3 papers
physics.ao-ph2026
ACE2-NEMO: Coupling an ML atmospheric emulator to a full-depth dynamical ocean model
Bobby Antonio, Kristian Strommen, Pablo Ortega +1
Understanding how fast atmospheric variability shapes slow climate variability and sensitivity remains a central challenge in Earth-system science. Recent advances in machine-learn…
physics.ao-ph2026
Role of the ocean for fast atmospheric evolution revealed by machine learning
Bobby Antonio, Kristian Strommen, Hannah M. Christensen
There have recently been many efforts to create machine learnt atmospheric emulators designed to replace physical models. So far these have mainly focused on medium-range weather f…
physics.ao-ph2025
Seasonal forecasting using the GenCast probabilistic machine learning model
Bobby Antonio, Kristian Strommen, Hannah M. Christensen
Machine-learnt weather prediction (MLWP) models are now well established as being competitive with conventional numerical weather prediction (NWP) models in the medium range. Howev…