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.
By Graham Clyne, Guillaume Couairon, Guillaume Gastineau, Claire Monteleoni, Anastase Charantonis
arXiv:2510. 02415v3 Announce Type: replace-cross Abstract: Machine learning models for the global atmosphere that are capable of producing stable, multi-year simulations of Earth's climate have recently been developed.
By Bosong Zhang, Timothy M. Merlis
arXiv:2605.16929v2 Announce Type: replace
Abstract: Global climate models are essential tools to simulate past and potential future pathways of climate change, as well as associated climate impacts....
By Graham Clyne, Julia Kaltenborn, Peer Nowack, Claire Monteleoni, Anastase Charantonis
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.
By Karandeep Singh, Stefan Rahimi, Chad W. Thackeray, Stephen Cropper, Alex Hall
We introduce a stretched-grid artificial intelligence (AI) weather forecasting model with 2-km resolution over the western United States and part of the Northeast Pacific and approximately 31-km resol...
arXiv:2608.30795v1 Announce Type: cross
Abstract: End-to-end weather forecasting systems produce skillful global gridded and station forecasts directly from raw Earth observations, replacing the nume...
By Rodrigo Almeida, Noelia Otero, Jost Arndt, Simon Baur, Wojciech Samek, Jackie Ma