Air and surface temperature are among the most important variables to study the urban climate and are closely linked with thermal comfort and human health. Despite their importance, for now only surface temperature can be estimated by remote sensing, which means that the spatial variability of urban air temperature data can only be studied with a dense set of weather stations, which are expensive and do not yet have a spatial resolution as good as remote sensing. Near-surface air exchanges heat mainly with the surface which suggests that their temperatures could be estimated by each other, but as air is a fluid and moves their relationship is complex, so this estimation cannot yet be done with enough precision. This study aims to help improve the estimation of air temperature with surface temperature using the concept of footprint/source areas, which are the average surface areas that air has most interacted with before reaching the sensor at a weather station. For that, footprint areas were approximated as circles around the weather station. Then, using Landsat 5 and 8 satellites data (which passes around 10 a.m. in local solar time), average surface temperatures at different radii around 51 weather stations at the Metropolitan Region of São Paulo, Brazil, were computed. Then, the Pearson Correlation Coefficient between air and surface temperature was computed for each radius, each weather station and different periods of the year, where the radius with maximum correlation would be an approximation of the true footprint area. The average surface temperature in this area is also a better value for estimating air temperature than the surface temperature in the original Landsat data (100 and 120 metters). 

How to cite: Lustosa, R. and Rocha, H.: Estimating air temperature based on satellite surface temperature in the Metropolitan Region of São Paulo, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-698, https://doi.org/10.5194/egusphere-egu24-698, 2024.

Timely information on the effects of the increasing intensity, frequency and duration of heatwaves on cities and critical infrastructure is needed for warning, emergency management and for developing context-specific climate adaptation strategies. Aside from the challenge of deploying sensor networks within built environments, there are hardly any operational city-wide networks that continuously measure and communicate human thermal comfort indices in public spaces. 

To address this gap, a two-tiered weather and outdoor human thermal comfort monitoring network was developed and deployed in Freiburg in 2022. The monitoring network comprises a total of 42 automatic weather stations primarily mounted on public lamp posts at a height of 3 m, with the Tier-I network consisting of 13 customised stations, which are equipped with an in-house developed data logging unit optimised for this application, that is extend by a spatially dense but less complex Tier-II network consisting of 29 commercial weather stations. Both networks collect data on air temperature, humidity and precipitation, with the Tier-I network providing additional data on wind, radiation, pressure, lightning, solar radiation and black-globe temperature to calculate human-biometeorological thermal indices such as the Physiological Equivalent Temperature (PET). 

Over the course of the first year of deployment (01-Sept-2022 to 31-Aug-2023), the stations have continuously collected high-resolution data (30 and 60 sec) with only little data loss. In a case study, the intra-urban differences in thermal comfort were analysed during the hot month of July 2023, in which five official heat warnings were issued by the German Meteorological Service (DWD). The results show expected intra-urban and urban-rural contrasts and that mid-density sites experience the highest number of summer days, totalling 22, compared to 19-20 in the city centre. The highest amount of moderate heat stress and higher (PET > 29°C) was observed in FRLAND (26,3%) compared to 13-19% at rural sites. Also more tropical nights were observed at inner city sites with 5-6, compared to 3 at outer, primarily suburban sites. Remote and rural sites reported no tropical nights. 

Over the full annual cycle and the entire network, the number of tropical nights ranged between 0 (rural) and 29 (inner city) per year. The highest number of summer days per year was recorded in industrial and suburban areas (up to 101) compared to 84-97 days in the city centre and 62-90 days at rural sites. The average annual air temperatures reveal a distinct long-term heat island with an annual mean temperature up to 14.0°C in the city centre, and 11.6°C - 12.7°C at rural sites of same elevation.

These results highlight the benefit of continued monitoring for real-time assessments, efficient identification of hot-spots for climate adaptation strategies, and model evaluation and to improve our understanding of urban heat islands and human thermal comfort patterns. In addition, an outreach platform and mobile app (uniWeather^TM) have been developed to provide end-users and the public with free access to real-time data and interpretation following FAIR principles.

How to cite: Feigel, G., Plein, M., Zeeman, M., Schindler, D., Matzarakis, A., Metzger, S., and Christen, A.: High spatio-temporal monitoring of weather and outdoor thermal comfort in urban environments: A modular sensor network, first year data and outreach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19427, https://doi.org/10.5194/egusphere-egu24-19427, 2024.

With growing urban population and expanding urban areas, the importance of understanding urban effects on precipitation keeps increasing. This study attempts to detect urban effects on precipitation in the Seoul Metropolitan Area (SMA), South Korea by analyzing hourly rain gauge data during 2005–2020. Precipitation events are categorized according to 850-hPa wind directions, and the precipitation increases from the upwind to downwind regions are examined for different duration and intensity classes of precipitation events. The downwind precipitation increase is largest in summer (39%), especially in August (64%). The August precipitation is analyzed in detail. Precipitation statistically significantly increases in Seoul for weak winds and 25–50 km downwind of the center of Seoul for westerly winds, and the precipitation increases are largest in the afternoon. For the precipitation increases, the increases in frequency and intensity of precipitation events are responsible. Short-duration and heavy precipitation events associated with small-sized precipitation systems initiated within the SMA are mainly responsible for the precipitation increases. The downwind precipitation increase also occurs for southwesterly, southerly, and southeasterly winds, but the increases are associated with large-sized precipitation systems.

How to cite: Hong, S.-H., Jin, H.-G., and Baik, J.-J.: Detection of urban effects on precipitation in the Seoul metropolitan area, South Korea, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4377, https://doi.org/10.5194/egusphere-egu24-4377, 2024.

The Delft Measures Rain Citizen-Science programme has been running for several years in the city of Delft, the Netherlands. Within this programme, interested citizens can apply to receive a low-cost Alecto WS5500 weather station, to measure local meteorological parameters in their own garden. Currently there are over 45 of these citizen-science weather stations spread across neighbourhoods in Delft, capturing rainfall variability in different urban microclimates. However, the scientific quality of these specific stations has never been tested, and from previous work we know that rigorous quality assurance is necessary in order to get meaningful (precipitation) data. Thus we have installed 8 Alecto stations in The Green Village outdoors urban climate field lab at the TU Delft. Stations have been explicitly installed in ways that a citizen might do: slightly tilted, next to a wall (simulating the limited open garden space of a Dutch urban residence), on top of a shed as well as free-standing. These different measurement setups, combined with a row of stations installed in the same way right next to one another, allow us to investigate the bias caused by less-than-ideal station installation, as well as systematic errors related to the tipping bucket mechanism and sensor drifts. Initial results show a general overestimation of the Alecto compared to reference stations and radar observations, and a discernible negative bias caused by sheltering effects of plants and, to a lesser extent by walls.

How to cite: Droste, A., Boonstra, M., Ten Veldhuis, M.-C., Bogert, M., Schleiss, M., and De Vries, S.: Assessing the quality of citizen-science rainfall data based on station setup, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2159, https://doi.org/10.5194/egusphere-egu24-2159, 2024.

Detailed measurements are indispensable in order to understand small-scale urban climate effects. With professional weather stations (PWS) mostly being available outside of cities with few sites per city, alternative data sources such as crowd-sourced weather data have proven to be valuable. Often the Urban Heat Island (UHI) is studied under ideal calm conditions when its development is strongest. At the same time, it has been shown that wind leads to advection of urban air, impacting regions downwind of urban areas and within the city.

We aim to provide insights into the effects of Urban Heat Advection (UHA) in the Urban Canopy Layer (UCL). The metropolitan regions of Paris and Berlin were studied, using four years (2019 - 2022) of quality-controlled crowdsourced air-temperature data from thousands of privately-owned Crowd Weather Stations (CWS). Those data were combined with global ERA5-Land data to overcome gaps in rural CWS coverage and globally-available Local Climate Zone (LCZ) information.

It is shown that wind causes increased exposure to urban heat for areas located downwind of the city core, which was derived using a LCZ-weighted centroid detection. 

For all observed wind directions, classified by dynamically moving wind sectors, differences in spatial patterns were visible with the effect being strongest with regional wind speeds of 3 m·s⁻¹. The results highlight the importance of considering the effects of UHA when studying the UHI to avoid underestimating the exposure to urban heat in downwind areas of the city. The results could be used as a starting point for coupling the conditions in the Atmospheric Boundary Layer with the resulting conditions in the UCL, utilizing a large database with crowdsourced CWS data.

How to cite: Kittner, J., Fenner, D., Demuzere, M., and Bechtel, B.: Quantifying the Effect of Urban Heat Advection using Crowd Weather Stations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1643, https://doi.org/10.5194/egusphere-egu24-1643, 2024.

Cities can offer an extraordinarily high or low urban level of climatic stressors depending on their location and topographical setting, infrastructural geometry and anthropogenic activities. To protect human well-being today and in the future, it is crucial to better understand how to mitigate temperature extremes in cities. Since cities are constantly growing and transforming in response to their residents’ needs, planning a foresighted sustainable climate-friendly infrastructure is critical. This need creates a niche for research to assess local climate effects that effect the lower atmosphere ground layer where human activity takes place. Large Eddy Simulation (LES) models can simulate heat transport and mixing processes by directly resolving large-scale turbulence and are often used to simulate urban development activities potentially mitigating the adverse effects of heatwaves in cities. Despite their growing use in forming recommendations, these models are inherently difficult to validate which leads to ‘simply believing them’.

We evaluate the performance of an urban LES model against a reference multi-station observational network focusing how well the space-time dynamics of distinct urban microclimate including densely-built hot spots, peri urban and park-cool islands agree. We selected a 72-hour extreme heatwave period in July 2019 in a mid-sized city in Germany which suffers from a similarly large urban heat island effect as larger cities. We investigated air temperature, air humidity, wind speed and direction as key elements impacting the perceived heat stress or relief by humans. Observations were compared to the PALM-4U LES model with a nested domain dynamically driven by the mesoscale COSMO-D2 output by the German Meteorological Service at spatial resolutions of 20 m and 5 m domain. We employed the stochastic multiresolution decomposition (MRD) technique applied to two-point correlation statistics for characterizing the space-time behavior.

Absolute air temperatures differences amounted to +5 K overestimation of modeled nocturnal air temperatures. A key finding from the MRD analysis is that correlation between stations does not follow separation distance (as expected for homogeneous domains) but rather the distinct urban microclimatic for air temperature and specific humidity in both observations and model at both resolutions. Separating the results into day and night shows distinct differences for air temperature and specific humidities for both model resolutions compared to the observations, but only small differences for near-surface winds. The model performance varies with its resolution and climate element: while winds are better represented in the finer 5 m resolution, specific humidity cannot be simulated properly by the model at night. Air temperature during day is better represented by the 20 m resolution, while the match between observations and the 5 m-prediction is better at night.

We show that the LES model can simulate the statistical space-time behavior of urban microclimates but performs poorly when absolute targets are modeled. Simulated air temperature and specific humidity follow mostly the implemented synoptic advective forcing large scale model which does not recognize local microclimatic effects. For near-surface winds, this model performs better with finer resolution as the larger eddies resolved depend on the geometry of the city.

How to cite: Sungur, L., Babel, W., Spaete, E., and Thomas, C.: Climate sensitive designs for policy makers: how well can LES models represent urban microclimates?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17406, https://doi.org/10.5194/egusphere-egu24-17406, 2024.

Over the past years, microclimate simulations and analyses became an important tool for the impact assessment of different planning scenarios of real estate projects on a local site. Based on the results of evaluated scenarios, the need for (additional) climate adaptation measures can be identified and improved design concepts might be realized. While this process led to several positive developments and best practice examples, the impact of a building project on the microclimate of the surrounding areas in spatial proximity to the development area is often still neglected. Especially if formerly green areas are sealed, cold-air production areas are lost, or cold-air corridors blocked. Even positively assessed microclimate studies for the local site itself, can have a negative effect on the microclimate of the surrounding area. While large urban planning projects (e.g., area size > 15 ha) in Austria require environmental impact assessments, policy makers and administrative units lack objective criteria to request spatially extended microclimate analyses for medium sized projects that not only affect the development area but also the neighbouring quarters.

In the prevalent research project, “Development of a criteria catalogue for requiring extended microclimate analyses”, funded by the Climate and Energy Fund and carried out under the program "Austrian Climate Research Programme Implementation", potential microclimatic impact of urban planning projects on their surroundings during autochthonous weather conditions in summer is evaluated through sensitivity experiments with the urban climate model PALM-4U. Based on the concept of Local Climate Zones (LCZ), idealized real estate projects are set up in two locations (inner city and periphery) of the city of Linz (Austria). For each location, the following selected characteristics of static input data are varied: (1) size of building site, (2) building footprint, (3) building height, and (4) degree of soil sealing. By comparing simulation results to the reference scenario of an unsealed, green area, the potential impact in terms of intensity and spatial range is assessed.

Results of the sensitivity experiments are used to compile a compact set of criteria, which allows policy makers and administrative units to request spatially extended microclimate analyses to evaluate effects of medium sized urban planning projects on the district-wide microclimate if impacts are expected.

How to cite: Schneider, M., Tötzer, T., and Bügelmayer-Blaschek, M.: Microclimatic effects of idealized urban planning projects on their surrounding area, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8755, https://doi.org/10.5194/egusphere-egu24-8755, 2024.

Cities alter the interactions between the surface and the atmosphere by modifying energy and water budgets. This is caused by the low albedo of urban environments, its high thermal conductivity, the increased surface roughness, and by greater surface imperviousness. Since the beginning of the 21st century, advances in high-performance computing allowed steady refinement of the numerical grids of climate models at the kilometer scale. At this resolution, representing the urban environment explicitly is necessary, as simplifying it to bare soil no longer suffices for accurate energy and water budget assessments and satisfies e.g. the representation of heat waves or urban runoff. In the ORCHIDEE model of the IPSL, cities are currently represented as bare soil, which fails to account for specific urban processes. To enhance ORCHIDEE's performance and study the impacts of specific urban processes on energy and water fluxes, an urban land cover was added to the existing land cover classes taken into account by the model. For this urban class, we prescribed specific parameters for soil imperviousness (though hydraulic conductivity), surface roughness, albedo, and thermal conductivity. All those parameters are cell-dependent, i.e. they account for the diversity of urban environments and cities as characterized by the WUDAPT database (Ching et al., 2018). By comparing model simulations with and without the urban module, we assess the sensitivity of simulated turbulent fluxes, infiltration, soil moisture, runoff, drainage, temperature, and compare them to available observations over France.

How to cite: Lalonde, M., Oudin, L., Ducharne, A., Bastin, S., and Arboleda-Obando, P.: Explicit representation of cities in the ORCHIDEE land surface model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6183, https://doi.org/10.5194/egusphere-egu24-6183, 2024.

Cities, as cradles of population and economic activities, constitute a crucial issue in the current environmental challenges. Their organisation is enduring major changes and the issues surrounding spatial planning are of growing interest in a context of climate change, since cities are particularly vulnerable to extreme events.

The evaluation of the climate change impacts requires to refine the climate projections provided by global and regional climate models down to a finer spatial scale more adapted to the city study. Besides their fine resolution, these models may include a dedicated surface model to represent explicitly the urban areas and the physical processes involved.

The CP-RCM (Convection-Permitting Regional Climate Model) AROME is coupled to the TEB urban canopy model with an horizontal resolution of 2.5 km, and uses the ECOCLIMAP land use and land cover database to characterise the surface properties. It is applied over the Paris region for a past period, forced by the ERA5 reanalysis, in order to assess local impacts of climate change.

Nonetheless, the land use map, used by the CP-RCM AROME and based on data from the 1990s without evolution in time, can be a limit to the realism of climate simulations. The expansion incurred by cities until the current period is not represented, nor the future dynamics. 

This study compares different climate simulations run with past, present and future land use maps over Paris region with the aim to quantify and analyse the impact of land use changes on the regional climate, as well as to explore the consequences in terms of population exposure to high heat conditions.

How to cite: Corneille, L., Lemonsu, A., Machado, T., and Viguié, V.: Sensitivity of the high-resolution regional climate model AROME to urban sprawl over Paris region, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6178, https://doi.org/10.5194/egusphere-egu24-6178, 2024.

Radiative cooling (RC) materials gained interest over the past decades, as these can help mitigating the urban heat island effect, fighting climate change and reducing the cooling demand for buildings. Their altered photonic properties, albedo and emissivity, enable these materials to cool down below ambient temperature and radiate heat in the atmospheric spectral window (8-13 µm), effectively releasing heat into space. Current RC materials typically consist of thin layers of metal and polymer, manufactured through energy-intensive and costly manufacturing processes. The Horizon 2020 project ‘MIRACLE’ is developing a new innovative radiative cooling material, that for the first time, is based on conventional concrete.

This study quantifies the effect of the Photonic Meta Concrete (PMC) on the climate of the highly urbanized region of Flanders, Belgium (13600 km²). Modelling such a large area allows to explore the impact on the urban heat island across multiple cities with diverse geometrical and geographical properties. More specifically, this study assesses the urban heat island effect of selected cities during a heatwave in August of 2019, comparing scenarios with and without the implementation of PMC in the built environment. The COSMO-CLM regional climate model, utilizing the TERRA-URB urban-canopy land-surface scheme, is employed for this assessment. Integration of the PMC’s photonic properties, i.e. the specific emissivity and albedo, into the urban canopy scheme is achieved by adapting the land surface parameters using the Semi-empirical Urban CanopY parametrization (SURY). Comparisons are made between scenarios incorporating specific albedo of the PMC, specific emissivity of the PMC or both against a baseline scenario without the PMC implementation. These comparisons aim to estimate the mitigation potential offered by this innovative material.

Initial findings suggest that the PMC shows promising potential for lowering city temperatures, with the albedo being identified as the primary factor in combating the urban heat island effect. In Brussels, surface temperatures drop by as much as eight degrees, while temperatures at a height of two meters decrease by up to two degrees

How to cite: Adams, N., Neirynck, J., Borgers, R., and Van Lipzig, N.: Implementation of a newly developed Photonic Meta-Concrete into the COSMO-CLM model to estimate the impact on the urban heat island: a case study of Flanders, Belgium, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9028, https://doi.org/10.5194/egusphere-egu24-9028, 2024.

Over the last decade, numerous urban canyon schemes have been developed, aiming to reproduce the interactions between the urban surface and the atmosphere. They have either used a bulk approach, where the general urban surface characteristics are modified, or a layered approach, in which single or multiple canyon levels are adopted, taking into account the contributions of individual urban facets: such as roofs, walls, and floors. Bulk schemes have often been the preferred approach in numerical weather prediction and climate models for their cost-efficient way of representing key atmosphere-canopy interactions and other important urban characteristics.
TERRA_URB is one of these bulk urban canopy models (UCM), originally developed for the COSMO atmospheric model. It has recently been integrated into the Icosahedral Non-hydrostatic Weather and Climate Model (ICON). In this study, we extended the preliminary implementation in ICON with the capability of representing morphological and material properties of the urban surfaces as spatially varying (instead of constant) fields in order to better represent the variability of energy, moisture, radiation, and momentum fluxes between the canopy and the atmosphere across a city. The spatially varying properties were derived from the Ecoclimap Second Generation (ECOCLIMAP-SG) land cover dataset, which is the latest version of ECOCLIMAP, incorporating local climate zones with a relatively high resolution of 300 meters. To assess the performance of ICON with TERRA_URB, we simulated the hot and dry summer period of mid-July to mid-August 2022 over the cities of Zurich and Basel in three configurations, (i) without TERRA_URB, (ii) with TERRA_URB in the preliminary and (iii) in the enhanced version. The three versions were compared against each other and evaluated against different types of observations, including standard weather stations, temperature sensor networks, and flux tower measurements.
Overall, our results reinforce the importance of incorporating accurate characterization of urban morphological and material properties into UCMs. Going forward, we will further improve these urban parameters by incorporating local datasets not accessible to a global product like ECOCLIMAP-SG. This will include, among others, detailed 3D building information, building material properties, surface reflectance (albedo) properties derived from remote sensing, and anthropogenic heat fluxes estimated from a detailed CO2 emission inventory. Our ultimate goal is to develop a comprehensive ICON-based urban modeling system that can be run with either a bulk UCM or the multilayer UCM BEP-Tree, previously developed in our group for COSMO. This novel modeling system will allow us to study the feedback between vegetation, carbon, energy, and water cycles in the urban environment.

How to cite: Dönmez, K., Emmenegger, L., and Brunner, D.: Urban Climate and CO2 Simulations with the New Atmospheric Model ICON-ART Accounting for Spatially Varying Urban Morphology and Material Properties, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3375, https://doi.org/10.5194/egusphere-egu24-3375, 2024.

The increasing in the resolution of atmospheric models for numerical weather prediction and climate simulations allows a more accurate description of the physical processes at urban scale. Furthermore, as the world continues to warm, urban areas are expected to face the brunt of the impacts due to large populations and higher temperatures.

In all this scenario, the interest in proper modelling the physical processes in urban areas has gained wide attention from the research community. In particular, the convection-permitting atmospheric models, associated with urban parameterizations, are able to resolve the heterogeneity of cities with applications for heat stress assessment and the development of urban climate adaptation and mitigation strategies. Generally, these schemes parametrize the effects of buildings, streets and other man-made impervious surfaces on energy, water and momentum exchanges between surface and atmosphere, accounting also for the anthropogenic heat flux, as a heat source from the surface to the atmosphere due to human activities.

In this perspective, a bulk urban canopy parameterization, TERRA_URB, has been developed for the multi-layer land surface scheme of the COSMO regional atmospheric model. This parameterization has already demonstrated to be able to properly take into account the overall properties of urban areas and to correctly reproduce the prominent urban meteorological characteristics for different European cities. Thus, in the framework of the transition from the COSMO model to the new Icosahedral Nonhydrostatic (ICON) Weather and Climate regional model, TERRA_URB needs to be implemented in ICON.

In this work, we present the results for TERRA_URB in the ICON-LAM (limited area model), for some cities of the Italian peninsula at 2km resolution. The main outcome of this study is that the porting of the TERRA URB scheme in ICON is satisfactorily completed, and it reasonably reproduces urban effects, like Urban Heat Islands, while improving air temperature forecasts for the investigated urban areas. The results constitute an updating of numerical weather prediction and climate simulations for urban modelling applications, although further investigations aimed at enhancing the calibration of the model parameterization and introduction of more realistic urban canopy parameters are needed.

How to cite: Campanale, A., Adinolfi, M., Raffa, M., Schulz, J.-P., and Mercogliano, P.: Evaluating the implementation of the new urban parameterization for the ICON atmospheric model: results over Italy, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15826, https://doi.org/10.5194/egusphere-egu24-15826, 2024.

Weather conditions play a large role in thermal comfort and energy consumption, such as during a cold spell (body hypothermia and building heating) or a heatwave (body overheating and building air conditioning). These weather conditions can be modified by urban factors and climate change, such as higher temperatures in city-center and in future periods. However, weather conditions used for building design and renovation are often taken for convenience from past data near airports. The present study aims to determine weather conditions with urban factors and climate change, as well as thermal comfort and energy needs for several building types in different environments. Measurements and simulations are combined to provide weather conditions and building estimations for different locations (rural, periurban, urban), seasonal cases (winter, summer, heatwave) and periods (recent past, mid-century, end-century). A first application of the approach is presented over the city of Strasbourg (France).

We find that the urban case has higher temperature, reduced windspeed and relative humidity, less energy for winter heating and less summer thermal comfort than the periurban than the rural case. Climate change leads to higher temperature and lower relative humidity, and to less summer thermal comfort especially during a heatwave and for older buildings. The combined effect of city, heatwave and climate change on outdoor air temperature reaches 8 to 11 degrees, and similarly for the indoor air temperature of very old buildings but 5 to 7 degrees for recent (well-insulated) buildings. This approach may support building renovation strategies and analyses of population vulnerability. The perspectives include the application to other regions, a comparison of urban climate models, and an investigation of urban scenarios.

How to cite: Breton, F., Micolier, A., Abdellatif, M., Mendez, M., and Blond, N.: Effect of city and climate change on weather conditions, building thermal comfort and energy consumption: application to Strasbourg region, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11633, https://doi.org/10.5194/egusphere-egu24-11633, 2024.

¹Centre de Recherches de Climatologie/Biogéosciences, Université de Bourgogne, Dijon, France - alexandre.berger@u-bourgogne.fr

²Laboratoire ThéMA, Université de Bourgogne, Dijon, France

Heat waves (HWs) become more frequent, severe and longer under climate change. In cities, their impact is exacerbated by urban heat islands (UHIs). Proposing efficient adaptation plans necessitates upstream studies to further understand air temperature space-time variability within cities during HWs, their drivers, and associated mechanisms and processes.

This study aims at understanding 2 m air temperature (T2m) space-time variability during the four HWs that occurred in Dijon during summer 2022 based on the dense MUSTARDijon network of 92 thermometers. We used a 150 m mesoscale simulations performed with the Meso-NH atmospheric model coupled with the TEB and ISBA surface schemes optimized for urban and rural environments, respectively. First, we evaluate the capability of Meso-NH to simulate the diurnal cycle of T2m for the four HWs over urban and rural environments. We show that Meso-NH more skillfully simulates the T2m diurnal cycle over urban than rural environments, despite a systematic cold bias in early morning and late afternoon. Second, we focus on the drivers of T2m space-time variability by using different predictors including land cover, energy budget, soil and atmospheric humidity and atmospheric dynamics. Buildings and roads contribute to warm the urban environment mostly at night, but these contributions are exaggerated by Meso-NH during all HWs. By contrast, vegetation contributes to cool the urban environment all day long for low vegetation and at night only for high vegetation in both observations and simulations. Also, wind speed seems having a strong impact on UHI intensity.

How to cite: Berger, A., Crétat, J., Pergaud, J., Pohl, B., Poupelin, M., and Richard, Y.: High-resolution air temperature modeling during the summer 2022 heat waves over Dijon, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12850, https://doi.org/10.5194/egusphere-egu24-12850, 2024.

Population exposure to high temperatures poses health risks and increases mortality, but comprehensive studies comparing impacts of building and street levels interventions on air temperature at urban scales are still lacking. High-albedo roofs (also called “cool roofs”) can lower the air temperature in urban areas, compared to standard low-albedo roofs. As part of the transition to renewable power generation rooftop regional authorities in the UK have set targets for rooftop solar panel capacity, but some recent studies have argued that solar panels may increase urban temperatures and therefore have unintended consequences. Using advanced urban climate modelling (WRF BEP-BEM), we model the impact of these cool roofs and rooftop photovoltaics on urban air temperature during the record-breaking hot summer of 2018, and estimate the impact these measures may have on heat-related mortality.

We find that cool roofs and rooftop photovoltaics both decrease modelled daily-mean temperature compared to standard low-albedo roofs. Rooftop photovoltaics may reduce heat-related mortality by 96 (12%), or cool roofs by 249 (33%), in scenarios where all roofs have these measures. Monetised using value of statistical life, we estimate benefits for solar roofs and cool roofs of £237M and £616M respectively for London July-August 2018 conditions, and we estimate 20 TWh of electricity, worth £3-6 billion, would be generated in the rooftop PV scenario. Our modelling indicates that, in the conditions of London July-August 2018, rooftop PV or cool roofs may reduce near-surface air temperatures and therefore heat related mortality, with cool roofs having a larger effect.

How to cite: Simpson, C. H., Brousse, O., Davies, M., and Heaviside, C.: Modelled outdoor temperature effects and heat-related mortality impact of cool roofs and rooftop photovoltaics in London, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16969, https://doi.org/10.5194/egusphere-egu24-16969, 2024.

Urban heat is characterised by elevated temperatures in cities, resulting not only from global climate change but also from urban development and human activities. Previous research on urban heat has predominantly relied on satellite-derived land surface temperature (LST) data to investigate the changes in near-surface thermal environments. However, the applicability of LST for examining the temporal variation of air temperature is still not well understood. Using crowdsourced air temperature observations and satellite imagery, we explore the temporal variation of air temperature and its relationship with LST in more than 50 populated cities worldwide. Results show that city-average air temperature values are highly correlated with LST. However, the intensity of this correlation differs by season, day/night cycle, and is further influenced by background climate. Using satellite LST data, we expanded our analysis to include over 1500 urban areas and evaluated temperature changes in the past two decades. We observed a general trend of increasing temperatures in cities globally, although the rates of warming vary. The highest rate of temperature change was found in cold climate cities, with a more rapid increase during winter days. These cities are predominantly located in Eastern Europe, extending into parts of Western Asia. These findings provide new insights into the application of satellite-based LST for predicting future air temperature changes and identifying areas most vulnerable to urban overheating.

How to cite: Naserikia, M., Hart, M., Nazarian, N., Sismanidis, P., Kittner, J., and Bechtel, B.: Urban heat trends across global cities, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7195, https://doi.org/10.5194/egusphere-egu24-7195, 2024.

At the end of the century, during the hot summer days, the thermal environment will be unbearable in most of the outdoor spaces of the cities. The few locations where the thermal environment will remain moderate will constitute heat shelters. The present study analyses the evolution of the quantity and the nature of the outdoor heat shelters, in a 27km² central portion of the Lyon conurbation, during a hot summer day that is representative of the climate at the end of the century.

A specific methodology has been applied to perform such an analysis. The weather data for the representative hot summer day (RHSD) was selected within a database of weather projections made available by the CORDEX project. The RHSD represents weather conditions that may happen statistically once every 5 years, during the 14^th of July, in Lyon, at the end of the century. Then, mean radiant temperature and operative temperature predictions were performed using the SOLWEIG micro-meteorological model. The heat shelters were defined as the outdoor locations where the operative temperature is below 37°C. With this definition, the heat shelters may not provide thermal comfort, but prolongated stays in the heat shelters would be safe for most of the population. The quantity of available heat shelters was measured through the heat shelter area per capita, which represents the total of the area covered by the heat shelters divided by the number of inhabitants in the domain.

During the RHSD, the heat shelters area goes below  per capita between 10:15 and 17:45, and reaches  between 15:00 and 16:00. During this period, the outdoor public domain is not able to provide heat shelters. This result suggests that people will have to adapt their way of life to the disappearance of heat shelters in the core of the afternoons, in order to avoid prolongated stays in the outdoor public environment.

The observation of the heat shelter maps reveals that, between 9:00 and at 19:00, the heat shelters are exclusively located within shaded areas. Continuous tree covers are more efficient than buildings to provide heat shelters: between 13:00 and 14:00, heat shelters are exclusively located in the core of the urban forests. In the streets, the capacity of shaded areas to provide heat shelters highly depends on the presence of surfaces (buildings façade or soil) that reflect the solar radiation toward the shaded areas. This result invites to rethink the way climate adaptation solutions should be designed in cities.

How to cite: David, D. and Salles, M.: Evaluation of the heat shelters availability in the city of Lyon, during a hot summer day at the end of the century., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16912, https://doi.org/10.5194/egusphere-egu24-16912, 2024.

Addressing the urban heat island effect requires informed and strategic planning of measures to mitigate urban heating. This is particularly important for highly urbanized and densely populated cities in the tropics, such as Singapore, which experience high levels of thermal discomfort due to urban heat island effect, and is further intensified by global warming. To assess the impact and effectiveness of different heat mitigation measures, we utilize a Digital Urban Climate Twin (DUCT) model of Singapore. The DUCT integrates the Weather and Research Forecasting model and Building Energy Model (WRF/BEM), with an added modification to account for near-surface anthropogenic heat sources such as power plants and traffic emissions. Next to these models the DUCT also integrates various data sources such as weather conditions, landcover, buildings, traffic etc. to describe the thermal behaviour of the city.

In this study, we use the DUCT to conduct a comprehensive testing of the sensitivity of urban temperatures to various heat mitigation measures such as increasing urban greenery, changing urban morphology and improvements in building efficiencies and traffic. Preliminary results indicate that designating forest land use and incorporating green areas are the most effective in reducing local and surrounding temperatures. This is followed by increasing the urban vegetation fraction in the pre-existing urban landscape, increase in electric vehicle usage, and improvements in building energy efficiencies, which show a more limited impact on temperatures. This work aims to highlight the capabilities of the DUCT as a versatile tool for planning agencies and policy makers to test the effectiveness of various policies and guide strategic planning for the management of urban heat.

How to cite: Wong, M. L., Zozaya, A., and Orehounig, K.: Assessing urban heat mitigation strategies in Singapore with a Digital Urban Climate Twin (DUCT), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7058, https://doi.org/10.5194/egusphere-egu24-7058, 2024.

Urban Heat Islands (UHIs) represent a climatic consequence of urbanization, leading to elevated temperatures within cities compared to surrounding rural and suburban areas. Addressing this human-induced phenomenon demands effective mitigation strategies. This study quantifies the UHI in the Kansas City Metropolitan Areas (KCMA) in the United States and investigates the potential of albedo modification, particularly through the cool roof implementation, as a means to mitigate UHI effects within the KCMA.

Utilizing the Weather Research and Forecasting (WRF) model, we first designed a suite of high-resolution simulations, examined UHI dynamics during a heatwave event across various scenarios within the KCMA, and determined the effectiveness of mitigation strategies in reducing temperatures within the KCMA. Specifically, we simulated two cool roof scenarios: one representing "newly installed" cool roofs with an albedo of 0.8 and another reflecting "aged" cool roofs with an albedo of 0.5. Our findings reveal that cool roof materials significantly mitigated surface UHI effects during evenings, delaying the onset of UHI effects until later in the day. Moreover, our study showcases the more profound impact of cool roofs on surface skin temperature, influencing the surface energy balance by altering sensible and ground storage heat fluxes and the planetary boundary layer.

Leveraging numerical modeling, we led and launched an Urban Heat Island Mapping Campaign in Kansas City. It is a volunteer-based community citizen science field campaign that builds upon local partnerships among academia, local government agencies, non-profits, and private sectors. This campaign engages Kansas City's local residents in a scientific study to map and understand how heat is distributed in the communities and the factors affecting the uneven distribution of heat. It raises awareness about the adverse impacts of extreme heat and excessive urban heat and presents actionable measures for urban planners and policymakers to address heat-related challenges in metropolitan areas.

How to cite: Sun, F.: Urban Heat and Mitigation Potential in the Kansas City Metropolitan Area: Insights from Integrated Numerical Modeling and Heat Mapping, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6510, https://doi.org/10.5194/egusphere-egu24-6510, 2024.

With recent advances in subkilometric numerical weather prediction (NWP) for urban areas¹, it has become possible to develop numerical platforms to assess landscape modifications and in particular heat mitigation scenarios in urban areas. One of the major barriers that exist for urban planners and health institutes to rely on such data is that they might be reluctant to consider the large amount of data produced by such numerical simulations.  This study aims at analyzing results in a more holistic approach, with the objectives of developing training data for statistical assessment of the impact of heat mitigation strategies in a particular city.

In a recent study², evaluations of scenarios for the urban landscape modifications were performed in Canada for Montreal and Toronto metropolitan areas with the Global Environmental Multiscale (GEM) atmospheric model with grid spacing of 250 m (with the Town Energy Balance TEB and the Interactions between the Soil, the Biosphere and the Atmosphere ISBA surface schemes) and applied during two overheating periods in 2010 when large impacts on the mortality rate were observed. More than 20 scenarios were assessed with realistic but ambitious scenarios, including increase of vegetation fraction with or without irrigation, and of thermal reflectivities. Various responses on the temperature reduction were found with an overall improvement, and down to -3 ^oC during the daytime, but negative effects were also found on the thermal stress during daytime when increasing albedo values.

More insights into the results are provided in this study, using various normalized efficiency metrics, as for example those based on previous work³ and extended to the mean radiant temperature and to thermal stress indices computed in these experiments (UTCI and WBGT⁴).  Dependencies of the measures on the various environmental conditions will be presented and greening strategies will be analyzed in combination with the soil water availability. 

References:

1.Leroyer, S., et al., 2022, https://doi.org/10.3390/atmos13071030

2.Leroyer, S., et al., 2019, https://doi.org/10.5281/zenodo.7075789

3.Krayenhoff, E.S., et al., 2021, https://doi.org/10.1088/1748-9326/abdcf1

4.Leroyer, S., et al., 2018, https://doi.org/10.1016/j.uclim.2018.05.003

How to cite: Leroyer, S., Bélair, S., Alavi, N., Nikiema, O., Munoz-Alpizar, R., and Popadic, I.: Contrasting Responses of Heat Mitigation Strategies on Surface and Air Temperature and on Thermal-Stress Indices Deduced from Mesoscale Modelling, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14149, https://doi.org/10.5194/egusphere-egu24-14149, 2024.

Increasing human activities and urbanization have posed huge challenges to the urban climate, such as the urban heat island effect, which makes air temperature in urban areas higher than that in suburban areas. Meanwhile, the urban heat island intensity (UHII) suffers impacts from the exacerbation of observed extreme heat events, but how extreme heat events affect UHII in different subdivided urban spaces remains unclear. In this paper, we attempt to address the impact of extreme heat days and nights on urban heat environment from the perspective of local climate zones (LCZs). Firstly, we propose a framework for LCZ classification for higher precision LCZ mapping over the Guanzhong Plain urban agglomeration in China. Secondly, to select extreme heat days and nights based on six extreme temperature indices (TXx, TNx, TX90p, TN90p, SU25 and TR20), the daily maximum, minimum and average seamless 1-km air temperatures are estimated using the random forest method for the period from 2000 to 2020. Finally, combining the LCZ map and gridded temperature product, we analyze variance in air temperature and UHII among different LCZs at daytime and nighttime, as well as the influence of extreme heat conditions on air temperature and UHII in different LCZs.

Our results indicate that the air temperature difference within LCZs is greater under extreme heat conditions compared against that under non-extreme conditions. Meanwhile, extreme heat conditions aggravate the urban heat risks at daytime, which is manifested in the following two aspects: (1) the temperature difference within LCZs on extreme heat days is greater than that on extreme heat nights; and (2) UHII at nighttime is stronger than that at daytime in most LCZs under non-extreme conditions, but under extreme heat conditions, it is the opposite. In addition, although the rank of UHII in different LCZs varies due to differences in time and definition of extreme heat days and nights, LCZ 6a (agricultural greenhouse) stands out for suffering the highest UHII under all conditions (different extreme temperature indices, on days and nights), to which particular attention should be paid. Our results could be contributed to conducting mitigation measures of urban heat risks and providing more explicit guidance to policymakers and urban planners.

How to cite: Wang, B., Gao, M., and Li, Z.: Impacts of extreme heat days and nights on urban heat environment: a perspective of local climate zones, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7102, https://doi.org/10.5194/egusphere-egu24-7102, 2024.

Summer temperature extremes are increasing rapidly under the current global climate change. Urban environments are among those most exposed to temperature extremes due to the urban heat island, and these exacerbated conditions significantly affect human health and activities, making urban heat load one of the most fundamental concerns for people living in cities. Our research quantifies spatio-temporal changes in urban heat load in two Central-European cities (Brno and Ostrava, Czech Republic) in different geographical configurations. We applied the urban climate model MUKLIMO_3, combined with the cuboid method, to simulate recent and future distributions of four summer climate indices. The simulation results clearly indicate continuous climate warming and project a significant increase in the mean annual values of summer climate indices by the end of the 21st century, particularly in the built-up areas with a predominance of impervious surfaces. Both model simulations and in-situ observations confirm that the magnitude of these changes can differ significantly from city to city suggesting the distribution of urban heat load is not only influenced by climate change, but also by local geography and anthropogenic factors. To determine the causes of the differences in urban heat load variability, we applied land use/land cover configuration metrics and correlation analysis using various geographical factors. Our results show that a compact and less fragmented land use/land cover structure can significantly increase the urban heat load. Altitude also has a strong influence on the heat load pattern in complex terrain. Therefore, some cities are and may continue to be extremely vulnerable to adverse summer temperature extremes. We suggest that urban planners should take into account the current impact of land use/land cover structure on temperature conditions when designing effective adaptation measures to mitigate urban heat load.

How to cite: Janků, Z., Dobrovolný, P., Geletič, J., and Lehnert, M.: The future changes in spatio-temporal distribution of urban heat load and factors that affect its variability, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-229, https://doi.org/10.5194/egusphere-egu24-229, 2024.

The urban heat island (UHI) is defined as the temperature difference between a city and its rural surroundings. It is one of the most studied urban phenomena and can have potential adverse effects on human well-being. Furthermore, the UHI may contribute to an increase in the urban energy consumption and ecological footprint, potentially exacerbating the impacts of climate change. The aim of this study is to evaluate the capability of different regional models from the EURO-CORDEX ensemble to accurately reproduce the UHI over several European large cities. Subsequently, the evolution of the UHI under climate change scenarios is studied using the models that demonstrate good performance. 

The employed data were extracted from the EURO-CORDEX EUR-11 project and the ERA5-land dataset. The historical data cover the period 1971-2000. The future model data under the climate change RCP8.5 scenario are divided into near future (2021-2050) and distant future (2071-2100) periods. There are multiple ways to perform the UHI intensity calculation. In this case, the urban temperature of each city is assigned as the temperature series of the most urbanized grid point. The reference rural temperature is defined as the mean temperature series of all the valid rural points inside a 1º box centered in the most urbanized point. A rural point is considered to be valid if its rural fraction falls below 5%, its land fraction is no lower than 50% and if its altitude does not differ more than 100 meters from the urban point altitude.

The results of this study show that several models do not simulate the timing of the UHI correctly, exhibiting its daily maximum during the daytime instead of the nighttime, as seen in other studies. ERA5-land data present similar limitations. However, the RegCM4-6 and HadREM3-GA7-05 models are two examples of regional models able to successfully reproduce the UHI effect and its annual and daily cycles. The differences between the historical and future mean annual cycles of the UHI daily maximum show small to no changes in most of the cities, with these small differences being generally negative. Barcelona and Lisbon present greater negative changes, with a reduction of the UHI intensity of around 0.2 ºC in the near future and a reduction of around 0.4 ºC in the distant future. In contrast, Porto and Toulouse present positive differences with an intensification of the UHI effect of around 0.3-0.4 ºC in the distant future. Furthermore, the greatest changes in each city occur during the summer season. No important changes in the hourly distribution of the UHI daily maximum are found. In conclusion, the UHI effect seems to generally not aggravate the rising temperatures due to climate change in urban areas.

The authors acknowledge the ECCE project (PID2020-115693RB-I00) of the Ministerio de Ciencia e Innovación/Agencia Estatal de Investigación (MCIN/AEI/10.13039/501100011033). ERL thanks her predoctoral contract FPU (FPU21/02464) to the Ministerio de Universidades of Spain.

How to cite: Raluy-López, E., Gallardo, V., Jiménez-Guerrero, P., and Montávez, J. P.: Urban Heat Island under Climate Change over European cities: Evaluation of the EURO-CORDEX ensemble performance in reproducing the UHI, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15357, https://doi.org/10.5194/egusphere-egu24-15357, 2024.

Increasing intensity, frequency, and duration of hot extremes has been one of the most pronounced aspects of climate change in Central Europe. At the same time cities and towns, where the majority of the population live, are affected by added urban heat load. Such circumstances require effective adaptation of the municipalities to heat extremes. On that account, the influence of blue and green features and various surfaces on thermal exposure, represented by MRT and physiological indices of Universal Thermal Climate Index (UTCI) and Physiological Equivalent Temperature (PET), has been investigated over a period of five years in a set of short-term measurement campaigns in several Czech cities. The results showed that trees in open public areas of Czech cities lead to a substantial decrease of thermal exposure during the daytime whereas it might slightly increase on-site thermal exposure during the night. Maintained turfs in open areas characteristically reduce thermal exposure only slightly, depending on grass height and density and soil properties. Similarly, the cooling or warming effect of blue elements differs with their character. The effect of fountains and misting systems in open areas of thermal exposure is usually hardly detectable; however, ground-based fountains moisturising the pavement seem efficient. Further results from a recently launched measurement winter season campaign are expected soon. 

How to cite: Lehnert, M., Květoňová, V., Koukalová, A., Jurek, M., and Geletič, J.: Investigation of diurnal/nocturnal and seasonal effect of blue and green features on thermal exposure in Czech cities, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4394, https://doi.org/10.5194/egusphere-egu24-4394, 2024.

Outdoor thermal comfort is influenced not only by meteorological variables air temperature, radiation and humidity at regional and local scales but also by local parameters such as mean radiant temperature and wind patterns, which vary at meter-scale within cities. All these factors can be affected by ongoing climate change. Hence, modelling future thermal comfort requires a multi-scale approach. Thermal comfort in outdoor settings can be quantified and described by thermal indices such as the Universal Thermal Climate Index (UTCI), which reflects the human response to environmental and physiological forcing. To date, several microscale modelling approaches have been proposed to model the meteorological and geometric variables that contribute to the UTCI, but they are all highly detailed, complex and computationally intensive. As a result, only individual heat waves, short case studies or single points have been modelled to estimate future heat stress conditions in cities.

This study introduces a novel and efficient deep-learning model that instantly and accurately predicts thermal comfort maps across entire cities and for long periods. This model is unique in its adoption of a solitary deep learning architecture, avoiding the use of sub-models that separately model, for example, air temperature or wind speed. We will refer to this model as the Unified Human Thermal Comfort Neural Network (UHTC-NN). Training and evaluation of the UHTC-NN is based on a machine learning model from a previous study, which combines four sub-models modelling air temperature, mean radiant temperature, wind speed, and relative humidity into UTCI. The UHTC-NN has a mean absolute error of 0.5 K compared to its preceding model. The UHTC-NN enables new applications of thermal comfort modelling, including meter-scale urban climate projection to support climate adaptation management in cities.

In a case study, we apply UHTC-NN to downscale 15 EURO-CORDEX climate projections with a 3-hour resolution over 30 years to generate high-resolution (1x1 m) street-level outdoor thermal comfort maps for the city of Freiburg, Germany. We compare the changes in UTCI frequency distribution and uncertainties of three different Representative Concentration Pathways (RCP2.6, 4.5 and 8.5) for the years 2070-2099 with the historical climate (1990-2019). Our study models the entire city center of Freiburg, with a domain size of 2.5x2.5 km, covering various aspects of the city's urban form. We show that the average number of hours per year with strong to extreme heat stress (UTCI >= 32°C) will increase up to three and six times for RCP2.6 and RCP8.5, respectively. The number of night-time hours with UTCI >= 20°C will increase by a factor of two and five, respectively for RCP2.6 and RCP8.5, compared to the 1990-2019 period. In addition, the 80th UTCI percentile shifts by 2°C and 4°C for RCP2.6 and RCP8.5, respectively. The presented high-resolution urban climate simulations allow us to identify intra-urban variability and daytime / nocturnal hot-spots where climate change will have the greatest impacts on outdoor thermal comfort. Such urban climate simulations therefore allow for an effective selection of areas where climate adaptation needs to be prioritized.

How to cite: Briegel, F., Schrodi, S., Sulzer, M., Brox, T., Christen, A., and Pinto, J. G.: Downscaling climate projections to map future outdoor thermal comfort in cities based on a deep learning approach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16110, https://doi.org/10.5194/egusphere-egu24-16110, 2024.

Urban Heat Islands (UHIs) are increasingly posing critical challenges to urban environments and human well-being. In response, we propose a novel methodology to identify Urban Heat Island Hotspots (UHIHs) to address the urgent need for effective management and mitigation strategies. Our research introduces the innovative concept of direct UHIHs detection and ranking, providing a framework for urban planning stakeholders to prioritise areas for regeneration based on UHIH severity.

A new concept is proposed, and it consists of: hotspot ranking in a given urban area, a new algorithm for hotspot detection, and a new tool for automatic detection and ranking hotspots. This approach greatly improves the effectiveness of interventions to mitigate the adverse impacts of UHIs on urban environments and public health.

This methodology addresses the critical importance of incorporating threshold percentiles and considering the spatial coverage of the study area. It relies on percentile-based thresholds, establishing the minimum acceptable values for individual cells and the mean values of the entire UHIH area. Through extensive experimentation with various threshold pairs, we identified the most suitable thresholds for further application, considering both LST values and non-climatic factors (e.g., urban fabric and imperviousness). The new hotspot identification algorithm calculates minimum acceptable values for individual cells and hotspot means, which plays a pivotal role in pinpointing UHI hotspots effectively. Each hotspot is identified on a step-by-step basis, starting with the identification of the highest temperature cell, which hasn’t been assigned to any other hotspot in previous steps. Further on, the algorithm searches among all surrounding cells and checks if they meet the two threshold conditions or not. In case of a positive result, the identified cell is assigned to the current hotspot and placed in a stack for its neighbours to be further considered. In addition to the detection process, this research introduces the concept of hotspot ranking based on their intensity. This innovative feature enhances the utility of our algorithm by prioritising the severity of UHI hotspots, facilitating data-driven decision-making for urban planning and climate mitigation strategies.

The practical implementation of the proposed algorithm is sustained by the use of the versatile R programming language, providing researchers and practitioners with a flexible and user-friendly tool.

This research addresses the complex challenges urban heat islands induce, offering a comprehensive approach readily adoptable by researchers and urban planners. It underscores the urgency of UHI management and its potential to enhance the well-being of urban populations.

In summary, this new approach and tool could become very useful in the urban planning process as they:

• Enhance the effectiveness by prioritising the assessment of UHIHs based on their severity in a given location;
• Provide a valuable tool for data-informed decision-making in urban planning and climate mitigation;
• Enables urban planners and stakeholders to allocate resources and interventions more strategically, focusing on the critical areas from the UHI perspective;
• Maximise the impact of the urban planners/stakeholders’ efforts in enhancing urban resilience and sustainability.

How to cite: Croitoru, A.-E., Magyari-Saska, Z., Horvath, C., and Pop, S.: A new method for automatic identification and ranking the urban heat island hotspots, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11609, https://doi.org/10.5194/egusphere-egu24-11609, 2024.

Background: Urban environments are increasingly recognized as both significant contributors to and primary victims of climate change. Buildings in urban settings are responsible for approximately 33% of global greenhouse gas emissions, while cities themselves are often situated on fertile land with high carbon sequestration potential. To mitigate these impacts on climate, adopting nature-based sustainable technologies is essential for developing climate-smart cities. Among these, green roofs have emerged as a critical solution for climate change mitigation.

Significance of Green Roofs: Originally designed for stormwater management, green roofs have demonstrated effectiveness in various environmental aspects. They mitigate urban heat island effects, reduce sound and air pollution, lower building energy consumption, enhance biodiversity, and have the potential for carbon sequestration. Recognizing these benefits, Toronto implemented a by-law in 2009 mandating green roofs on all new large buildings with flat roofs larger than 2,000 m², complemented by incentives for the private sector. Despite the increase in green roof installations, there is a lack of efficient monitoring, leading to concerns about maintenance and compliance.

Challenges in Monitoring: The absence of an efficient green-roof monitoring system is a widespread problem. Traditional monitoring techniques face limitations, including on-site inspections and satellite imagery analysis. High-resolution satellite data are costly, while freely available images (e.g., from Landsat) lack the necessary resolution for small-scale green roof analysis. This gap highlights the need for an efficient, automated, and accurate green roof monitoring system.

Methods: To address this need, we developed an automated, deep-learning-based toolbox (GreenRoofNet) for monitoring green roofs using high-resolution (8 cm) orthoimages collected by the City of Toronto for other purposes. We segmented these images into 299x299 pixel tiles with a 20% overlap to ensure comprehensive coverage, particularly of smaller green roofs. Using 500 labeled images for training and validation, and the remainder for testing, we employed the Inception v4 architecture in TensorFlow. This deep convolutional network model was selected for its ability to extract detailed features crucial for accurate green roof detection. The model training involved a cross-entropy loss function, an Adam optimizer, and a dynamic learning rate, with a 50-epoch limit and early stopping to prevent overfitting. Post-processing of tiles was conducted using maximum confidence scores to amalgamate overlapping detections.

Results and Implications: The model has successfully identified green roofs with approximately 95% accuracy and detected their boundaries with about 90% precision. Preliminary analysis reveals that a segment of Toronto's green roofs is undergoing degradation, whereas a substantial proportion remains in good condition, with a smaller segment being currently undetectable or missing. Further testing is underway, with plans to package the results of this project in a web application featuring an open-source map. This tool will play a pivotal role in assessing the effectiveness of the green roof by-law, aiding in the verification of subsidies, guiding the maintenance of green roofs, and facilitating the estimation of their environmental benefits.

How to cite: Halim, M. A., Liao, W., Kayes, I., Drake, J., Margolis, L., Wunch, D., and Thomas, S.: GreenRoofNet: Integrating High-Resolution Aerial Imagery with Deep Learning for Efficient Green Roof Monitoring, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4737, https://doi.org/10.5194/egusphere-egu24-4737, 2024.

The building size and distribution has a big impact on atmospheric properties such as wind speed, air temperature, air pollution, etc. This impact may be quite different depending on the lattitude and the climate where a city is located. Nowadays, climate simulations performed over city territories consider average building properties (building height, distance between buildings, etc.) over one to several hundred meters grid cells. However, it is difficult to find a homogeneous building dataset that would be used over the world to observe the effect of a same building organisation between two regions of the world located at a different lattitude or in a different climate zone.

This work is dedicated to the evaluation of several building datasets (OpenStreetMap, BING, Global Human Settlement, etc.) that are available over several continents. The building footprint and height of each dataset are compared to local reference data for different parts of the world. The objective is to identify which dataset would be preferable to use depending on its quality and availability.

How to cite: Bernard, J., Wurtz, J., Masson, V., and Bocher, E.: Which dataset should be used to get building footprint and height worldwide ?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5096, https://doi.org/10.5194/egusphere-egu24-5096, 2024.

The rise in high-resolution satellite technology and computational advancements has enabled the development of global urban land cover datasets, crucial for understanding climate risks in our increasingly urbanizing world. Here, we analyze urbanization patterns across spatiotemporal scales from several such widely used current-generation datasets and find substantial discrepancies in percentage of urban land influenced by differing urban definitions and methodologies. Despite these inconsistencies, the datasets show a rapidly urbanizing world, with global urban land nearly tripling between 1985 and 2015. We also discuss the implications of these inconsistencies for several use cases, including for monitoring urban climate impacts, such as localized urban warming and urban flood risks, and for modeling urbanization and its influence on weather and climate from regional to global scales. Our results demonstrate the importance of choosing application-appropriate datasets for examining specific aspects of historical, present, and future urbanization with potential implications for informing sustainable development, resource allocation, and quantifying climate impacts.

How to cite: Chakraborty, T., Venter, Z., Zhao, L., Demuzere, M., Zhan, W., Gao, J., and Qian, Y.: Disagreements among current-generation global urban estimates across scales, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13324, https://doi.org/10.5194/egusphere-egu24-13324, 2024.

Climate modelling needs to have accurate informations about topography, type of land and land-use, size and type of wind or radiative obstacles such as trees or buildings. Explicits climate models solves heat and mass equations for each individual surfaces but they cannot be applied at regional scale for long time periods due to computational limitations. Parameterized climate models can overpass this limitations considering that within a given grid cell (being from one to several hundred meters wide), the obstacles and lands follow a given setting (e.g. street canyon for cities, with or without a garden). The heat and mass balances are applied for each of the grid cells using urban canopy parameters summarizing the main relevant parameters describing an area (e.g. mean building height, canyon aspect ratio, building fraction, fraction of road, building type and use, etc.). However, there is currently no datasets over Europe that would accurately describe all these informations.

OpenStreetMap (OSM) is a free, open geographic database updated and maintained by a community of volunteers via open collaboration. It contains most of the informations needed by climate models. It can cover any part of the world and is particularly well fullfilled for the European continent. One of its limitation is the lack of building height information. GeoClimate is a tool that calculates urban canopy and land cover parameters as well as Local Climate Zones (LCZ). GeoClimate uses vector data such as the ones available through the OSM project and uses machine learning algorithm to estimate the height of building missing such information. GeoClimate has recently used the OSM data to calculate the needed informations needed by parameterized climate models such as SURFEX-MesoNH or WRF over Europe. The presentation will describe the way GeoClimate works and will show some of the results of the resulting dataset.

How to cite: Wurtz, J., Bernard, J., Masson, V., and Erwan, B.: Urban canopy parameters and local climate zones over Europe using OpenStreetMap data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8061, https://doi.org/10.5194/egusphere-egu24-8061, 2024.

Urban climate conditions under global or regional change are a major stake for city planners and policy makers.

Members of the different involved communities need a common way to describe urban territory, a way that urban planners and policy makers can easily comprehend and that at the same time can be validated by specialists of the different science relevant fields : climatology, meteorology, building energy, environment and social sciences...

Local Climate Zones (LCZ) have proven their usefulness as an easy to use and scientifically founded concept.

Several methods aim at classifying a territory into LCZ, and a few workflow even allow an automatic classification. These methods produce maps which are often similar, but may show some differences, due to input data or implemented algorithm.

Outside these technical considerations, to assess the impact of urban planning scenarios, one may also want to compare maps before and after the planned urban renovation projects.

Therefore, the need for automation of LCZ map comparison asks for an easy to use tool.

The `lczexplore` package is a free open-source package that allows to easily import, visualize, group and compare LCZ maps, even if they do not use the same spatial units / resolution. These LCZ maps can come from vector layers or raster layers. Five steps are usually performed by the tool:

1. The LCZ classifications (or any other qualitative variables) are imported from a file (geojson or shapefile format)

2. Each LCZ classification can then be visualized

3. Some LCZ levels may be grouped in broader categories

4. A pair of LCZ classifications (or qualitative variable maps) can then be compared:

- a map of agreement/disagreement is produced,

- the general agreement and a pseudo-kappa indicator of agreement are computed,

- the summed surface of each LCZ type is computed for each classification,

- a confusion matrix shows how the levels of one LCZ classification break up into the levels of the other

5. Influence of the level of confidence on the agreement between classifications is performed (sensitivity analysis)

To illustrate the use of LczExplore, we propose a study case where three different maps are compared :

- one created within the WUDAPT project, an approach using remote sensor data and machine-learning algorithm

- a second created using the GeoClimate software and geographical data provided by an institutionnal actor (the French National Geographical Institute)

- a third created using the GeoClimate software and crowdsourced geographical data (from OpenStreetMap).

The use of lczexplore package allows to easily visualize how the different map agree or differ, and gives insight on how methods can be complementary to each other, and how input data or algorithms could be improved in the future.

How to cite: Bocher, E., Gousseff, M., Jérémy, B., Le Saux Wiederhold, E., Alglave, B., and Kerjouan, E.: Comparison of different methods to produce local climate zone maps using the LczExplore tool, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15984, https://doi.org/10.5194/egusphere-egu24-15984, 2024.

Heatwaves are becoming more frequent because of climate change, and this trend is exacerbated in cities due to the urban heat island effect. With more than half of the world’s population living in cities, it is essential to quantify the future evolution of heat stress and develop smart adaptation strategies to counter its impacts. This requires the capturing of fine-grained variations in heat-related hazards within the urban fabric. However, the coarse resolutions of Earth System Models makes it difficult to model urban areas explicitly. Moreover, high-resolution modelling of future climate conditions in cities is often conducted for select cities only, in very focused studies or by private companies, thus limiting the availability of its results in the public domain. Additionally, there is limited understanding of the potential of climate-smart urban development for reducing heat stress.

In the H2020 PROVIDE project, we use the urban boundary layer climate model UrbClim to generate projections of urban heat stress at a 100-meter resolution, for about 20 indicators in 140 urban centres across the world. UrbClim consists of a land surface scheme with simplified urban physics coupled to a 3-D atmospheric boundary layer module, and can represent the effect of varying land cover conditions on local climate. We consider three emission scenarios: 1) compatible with the 1.5°C goal of the Paris Agreement, 2) representative of the trend from current policies, and 3) an intermediary scenario. The forcing data corresponding to these scenarios is generated by coupling the emulator for Global Mean Temperature FaIR, with the Earth System Model emulator with spatially explicit representation MESMER. This allows us to account for uncertainties in the forcing data arising from both the response of Global Mean Temperature (GMT) to emissions, and the response of large-scale climate conditions above each city included in the study to rising GMT.

The resulting database is integrated into the PROVIDE climate risk dashboard, an open-access and user-friendly online tool that allows visualization of global-to-local future climate impacts depending on mitigation outcomes. The dashboard also contains a module that allows its users to first select a critical heat stress level of their choice, and then get information about the emission scenarios that would enable to avoid exceeding that level in their city of interest. This more impact-centered perspective on the UrbClim results provides information on future heat stress in a way that better reflect how climate impact information is accounted for in local adaptation processes.

Furthermore, we explore the potential for urban greening plans co-developed by urban planners and city-level stakeholders to reduce heat stress by running UrbClim at very high resolution (down to 1 meter) for the cities of Lisbon (Portugal), Bodø (Norway), Islamabad (Pakistan), and Berlin (Germany). These new results will eventually also be made available in the PROVIDE climate risk dashboard. Together with the insights from the urban planners and stakeholders’ needs, they offer more practical and policy-relevant insights for adaptation practitioners at the municipal level on the potential for climate-smart urban development to reduce heat stress.

How to cite: Lejeune, Q., Souverijns, N., Georgiou, S., Schwind, N., Ali, S., Capela Lourenço, T., Irfan, K., Lauwaet, D., Gomes Marques, I., Gonzales Lindberg, H., Menke, I., Nath, S., Pfleiderer, P., Pires Costa, H., Saeed, F., Saleh Khan, M., Schmidt, S., Theokritoff, E., Yesil, B., and Schleussner, C.-F.: A public database of future heat stress in 140 cities to examine the potential for heat reduction via climate-smart urban development , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15412, https://doi.org/10.5194/egusphere-egu24-15412, 2024.

More than 50% of people worldwide live in cities with upward projections. Ensuring health, public safety, and maintaining a high quality of life is a challenge within cities due to the accelerated growth of urbanization, climate change, and the limited resources available for urban management and planning. Strategies for sustainable urban planning are a need that has become relevant worldwide. The concept of a Smart City is presented as an approach that integrates many inputs from different sources to make decisions based on reliable and updated information, considering, for example, environmental monitoring. However, capturing the spatiotemporal variability of processes within the city requires a significant investment in time and resources, especially for medium-sized cities in Latin America, where little information is available. Earth Observation based on open-access information offers essential opportunities to obtain information of interest, contributing to cities' physical and environmental characterization. Satellite sensors allow cities to be characterized in terms of the presence and state of vegetation, surface temperature, changes in the urban footprint, level of luminosity, and atmospheric pollution, among other parameters. This ongoing project is progressing with a web platform containing urban-environmental indicators derived from satellite images to support intelligent planning and management of sustainable development strategies in the city. The platform is currently undergoing pilot development in the city of Quilpué, Valparaíso Region, with the potential for scaling to other territories at the regional and national levels. Preliminary findings with satellite data reveal adverse trends in surface temperature, vegetation health, and air quality in Quilpué City, which are currently undergoing validation with on-site data. Efforts were focused on merging socio-economic and environmental data to pinpoint areas with vulnerable populations. Despite the emphasis on environmental variables, gaps exist in analyzing population exposure to these factors due to outdated demographic information. This underscores the importance of nature-based solutions to exposure variables such as air quality, surface temperature, and proximity of green areas, which could be addressed by governance risk management and public policy planning. This approach offers substantial potential for informed decision-making and risk mitigation strategies.

How to cite: Medina, J., Hernández-Duarte, A., Saavedra, F., Contreras, V., Leguía, M., and Romero, C.: “GENIUS: Satellite Monitoring Platform for City Management and Planning”, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13679, https://doi.org/10.5194/egusphere-egu24-13679, 2024.

The strategic, serious games could be one of the most interesting and effective educational tools in climate change action methods. This is why interdisciplinary project Co-Adapt - Communities for Climate Change Action (NOR/IdeaLab/Co-Adapt/0002/2020-00; https://coadapt.pl/en)​ aims to develop an integration toolkit based on both board and  computer game to support resiliency and citizen engagement in city-communities, empowering them in responding to new climate change challenges with bottom-up involvement.

The game features simulations that allow local community to transform their neighborhoods into more resilient to the climate change. The game is adapted to local environmental and spatial conditions so people can play in a group on their real neighborhoods maps what stimulate higher motivation for participation in climate change transformation. The residents play together and they are forced to co-operate. They will explore various choices available for their neighborhoods (from wide, but limited and detailed range of solutions connected with green and blue infrastructure, renewable resources, climate-friendly changes of colors of facades and roofs etc.) and consequences (costs, savings, climatic benefits). The workshop toolkit integrates best practices collected from communities that are already involved in climate change actions in Norway, Denmark, France or USA and which were visited by project’ leaders.

The pilot board games were played October-November 2023 in five neighbourhoods in Warsaw diversified in relation to exposure to urban heat island, flood risk etc., urban structure and socioeconomic factors. They were carefully chosen after consultations with Warsaw City Council out of the most active local communities and on city-owned land. City ownership is crucial because at the end of the game each of the community will be able to implement some solutions from the game up to the sum of c. 6800 € (30 000 PLN). The residents could eg plant the trees, sow a flower meadow, create bioswale trough or start a small orchard.

Co-Adapt game is completely new idea of ​​implementing science into the behavior of local communities in order to arouse their will to act together, to improve their living environment, to adapt to climate change and to mitigate this change.  

How to cite: Kuchcik, M., Cieszewska, A., Adamczyk-Jabłońska, J., Dudek-Klimiuk, J., Giedych, R., Klimaszewski, K., Łączyński, M., Maksymiuk, G., Pusłowska-Tyszewska, D., and Wałdykowski, P.: Serious game as a tool for understanding the need for adapting our neighbourhoods to climate change, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3171, https://doi.org/10.5194/egusphere-egu24-3171, 2024.

Cities such as Austin, Texas have in the last decade noted increase in severity and frequency of weather extremes, causing loss of life and millions in damaged property and  critical infrastructure. Communities with the fewest resources often experience the greatest burden from these events and struggle to “bounce back.” The City of Austin, as well as all other cities around the world, are currently scrambling to understand how to plan for an uncertain future. 

The University of Texas (UT)-City Climate CoLab is a novel initiative that builds on the success of national climate assessments, the state and regional climate centers, and highlights and fills the void of creating a city climate office. The UT-City of Austin CoLab develops Austin and City-specific climate information, data products, tools, and assessments to drive innovation and investment in research, policy, governance, and education. This CoLab is the first City-academia  climate collaboratory in the US through the city council.

City Council, planners, engineers, and other decision-makers are using the past to predict the future, and with climate change, that approach is no longer sufficient. This presentation will bring out the workings of this colab with the City staff and community group on extreme weather and climate projects. The City Colab has been working on different needs/problems to solve:

• Specific climate data and models needs that are often confusing for community and City project teams and staff, therefore not immediately useful for planning and policy purposes;
• Academic research can be made accessible to different City departments, agencies, and programs to improve decision-making -- but is not easily usable; 
• Currently there is no entity that directly supports municipal climate data needs. Climate aligns with multiple departments’ work but needs differ across teams. Need more coordination across departments and to connect data to city department decision making;
• Currently, academia / City climate research projects are selected per faculty interest and a much more strategic approach is needed.

Types of Projects: (a) City Climate Assessment (coinciding with global climate assessment from IPCC; (b)  Data products:  Provide data based on different department needs; Develop data products and downscale the needed climate model information so that it is useful at city scale i.e. 100 km uniform grid information to gridded neighborhood scale (target 1 km x 1 km data output or even finer); (c) Communication: Collaborate with local news weather teams to share climate information; Connect through the City Public Information Office to share takeaways in official city press releases; (d)  Policy and Governance: Map intra- and inter-agency climate governance networks to understand key relationships, programs, and community organizations for outreach;  Research policy and governance frameworks;  Connect climate modeling data products to social and policy science, including social vulnerability;  Develop a stakeholder database and platform. (e) Outreach: Workshop and outreach for assessing media, community and stakeholder needs; Conduct public participation in scientific research by collecting and sharing data based on community feedback; Advance community science and volunteer monitoring efforts. 

A number of research topics are underway  and an outline of these activities, lessons learnt, and path ahead will be presented. 

How to cite: Niyogi, D.: The University of Texas Austin City Climate CoLab - Localizing Climate Decision using Data and Community Partnerships, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14394, https://doi.org/10.5194/egusphere-egu24-14394, 2024.

Cities worldwide are in the phase of either acknowleding the need for heat action plans or are already in the phase of improving their existing plans. Due to bad ventilation conditions heat plays a major role in city dwellers‘ life. Heat actions plans are therefore a strongly advised intrument by many experts. Main tools are urban climatic maps (UCM) and their recommendation plans.

This article is about the methods during the development phase of heat action plans with a focus on urban climatology. We suggest to use urban climate maps and recommendation maps under the framework of VDI Guidelines „urban climate and planning“ to locate areas, institutions and livinghoods facing heat and to develop recommendations to decrease vulnerability.

With the example of a small city in Western Germany the methodology is shown. Based on urban climate map and recommendation map those loactions were identified which are moderately hot or show inconvenient ventilation conditions. Together with demographic statistics (age), vunerable groups were identified: Children under 6 years and people over 65 years. Further, we analysed the location of institutions which become frequently visited by vulnerable people: i.e. kindergartens, schools, care institituions for older people. We added urban green infrastructure (UGI) as places for recreation during heat phases.

WIth the help of geoinformation services (GIS) we were able to combine the different information from UCM, recommendation map, UGI, demographic statistics and the location of the „sensitive institutions“ to find spots most attractive for recreation as well as spots less attractive or even dangerous in terms of health during heat. This technique gives valuable and localised information for developing heat action plans.

How to cite: Hahne, J., Katzschner, L., and Kupski, S.: Use of urban climate recommendation maps for heat action plans, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8483, https://doi.org/10.5194/egusphere-egu24-8483, 2024.

The third United Nations Conference on Housing and Sustainable Urban Development (HABITAT-III) in October 2016 adopted the New Urban Agenda (United Nations, 2017), which brings into focus urban resilience, climate and environment sustainability, and disaster risk management.
Following the event at the United Nations Economic and Social Council, efforts are required from WMO to consolidate its input to the revision of the New Urban Agenda (NUA) and support urban related activities in a comprehensive manner. Urban development is now a cornerstone of the United Nations 2030 Sustainable Development Goals. It has its own sustainable development goal (SDG 11): Make cities inclusive, safe, resilient and sustainable.
To support implementation of urban activities the WMO inter-programme Urban Expert Team under the Commission for Atmospheric Sciences and Commission for Basic Systems (2018) supported by a dedicated team of urban focal points in the Secretariat developed the Guidance on Integrated Urban Hydro-Meteorological, Climate and Environmental Services (IUS). The needs for integrated urban services (IUS) include information for short-term preparedness (e.g. hazard response and early warning systems), longer-term planning (e.g. adaptation and mitigation to climate change) and support for day-to-day operations (e.g. water resources). The aim is to build urban systems and services that meet the special needs of cities through a combination of dense observation networks, high-resolution forecasts, multi-hazard early warning systems, disaster management plans and climate services. This approach gives cities the tools they need to reduce emissions, build thriving and resilient communities and implement the UN Sustainable Development Goals.

The ways and approaches, as well as priorities for relization of such systems depend on specific climatic, geographical, economical and environmental conditions specific cities. In this presentation we will classify and concider different approaches, methodologies and tools for selected cities in different climate zones (e.g. northern, tropical), economical conditions (developed and developing worlds) and combinations of risk factors (e.g., multi-hazards, heat stress, floods, air quality). Specific focus will also be done on the mitigation and adaptation strategies and their combinations. 

How to cite: Baklanov, A.: Integrated Hydrometeorology, Climate and Environmental Systems and Services for Sustainable Cities: Approaches for different regions and countries. , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19983, https://doi.org/10.5194/egusphere-egu24-19983, 2024.

Urban hydrometeorological (UHM) research is important for managing the challenges that arise from the complex interactions between climate change, meteorological processes and the water cycle in urban environments. It provides valuable insights for sustainable urban development, infrastructure planning, climate change adaptation, public health and improving the overall resilience of cities to weather, water and climate-related challenges. This bibliometric research analyses published literature on the research topic of UHM. In total, 507 studies were assessed in the period 1975-2023 based on the Web of Science database, covering almost half of the century of UHM research. Three subperiods with different publication trends were noticed. The first publication subperiod is the longest (1975-2020), but with the fewest publications (45), while the second subperiod is substantially shorter (2011-2017), but with a significant increase in the number of publications (122). The third subperiod is the shortest, i.e., from 2018 to 2023, and it is characterized by further substantial increase in the number of publications (340); although the shortest, the third subperiod contains 67% of published UHM studies, thus showing the increased interest in this research topic during the recent years. Furthermore, majority of UHM studies were published in the research fields of: 1) Environmental Sciences (175 studies), 2) Water Resources (165 studies); and 3) Meteorology and Atmospheric Sciences (150 studies). Countries/regions leading the way in UHM research and publishing are the USA, China and England, while there is a noticeable lack of UHM studies from Global South. Regarding sustainable development, UHM studies mostly contributed to the research on SDG 13 (Climate Action), SDG 6 (Clean Water and Sanitation) and SDG 11 (Sustainable Cities and Communities). The keyword analysis further revealed the changes in the main research themes during the last decades of the 20th century and the first decades of the 21st century. This study can be beneficial for those interested in acquiring more knowledge about UHM research and its application.

How to cite: Milošević, D., Teuling, R., Paparrizos, S., and Steeneveld, G.-J.: Urban Hydrometeorology: an overview and bibliometric analysis of published research, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8402, https://doi.org/10.5194/egusphere-egu24-8402, 2024.

The study of urban climates is at a critical juncture in its development as its subject matter is viewed as increasing relevant to a number of intersecting concerns across a hierarchy of scales. These concerns include global climate change and its drivers and consequences, which are focused on cities where most reside. Addressing these concerns requires an integrated science of cities, which does not yet exist. Our current urban climate knowledge framework developed as a series of specialist endeavours concentrating on aspects of the outdoor and of the indoor environments. As a result, much of the training, methodologies, technical language and data that are associated with these specialist fields are distinct and not easily transferable. In the climate field, there is a clear division between the outdoor and indoor climates and addressing each independently makes it difficult to find solutions to urban challenges, such as achieving zero Carbon cities. Moreover, the lack of a common framework causes confusion when articulating findings. As examples, the urban canopy layer (UCL) in urban climatology commonly refers to the outdoor space below roof level and is bounded by the ground, the walls of adjacent buildings and the interface at roof level; the walls are also part of the indoor canopy, which is bounded by the walls and the roof. Clearly these spaces are strongly connected by exchanges of energy and mass and by the movement of people across the wall interface, yet these receive little attention. In this presentation we will discuss the emergence of indoor and outdoor climate sciences and the potential for integration within an urban climate science.

How to cite: Mills, G. and Erell, E.: New directions for urban climate science, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-22059, https://doi.org/10.5194/egusphere-egu24-22059, 2024.