Projected Climate Change Indices over Central Europe Using Dynamically Downscaled CMIP6 Models

Reliable climate projections foremost for temperature and precipitation are essential for countries adaptation and mitigation planning. The newest ensembles of climate models, used to construct projections of future climate, is CMIP6. This study will use CMIP6 regional climate model daily data from the portal of World Climate Research Program (WCRP) Coordinated Regional Downscaling Experiment (CORDEX). The main goal of this study is to estimate changes in climate indices for temperature (T) and precipitation (P) in Central Europe. Three models are selected (MIROC6, MRI-ESM2-0 and TaiESM1(AS-RCEC)) for three scenarios (SSP 245, SSP 370 and SSP 585). We use a baseline period of 1991-2010 and two future periods: 2031-50 (near future) and 2081-2100 (far future). Three temperature indices were considered: (i) number of frost days with minimal air T under 0 °C, (ii) number of ice days with maximal T below 0 °C and (iii) tropical nights (TN) index when minimal T exceed 20°C. Five precipitation indices were chosen to estimate climate change: (i) yearly mean precipitation, (ii) Simple daily intensity, (iii) number of extreme days with more than 20 mm/day, (vi) Consecutive dry and (v) wet days.

The study of the three model's ensemble concludes, a decrease by more than 30 % and 50 % in the freezing days (frost and ice) respectively for near and far future by SSP 585, the number of tropical nights is multiplied by 6 to 9 times foremost for South Germany and Austria. Regarding precipitation parameter, the annual maximum precipitation will increase by more than 100 mm/year with a slightly added rainfall amount (0.4 mm/day) for of SD index. Extreme rainy days rise by about 30 and 50 days by SSP 585 scenario respectively during the near and far future. The analysis of the wet/dry indices showed a slight increase foremost for the dry period length and the number of CDD period. A natural progression of this work is to analyze all the CMIP6 model's ensemble to set up more robust results.