Emulating the Forced Response of Climate Models with Generative Machine Learning
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
The Flow has not summarised this story yet — read it at arXiv Machine Learning.
arXiv:2606. 19302v1 Announce Type: cross Abstract: As deep learning for physical systems continues to grow in popularity, efforts to improve generalizability have primarily focused on designing architectures that embed physical constraints.
As deep learning for physical systems continues to grow in popularity, efforts to improve generalizability have primarily focused on designing architectures that embed physical constraints. However, for machine-learning surrogate climate models (emulators), we show that the low structural diversity in existing scenarios commonly used to generate training data places a ceiling on predictive skill.
arXiv:2509. 15942v3 Announce Type: replace-cross Abstract: Internal variability is a dominant contributor to the uncertainty of predictions at the interannual to decadal timescale.
arXiv:2603. 22320v2 Announce Type: replace Abstract: For decades, physics-based climate models have been used to provide insights for climate decision-making.
arXiv:2603. 22320v3 Announce Type: replace Abstract: For decades, physics-based climate models have been used to provide insights for climate decision-making.
arXiv:2606. 07898v1 Announce Type: new Abstract: High-resolution regional climate simulations provide critical information for climate impacts assessments but remain computationally expensive, motivating the development of machine-learning downscalers and emulators.