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. 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 variabil
The continuous advancements in AI and deep learning are enabling the emulation of complex physical systems with increasing accuracy, addressing the computational bottleneck of traditional models.
Improving the accuracy and scalability of ocean emulators is critical for climate science, allowing for more robust predictions and scenario planning, which impacts policy and resource management.
The ability to run multi-year, high-resolution ocean simulations much faster could accelerate climate research and improve the fidelity of climate models necessary for critical decision-making.
- · Climate scientists
- · Policymakers
- · AI research community
- · Environmental research institutions
- · Traditional high-performance computing centers (if AI emulators significantly re
- · Legacy climate modeling approaches
Faster and more detailed climate simulations become accessible to a wider range of researchers, enabling more extensive studies.
Improved climate predictions could lead to better disaster preparedness and more effective climate change mitigation strategies globally.
Enhanced understanding of ocean dynamics could inform new approaches to sustainable resource management, marine conservation, and even geopolitical strategies related to maritime territories.
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