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The Ocean Model Kept Its Climate for Years
A probabilistic neural model produced stable multi-year upper-ocean and sea-ice ensembles under atmospheric forcing.
Summary
A probabilistic neural model produced stable multi-year upper-ocean and sea-ice ensembles under atmospheric forcing.
DLESyM-Ocean is trained with a patch energy-score loss to simulate global upper-ocean and sea-ice conditions. When driven by atmospheric forcing, it produced spatially coherent ensembles with limited bias relative to reanalysis and remained stable during multi-year autoregressive runs. Case studies included a sea-ice extreme, a marine heatwave, the 2023 El Niño transition and the 2023 global-temperature spike. The evidence is comparison with reanalysis and selected historical cases, not a coupled operational forecast deployment.
Why it matters
A probabilistic neural model produced stable multi-year upper-ocean and sea-ice ensembles under atmospheric forcing.
Limits and context
- The evidence is comparison with reanalysis and selected historical cases, not a coupled operational forecast deployment.
Key claims
A probabilistic neural model produced stable multi-year upper-ocean and sea-ice ensembles under atmospheric forcing.
Qualification: The evidence is comparison with reanalysis and selected historical cases, not a coupled operational forecast deployment.
Evidence: source-2026-08-13-012
Sources
- arXiv preprint 2608.11545arXiv · primary research
Corrections
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