arXiv:2606. 02610v1 Announce Type: cross Abstract: Ocean general circulation models (OGCMs) are essential to climate science but computationally expensive, limiting ensemble size and forcing scenarios.
By Yuan Yuan, Jesse Rusak, Alexander Merose, Adam Subel, Pavel Perezhogin, Alistair Adcroft, Carlos Fernandez-Granda, Laure Zanna
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: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
HClimRep‑Ocean is a machine‑learning emulator that operates directly on the native unstructured mesh of the FESOM2 ocean model, trained on a 209‑year AWI‑CM3 control run and run without atmospheric forcing except at initialization. It shows strong skill for current forecasts at 30‑day lead times, outperforming all references, while temperature and salinity forecasts are best served by a damped‑anomaly persistence approach. In independent OceanBench testing, a reanalysis‑trained variant achieves the lowest RMSE against GLORYS reanalysis, demonstrating the competitiveness of the native‑mesh approach.
By Kacper Nowak, Aleksei Koldunov, Nikolay Koldunov, Savvas Melidonis, Ankit Patnala, Simon Grasse, Julius Polz, Christian Lessig, Martin Schultz, Thomas Jung
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.
By Christopher B. Womack, Shahine Bouabid, Andrei Sokolov, Popat Salunke, Glenn Flierl, Sebastian D. Eastham, Noelle E. Selin
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.