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.
By Davide Donno, Italo Epicoco, Massimo Cafaro, Gabriele Accarino, Mohammad M. Amirian, Viviana Acquaviva, Paola Nassisi, Doroteaciro Iovino, Annalisa Bracco, Simona Masina, Pierre Gentine
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.
By Yuan Yuan, Jesse Rusak, Alexander Merose, Adam Subel, Pavel Perezhogin, Alistair Adcroft, Carlos Fernandez-Granda, Laure Zanna
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.
By Simon Donike, Ruben Cartuyvels, Antonino Ian Ferola, Elisa Carli, Diego Fernandez Prieto, Marie-Helene Rio
arXiv:2608. 10022v1 Announce Type: cross Abstract: The large-scale oceanic and atmospheric forecasts provided by global climate models typically lack sufficient resolution to accurately capture the response of the coastal ocean to atmospheric forcing and coastal circulation that drive fine-scale SST variability.
By Onkar Jadhav, Tim French, Ivica Janekovic, Nicole L. Jones, Matthew Rayson
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.
By Sherkhon Azimov, Susana L\'opez-Moreno, Eric Dolores-Cuenca, JinYong Choi, Sangil Kim
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.
By Gabriela Martinez Balbontin, Anastase Charantonis, Dominique Bereziat, Stefano Ciavatta
arXiv:2607. 19147v1 Announce Type: cross Abstract: Data-driven methods have revolutionized ocean modeling, yet current approaches rely heavily on complete reanalysis datasets, imposing computational constraints and limiting model performance to that of the training data.
By Yangyang Kong, Yutong Jiang, Yanhai Gan, Junyu Dong, Feng Gao, Xiaopei Lin
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.
By Matteo Peduto, Qidong Yang, Jonathan Giezendanner, Devis Tuia, Sherrie Wang
Reliable global ocean forecasting is critical for climate monitoring, marine navigation, and extreme event early warning. Physics-based ocean forecasting models impose prohibitive computational costs,...
arXiv:2608. 16070v1 Announce Type: cross Abstract: Reliable global ocean forecasting is critical for climate monitoring, marine navigation, and extreme event early warning.
By Wei Wu, Xiang Wang, Hongze Leng, Qingye Min, Junxing Zhu, Junqiang Song
arXiv:2608.21767v1 Announce Type: new
Abstract: Accurate modeling of sea ice concentration (SIC) evolution is essential for polar climate assessment and short?range sea ice prediction. Numerical and...
By Maqun Zhang, Feng Gao, Wankun Chen, Hui Yu, Yanhai Gan, Junyu Dong
arXiv:2608. 04230v1 Announce Type: new Abstract: Deep learning models for scientific spatio-temporal downscaling often minimize reconstruction error while failing to preserve physically meaningful multi-scale structure.
By Parth Doshi, Priyanka Aravindan, Vaishnav Vaidheeswaran, Md Mahbub Alam, Gabriel Spadon