GCM-based future projections of European weather types obtained by Self‑Organizing-Maps and a novel GCM selection technique

The interdisciplinary research project "BayTreeNet" examines the reactions of forest ecosystems to climate dynamics. To establish a relationship between tree growth and climate, it is important to know that in the mid-latitudes, local climate phenomena often show a strong dependence on the large-scale climate weather types (WT), which significantly determine the climate of a region through frequency and intensity. Different WT show various weather conditions at different locations, especially in the topographically diverse region of Bavaria. The meaning of every WT is the physical basis for the climate-growth relationships established in the dendroecology sub-project to investigate the response of forests to individual WT at different forest sites. Complementary steps allow interpretation of results for the past (20th century) and projection into the future (21st century). One hypothesis is that forest sites in Bavaria are affected by a significant influence of climate change in the 21st century and the associated change in WT.

The automated classification of large-scale weather patterns is presented by Self-Organizing-Maps (SOM) developed by Kohonen, which enables visualization and reduction of high-dimensional data. The poster presents the SOM-setting which was used to classify the WT and the results of past environmental conditions (1990-2019) for different WT in Europe based on ERA5 data. Morover, it shows a future projection until 2100 for European WT and their respective environmental conditions. The projections are based on a novel GCM selection technique for two scenarios (ssp1-2.6 and ssp5-8.5) to obtain a range of the most likely conditions.