Session Description

 Downscaling is a key aspect in most studies of climate change and its consequences, as it reduces the gap between climate model output and observations. There are two widely used methods that are developed by two separate scientific communities: dynamical and empirical-statistical downscaling. While the dynamical downscaling approaches are primarily based on the physical understanding of processes and phenomena, statistical downscaling techniques focus more on reproducing the statistical properties of particular climate variables or phenomena such as the mean, variability and extremes. The two different approaches have different strengths and weaknesses, which implies that they can complement each other.

In this session we invite the submission of abstracts on efforts combining the two downscaling techniques and discussing how the methods can benefit from one another to produce a more reliable and robust climate and climate change signal along with associated uncertainties. This session will include contributions related to statistical analysis techniques that make use of CORDEX datasets, innovative methods of combining different sources of regional downscaled climate information/data, and statistical approaches dealing with the uncertainty inherent in these datasets.

The session will include both oral presentations and posters as well as open discussions.