Diffusion-Based Joint Temperature and Precipitation Emulation of Earth System Models

arXiv:2404.08797v1 Announce Type: cross
Abstract: Earth system models (ESMs) are the principal tools used in climate science to generate future climate projections under various atmospheric emissions scenarios on a global or regional scale. Generative deep learning approaches are suitable for emulating these tools due to their computational efficiency and ability, once trained, to generate realizations in a fraction of the time required by ESMs. We extend previous work that used a generative probabilistic diffusion model to emulate ESMs by targeting …

abstract arxiv climate climate science computational cs.lg deep learning diffusion earth efficiency emissions future generate generative global physics.ao-ph physics.geo-ph precipitation regional scale science tools type