Downscaling aims to process and refine global climate model output to provide information at spatial and temporal scales suitable for impact studies. In response to the current challenges posed by climate change and variability, downscaling techniques continue to play an important role in the development of user-driven climate information and new climate services and products. In fact, the "user's dilemma" is no longer that there is a lack of downscaled data, but rather how to select amongst the available datasets and to assess their credibility. In this context, model evaluation and verification is growing in relevance and advances in the field will likely require close collaboration between various disciplines.
Furthermore, epistemologists have started to revisit current practices of climate model validation. This new thread of discussion encourages to clarify the issue of added value of downscaling, i.e. the value gained through adding another level of complexity to the uncertainty cascade. For example, the ‘adequacy-for-purpose view’ may offer a more holistic approach to the evaluation of downscaling models (and atmospheric models, in general) as it considers, for example, user perspectives next to a model’s representational accuracy.
In our session, we aim to bring together scientists from the various geoscientific disciplines interrelated through downscaling: atmospheric modeling, climate change impact modeling, machine learning and verification research. We also invite philosophers of climate science to enrich our discussion about novel challenges faced by the evaluation of increasingly complex simulation models.
Contributions to this session may address, but are not limited to:
- newly available downscaling products,
- applications relying on downscaled data,
- downscaling method development, including the potential for machine learning,
- bias correction and statistical postprocessing,
- challenges in the data management of kilometer-scale simulations,
- verification, uncertainty quantification and the added value of downscaling,
- downscaling approaches in light of computational epistemology.