Towards diffusion models for large-scale sea-ice modelling

Understand

We make the first steps towards diffusion models for unconditional generation of multivariate and Arctic-wide sea-ice states.

• While targeting to reduce the computational costs by diffusion in latent space, latent diffusion models also offer the possibility to integrate physical knowledge into the generation process.
• We tailor latent diffusion models to sea-ice physics with a censored Gaussian distribution in data space to generate data that follows the physical bounds of the modelled variables.
• Our latent diffusion models reach similar scores as the diffusion model trained in data space, but they smooth the generated fields as caused by the latent mapping.