity to soil moisture and is predominantly controlled by energy availability.
In conclusion, our results provide clear quantitative evidence that urban evapotranspiration alternates between energy-limited and water-limited regimes, with shallow soil moisture exerting a dominant control during dry periods. These findings highlight the vulnerability of urban vegetation to soil drying and have important implications for urban climate adaptation, green infrastructure management, and land-atmosphere modeling under increasing drought frequency.
How to cite: Aljoumani, B., Durate Rocha, A., Vulova, S., Meier, F., Dechant, B., Saidi, S., and Wallis, C.: When is evapotranspiration at an urban tree site energy-limited versus water-limited? Evidence from eddy-covariance and soil moisture measurements in Berlin, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4480, https://doi.org/10.5194/egusphere-egu26-4480, 2026.
This study investigates the impact of changes in the radiative properties of building façades and ground surfaces on the urban thermal environment in a high-density urban district characterized by diverse building heights and surface covers. To this end, surface temperatures were calculated using CitySim Pro—which simulates detailed thermal properties and inter-building radiative exchange—and were subsequently integrated as boundary conditions for a high-resolution CFD model. After validating the CitySim Pro–CFD coupling method against in situ observation data in a control experiment (CNTL), sensitivity experiments were conducted across various scenarios involving the application of cool coatings to building façades and ground surfaces.
The results indicate that while increased reflectance from cool coatings led to reduced daytime surface temperatures, the cooling intensity was spatially non-uniform due to multiple reflections and radiation trapping between building façades and ground surfaces. Under certain conditions, this was accompanied by localized nighttime warming. Applying cool coatings solely to ground surfaces resulted in a moderate decrease in average air temperature at pedestrian height during both day and night, suggesting the potential for mitigating heatwaves and tropical nights. In contrast, applications to building façades only, or to both building façades and ground surfaces, led to localized nighttime air-temperature increases due to enhanced longwave radiative coupling; however, a daytime air-temperature reduction of up to approximately 2.0 °C was confirmed.
In terms of outdoor thermal comfort accounting for solar radiation, ground-surface-only application maintained Universal Thermal Climate Index (UTCI) levels similar to the control while reducing air temperatures. Conversely, façade application showed a distinct trend of increasing daytime UTCI due to increased exposure to reflected shortwave radiation. These findings imply that air-temperature reduction does not always directly translate into improved thermal comfort and that the effectiveness of cool coatings can vary spatiotemporally due to multiple reflections and radiation trapping. Therefore, effective application strategies must be optimized according to site usage and pedestrian exposure, balancing the benefits of surface cooling against the potential negative impacts of increased radiative loads.
This work was funded by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (RS-2024-00341302).
How to cite: Mun, D., Kaempf, J. H., and Kim, J.: Impacts of Surface Radiative Properties on the Urban Microclimate and Outdoor Thermal Comfort in a High-Density Urban Area, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4601, https://doi.org/10.5194/egusphere-egu26-4601, 2026.
Against the backdrop of unprecedented rapid urbanization and the continuous implementation of clean air actions in China, how sunshine duration (SSD) has changed and the extent to which it has been affected remain insufficiently understood. Based on homogenized daily SSD from 2,364 meteorological stations across China (1993-2023), this study accounts for station relocations and evaluates the urbanization effects (UE) and contribution (UC) using a dynamic urban-rural classification derived from harmonized nighttime-light-based urban extents. This study finds that SSD decreased nationwide during 1993-2013, with more pronounced declines in highly urbanized regions; first-tier and new first-tier cities exhibited positive UE because rural SSD declined more rapidly, whereas second-tier and third-tier cities showed negative UE. Following the implementation of the two phases of clean air actions during 2014-2023, a widespread national brightening emerged, and SSD recovered more rapidly in rural areas than in urban ones, while negative UE were observed across all city tiers except third-tier cities. The substantial decreases in total cloud cover (TCC), PM2.5 and PM10 effectively explain the nationwide SSD recovery after 2014, highlighting the crucial role of clean air policies in promoting China’s brightening trend.
How to cite: Zhou, J., He, Y., Zhang, M., Song, X., and Zhou, Y.: Impacts of Urbanization on Sunshine Duration across China: The Role of Clean Air Policies, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5149, https://doi.org/10.5194/egusphere-egu26-5149, 2026.
This study develops and evaluates an integrated, fully cloud-based workflow implemented in Google Earth Engine (GEE) to generate high-resolution LST products for urban climate applications. The approach combines spatio-temporal satellite data fusion with machine-learning-based thermal pansharpening to overcome the trade-off between spatial and temporal resolution inherent in current thermal infrared observations. The workflow targets the production of near-daily LST at Landsat-like resolution, subsequently refined to 10 m to enable microclimate analysis.
The study area is the Iasi metropolitan region, northeastern Romania, a medium-sized Eastern European city characterized by heterogeneous urban fabric, variable vegetation cover, and moderate topographic relief. Input datasets include MODIS MYD11A2 8-day LST composites (1 km), Sentinel-2 MSI multispectral imagery (10 m), and SRTM GL1 elevation data (30 m). The temporal coverage spans 2014–2024 for fusion development, with a focus on the summer season (June–August).
Spatio-temporal fusion is applied to reconcile the high temporal density of MODIS thermal observations with the finer spatial detail of Landsat, generating temporally continuous downscaled land surface temperature (DLST) fields at Landsat spatial scale. These methods exploit coincident MODIS and Landsat observations to predict Landsat-like LST on non-overpass days.
To further capture fine-scale thermal variability relevant for intra-urban climate analysis, the DLST products are subsequently sharpened to 10 m using supervised machine-learning regression models, leveraging the high spatial resolution and spectral richness of Sentinel-2 to better represent vegetation structure, surface moisture, and built-up heterogeneity. The evaluation focuses exclusively on Ridge Linear Regression, Random Forest, and Gradient Boosting, which are trained to model the relationship between LST and surface characteristics. Predictor variables are derived from Sentinel-2 multispectral reflectance and spectral indices (NDVI, NDWI, NDMI, NDBI, Urban Index, bare soil index), supplemented by terrain parameters (elevation, slope, and aspect). Model training is conducted using randomly sampled pixels within the study area, applying a 70/30 hold-out split and simulated 10-fold cross-validation. Model performance is quantified using mean absolute error (MAE), root mean square error (RMSE), and the coefficient of determination (R²).
Results indicate that ensemble tree-based models outperform the linear baseline, achieving RMSE values close to 1.2 °C and R² around 0.82, consistent with recent studies. Variable importance analysis highlights vegetation and moisture indices as dominant negative controls on LST, reflecting evapotranspirative cooling, while built-up and bare soil indicators exert positive effects associated with heat storage and reduced latent heat flux. Topographic influence is secondary in the relatively gentle relief of the Iasi basin. The resulting 10 m LST products enable detailed mapping of SUHI intensity and fine-scale thermal gradients across urban neighborhoods.
Acknowledgement. This work was supported by a grant of the Ministry of Research, Innovation and Digitization, CNCS-UEFISCDI, project number PN-IV-P1-PCE-2023-0897, within PNCDI IV.
How to cite: Corocăescu, A.-C., Sfîcă, L., Ichim, P., Brenning, A., Crețu, Ș.-C., Grozavu, A., and Croitoru, I.-M.: High-Resolution Land Surface Temperature Mapping for Urban Climate Applications Using Satellite Fusion and Machine Learning in Google Earth Engine, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5920, https://doi.org/10.5194/egusphere-egu26-5920, 2026.
Over the past decade, artificial intelligence (AI) and machine learning (ML) have rapidly advanced, becoming powerful tools in weather forecasting and climate research. At present, there is an increasing interest in integrating ML and recent AI developments into urban climate studies. This fast-evolving field encompasses a wide range of research approaches. Models with varying levels of complexity have been developed to make, for example, frequent predictions at point locations or high-resolution simulations at the scale of a neighborhood, city, or many cities for one or multiple (bio)meteorological variables. AI- or ML-based approaches are also used to create scenarios that examine the impacts of urban greenery or different global warming levels on urban climate, supporting the identification and design of future outdoor cool spaces or to assess individuals’ thermal comfort. In addition, ML techniques are applied in diverse ways to generate boundary conditions for micro-scale models.
The emergence of this very broad and dynamic research field comes with new challenges. It is, for example, unclear what is contained under “AI/ML for urban climate” as there are a multitude of approaches, spatial-temporal scales, applications and datasets being used. This makes it unclear in what direction the field can and should evolve and what the priorities are. Moreover, as scientists meet in an urban climate or AI context, there is a lack of a common research network leading to a high chance of duplicate research efforts. Hence, by setting up the urban climate-AI working group AI4UrbanClimate, we aim to bring people together with similar research interests to define a common understanding of AI/ML for urban climate applications by reviewing existing research. International collaboration within the AI4UrbanClimate initiative will make it possible to identify persistent challenges, gaps and priorities to advance this research field in a coordinated way to improve and accelerate ongoing research in this field.
As a first activity we try to get an overview of ongoing work in AI/ML for urban climate. Only then it is possible to create the highly needed benchmarks that are suited as raised within the AI4UrbanClimate working group. Outcomes of a questionnaire and the kick-off meeting will be presented and you will learn about the opportunity on how to join and collaborate in this novel AI4UrbanClimate community.
How to cite: Top, S., Pierce, C., Kittner, J., Wolf, L., and Bechtel, B.: The AI4UrbanClimate working group initiative, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7256, https://doi.org/10.5194/egusphere-egu26-7256, 2026.
Urban warming differs markedly from regional background trends, highlighting the unique behavior of urban climates and the challenges they present. Accurately predicting local urban climate necessitates modeling the interactions between urban surfaces and atmospheric forcing. Although off-the-shelf machine learning (ML) algorithms offer considerable accuracy for climate prediction, they often function as black boxes, learning data mappings rather than capturing physical evolution. As a result, they struggle to capture key land-atmosphere interactions and may produce physically inconsistent predictions. To address these limitations, we propose UCformer, a novel multi-task, physics-guided Transformer architecture designed to emulate nonlinear urban climate processes. UCformer jointly estimates 2-m air temperature , specific humidity , and dew point temperature in urban areas, while embedding domain and physical priors into its learning structure. Experimental results demonstrate that incorporating domain and physical knowledge leads to significant improvements in emulation accuracy and generalizability under future urban climate scenarios. Further analysis reveals that learning shared correlations across cities enables the model to capture transferable urban surface–atmosphere interaction patterns, resulting in improved accuracy in urban climate emulation. Finally, UCformer shows strong potential to fit real-world data: when fine-tuned with limited observational data, it achieves competitive performance in estimating urban heat fluxes compared to a physics-based model.
How to cite: Xia, J., Ling, F., Li, Z., Yu, J., Zhang, H., Topping, D., Bai, L., and Zheng, Z.: Learning Urban Climate Dynamics via Physics-Guided Urban Surface–Atmosphere Interactions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10179, https://doi.org/10.5194/egusphere-egu26-10179, 2026.
Cities are known to alter their surrounding atmosphere leading to a distinct urban canopy. The effect of cities on temperature is well understood and can be modeled with different methodologies. However, the urban form also has an impact on windspeed and direction, which are important variables for the ventilation of cities and are also needed to calculate thermal comfort indices. Thus, also the modeling of urban winds is of interest for urban planning and city administrations.
To date, wind is typically modeled through computationally intensive fluid dynamic simulations, requiring prohibitively many CPU hours to model winds over a whole city for longer periods. In this study, we compare two fast approaches to model urban windspeeds at 10m height. The first method is a static morphometric approach where we scale down ERA5 100m windspeeds, taking into account logarithmic, intermediary and canopy wind velocity profiles. The second approach is data-driven using XGBoost and in-situ observations in order to leverage the relationships between coarse ERA5 meteorological drivers and urban features such as buildings and trees. The approaches are developed to be applied to any city in Europe and are tested against wind measurements in select European cities. Their advantage lies in their fast computational times, modeling windspeeds for a whole city at hourly resolution for a year within minutes. However, despite no complex urban characteristics being captured or resolved, the models may still inform policy makers and urban planners on mean windspeeds and their effects on perceived temperature in different neighborhoods.
How to cite: Pierce, C., Burger, M., and Brönnimann, S.: Urban windspeed modeling: from physical to data driven, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10946, https://doi.org/10.5194/egusphere-egu26-10946, 2026.
Adaptation to climate change in urban areas is increasingly based on integrated models that combine high-resolution geospatial data, urban morphology and physically based climate simulations. However, in practice, the application of advanced urban climate models is often limited by complex user interfaces and workflows, incomplete data structures and high computational requirements.
As part of the EU-funded project ‘SMART-TWIN – AI-supported planning tool for climate-friendly green urban development in Bavaria’ the existing digital twin for the City of Würzburg (Germany) is further developed to fill the gap between urban climate science and operational planning practice. The digital twin acts as a central platform, integrating geodata sets such as 2D and 3D city models, land use, building characteristics, green and blue infrastructure, and environmental observations, and presenting them to the user in a visual and interactive way.
The innovation of SMART-TWIN is the close coupling of the digital twin with the high-resolution urban climate model PALM-4U. This model provides a spatially explicit scenario parametrisation layer that allows local authorities and planning offices to create real or hypothetical planning measures such as new buildings, demolition, unsealing or greening strategies directly within the digital twin. The base geospatial data of the digital twin as well as the modified scenario inputs are automatically harmonised, pre-processed and transferred to PALM-4U via standardised data pipelines. Simulation results such as air temperature, wind fields or heat stress metrics are post-processed automatically and transferred back to the digital twin where these thematic layers are visualised. This enables a comparison between alternative planning scenarios and between different current or feature climate scenarios.
For the initial test runs, five public locations within Würzburg were selected, for which modelling was simulated under typical climatic conditions at a resolution of 1 m. Subsequently, individual changes, such as the creation of additional green spaces, were simulated. The automatic data processing worked to a high degree, and the results of the urban climate model also show promising results regarding the effects of infrastructure modification.
How to cite: Roy, E., Schaefer, F., and Paeth, H.: SMART-TWIN: Transforming urban climate science into planning practice using a geospatial digital twin, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11362, https://doi.org/10.5194/egusphere-egu26-11362, 2026.
This work presents the EU-funded project “SMART-TWIN - AI-supported planning tool for a climate-adapted green urban development in Bavaria”. Climate resilience in urban planning is becoming increasingly important, especially regarding the adaption to extreme weather events and mitigation of associated risks. For a more sustainable and efficient urban and land-use planning, the SMART-TWIN project develops an AI-supported digital planning tool for the city of Würzburg. Main objective of the project is the extension of the existing digital twin by integrating the urban climate model PALM-4U. This tool enables local authorities and planning offices to simulate different scenarios for real or potential construction projects as well as modifications to green and blue infrastructure, and to assess their impacts on the urban climate, under both current and prospective (extreme) weather conditions. This way, planning processes can be carried out with greater precision, speed, and cost-efficiency. Würzburg offers particularly suitable conditions for this project: its warm and dry climate, dense building structures, and low proportion of green spaces make the city a climate change hotspot in Central Europe.
As a practical contribution to the planning tool, we provide our own simulations for typical climate and urban planning scenarios for five public areas in Würzburg at a spatial resolution of 1 m: For this purpose, we implement modifications of the urban infrastructure by creating real or potential building projects, such as the unsealing of parking areas, tree plantings on town squares or along major roads, changes in surface albedo, or the conversion of sealed areas into inner-city parks. In addition, a selection of characteristic (extreme) weather conditions is considered for each planning scenario, including a heat day, a tropical night or a day with intense solar radiation. The climate impact on the entire city area is represented at a spatial resolution of 10 m.
The simulations are driven by boundary conditions from the COSMO-CLM 5.10 Model with hourly resolution for the period January to December 2019. The preliminary PALM-4U configuration and domain setup promise comprehensive results for the early identification of urban hotspots and urban heat island patterns and the positive effect of shading and urban vegetation.
How to cite: Schaefer, F., Roy, E., and Paeth, H.: SMART-TWIN: Setup and Application of the Urban Climate Model PALM-4U for the City of Würzburg, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11376, https://doi.org/10.5194/egusphere-egu26-11376, 2026.
Understanding and simulating urban climate processes, as well as how climate change affects cities is crucial for designing effective mitigation and adaptation strategies and policies. However, producing climate projections at the city scale requires very high-resolution physically-based models, which are computationally demanding and time-consuming to run. Deep Learning (DL) downscaling and offline simulations of land surface models offer cost- and time-effective alternatives.
Here, we present a comparison between DL-based downscaling and offline simulations performed using the SURFEX land surface model for the city of Paris, France. Two lightweight 3-layer Convolutional Neural Network (CNN) architectures are trained to downscale ECMWF ERA5 reanalysis for the 2004-2012 period. The CNNs generate hourly predictions of 2-meter temperature (T2m) at point-level (using data from 24 in-situ observational stations) and Land Surface Temperature (LST) at a spatial resolution of ~5 km, respectively. The SURFEX land surface model (versions 8.1 and 9.0) is run at two different spatial resolutions (5 km and 1 km) for the 2013-2022 period. DL and SURFEX output are compared in terms of their representation of T2m, LST, and their respective urban heat island (UHI), surface urban heat island (SUHI) and extremes, in present climate (2013-2022). DL-based downscaling presents improved performance metrics in relation to SURFEX. DL also presents a diurnal cycle closer to the observations. Both DL-downscaling and SURFEX replicate the Parisian UHI effect, described in previous studies and in the observational data used to train the CNNs. This work supports the use of DL-based downscaling for urban climate studies as a viable alternative to more computationally and time-heavy approaches.
Acknowledgements: This work is supported by FCT, I.P./MCTES through national funds (PIDDAC): LA/P/0068/2020 - https://doi.org/10.54499/LA/P/0068/2020, UID/50019/2025, https://doi.org/10.54499/UID/PRR/50019/2025, UID/PRR2/50019/2025. The authors would like also to acknowledge the project “Elaboração do Plano Municipal de Ação Climática de Barcelos" (PMACB).
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.: Comparing Deep Learning-based downscaling and the SURFEX land surface model on representing temperature extremes and the urban heat island in Paris, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11400, https://doi.org/10.5194/egusphere-egu26-11400, 2026.
Urban areas concentrate a significant (and growing) fraction of the global population and economic activity, making them particularly vulnerable to the evolving climate change risks. While Earth System Global Climate Models (ESGCMs) provide essential long-term climate projections, their coarse spatial resolution and lack of urban parameterizations often fail to capture the complex physical processes within the cities. Therefore, downscaling urban climate characterization under future climate projections is essential for providing high-resolution data, necessary for developing effective mitigation and adaptation strategies, which are ultimately paramount for safeguarding societal well-being and urban resilience.
Within this framework, Artificial Intelligence methodologies – an evolving branch of statistical downscaling methods – offer an alternative approach to traditional ESGCMs for characterizing climate at the urban scale. In this study, Deep Learning (DL) is applied to generate high-resolution (0.05º) present and future urban climate projections for the megacity of São Paulo (SP), Brazil. Firstly, sensitivity cases were performed to evaluate the performance of convolutional neural network (CNN) architectures in predicting near-surface air temperature (daily maximum and minimum, T2max and T2min, respectively) and Land Surface Temperature (LST), using observational datasets as local predictands and ERA5 reanalysis as large-scale predictors. Secondly, a multi-model ensemble of CNN-based downscaled projections was developed to project T2max, T2min, and LST, along with their associated Urban Heat Island phenomena (UHI and SUHI, respectively), throughout the 21st century. These projections were developed for four Shared Socioeconomic Pathway (SSP) scenarios at a daily scale. The resulting DL-downscaled projections demonstrate overall agreement with the CMIP6 ESGCM ensemble in the magnitude for the projected temperatures
This work is supported by FCT, I.P./MCTES through national funds (PIDDAC): LA/P/0068/2020 - https://doi.org/10.54499/LA/P/0068/2020, UID/50019/2025, https://doi.org /10.54499/UID/PRR/50019/2025, UID/PRR2/50019/2025. The authors would like also to acknowledge the project “Elaboração do Plano Municipal de Ação Climática de Barcelos (PMACB). A.B. also acknowledge individual funding from FCT, I.P./MCTES grant UI/BD/01324/2024
How to cite: Bushenkova, A., M. M. Soares, P., M. Trigo, R., Libonati, R., M. Cardoso, R., and Johannsen, F.: Deep Learning-Driven Downscaling for the Urban Climate of the Megacity of São Paulo at High Resolution, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12414, https://doi.org/10.5194/egusphere-egu26-12414, 2026.
The average air temperature in urban areas is usually elevated relative to the surrounding rural areas, commonly referred to as the Urban Heat Island (UHI) effect, leading to increased heat exposure and associated health risks. Under ongoing climate change and with a large and increasing share of the global population living in cities, the study of urban temperature is increasingly important as it provides a basis to study associated health risks and urban adaptation strategies.
Hectometre-scale heat risk information is crucial to implement effective measures to protect vulnerable groups in exposed neighbourhoods. Physics-based urban climate models provide this information, but their substantial computational costs limit their spatiotemporal coverage and practical usability. By leveraging deep learning, we provide rapid surrogates that deliver high-resolution temperature information at low computational cost.
We present EU-HEAT (European Urban High-resolution Emulator for Air Temperature), a machine-learning emulator of the UrbClim model to enable rapid inference of 2-m air temperature (t2m) fields at 100 m resolution across European cities. UrbClim is an urban-slab model that downscales t2m using large-scale meteorological forcing and surface representation data [1]. EU-HEAT v1 is a U-Net model trained on a dataset covering 100 European cities, generated with UrbClim [2]. Since we focus on emulating UrbClim, input features are selected to closely mirror the drivers of the physics-based model.
We will present a qualitative and quantitative validation of EU-HEAT v1 for representative hold-out cities. These results will be compared with scores obtained from the European Random Forest Urban Climate Emulator (Eu-RaFUCE), which was trained on the same UrbClim dataset [3].
References
[1] De Ridder, K., Lauwaet, D. and Maiheu, B. (2015), ‘UrbClim – A fast urban boundary layer climate model’, Urban Climate, Vol. 12, pp. 21–48, https://doi.org/10.1016/j.uclim.2015.01.001.
[2] Lauwaet, D., Berckmans, J., Hooyberghs, H., Wouters, H., Driesen, G. et al. (2024), ‘High resolution modelling of the urban heat island of 100 European cities’, Urban Climate, Vol. 54, p. 101850, https://doi.org/10.1016/j.uclim.2024.101850.
[3] Top, S., Blancke, J., Covaci, A., Caluwaerts, S., Hamdi, R. et al. (2025), ‘Emulation of a numerical urban model to create high-resolution near surface air temperature over European cities.’, ESS Open Archive, https://doi.org/10.22541/essoar.173671231.11835703/v1.
How to cite: Speelman, S., Covaci, A., Wang, Y., De Kock, S., Top, S., and De Cruz, L.: A 2-m temperature deep learning emulator of the UrbClim model for European cities, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13797, https://doi.org/10.5194/egusphere-egu26-13797, 2026.
Urban heat is a rapidly intensifying risk for European cities, affecting public health, energy demand and the quality of urban life. This contribution demonstrates how urban hydrometeorological observations can be transformed into actionable climate services and adaptation strategies using three European cities with different local climates and urban forms: Amsterdam (Netherlands), Ghent (Belgium) and Novi Sad (Serbia). Each city operates an urban meteorological network (UMN) that provides high-resolution observations across Local Climate Zones (LCZs), enabling detailed identification of heat-risk patterns within the urban fabric. Multi-year observations show that compact, densely built mid-rise districts (LCZ 2) consistently experience the strongest nocturnal urban heat island (UHI) effects, while areas with dense vegetation and water bodies remain significantly cooler. Average summer UHI intensities reach about 1 °C in Amsterdam and Ghent and 2 °C in Novi Sad, while extreme heat-wave conditions produce hourly UHI values exceeding 6–9 °C.
These spatially explicit data reveal where heat stress is most severe and where cooling potential is greatest, providing a scientific basis for targeted interventions. The observational evidence has directly supported urban climate action. In Amsterdam, UMN data underpin heat-stress maps and “find-your-cool” tools for citizens, as well as the deployment of large-scale blue-green roofs. In Ghent, monitoring and modelling guide the design of green corridors, neighborhood parks and façade gardens to enhance cooling and ventilation. In Novi Sad, fixed and mobile measurements have steered the installation of green roofs and vertical greening on public buildings in the most heat-exposed districts. Together, these case studies show how urban hydrometeorological and climate observations, combined with LCZ analysis and stakeholder engagement, enable cities to move from heat diagnostics to evidence-based, locally tailored climate adaptation.
Acknowledgements. DM and GJS acknowledge support from the 4TU-program HERITAGE (HEat Robustness In relation To AGEing cities), funded by the High Tech for a Sustainable Future (HTSF) program of 4TU, the federation of the four technical universities in the Netherlands.
How to cite: Milošević, D., Steeneveld, G.-J., Savić, S., and Caluwaerts, S.: From Urban Hydrometeorological Observations to Action Across Three European Cities, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14329, https://doi.org/10.5194/egusphere-egu26-14329, 2026.
Anthropogenic climate change is amplifying heat extremes worldwide. Urban areas are particularly vulnerable to prolonged warm minimum air temperatures (Tmin) due to the Urban Heat Island (UHI) effect, where cities' Tmin exceed those of surrounding rural areas as a result of altered surface properties, urban geometry and anthropogenic heat emissions.
To understand and mitigate the UHI of a particular city, high-resolution, accurate urban air temperature (Tair) data is needed. However, automated high-quality measurement stations in urban environments are both scarce and expensive. To address this limitation, this study uses high-quality, street-level air temperature observations from the open-access FAIRUrbTemp dataset, which harmonized and quality-controlled measurements from 12 urban monitoring networks across Europe. Based on this data, we are using a Land Use Regression (LUR) modelling approach to model the Tair at high spatial resolution (50 m) in the studied areas.
LUR modelling works particularly well for UHI studies because it directly relates measured Tair to key features of the urban environment, such as land cover, vegetation, surface sealing, anthropogenic heat and urban geometry. This allows the model to capture fine-scale spatial temperature variability and to reliably extrapolate Tair patterns to areas without measurement stations. To ensure the broad applicability of our approach, we combined these standardized temperature observations with open-access geospatial and meteorological predictors. These include land-use and urban morphology variables, vegetation and surface sealing indicators derived from Copernicus datasets, as well as atmospheric and meteorological data from ERA5-Land, and ERA5 pressure-level products. This consistent data framework enables the application of the model across diverse cities.
A key objective of this study is to assess the transferability of LUR models across cities with varying climates, urban forms, and monitoring densities. By evaluating model performance across multiple European urban environments, we can identify robust predictors of UHI intensity and assess the conditions and coefficients under which models can be transferred between cities. The resulting high-resolution temperature maps will help identifying vulnerable populations and priority areas for intervention by illustrating intra-urban heat patterns and hotspots. Additionally, it can serve as a good foundation for further climate adaptation studies.
How to cite: Amini, S., Top, S., Burger, M., and Brönnimann, S.: Transferable Land Use Regression Models for Urban Heat Island Assessment Using Street-Level Air Temperature Data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14886, https://doi.org/10.5194/egusphere-egu26-14886, 2026.
Reliable early warning systems for heat hazards require not only high-resolution meteorological modelling, but also robust, scalable and accurate in-situ observations. However, existing Wet Bulb Globe Temperature (WBGT) sensors are poorly suited for dense spatial monitoring and operational deployment: they often rely on maintenance-intensive wet-bulb thermometers, lack integrated geolocation, are sensitive to sun radiation, and are difficult to deploy consistently by non-expert users in particular in urban environments.
To address these limitations, VITO has developed a new generation of heat-stress sensors designed for both stand-alone field campaigns and large-scale sensor networks. The algorithm eliminates traditional wet-bulb hardware by computing wet-bulb temperature psychrometrically from shielded air temperature, humidity measurements, black globe and wind, integrates automatic GPS-based geolocation and applies quality control using dedicated light sensors. A modular hardware architecture allows deployment either as an autonomous SD-logging device or as a real-time LoRa-connected network node, enabling synchronised, spatially distributed WBGT monitoring across urban and occupational environments.
In addition, VITO developed a dynamic heat-stress sensor, HEATCAP, which enables instantaneous heat-stress measurements. Unlike the traditional black-globe method—which requires 10–15 minutes to stabilise—the HEATCAP sensor replaces the globe thermometer with shortwave and longwave radiation sensors that capture radiative fluxes from all directions. This design allows immediate assessment of heat stress under rapidly changing conditions, such as during commuting, intermittent cloud cover, or movement between sun and shade.
Together, these sensor technologies enable high-resolution and operationally robust heat-stress observations that directly complement high-resolution urban climate models. They support both mobile and permanent long-term monitoring applications, ranging from urban exposure assessments and early warning systems to indoor and outdoor occupational settings, such as construction workers and delivery drivers, including environments where heat exposure is amplified by industrial processes (e.g. furnaces in the steel industry). The resulting dense point measurements enable the identification of true heat-stress hotspots, support assessments of vulnerable population groups and animal wellbeing, and provide a potential direct bridge to high-resolution modelling, where sensor observations can be interpolated and assimilated into area-wide heat-stress maps for near-real-time warning and long-term planning.
How to cite: Takacs, S., De Ridder, K., Souverijns, N., Hellebosch, I., Lauwaet, D., Lefebre, F., Wouters, H., Veldeman, N., Broeckx, J., Pereira, F., Theunis, J., Lanssens, B., Hosseinzadehtalaei, P., and Theunissen, R.: Sensors for climate services, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18614, https://doi.org/10.5194/egusphere-egu26-18614, 2026.
Climate change with increasing air temperatures results in amplified hazards for human health by excessive heat. Compared to their non-urban surroundings, cities typically show elevated air temperatures, causing urban dwellers to be even more threatened by heat in warmer conditions. Up until now, studies could not conclusively clarify how climate change and urban effects on air temperature interact with each other over time scales covering decades since multi-decadal measurements from urban climate observation networks are generally scarce. Here, we present robust air-temperature trends for the Climate Normal 1991-2020 using quality-controlled data from eleven urban and 14 non-urban weather stations in the Berlin region, Germany, covering a wide range of urban and non-urban settings. We analyse trends for four daily variables as annual and seasonal means, as well as during heatwaves. Our findings highlight that climate change and the city interact linearly on the analysed time scales. This results in similar air-temperature trends in urban and non-urban areas, yet at different absolute levels. An exception is the daily minimum air temperature in spring, which shows different trends for urban and non-urban stations. Investigation of the built-up area around the stations and in the study region shows no significant change during the study period. This highlights that the observed warming is due to regional climate change and not related to urbanisation processes. By comparing trends for the last 30 years (1991-2020) with observational data since the end of the 19th century, we show that the recent rise in air temperature is unprecedented in the study region, indicating accelerated regional climate change. Our study, a first presenting 30 years of data from an urban climate observation network, offers a blueprint for investigating climate change in other cities with sufficient data.
How to cite: Fenner, D., Meier, F., Holtmann, A., Otto, M., and Scherer, D.: Unprecedented warming over the past 30 years in Berlin, Germany, unaffected by urban effects, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18723, https://doi.org/10.5194/egusphere-egu26-18723, 2026.
Recent heat extremes have highlighted both existing and emerging vulnerabilities in European cities, underscoring the need for robust climate information to support future urban adaptation. These risks are projected to intensify under climate change, particularly in densely populated urban environments. Accurately representing urban surface and atmosphere interactions therefore remains a key challenge for regional climate modelling at city scales. Convection-permitting regional climate models (CPRCMs) resolving climate processes at the kilometer-scale provide new opportunities to simulate urban areas and their local climate processes through urban parameterizations.
We investigate how urban representation modifies present-day and future urban climates in CPRCM simulations over Europe. We use the regional climate model REMO2020 at 3 km horizontal resolution, newly coupled with the Town Energy Balance (TEB) urban canopy model. Historical and future climate simulations are conducted for the Horizon Europe project Impetus4Change over two European domains covering cities across contrasting continental and maritime climates, including southern and northern European urban environments.
The analysis focuses on urban heat extremes and their diurnal characteristics, including urban-rural temperature contrasts and the persistence of elevated night-time temperatures. The added value of the urban canopy model TEB is assessed compared to a bulk approach and coarser-resolution simulations in REMO. By comparing present-day conditions with end-of-century climate projections, we assess how urban heat extremes and urban-rural temperature patterns evolve with warming and whether urban effects scale with increasing background temperatures.
How to cite: Freisen, P., Teichmann, C., Pietikäinen, J.-P., and Buntemeyer, L.: Urban heat extremes in REMO2020-TEB convection-permitting regional climate simulations over Europe, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20329, https://doi.org/10.5194/egusphere-egu26-20329, 2026.
Urban climate conditions in lakeside cities are shaped by the interaction between the urban morphology and atmospheric processes modified by the lake. It is essential to understand how and where the urban effect intensifies in order to develop effective adaptation strategies under increasingly frequent and intense extreme climate events. In this study, the city of Konstanz, located on Lake Constance, is used as a case study to examine urban-induced climate responses during extreme conditions.
To assess urban climate impacts, high-resolution (1 km) simulations were conducted using the Weather Research and Forecasting (WRF) model with Building Effect Parametrization and Building Energy Model (BEP/BEM) integration. We simulated multiple extreme weather events, including heatwave and heavy rainfall events. Urban effects were quantified by contrasting current urban land use patterns with a hypothetical non-urban surface representation, allowing the evaluation of urban climate signals under lake-influenced conditions.
Results indicate that the urban thermal effect is particularly pronounced under nighttime conditions. Compared to non-urban conditions, urban areas exhibit enhanced nighttime warming, with surface skin temperatures rising by approximately 1–2.5°C during heatwaves. These patterns suggest that urban heat storage and release significantly contribute to the nighttime thermal conditions. Furthermore, this nighttime warming varies with lake proximity and land-use characteristics, indicating that these factors influence the spatial distribution of urban heat during extreme events.
Based on the findings, spatially targeted urban adaptation strategies are explored through the application of mitigation measures in areas experiencing persistent thermal stress. This study suggests that targeted approaches can effectively reduce local heat stress while limiting the extent of mitigation strategy application, emphasizing the potential for more strategic and efficient urban climate adaptation. This perspective provides useful context for climate-responsive urban planning approaches that give priority to impact-prone areas under increasing extreme events.
How to cite: Choi, S., Samakinwa, E., Rechid, D., and Brasseur, G.: Urban Heat Effects and Targeted Adaptation under Extreme Events in the Lakeside City: A Case Study of Konstanz, Germany , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20489, https://doi.org/10.5194/egusphere-egu26-20489, 2026.
Urban environments are hotspots of anthropogenic emissions, impact the warming rate over cities, and induce changes in several relevant meteorological variables such as wind speed, humidity and temperature, which in turn affect air quality and human health. Therefore, it is important to identify how urban parameterizations impact the regional-to-local scale processes in regional climate model simulations. To evaluate these impacts, we use the Weather Research and Forecasting (WRF) model with different urban schemes. The simulated period covers a timespan of 10 year and has an especial focus on the city of Prague (Czech Republic). To evaluate the results obtained, data from observations from the Czech Hydrometeorological Institute were used. Changes in temperature and specific humidity are mostly sensitive to the urban scheme selected, while changes in precipitation and cloud cover are less sensitive to the urban parameterization but more sensitive to the parameterization of convection and microphysics.
How to cite: Villalba-Pradas, A., Karlický, J., Huszár, P., Žák, M., and Halenka, T.: Long-term impact of urban areas on meteorological conditions on Prague urban climate, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22596, https://doi.org/10.5194/egusphere-egu26-22596, 2026.
Port cities are dynamic socio-ecological systems where rapid infrastructure development interacts with coastal processes and urban growth. Hai Phong Port, the largest seaport complex in northern Vietnam, has undergone substantial spatial expansion over the past two decades, driven by increasing trade demand and regional economic integration. However, comprehensive assessments of the spatio-temporal evolution of port infrastructure and its socio-economic implications remain limited.
This study applies multi-temporal remote sensing and Geographic Information Systems (GIS) to quantitatively analyze the spatial dynamics of Hai Phong Port and adjacent urban areas from 2000 to 2025. Landsat and Sentinel satellite imagery were processed using supervised classification and post-classification change detection techniques to derive land use and land cover (LULC) transitions, shoreline modifications, and port-related land reclamation. Spatial metrics were employed to characterize the magnitude, rate, and spatial configuration of port expansion.
To evaluate socio-economic implications, geospatial indicators extracted from remote sensing data were integrated with district-level statistics on population density, industrial activity, employment, and cargo throughput. Spatial correlation and trend analyses were conducted to examine linkages between port expansion and socio-economic development patterns. The results indicate a pronounced seaward and linear expansion of port infrastructure, accompanied by accelerated industrialization and urban growth in surrounding districts. While these transformations have contributed to regional economic growth and improved logistics connectivity, they have also intensified land-use conflicts and pressures on coastal ecosystems and local livelihoods.
This research demonstrates the value of integrating remote sensing and GIS for long-term monitoring of port city dynamics and their socio-economic impacts. The proposed framework provides a transferable methodological approach to support sustainable planning and policy development in rapidly growing port cities, particularly in coastal regions of Southeast Asia.
How to cite: Nguyen, K.-A., Liou, Y.-A., and Cham, D. D.: Spatio-Temporal Analysis of Port Expansion in Hai Phong, Vietnam, Using Remote Sensing and GIS and Its Socio-Economic Implications, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4180, https://doi.org/10.5194/egusphere-egu26-4180, 2026.
Climate change is rapidly intensifying the frequency and severity of urban heatwaves, presenting profound risks to urban resilience and public health. Urban heatwaves are inherently three-dimensional phenomena, shaped by complex interactions between building morphology, surface characteristics, and air exchange processes within the urban canyon. Conventional evaluation methods, which primarily rely on two-dimensional surface temperature data or isolated point-based measurements, often fail to capture the nuanced micro-scale temperature variations across diverse spatial dimensions and vertical profiles. This study introduces a three-dimensional, zone-based air temperature framework as an effective alternative for assessing heatwave conditions within urban environments.
The framework operates by first simplifying complex building geometries into structured, representative forms to optimize the simulation process. Based on these simplified building configurations, the urban exterior space is then partitioned into automated 3D zones. The model tracks temperature dynamics by calculating air exchange rates between these zones and convective heat transfer from urban surfaces, such as walls and rooftops. Furthermore, the framework allows for the simulation and visualization of various adaptation scenarios, such as the implementation of green walls. This allows users to virtually implement different technologies and observe how they might alter local temperature patterns across various heights and spatial locations. By offering a middle ground between simple 2D maps and complex simulations, this approach helps planners create better heat-reduction policies and improves our fundamental understanding of urban resilience for a sustainable future.
How to cite: Park, B. J. and Lee, D. K.: Assessing urban heatwave conditions using a three-dimensional zone-based air temperature framework, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15807, https://doi.org/10.5194/egusphere-egu26-15807, 2026.
Heat waves are becoming more intense and frequent due, in part, to land cover changes and urbanization that impacts the urban climate. Moreover, city morphology directly influences pollutant dispersion, resulting in major problems on residents’ comfort and well-being. Given cities’ increased vulnerability to urban overheating and air pollution, it is essential to have a high spatial and temporal resolution description of urban physical phenomena (temperature, pollutant concentrations etc.). Therefore, modeling and mapping are valuable tools, particularly for assessing vulnerability, anticipating risks, and supporting decision-making for adaptation policies in urban planning.
In this context, the ANR City-FAB project brings together local authorities and researchers from Université Gustave Eiffel to support the transition to more sustainable territories. For the Hauts-de-Seine département, the project assists local authorities in the redeveloping of the RD920 roadway to improve coexistence between pedestrians, cyclists and vehicles, enhance safety, and address climate change challenges. In the City-FAB project, we propose a digital twin to evaluate urban comfort and air quality based on different planning scenarios, while facilitating dialogue with users and suggesting complementary approaches.
We employ a multi-physical approach with high-resolution ENVI-met microclimatic and air quality simulations. Starting with a detailed geographic reconstruction of the study area using data from the National Institute of Geographic and Forest Information (IGN) and the Hauts-de-Seine Council (CD92) databases and creating a sufficiently accurate representation using an open-source Geographic Information System (QGIS). From the 3D geometric model, we conduct microclimatic simulations to generate district-scale maps at a 2-meter resolution of key physical phenomena, of pollutant concentrations and of thermal comfort, quantified by the Universal Thermal Climate Index (UTCI).
Preliminary numerical results for the day of August 12th, 2022 showed that in exposed mineral areas, air temperatures are above 34 °C and mean radiant temperatures exceed 50 °C, leading to high UTCI values indicative of severe thermal stress for about 8 hours of the day. Importantly, shading provided by street trees on RD920 significantly decreases the heat stress by reducing UTCI values up to 5 °C on sidewalks. Regarding air quality, the simulation results show that several zones are exposed to high NO2 concentrations due to morning traffic, resulting in elevated ozone (secondary pollutant) concentrations by mid-day. A sensitivity analysis tool is then proposed to identify and quantify the parameters that most critically impact thermal comfort. This tool is thus useful to inform local decision-makers about the factors to prioritize in urban planning.
In future work, we plan to refine and calibrate the model and validate our simulations using data from in-situ measurement stations. This will enhance its role as a predictive tool and provide valuable guidance for urban redevelopment strategies that adapt to evolving climatic challenges and ensure resident comfort.
How to cite: Benkherfallah, M., Parison, S., Hendel, M., and Waeytens, J.: Digital Strategy for Evaluating Thermal Comfort and Air Quality in Urban Planning of RD920 in the Île-de-France Region, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13641, https://doi.org/10.5194/egusphere-egu26-13641, 2026.
Urban green spaces (UGS) are widely promoted as nature-based solutions to reduce heat risk in Urban Heat Islands. To quantify the cooling effects of UGS the use of remote sensing-based Land Surface Temperature (LST) indicators, such as the Park Cool Island Intensity (PCII) or Cooling Effect Intensity (CEI), has proven beneficial. However, these indicators heavily depend on the urban reference the UGS LST is compared with. This limits the comparability of the cooling performances of UGS across cities and urban forms and furthermore obscures the fact that UGS may themselves experience warming effects from adjacent urban areas in return, a mechanism which is still underrepresented in quantitative research.
In this study, we therefore develop and test a transferable approach for determining UGS cooling deficits (ΔLST) by consistently deriving UGS LST relative to a standardized rural baseline derived from Local Climate Zones (LCZ). Using a on a long-term (1984–2025) Landsat LST time series, we analyze several municipalities in Hesse, Germany, and compare summer patterns of ΔLST within and between municipalities.
UGS are characterized by size, tree cover, and vegetation state according to the Normalized Difference Vegetation Index. Surrounding urban structure is quantified using buffer-ring metrics and indicators of built form and land cover (e.g., imperviousness and building density) to capture how different urban contexts modulate ΔLST. This allows the evaluation of the warming effects of different urban areas on different UGS. To disentangle drivers of cooling deficits, we fit multivariate models that account for nested spatial structure (mixed-effects regression) and complement them with a nonlinear benchmark (e.g., random forest). Finally, we analyze to what degree antecedent weather conditions (air temperature, precipitation, and relative humidity) in different time periods (e.g., 7, 14, 21 days) prior to a Landsat acquisitions modulate ΔLST.
This approach provides a transferable, planning-relevant metric that allows UGS to be classified not only as "cool" or "warm," but also as more or less effective relative to a clearly defined rural reference state. This improves comparability across time, space, and different urban structures, and creates a robust basis for prioritized adaptation measures.
How to cite: Sakretz, D., Conrad, C., and Koza, M.: Impact of urban form and antecedent weather on urban green space cooling deficits derived from multi-decadal Landsat LST, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19656, https://doi.org/10.5194/egusphere-egu26-19656, 2026.
In mature urban areas where space for nature infrastructure is limited, high albedo pavements provide a practical alternative for urban heat mitigation; however, their integrated impacts on urban microclimate and building energy demand lack quantification at fine spatial and temporal scales. We developed a multiscale urban energy microclimate coupling framework to assess the cooling effects of high albedo pavements in Antwerp. Urban microclimate simulations capture pavement induced modifications in surface temperature, near-surface air temperature, and radiative fluxes under summer conditions. We dynamically coupled these outputs with building energy models to evaluate how altered microclimatic conditions influence cooling energy demand at the neighborhood scale. We examined multiple pavement albedo enhancement scenarios while controlling for urban morphology, meteorological conditions, and building characteristics.
Our case study demonstrates that increasing pavement albedo reduces average near-surface temperatures and lowers building cooling energy demand in many neighborhoods. The effects are strongest in densely built and medium density areas, while more heterogeneous or low density neighborhoods show mixed outcomes. Moreover, high albedo pavement across the city would generate significant cumulative reductions in building energy related greenhouse gas emissions over multiple decades, demonstrating the climate mitigation potential of urban surface interventions.
How to cite: Xu, Y., Vuye, C., and Omranian, S. R.: Coupling Urban Energy and Microclimate Models to Quantify High Albedo Pavement Cooling Effects in Antwerp, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14185, https://doi.org/10.5194/egusphere-egu26-14185, 2026.
Posters virtual: Fri, 8 May, 14:00–18:00 | vPoster spot 4
EGU26-14400 | ECS | Posters virtual | VPS7
Development of a Multi-Criteria Framework for Identifying Intra-Urban Heat Islands in Support of Urban Heat Mitigation in Athens, GreeceFri, 08 May, 14:39–14:42 (CEST) vPoster spot 4
This study develops a novel multi-criteria framework for the identification of intra-urban heat islands by integrating indicators related to three-dimensional urban morphology (e.g., height-to-width ratio and sky view factor), land cover characteristics, satellite-derived land surface temperature, and thermal comfort conditions. The proposed framework practically enables the delineation of urban areas with distinct thermal and morphological profiles, thereby providing a robust basis for targeted, site-specific intervention strategies.
Subsequently, a range of bioclimatic heat mitigation measures is assessed for selected hotspots, including nature-based solutions such as increased tree planting and green roofs, as well as the application of high-albedo (cool) materials. The effectiveness of these measures is evaluated using advanced urban climate simulation models (ENVI-met and UT&C), allowing for a comparative assessment of their performance under varying spatial configurations and microclimatic conditions.
Overall, the study provides evidence-based guidance for urban heat mitigation and supports climate-resilient urban planning in Mediterranean cities, with Athens serving as a representative case study.
How to cite: Oikonomou, M., Agathangelidis, I., and Cartalis, C.: Development of a Multi-Criteria Framework for Identifying Intra-Urban Heat Islands in Support of Urban Heat Mitigation in Athens, Greece, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14400, https://doi.org/10.5194/egusphere-egu26-14400, 2026.
