21.10.2025 BOKU-Met Seminar

n this talk, I will present the first paper of my doctoral thesis, which explores the capabilities of state-of-the-art climate projections for impact studies in the energy infrastructure sector. As even regionalized climate projections often lack the fine spatial resolution necessary to accurately represent valleys in mountainous regions like Western Austria, downscaling, bias adjustment, and machine learning techniques are essential for producing reliable results. Still, the challenge remains that certain localized phenomena like foehn are not resolved by the climate models due to the smoothing of complex orography. In this paper, we show how machine learning can be utilized to link synoptic weather patterns to foehn occurrences in the Rhine and Inn valleys. Since foehn directly influences wind power potential in mountainous regions, we investigate how well climate projections can reproduce the conditions linked foehn in a warming climate. Weights for individual climate projections are derived by analysing the performance of EURO-CORDEX models in their ability to produce foehn-enabling conditions in the historical period. Our results reveal a systematic negative bias in annual foehn occurrence for two GCMs and present a weighted trend analysis of foehn occurrence, showing projected changes in annual foehn occurrence as well as in seasonality.

https://bokuvienna.zoom.us/j/95795467199