Specifically in mountains, the local climate is very variable over space and time and available data have large uncertainties. Robust estimates of temperature, precipitation or other climatological variables are essential for calibration and future projections of different types of models in mountainous regions (e.g.; glacier, hydrological or snow models, but also natural hazards or vegetation models). To use the sparse direct measurements or gridded climate products for a specific location, we almost always need to correct or downscale them. The used approaches can range from simple corrective factors or gradients to more complex statistical or dynamical downscaling methods, which account for changes in higher-order statistical properties such as the observed distributions. Even local-scale models with AWS-station observations available need to correct their climatic variables (for precipitation e.g. wind undercatch or orographic effects).

This session is open to anyone interested in an exchange of experiences and struggles when correcting or downscaling climate data, either to allow for better local-scale climate predictions or to use it as input data for impact models (for both local-scale or large-scale models).

The session will start with a short PICO round (3-5 min. presentations depending on the number of participants) where we ask participants to introduce their climate downscaling approach together with the applied spatio-temporal resolution and if relevant their impact model. Depending on the participant’s expertise and interest, there could be some longer methodological inputs before starting the open discussion.