In the scenic project the focus lies on the investigation of extreme weather events in future (warmer) climate scenarios. For that purpose, tropospheric winds in climate models are adapted such that certain recent extreme weather events are reproduced by the model. Using those boundary conditions climate models are run with future elevated CO2 concentrations in order to investigate how the extreme events would enfold under such conditions. The future climate conditions applied correspond to 2K or 4K warming, respectively.

Our part at GFZ is concerned with the impacts of climate change on certain socio-economic parameters. For that we use machine learning methods. Those methods can describe complex relationships between input data (here the results of climate models) and target data (here the socio-economic parameters). When these relationships are described numerically (e.g. by a trained artificial neural net) it is possible to estimate the target parameters under future climate conditions from the climate models in the future scenario.