This study explores the spatiotemporal variations in Land Surface Temperature (LST) and vegetation indices in relation to Local Climate Zone (LCZ) classification in a coastal, temperate climate city across multiple seasons. The study focuses on Cardiff, the capital and largest city of Wales, located only 2.4 km from the sea. The findings of this study extend our scientific understanding of the interrelations between LST and morphological and surface properties of the built environment and urban vegetation for various LCZ classes in Cardiff. Results showed a significant variation in Surface Urban Heat Island (SUHI) intensity in spring, summer, and winter. LST and Normalised Difference Vegetation Index (NDVI) were found to vary significantly across the LCZ classes demonstrating their association with the local urban form and morphology. For built-up areas, LCZ classes with lower vegetation cover and higher building density showed higher LST. For natural areas, LCZ F (Bare soil or sand) had higher LST than LCZ A (Dense trees). The high-density, built-up LCZ classes have a greater UHI compared to the natural classes. In addition, the results showed that LST and NDVI are significantly affected by the morphological and surface properties for each LCZ classes. Building surface fraction, impervious surface fraction and surface admittance were found to have a positive correlation with LST. Sky View Factor, surface albedo and pervious surface fraction, on the other hand, showed a negative correlation with LST. Opposite associations were found with the NDVI. Urban planners and designers will find the study useful to develop heat mitigation strategies while planning, designing, or improvising the new and existing urban areas in Cardiff. In addition, the LCZ map produced in this study for Cardiff using local expert knowledge will enable international comparison and testing of proven climate change adaptation and mitigation techniques for similar urban areas.

How to cite: Sharmin, T., Lannon, S., and Chappell, A.: Spatio-temporal variability of land surface temperature and vegetation indices within Local Climate Zone classes in Cardiff, Wales, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8591, https://doi.org/10.5194/egusphere-egu23-8591, 2023.

The inverse S function can not only fit the spatial attenuation of construction land density, but also fit the spatial attenuation characteristics of various urban characteristics. Therefore, we assume that the inverse S-function curve is also applicable to the spatial variation law of urban LST. We hope to conduct an inverse S function model fitting analysis of the surface temperature of the three major cities in Beijing, Tianjin, Hebei, China's capital economic circle in 2001 and 2020 in winter, summer, day and night in eight periods to verify that they all conform to the characteristics of the function curve, and use the fitting parameters to analyze the urban development process and its impact on the thermal environment.

First, we draw concentric circles at intervals of 1KM from the center points of the three cities, and then extract the land surface temperature (LST) of each circle and process it dimensionlessly. Finally, the inverse S function model is fitted to all LST data, and the expression of the inverse S function is as follows. And combined with the characteristics of LST, the fitting parameters in the function are given corresponding meanings.

Analyzing the results of fitting parameters, LST conforms to the law of the reverse S-curve model in most cases.

Since the LST in the most periods can be simulated by the inverse S model, it is proved that their change law is that they first decrease slowly with the increase of the radius of the concentric circle, then decrease rapidly, and finally decelerate to zero.

The fit parameter "a" controls the slope of the curve. The larger "a" is, the faster the curve decays, indicating that the urban thermal environment is more compact.

The "a" of each city of winter is greater than that of summer.

Except for the smallest "a" in winter night in Beijing in 2020, the "a" in summer in 2001 was the smallest in other cities. The distribution of urban thermal environment in this period is the most scattered.

The "a" results for Beijing and Tianjin are similar every time, but Beijing has a wider range of values. Tianjin's is generally larger than them.

The fitting parameter "c" is the mean value of surface temperature at the city fringes.

The most cities are distributed between 0 and 0.2.

Only Tianjin Xiaye in 2020 reached 0.53. It shows that the temperature around Tianjin is on the high side during this period.

The fitting parameter "D" reflects the radius of the urban thermal environment.

The "D" of each city sample has increased to varying degrees, indicating that the urban high-temperature thermal environment has also expanded.

The thermal environment radii of Beijing and Shijiazhuang are the smallest at night in winter, while Tianjin is the smallest at night in summer.

The fastest growth rate was during summer nights, with each city adding more than 10 kilometers.

The slowest growth in Beijing is during the daytime in summer, while that in Tianjin and Shijiazhuang is during the night in winter.

How to cite: Zhang, X., Roca Cladera, J., and Arellano Ramos, B.: Research on the effect of urban territorial expansion on thermal environment using the inverse S-function curve- Taking Beijing-Tianjin-Hebei China Capital Economic Circle as an Example, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15292, https://doi.org/10.5194/egusphere-egu23-15292, 2023.

Urban anthropogenic heat has a direct impact on urban climate. However, due to a warm feedback loop, the attributions of urban anthropogenic heat in urban area are unclear. This research carried out an attribution analysis of anthropogenic heat index (AHI) derived from remote sensing over global 1386 cities to investigate the contribution of 13 environmental variables to global urban anthropogenic heat based on GEE environment. 13 independent variables are categorized the groups of human activities, land morphology, vegetation, climate and atmospheric environment on anthropogenic heat. The results show that although human activities are considered as the main source of the anthropogenic heat, other factors have more impacts on the anthropogenic heat pattern in the urban area due to the feedback loop of urban thermal environment. Climate played a leading role in the impacts on anthropogenic heat with a contribution rate of 30-50% in most background contexts. The impact rate of human activities and landforms on anthropogenic heat accounts for 20% in most background scenarios. The findings of this research can contribute to the solution of mitigating urban anthropogenic heat and expanded the research scope of urban anthropogenic heat in the urban area.

How to cite: Wu, H. and Huang, B.: Attributes analysis of global urban anthropogenic heat index with multi-sources remote sensing data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16489, https://doi.org/10.5194/egusphere-egu23-16489, 2023.

There is often a lack of meteorological stations in cities, which makes it difficult to examine their microclimate. Alternatively, it is possible to get around this by fixing measuring instruments on bicycles and traversing through the city to observe spatial patterns in the urban canopy layer. The "traverse" approach enables insight into the spatial variation of air temperature in relation to urban form.

In this research, air temperature measurements were carried out using bicycles and an IoT MF-300 instrument with a synchronized GPS receiver and a temperature probe under different weather conditions throughout 2021 and 2022 in Zagreb, Croatia. The GPS receiver's high sensitivity and position accuracy allowed measurements in places like urban canyons and dense foliage environments. The routes were carefully designed to pass through morphologically diverse parts of Zagreb to emphasize the heat characteristics of the city's microclimate. We specifically analysed the spatial distribution of air temperature and land surface temperature (LST) during a heat wave event on the 24^th of June 2021. On that day mobile measurements were conducted between 10:30 and 11:15 AM local time to match the LST measurements of the Landsat-8 satellite that overpassed Zagreb at approximately 10:45 AM. Additional measurements were carried out in other seasons and at different times of the day.

Results show thermal differences between surface types and urban forms. During a heat wave event, air temperatures reached up to 35 °C, and LST was above 40 °C, which are high temperatures considering the time of measurement. However, mobile measurements showed that city parks can be even 3 °C cooler compared to densely built-up city areas. Due to the high response time of the instrument, the effect of microscale city properties, such as tree lines, was also observed. These results indicate the cooling effect of green areas in Zagreb and the importance of their preservation for heat load reduction and mitigation of the negative effects of the urban heat island.

How to cite: Žgela, M. and Herceg Bulić, I.: Urban heat load assessment in Zagreb, Croatia: a multi-scale analysis using mobile measurements and satellite imagery, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-290, https://doi.org/10.5194/egusphere-egu23-290, 2023.

Detailed knowledge about the intra-urban air temperature variability within a city is crucial for the implementation of adaptation strategies to counteract the negative effects of urban heat stress. Various methods to model urban-rural temperature differences exist, but they often only cover certain periods (heatwave, hot day) or meteorological conditions (sunny and calm) due to computational limitations or limited data availability. Land use regressions, which are usually based on fine scaled measurements and high resolution spatiotemporal data, are one promising method to overcome those limitations and to conduct daily urban temperature fields.

In the city of Bern, Switzerland, a very dense urban temperature network (about 1 station per 1.5 km²) is operated since summer 2018. With that detailed information on temperature and publicly available land use and meteorological data, different land use regression types with a differing degree of complexity were tested in the recent past. One main outcome of the application of the method in Bern is an urban temperature dataset that covers the temperature distribution of all nights of the metropolitan area of the summers 2007 to 2022 with a resolution of 50 meters. In this talk, we would like to present the different models applied in Bern, analyze the potential of land use regression approaches in urban climate studies, and discuss possible applications of the dataset regarding urban planning and heat stress studies.

How to cite: Burger, M., Gubler, M., and Brönnimann, S.: Modeling daily urban temperature fields using land use regression approaches, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1476, https://doi.org/10.5194/egusphere-egu23-1476, 2023.

Urban heat is a local scale warming effect associated with urban areas where most of the world's population live. Due to the scarcity of air temperature (Ta) data, urban heat studies have been mostly focused on Land Surface Temperature (LST) extracted from satellite imagery and a quantitative understanding of how LST interacts with Ta within a city is still lacking. Using crowdsourced weather station data in Sydney, Australia, combined with high resolution satellite images and urban datasets (such as Local Climate Zone (LCZ) and building-level urban data), we explore the interaction between Ta and LST, and their intra-urban variabilities during different seasons. We found that LST and Ta have different characteristics and their dependency varies by season and LCZ. When exploring the relationship between Ta, LST, and variables describing the urban structure, such as building fraction, the correlation between LST and urban structure was stronger and more seasonal dependent than the Ta-urban form relationship. Moreover, stronger correlations between LST and Ta were observed in the less built-up areas within the city. We also found that the determinants of LST variability are different from the contributing factors of Ta. These findings provide new insights for quantitatively investigating surface and canopy urban heat and their relationship with land cover, providing fit-for-purpose information to mitigate the adverse effects of urban overheating at local and global scales. 

How to cite: Naserikia, M., Hart, M. A., Nazarian, N., Bechtel, B., and Nice, K. A.: Understanding within-city interaction between surface and air temperatures, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10461, https://doi.org/10.5194/egusphere-egu23-10461, 2023.

How to cite: Mitchell, T. D. and Fry, M. J.: The use of crowdsourced observations to build climate grids and assess urban heat hazard., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14110, https://doi.org/10.5194/egusphere-egu23-14110, 2023.

Due to climate change and urbanization, the world's population is increasingly exposed to extreme heat, posing a threat to public health. Despite people spending ~90% of their time indoors, heat effects in buildings have been studied far less than outdoor heat island effects. This study aims to observe, understand and model the behaviour of indoor air temperatures (T_in) during summer heat. As a proof of concept, we present and analyse up to 27 years of individual T_in timeseries of seven citizen weather stations (CWS) across the Netherlands. First, we find that typically T_in increases slower, but also cools down slower than T_out with a lag difference of ~130 minutes in the diurnal cycle. We demonstrate that nocturnal indoor human thermal comfort (HTC) can be worse than outdoor HTC even for days after a heatwave.

Second, to model T_in behaviour, we simulate six-hour changes in T_in behaviour with a physics-based statistical model by Vant-Hull et al. (2018) that has an outdoor conduction, indoor conduction and solar transfer component. Preliminary results of this computationally-fast model for each of the seven houses are promising, showing on average a R-squared of 0.74 and a root mean squared error of 0.13 K. Third, in the next research steps, we are also interested in how T_in may evolve due to climate change. We will study this by converting the T_in measurements to 2050 and 2085 values based on the Royal Netherlands Meteorological Institute 2014 climate scenarios (or 2023 if available).

Finally, we will scale up our proof-of-concept analyses to 100 indoor CWS placed in Amsterdam. The participating households receive a CWS for three years to measure their indoor climate – temperature, relative humidity, CO₂ concentrations – in the bedroom and living room. Based on our insights, we will make recommendations for climate-sensitive urban design to reduce indoor heat stress.

How to cite: Peerlings, E. and Steeneveld, G.-J.: Hot weather impacts on urban indoor air temperature assessed through citizen science observations in the Netherlands, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7886, https://doi.org/10.5194/egusphere-egu23-7886, 2023.

The wind speed varies significantly within a neighborhood due to building and vegetation size and position. A good estimation of these spatial variations is useful for several applications (outdoor thermal comfort, air pollution, building energy consumption and thermal comfort, etc.) but might be time-consuming using Computational Fluid Dynamic tools and difficult to produce for non experts.

URock is a Python library that has been developped within UMEP, a city-based climate service tool integrated as plug-in in the free and open source QGIS software. It is based on the Röckle approach already used in non open source softwares such as QUIC-URB and SkyHelios: first an initial wind field is set according to empirical laws derived from wind tunnel observations; second the mass air flow is balanced minimizing the modifications of the initial wind field. This method is less accurate than traditional CFD method but quicker and simple to implement for non specialists. This work presents the evaluation of URock against wind tunnel observations.

Acknowledgement

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 896069.

How to cite: Bernard, J., Lindberg, F., and Oswald, S.: Quick calculation of wind field in urban area within a free and open source GIS: evaluation of the URock model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16480, https://doi.org/10.5194/egusphere-egu23-16480, 2023.

During the last decade, atmospheric measurement networks in urban areas have become important drivers for global pollution and greenhouse gas (GHG) mitigation. For city stakeholders to effectively plan GHG emissions mitigation measures and to monitor changes in emissions, GHG concentration data both, from measurements and simulations on high resolution, are required, but still lacking in most cities. The accurate simulation of high-resolution dispersion in urban areas enables the interpretation of concentration measurements as well as quantitative estimates of local emissions in an inverse modelling framework.

High-resolution dispersion simulations on neighborhood scale are generally computationally costly, preventing the analysis of long time periods. To overcome this limitation, we use the coupled GRAMM/GRAL model which is computationally efficient due to a ‘catalogue approach’. GRAMM/GRAL is composed of the mesoscale model GRAMM, solving the Reynolds Averaged Navier Stokes equations for an outer domain (resolution 100 m), and the computational fluid dynamics model GRAL for an inner domain (resolution 10 m). We run GRAMM for 1008 different wind situations differing in synoptic wind forcing and stability class. GRAL is initialized by the GRAMM fields and calculates wind fields taking the flow around buildings into account.  A time series of hourly, 10 m resolved wind fields and concentrations can be obtained by matching catalogued, simulated wind fields with measurement of a local wind measurement network (‘catalogue approach’).

Here, we evaluate the GRAMM/GRAL model in the urban area of Heidelberg for 12 months. 14 wind measurement stations within the inner GRAL domain enable a thorough evaluation of GRAL for yearly time periods and for a 12x12 km² area under challenging topography. Our evaluation also includes wind profile measurements (up to 200 m) from a LIDAR. We find good agreement between modelled and simulated wind directions. Wind speeds can be simulated with an overall root-mean square difference of about 1 ms^-1 and a mean bias of about -0.3 ms^-1. Measurement sites with poorer model representation are located in the forest or the outer domain with coarser resolution. We conclude that GRAMM/GRAL is capable of simulating high-resolution wind fields in urban areas of complex topography. We showcase first dispersion simulations for carbon dioxide in Heidelberg.

How to cite: May, M., Wald, S., and Vardag, S. N.: Comprehensive analysis of high-resolution dispersion simulations in urban area using the GRAMM/GRAL model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3458, https://doi.org/10.5194/egusphere-egu23-3458, 2023.

The mitigation of urban microclimatic deficiencies as a result of the urbanized environment receives increasing attention by cities, municipalities and the planning community. Priority areas for the implementation of multi-beneficial climate change adaptation measures and the development of mitigation plans need to be identified. Therefore modelling methods producing good results while only requiring moderate data input and computational effort are needed for the widespread application in planning processes. In this study, we test the rapid fine-scale methodology for simulating urban bioclimatic conditions in a 2D environment that was previously introduced by Back et al. (Back et al., 2021). The original methodology uses high resolution land cover classification (Hiscock et al., 2021) from multi-spectral aerial imagery, digital elevation data, and a vector layer of buildings to calculate land surface temperature (LST), mean radiant temperature (MRT), and the Universal Thermal Climate Index (UTCI). We apply this methodology to the rural municipality of Feldbach, Austria, using commercially and openly available satellite imagery. The simulated data is validated against the publicly available monitoring data from the WegenerNet climate station networks (Fuchsberger et al., 2022), which provides high spatial and temporal resolution measurements. The results are evaluated regarding the agreement of relative spatial differences of the simulated variables with the observed data. To be suitable for the identification of priority areas for the implementation of climate change adaptation measures, the methodology is expected to accurately reflect the spatial variability of the simulated variables.

Project supported by ESA Network of Resources Initiative.

Back, Y., Bach, P. M., Jasper-Tönnies, A., Rauch, W., & Kleidorfer, M. (2021). A rapid fine-scale approach to modelling urban bioclimatic conditions. Science of The Total Environment, 756, 143732. https://doi.org/10.1016/j.scitotenv.2020.143732

Fuchsberger, J., Kirchengast, G., Bichler, C., Leuprecht, A., & Kabas, T. (2022). WegenerNet climate station network Level 2 data [Text/csv,application/x-netcdf]. Wegener Center for Climate and Global Change, University of Graz. https://doi.org/10.25364/WEGC/WPS7.1:2022.1

Hiscock, O. H., Back, Y., Kleidorfer, M., & Urich, C. (2021). A GIS-based Land Cover Classification Approach Suitable for Fine‐scale Urban Water Management. Water Resources Management, 35(4), 1339–1352. https://doi.org/10.1007/s11269-021-02790-x

How to cite: König, A., Pichler, M., and Muschalla, D.: Validation of a 2D modelling approach for urban microclimatic conditions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7691, https://doi.org/10.5194/egusphere-egu23-7691, 2023.

Urban areas are known to be exposed to higher temperatures than rural areas making urban citizens particularly vulnerable to extreme heat events. Large eddy simulation (LES) models can simulate micrometeorological heat transport and mixing processes by directly resolving large-scale turbulence. These models can be used to simulate urban development strategies aiming at mitigating the adverse effects of heat waves in cities by analyzing their influence on urban microclimate. Despite their use in formulating recommendations for city planning, these models are often not validated with observed meteorological data. We here present results from conducting a model-observation comparison for a mid-size city in Germany. Model simulations were computed with the LES model PALM4U run at two different resolutions (Δx,y,z = 5 and 20 m) and evaluated against observations from a network of microweather stations for a heat wave in 2019 reaching maximum near-surface air temperatures of 37 °C.

During daytime, differences between observed and modeled near-surface air temperatures were small (-3.8 to 1.1 K, mean = 0.9 K), but much larger during night-time and the early morning transition. The latter findings can be explained by an overestimated modeled ground heat flux resupplying too much energy offsetting the radiative cooling leading to overestimated modeled air temperatures by up to +9 K (mean = 5.3 K). Further, results showed that in areas where the actual urban structure is reproduced well by the model resolution, differences between observed and modeled wind speeds were lower. Our findings indicate that a spatial resolution smaller than the mean building height produce more accurate model results for wind speeds. Many differences in model-observation intercomparison are explained by an overestimated modeled turbulent kinetic energy (TKE) causing inflated turbulent mixing in the air, which leads to distorted model output particularly for the urban nocturnal boundary layer.

How to cite: Späte, E., Sungur, L., Schneider, J., Babel, W., and Thomas, C. K.: Simulating an extreme heat event in a mid-sized city in Europe: validating and analyzing the relevance of spatial resolution in the urban LES model PALM4U with an observation network, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15015, https://doi.org/10.5194/egusphere-egu23-15015, 2023.

Climate change and a rapidly increasing urban population make cities more vulnerable to hazardous weather events. The need for a better understanding of meteorological processes and an improvement of the weather prediction system in an urban environment is crucial to mitigate these impacts and protect the population. 

The “Paris Olympics” international Research and Demonstration Project, endorsed by WMO, aims at improving meteorological research in urban meteorological processes and weather forecasting systems at 100m resolution. It mainly focuses on extreme weather events in urban areas such as urban heat islands and thunderstorms. Several study cases have been proposed. They are golden cases observed during the PANAME2022 field campaign in Paris and they represent interesting weather situations to test numerical weather prediction capacities.

The objective of this study is to investigate through the selected cases, the influence of Paris’s urban environment on thunderstorms. An ensemble of hectometric simulations is built using the Meso-NH research atmospheric model initialized and forced by the members of the AROME-EPS ensemble prediction system which has a 1.3 km horizontal resolution. For each case, two sets of ensemble simulations are performed; to identify the main processes driving the interactions between urban environment and thunderstorms: one with a fine-scale surface description of the city (using a multi-layer urban scheme) and another one where the urban surface is replaced with vegetation.

The first results show that it remains challenging to correctly simulate the location of thunderstorms. Nevertheless, the ensemble technique combined with urban and non-urban city description is effective to discriminate random effects from real trends on  the urban environment impacts on thunderstorms.

How to cite: Forster, A., Masson, V., and Augros, C.: Study of the urban effect of Paris on several thunderstorm cases in 2022, using hectometric ensemble simulations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3249, https://doi.org/10.5194/egusphere-egu23-3249, 2023.

Increasing urbanization and its implication for human health, outdoor thermal comfort, air quality or energy consumption necessitate a need for high-resolution urban modelling. In this study, we evaluate uSINGV, a coupled urban-atmosphere research model used by the Singapore Meteorological Service, for four clear-day over Singapore using two different spatial resolutions of 300 and 100 m, respectively. The model is modified to incorporate urban morphology and land use/land cover datasets which are based on European Space Agency climate change initiative data (ESA CCI) at 300 and local datasets at 100 m spatial resolution. The evaluation is carried out for near-surface variables such as temperature, specific humidity, wind speed, and turbulent surface fluxes using Kling-Gupta efficiency (KGE'), root mean square error (RMSE) and mean absolute error (MAE) as model evaluation metrics. Results suggest that temperature and specific humidity are similar for 300 m and 100 m spatial resolution. On the other hand, for 100 m (300 m), the 10 m wind speed has a KGE’ of 0.45 (0.15), RMSE of 0.69 (1.42) m/s, and MAE of 0.55 (1.26) m/s, hence showing improvements from 300 to 100 m spatial resolution. In addition, sensible heat flux for 100 m resolution simulations is closer to observations, while latent heat flux is overestimated. Overall, uSINGV is able to produce reliable simulations at 100 m spatial resolution, thereby showing promise for improved understanding of detailed urban climate processes.

How to cite: Patel, P., Chen, S., Dipankar, A., Roth, M., Lean, H., Zhang, H., and Moise, A.: Comparison of 300 m and 100 m uSINGV clear-day simulations for Singapore, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10849, https://doi.org/10.5194/egusphere-egu23-10849, 2023.

The urban environment and climate change are essential factors to consider for applications involving urban planning and human health. Although these factors influence estimations of energy consumption and thermal comfort, buildings in France are still generally designed and renovated without accounting for these specific conditions but by considering present rural weather conditions. The first objective of this study is to develop an approach to explore building design and renovation choices while accounting for the urban environment and climate change. The second objective is to find which design and renovation choices are relevant to improve thermal comfort and reduce energy consumption (and therefore GHG emissions).

First, we use observations and simulations of weather conditions in several cities of France (representing different climatic zones), for the present and future climate (2050), to analyze urban conditions and estimate energy consumption. Second, we run building simulations for rural and urban situations, and for present and future climate conditions, to investigate the effect of the urban environment and climate change on the operation of buildings, and the effect of building scenarios on the urban climate.

How to cite: Blond, N., Breton, F., Micolier, A., and Mendez, M.: A modeling approach to address building energy consumption and thermal comfort under urban climate change, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14169, https://doi.org/10.5194/egusphere-egu23-14169, 2023.

How to cite: Vurro, G., Constantinidou, K., and Hadjinicolaou, P.: Comparison of urban canopy models (UCMs) over the area of Nicosia, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5473, https://doi.org/10.5194/egusphere-egu23-5473, 2023.

This study summarizes the inter-comparison of three urban canopy models that are able to derive local-scale urban air temperature from rural atmospheric data and urban land use and built form characteristics at an hourly resolution. The focus of this presentation are the findings of the sensitivity analysis and the lessons learned in the process. The evaluated models are the Urban Weather Generator (UWG), the Vertical City Weather Generator (VCWG), the Surface Urban Energy and Water Balance Scheme (SUEWS). The evaluation is done against a two-week-long air temperature and relative humidity measurement conducted in the neighborhood of Újlipótváros in Budapest, Hungary. 
 
The study found a good agreement between modeled and observed air temperature values with a root mean square error (RMSE) remaining between 1–2ºC when calculated for the entire period. However, when separated per day- and nighttime, as well as per cyclonic and anticyclonic periods, the RMSE of the models increased up to 2–3ºC—particularly when calculated for nighttime and/or for anticyclonic periods. The sensitivity analyses shed light on additional shortcomings in the models. It revealed UWG’s low sensitivity to trees and vegetation. In this regard, especially the presence of trees in the urban canopy were not captured by the model. The analysis also found discrepancies regarding VCWG’s model physics, as the model responded to the increase in shortwave radiative forcing with decreasing air temperatures. The study will conclude with a set of recommendations for model developers and users.

How to cite: Gal, C.: A model inter-comparison and sensitivity analysis of three urban canopy models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15513, https://doi.org/10.5194/egusphere-egu23-15513, 2023.

Snowfall and snow depth are important elements of the climate system which can have a significant impact on the transport sector, local economy, water resources and local thermal regime.

This study aims to identify the future trends of snowfall and snow depth in the most important urban areas in Romania, based on high-resolution regional climate models (RCM) data, made available through the EURO-CORDEX, and bias-corrected RCM simulations available în the RoCliB dataset. Ten different regional climate models with a target resolution of 10 km and two emission scenarios were considered, namely the moderate (RCP4.5) and business-as-usual (RCP8.5) scenarios. The study covers the interval from 2021 to 2100.

In order to predict the future snow depth, snow cover duration and snowfall amounts based on the available parameters from the RCM simulations, it was necessary to identify the complex relationship between snowfall, snow melting, temperature and precipitation. In order to do this, we first extracted the ERA5 reanalysis data from 1981 to 2020 for each urban area, and then employed a Bayesian Regularized Neural Network (BRNN). The resulting model was used to predict the future variables for all major urban areas in Romania.

A general trend of decreasing snowfall amounts, mean snow depth and snow cover duration was observed for all analyzed areas and for both emission scenarios. Important regional variations were also observed, with some areas no significant change for the 2021 – 2050 interval compared to the observation period, which could be explained mostly by the increasing winter precipitation predicted by the RCM simulation. The results also showed an increased possibility of some years virtually lacking any snow cover and snowfall precipitation, especially after 2050 in the business-as-usual scenario (RCP8.5). However, an increased variability was also observed, with extreme snowfall events remaining possible even in the latter half of the study interval.

How to cite: Hrițac, R., Sfîcă, L., Breabăn, I.-G., and Amihăesei, V.-A.: The expected effect of climate change on snowfall amounts and snow depth in the major urban areas of Romania, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12497, https://doi.org/10.5194/egusphere-egu23-12497, 2023.

According to NASA's temperature record, Earth in 2021 was about 1.1 degrees Celsius warmer than the late 19th century average, the start of the industrial revolution. The rate of Global Warming (GW), however, differs across different regions of the planet. The Mediterranean is one of the "hotspots" of climate change, with more prominent temperature increases throughout the 20th and 21st centuries (Giorgi 2006). Since the mid-20th century, the average temperature over the Mediterranean has been increasing above the global average. The recent temperature record reveals an annual mean temperature for the entire basin that is approximately 0.4°C above the global mean (Lange 2021). This increase is even higher on the Spanish coast, which has experienced increases of more than 2°C (Arellano 2022).

The aim of this paper is to analyze the warming process in the main Spanish urban areas since unified records were kept in the early 1970s. For this purpose, the evolution experienced by temperatures between 1971 and 2022 in 21 meteorological stations representative of all the Spanish Autonomous Communities is analyzed. Barcelona, Madrid, Valencia, Zaragoza, Seville, Malaga, Bilbao, Valladolid, Ciudad Real, Badajoz, Asturias, Corunya, Ourense, Murcia, Logroño, Palma de Mallorca, Las Palmas de Gran Canaria and Santa Cruz de Tenerife, are studied.

The results show that, if on a global scale temperatures have risen 0.94°C since 1971, the increase in the main cities of peninsular Spain has been 2.17°C. And 2022 will be the warmest year on record. The research carried out differentiates the evolution experienced by maximum and minimum temperatures, showing that the continental influence is mainly manifested in the increase of maximum temperatures, while in the area of Mediterranean influence, the increase of minimum temperatures is more pronounced. On the other hand, the Cantabrian and Atlantic coasts, as well as, above all, the Canary Islands, show less pronounced increases, below 2°C.

The study also presents the heat and cold waves (Serra 2022) experienced by the cities studied. Diurnal heat waves (DHW) have increased from 0.6 per year per weather station in the decade 1971-1980, to 1.71 in 1981-1990, 1.81 in 1991-2000, 2.72 in 2001-2010, and 3.84 in 2011-2020. 2022, with 7.11 DHW per station, is the year with the highest number of diurnal heat waves in the entire series. Regarding nocturnal heat waves (NHW) they have increased from 0.47 per station per year in the decade 1971-1980, to 1.53 (1981-1990), 1.57 (1991-2000), 3.55 (2001-2010), and 4.63 (2011-2020). Again 2022 is the year with the highest number of NHW, with 7.61 per weather station.

2022 appears, therefore, as the warmest year since records have been kept, and the one in which a greater number of NHW has been experienced.

How to cite: Roca, J., Arellano, B., and Zhang, X.: Global Warming in Spanish Cities (1971-2022), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16349, https://doi.org/10.5194/egusphere-egu23-16349, 2023.

Poli-HE, a coupled hydrological-energy budget model was developed to simulate the surface water, and energy fluxes between soil and shallow atmospheric boundary for the city of Milano, Northern Italy. So doing, we describe the urban heat island effect, i.e. differences in land surface temperature (LST) between paved/urban, and green/natural areas, i.e. parks, and suburban agricultural patches.

Energy and water balance equations are linked through soil water content (W), and latent heat flux (LE), calculated as a function of the LST. W in turn drives (actual) evapotranspiration, thus driving (water) mass balance. Input variables were used from i) meteorological stations, air temperature (Ta), net radiation (Rn), rainfall (R), and ii) satellite images, giving leaf area index (LAI) and land surface temperature were used for model tuning.

The results show large differences in LST between urban/green areas, high during summer, viz 3-4 °C, lower in winter, viz 0.3°C. During 2010-2021 max surface temperature in Milano was +37.3°C in urban areas, and to +33.6°C in the green areas.

The model was then used for future projections of LST, using outputs of the Global Circulation Model EC-Earth3.0, constrained to shared socio-economic pathways SSP1-2.6 and SSP5-8.5 of the CMIP6. We analysed near (2030-2041), medium (2050-2061) and long-term (2080-2091). On average in the city of Milano, LST is projected between +0.14°C (SSP 2.6 in the medium-term), and +6.35°C (SSP 8.5 in the long-term). The jump of LST between urban/green areas would reduce against average increase of air temperature, and LST, i.e. large increase of air temperature will be less and less dampened by the present green area cover.

How to cite: Casale, F., Zhang, W., and Bocchiola, D.: Urban heat island under climate change. The case study of Milano, Italy., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12896, https://doi.org/10.5194/egusphere-egu23-12896, 2023.

In this study, land use/land cover changes were examined to investigate their impact on the urban heat load of the City of Dubrovnik in the present and future climate. Dubrovnik is situated in the Mediterranean, which has been referenced as one of the most responsive regions to climate change. Therefore, it is crucial to investigate the effects of different substrates on the heat load and its possible mitigation. Firstly, urban heat load, in the current morphology of the city, is investigated in the present and future climate conditions by using data observed at the local meteorological station and data obtained from regional climate models of the EURO-CORDEX initiative. Also, the urban climate model MUKLIMO_3 is utilized to obtain the spatial distribution of the heat load. Climate indices based on measured data (summer days and tropical nights) show that the heat load has been increasing in the last 50 years. The spatial distribution of the heat load in the City of Dubrovnik in the present climate indicates that the highest heat load is in the public and residential parts of the city. Furthermore, during the nighttime, heat load decreases with a reduction in the density of buildings. Climate indices obtained by simulations of the model MUKLIMO_3 for future climate scenarios (rcp4.5 and rcp8.5) show that the heat load will increase in the entire city domain, with the strongest increase in its urbanized parts. In this study, the impact of modifications in land use/land cover (like changes in the fraction of buildings, impervious surfaces, vegetation and albedo of the roofs) on the heat load are examined. It is demonstrated that these changes will decrease the heat load to some extent. However, the impact is locally limited and significantly smaller than the contribution of global warming. Therefore, land use/land cover changes can mitigate the urban heat load. However, even more comprehensive interventions cannot eliminate the overall increase in the urban heat load due to global warming.   

How to cite: Boras, M., Žgela, M., and Herceg Bulić, I.: Impact of land use/land cover changes on the urban heat load - a case study for the city of Dubrovnik, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5207, https://doi.org/10.5194/egusphere-egu23-5207, 2023.

Territories are complex environments with many issues at stake. The issues of human health and energy consumption for instance are linked to local weather and climate change. Despite the scientific and societal developments of the last decades, some of the physical and social processes of territorial climate change are still not well understood, and the implementation of mitigation and adaptation is slow. The following studies address these aspects of processes, mitigation, and adaptation, based on different methods in climate and social sciences.

The first study investigates the seasonality of weather conditions in Europe by using classification approaches (weather types, local analogues) on climate observations, simulations and projections. Simulations are close on average to the observed variability, winter conditions decrease while summer conditions increase, and Mediterranean seasonality expands Northwest while Scandinavian seasonality declines. 

The second study investigates the social perception of climate change and the acceptability of territorial options (mitigation, adaptation) by inhabitants and decision-makers, based on field interviews and foresight activities in the Gulf of Morbihan (France). A strong territorial seasonality (climate, socio-economy) and a complex role of climate change are found, as well as general agreement between local experiences and scientific knowledge. Despite divergent visions among inhabitants, two long-term scenarios and about twenty short-term actions emerged from foresight activities.

A third study investigates the effect of urban parameters on city temperature, and how urban planning options can optimize thermal comfort and reduce energy consumption and GHG emissions, based on urban climate observations and simulations. The city size drives urban warming, followed by urban fraction but building heights can cool the city depending on the season. Model adjustment and sensitivity simulations are presented for the urban planning approach.

How to cite: Breton, F.: Territorial climate change: understanding, mitigating and adapting, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14056, https://doi.org/10.5194/egusphere-egu23-14056, 2023.

The population growth and its concentration within cities, on the one hand lead to an increment of water demand and on the other hand affect the urban water cycle with frequent urban flood events associated to rain extreme events. New approaches to the water management are currently being developed where a role is assigned to rain water harvesting (RWH). RWH provides water for non-potable uses (private and public, indoor and outdoor), reducing water consumption. Moreover, RWH allows to partially retain water on site, reducing the probability of failures of the sewerage system during heavy rain events.

These positive effects are easily evaluable at a building scale when well-known behavioral models are used, while the evaluation becomes often more complex at an urban scale, due to the lack of characteristics and demographic data about all the buildings in the city. In our work, we consider RWH impact at the urban scale, by means of the representative building concept.

We focus on several hypothetical retrofitting scenarios for the residential buildings of Turin (Italy): 1) domestic use of rainwater (e.g., toilet flushing and the washing machine), where buildings are independent of each other, and 2) two public uses of rainwater (the irrigation of public green areas and street washing), for which we have hypothesized that the rainwater collection takes place at a district scale. We estimate a reduction of 42% in the non-potable water consumption for domestic use (values vary across the municipal districts from 29% to 62%, according to the characteristics of the buildings), while for irrigation and street washing, that require a lower amount of water, about 80% of the water can be provided by RWH. The highest reduction of the flow peak conveyed to the sewerage system during extreme storms is reached in the domestic use scenario (about 60%, quite constant across the city), while for public uses the retention capacity is very low.

Finally, our estimations based on historical rainfall series are examined against different climate change scenarios.

How to cite: Carollo, M., Butera, I., and Revelli, R.: Rainwater harvesting potential: the Turin case for an analysis at the urban scale, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-848, https://doi.org/10.5194/egusphere-egu23-848, 2023.