Samudra 2: Scaling Ocean Emulators across Resolutions
Ocean general circulation models (OGCMs) are essential to climate science but computationally expensive, limiting ensemble size and forcing scenarios. Neural emulators promise orders-of-magnitude speedups, yet existing ocean emulators have not combined fine spatial resolution with multi-year autoregressive rollouts. Samudra, the first autoregressive neural ocean emulator to produce multi-decade global rollouts, is limited to $1^\circ$ resolution and exhibits two long-horizon failure modes: \emph{variance collapse}, the loss of temporal variability, and \emph{imprinting artifacts}, in which velocity patterns leak into deep-ocean fields. We present Samudra 2, which introduces a wider U-Net ...
| Authors | Yuan Yuan, Jesse Rusak, Alexander Merose et al. |
|---|---|
| Primary category | cs.CE |
| Published | 24 May 2026 |
| Last updated at source | 21 June 2026 |
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