Using Cascaded Diffusion Models and Multi-Channel Data Integration for High-Resolution Statistical Downscaling of ERA5 over Denmark

Central to understanding climate change impacts and mitigation strategies is the generation of high-resolution, local-scale projections from global climate models. This study focuses on Danish hydrology, developing models finely tuned to generate essential climate fields such as temperature, precipitation, evaporation, and water vapor flux.

Employing advancements in computer science and deep learning, we introduce a pioneering Cascaded Diffusion Model for high-resolution image generation. This model utilizes our understanding of climate dynamics in a hydrological context by integrating multiple climate variable fields across an expanded North Atlantic domain to produce a model for stable and realistic generation. In our approach, 30 years of low-resolution daily conditioning data (ERA5) are re-gridded to match the 2.5x2.5 km 'ground truth' data (30 years of DANRA), and preprocessed by shifting a 128x128 image within a larger 180x180 pixel area, ensuring varied geographic coverage. This data, along with land-sea masks and topography, is fed as channels into the model. A novel aspect of our model is its specialized loss function, weighted by a signed distance function to reduce the emphasis on errors over sea areas, aligning with our focus on land-based hydrological modeling.

This research is part of a larger project aimed at bridging the gap between CMIP data models and ERA5 and DANRA analysis. It represents the first phase in a three-step process, with future stages focusing on downscaling from CMIP6 to CORDEX-EUROPE models, and ultimately integrating model and analysis work to form a complete pipeline from global projections to localized daily climate statistics.