Probabilistic Emulation of a Global Climate Model with Spherical DYffusion

Probabilistic Emulation of a Global Climate Model with Spherical DYffusionby Salva Rühling Cachay, Brian Henn, Oliver Watt-Meyer, Christopher S. Bretherton, Rose YuThe paper introduces Spherical DYffusion, the first conditional generative model for probabilistic emulation of a realistic global climate model, offering efficient and accurate climate ensemble simulations.It demonstrates that weather forecasting performance is not a strong indicator of long-term climate performance, a crucial insight for developing climate models.Spherical DYffusion significantly reduces climate biases compared to existing baselines like ACE and DYffusion, achieving errors often closer to the reference simulation's "noise floor".The model generates stable, 10-year-long probabilistic predictions with minimal computational overhead, being more than 25 times faster than the physics-based FV3GFS model it emulates, while also reproducing consistent climate variability.