The climate warming trend and city growth contribute to the generation of excessive heat in urban areas. This could be reduced by introducing vegetation and open water surfaces in urban design. This study evaluates the cooling efficiency of green and blue infrastructure to reduce urban heat load using a set of idealized case simulations and a real city model application for Vienna. The idealized case simulations show that the cooling effect of green and blue infrastructure is dependent on the building type, time of the day and in case of blue infrastructure, the water temperature. The temperature reduction and the size of the cooled surface are largest in densely built-up environments. The real case simulations for Vienna, which include the terrain, inhomogeneous land use distribution and observed climate data, show that urban planning measures should be applied extensively in order to gain substantial cooling on the city scale. The best efficiency can be reached by targeted implementation of minor but combined measures such as a decrease in building density of 10 %, a decrease in pavement by 20 % and an enlargement in green or water spaces by 20 %. Additionally, the modelling results show that equal heat load mitigation measures may have different efficiency dependent on location in the city due to the prevailing meteorological conditions and land use characteristics in the neighbouring environment.

Extreme heat events are natural hazards affecting many regions of the world. This study uses an example of the six largest cities in Austria to demonstrate the potential of urban climate model simulations applied in prediction mode providing detailed information on thermal conditions. For this purpose, the urban climate model MUKLIMO_3 of the German Meteorological Service (DWD) coupled with the hydrostatic numerical weather prediction model, ALARO, is used to simulate the development of the urban heat island (UHI) in Austrian cities for the summer period of 2019 with a horizontal resolution of 100 m. In addition to the evaluation of UHI predicting skills, other relevant variables, such as humidity and wind characteristics on hourly basis, are also analysed in this paper. Model evaluation confirmed that the MUKLIMO_3 microscale model had the capacity to simulate the main thermal spatiotemporal patterns in urban areas; however, a strong dependence on the input data from the mesoscale model was found. Our results showed large benefit in prediction of maximum air temperatures in urban areas, while the relative humidity predictions of MUKLIMO_3 appear to be much less plausible and show large variety of model prediction skills. Urban climate model simulations using real atmospheric conditions can facilitate better quantification and understanding of day-to-day intra-urban variations in microclimate as well as provide a basis for evaluation of the microclimate prediction skills of mesoscale numerical models with urban extensions.

Frequent heat waves in the latest decade have drawn attention due to large mortality and economic losses. Urban areas are in additional danger of extreme heat due to the well‐known urban heat island (UHI) phenomena. Here, we investigate the effect of the North Atlantic Oscillation (NAO) in modifying urban heat in Zagreb, Croatia. Both winter (wNAO) and summer (sNAO) NAO components are analysed in terms of indirect (lagged) and direct effects on the urban heat load. The strongest increase in heat load was found when positive wNAO was followed by negative sNAO, while the opposite situation led to the strongest decrease. Situations with the same wNAO and sNAO polarity resulted in much weaker amplitude of changes due to opposing effects on climate parameters over investigated area. These findings were confirmed using ground measurements from meteorological station, as well as an urban climate model. However, not only changes in the total heat load, but also differences regarding UHI intensity are detected. It is found that soil moisture, at least partially, drives such behaviour. The combination of positive wNAO followed by negative sNAO leads to a longer drier and warmer conditions over the Zagreb area and vice versa. Hence, in situations with an extended drier period the increase in heat load over green areas is found to be stronger compared to the densely built‐up regions resulting in lower spatial gradient. This was confirmed by additional modelling experiments where the standardized precipitation evapotranspiration index (SPEI) was used to analyse the effect of drought conditions on UHI. These findings are additionally confirmed using satellite land surface temperature data. The potential applicability of this study includes UHI mitigation by irrigation of green urban areas in situations when seasonal forecasts indicate that warmer and drier conditions are expected.

The numerical model MUKLIMO_3 is used to simulate the urban climate of an imaginary city as an illustrative example to demonstrate that the residential areas with deprived socio-economic conditions can exhibit an enhanced heat load at night, and thus more disadvantageous environmental conditions, compared with the areas of higher socio-economic status. The urban climate modelling simulations differentiate between orographic, natural landscape, building and social effects, where social differences are introduced by selection of location, building type and amount of vegetation. The model results show that the increase of heat load can be found in the areas inhabited by the poor population as a combined effect of natural and anthropogenic factors. The unfavourable location in the city and the building type, consisting of high density, low housing with high fraction of pavement and small amount of vegetation contribute to the formation of excessive heat load. This abstract example shows that the enhancement of urban heat load can be linked to the concept of a socially stratified city and is independent of the historical development of any specific city.

Urban environments experience amplified thermal stress due to the climate change, leading to increased health risks during extreme temperature events. Existing numerical weather prediction systems often lack the spatial resolution required to capture this phenomenon. This study assesses the efficacy of a coupled modeling system, the numerical weather prediction AROME model and the land-surface model SURFace EXternalisée in a stand alone mode (SURFEX-SA), in forecasting air temperatures at high resolutions ($2.5$km to $100$m) across four Austrian cities (Vienna, Linz, Klagenfurt and Innsbruck). The system is updated with the, according to the author’s knowledge, most accurate land use and land cover input to evaluate the added value of incorporating detailed urban environmental representations. The analysis focuses on the years 2019, 2023, and 2024, examining both summer and winter seasons. SURFEX-SA demonstrates improved performance in specific scenarios, particularly during nighttime in rural and suburban areas during the warmer season. By comprehensively analyzing this prediction system with operational and citizen weather stations in a deterministic and probabilistic mode across several time periods and various skill scores, the findings of this study will enable readers to determine whether high-resolution forecasts are necessary in specific use cases.