Projected changes in heat load in Carpathian Basin cities during the 21st century

In this study the changes in the nighttime heat load in Carpathian Basin cities during the 21st century were examined. To quantify the heat load, the tropical night climate index was used. The MUKLIMO_3 local scale climate model was used to describe the urban processes and the land use classes were defined by the local climate zones. The expected change was examined over three periods: the 1981–2010 was taken as reference period using the Carpatclim database and the 2021–2050 and 2071–2100 future periods using EURO-CORDEX regional model simulation data for two scenarios (RCP4.5 and RCP8.5). To combine the detailed spatial resolution and the long time series, a downscaling method was applied. Our results show that spectacular changes could be in the number of tropical nights during the 21st century and the increasing effect of the urban landform is obvious. In the near future, a slight increase can be expected in the number of tropical nights, which magnitude varies from city to city and there is no major difference between the scenarios. However, at the end of the century the results of the two scenarios differ: the values can be 15-25 nights in case of RCP4.5 and 30-50 nights in case of RCP8.5. The results show that dwellers could be exposed to high heat load in the future, as the combined effect of climate change and urban climate, thus developing various mitigation and adaptation strategies is crucial

Quality of the Governing Temperature Variables in WRF in Relation to Simulation of Primary Biological Aerosols

We have evaluated three prognostic variables in Weather Research and Forecasting (WRF) model, mean daily temperature, daily maximum temperature, and daily minimum temperature using 9 months of model simulations at 36 and 12 km resolution, and compared the results with 1182 observational sites in north and central Europe. The quality of the results is then determined in the context of the governing variables used in crop science, forestry, and aerobiological models. We use the results to simulate the peak of the birch pollen season (aerobiology), growth of barley (crop science), and development of the invasive plant pathogen Hymenoscyphus pseudoalbidus (the cause of ash-dieback). The results show that the crop and aerobiological models are particularly sensitive to grid resolution and much higher quality is obtained from the 12 km simulations compared to 36 km. The results also show that the summer months have a bias, in particular for maximum and minimum temperatures, and that the low/high bias is clustered in two areas: continental and coastal influenced areas. It is suggested that the use of results from meteorological models as an input into biological models needs particular attention in the quality of the modelled surface data as well as the applied land surface modules

Subsampling impact on the climate change signal over poland based on simulations from statistical and dynamical downscaling

Most impact studies using downscaled climate data as input assume that the selection of few global climate models (GCMs) representing the largest spread covers the likely range of future changes. This study shows that including more GCMs can result in a very different behavior. We tested the influence of selecting various subsets of GCMs on the climate change signal over Poland from simulations based on dynamical and empirical-statistical downscaling methods. When the climate variable is well simulated by the GCM, such as temperature, results showed that both downscaling methods agree on a warming over Poland by up to 2° or 5°C assuming intermediate or high emission scenarios, respectively, by 2071-2100. As a less robust simulated signal through GCMs, precipitation is expected to increase by up to 10% by 2071-2100 assuming the intermediate emission scenario. However, these changes are uncertain when the high emission scenario and the end of the twenty-first century are of interest. Further, an additional bootstrap test revealed an underestimation in the warming rate varying from 0.5° to more than 4°C over Poland that was found to be largely influenced by the selection of few driving GCMs instead of considering the full range of possible climate model outlooks. Furthermore, we found that differences between various combinations of small subsets from the GCM ensemble of opportunities can be as large as the climate change signal. © 2019 American Meteorological Society