arXiv:2607. 16731v1 Announce Type: new Abstract: Marine biogeochemical forecasting is increasingly important for managing marine ecosystems and the carbon cycle, yet global, seasonal forecast products lag far behind physical oceanography, held back by the complexity of the processes involved and by data scarcity.
arXiv:2606. 12141v1 Announce Type: new Abstract: Accurate forecasting of sea surface temperature (SST) in regional seas such as the East Sea is crucial for monitoring marine ecosystems, assessing climate risks, managing fisheries, and conducting naval operations.
Subseasonal-to-seasonal (S2S) forecasting is societally critical, supporting decision-making in sectors ranging from water and agricultural management to disaster risk reduction, energy planning, and...
arXiv:2512. 03606v2 Announce Type: replace Abstract: Accurate marine wind forecasts are essential for safe navigation, ship routing, and energy operations, yet they remain challenging because observations over the ocean are sparse, heterogeneous, and temporally variable.
Neptune is an end‑to‑end data‑driven framework that emulates global ocean and sea‑ice states for subseasonal‑to‑seasonal (S2S) forecasting up to 60 days. It combines Convolutional Neural Networks and Spherical Fourier Neural Operators to capture both local features and global cross‑scale interactions, producing daily outputs for temperature, salinity, currents, sea‑surface height, and sea‑ice metrics at 1° and 0.25° resolution. Evaluated against metrics such as RMSE, CRPS, ACC, and climate indices (ENSO, IOD), Neptune reproduces the spatio‑temporal evolution of oceanic fields and remains stable over long timescales.
arXiv:2408. 11336v3 Announce Type: replace Abstract: Climate change stands as one of the most pressing global challenges of the twenty-first century, with far-reaching consequences such as rising sea levels, melting glaciers, and increasingly extreme weather patterns.
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.
OceanDepths is the first open, global, AI‑ready dataset that pairs satellite‑derived sea surface temperature, salinity, and height with co‑located EN4 subsurface temperature and salinity profiles, complemented by GLORYS12 reanalysis data. It covers 2000–2024 at 0.1°×0.1° spatial resolution and weekly temporal resolution, providing over 9.5 million paired profiles interpolated to 50 depth levels. The dataset’s 4‑D multivariate structure, high resolution, long temporal extent, and extreme sparsity of subsurface observations make it a challenging testbed for novel AI methods, with demonstrated use in subsurface state reconstruction and potential for observation‑based forecasting.
SimCast‑S2S is a generative latent‑diffusion framework designed for probabilistic subseasonal‑to‑seasonal precipitation forecasting. It tackles three key challenges: it uses a diffusion‑based generative pipeline for uncertainty quantification, operates in a compact latent space learned by VAEs for efficient large‑ensemble generation, and employs transfer learning with LoRA to overcome limited training data. On reanalysis data, it outperforms deep‑learning baselines and competes with or surpasses state‑of‑the‑art operational systems such as ECMWF‑S2S.
SimCast‑S2S is a generative latent‑diffusion model designed for probabilistic subseasonal‑to‑seasonal precipitation forecasting. It tackles three key challenges: it uses a diffusion pipeline to capture uncertainty, operates in a compact latent space to enable efficient large‑ensemble generation, and leverages transfer learning with low‑rank adaptation to train on limited reanalysis data after pretraining on climate simulations. The model outperforms deep‑learning baselines and competes with, or surpasses, operational systems such as the ECMWF‑S2S baseline without requiring extensive post‑processing.
arXiv:2508. 10178v3 Announce Type: replace-cross Abstract: Shelf seas are important for the economy and the carbon cycle, but shelf sea observations for carbon pools are often sparse, or highly uncertain.
arXiv:2606. 16076v1 Announce Type: cross Abstract: Multivariate forecasting in physical systems requires models that predict coupled temporal variables while preserving meaningful state evolution.
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.