During the simulation of the urban heat island phenomenon, the accurate representation of urban geometry in numerical models is crucial. In this study, the local climate zone (LCZ) system was incorporated into the Weather Research and Forecasting (WRF) model in order to facilitate proper land surface information for the model integrations. After the calculation of necessary input canopy parameters, based on local static datasets, simulations were performed to test the model’s performance in predicting near-surface air temperature (T_a) and urban heat island intensity (ΔT) under a heatwave period in July 2017. The modelled values were evaluated against the observations of the local urban climate monitoring system. The results suggest that WRF with a single-layer canopy scheme and the LCZ-based static database was able to capture the spatiotemporal variation of the aforementioned variables reasonably well. The daytime T_a was generally overestimated in each LCZ. At nights, slight overestimations (underestimations) occurred in LCZ 6, LCZ 9, and LCZ D (LCZ 2 and LCZ 5). The mean ΔT was underestimated in the night-time; however, the daytime ΔT was estimated accurately. The mean maxima (minima) of ΔT were underestimated (overestimated) with around 1.5–2 °C, particularly in LCZ 2 and LCZ 5. Some components of the surface energy budget were also computed to shed light on the inter-LCZ differences of T_a. It was concluded that the nocturnal ground heat flux was about five times higher in urban LCZs than in the rural LCZ D, which resulted in a reduced cooling potential over the urbanized areas.

Climatic factors significantly influence travellers’ destination selection and demand for tourism products. To maintain sustainable tourism development, it is essential to monitor how tourist destinations will be affected by the projected future changes in the climate system. This study aims to reveal the future impact of climate change for tourism sector in Hungary using the Holiday Climate Index specified for urban tourism (HCI-Urban) and a modified version of Tourism Climate Index (mTCI). The paper employs an ensemble of multiple climate models (ALADIN5.2 and REMO2015) and emission scenarios (RCP4.5 and RCP8.5) to determine the future evolution of indices. Initially, the reference climatic conditions derived from the observational dataset CarpatClim-HU are presented. Then, the climate model results are validated against the observational database for both indices. Afterwards, bias-corrected model outputs are analysed for the periods 2041–2070 and 2071–2100 based on the ensemble of models and scenarios. The spatial distributions of indices are depicted on a monthly level and at a Hungarian district spatial scale. Both the observational and model results highlight that the climatic conditions are more favourable in spring and autumn compared to the summer months. It is also demonstrated that the climatic conditions in Hungary are expected to be more favourable or unchanged from autumn until spring, while there is a general worsening in summer. In terms of the future directions, larger uncertainties are found in May and September. Based on the anticipated trends, the paper pinpoints sector-specific recommendations for adapting tourism services to the altered climatic conditions.

This study evaluates the pattern of a nighttime climate index namely the tropical nights (Tmin ≥ 20ºC) during the 21st century in several different sized cities in the Carpathian Basin. For the modelling, MUKLIMO_3 microclimatic model and the cuboid statistical method were applied. In order to ensure the proper representation of the thermal characteristics of an urban landscape, the Local Climate Zone (LCZ) system was used as landuse information. For this work, LCZ maps were produced using WUDAPT methodology. The climatic input of the model was the Carpatclim dataset for the reference period (1981–2010) and EURO-CORDEX regional model outputs for the future time periods (2021–2050, 2071–2100) and emission scenarios (RCP4.5, RCP8.5). As results show, there would be a remarkable increase in the number of tropical nights along the century, and there is a clearly recognizable increase owing to urban landform. In the near past, the number of the index was 6–10 nights higher in the city core than the rural area where the number of this index was negligible. In the near future this urban-rural trend is the same, however, there is a slight increase (2–5 nights) in the index in city cores. At the end of the century, the results of the two emission scenarios become distinct. In the case of RCP4.5 the urban values are about 15–25 nights, what is less stressful compared to the 30–50 nights according to RCP8.5. The results clearly highlight that the effect of urban climate and climate change would cause serious risk for urban dwellers, therefore it is crucial to perform climate mitigation and adaptation actions on both global and urban scales.

DOI: https://doi.org/10.14232/acta.clim.2018.52.4