Deep graph neural networks for spatiotemporal forecasting of sub-seasonal sea ice: A case study in Hudson Bay
Bibliographic record
Abstract
This study introduces GraphSIFNet ( Graph S ea I ce F orecast neural Net work), a novel graph-based deep learning framework for spatiotemporal sea ice forecasting. GraphSIFNet employs a Graph Long-Short Term Memory (GCLSTM) module within a sequence-to-sequence architecture to predict daily sea ice concentration (SIC) and sea ice presence (SIP) in Hudson Bay over a 90-day time horizon. The use of graph neural networks (GNNs) allows the domain to be discretized into arbitrarily specified meshes, allowing more explicit spatial modeling than approaches based on the convolutional neural network (CNN). This study demonstrates the model’s ability to forecast over an irregular mesh with higher spatial resolution near shorelines. The model is trained using atmospheric data from ERA5 and oceanographic data from GLORYS12. Results demonstrate the model’s superior skill over a linear combination of persistence and climatology as a statistical baseline. The model showed skill particularly in short- to medium-term (up to 35 days) SIC forecasts, with a noted reduction in root mean squared error (RMSE) by up to 10% over the statistical baseline during the break-up season, and up to 5% in the freeze-up season. Long-term (up to 90 days) SIP forecasts also showed significant improvements over the baseline, with increases in accuracy of around 10% even at a lead time of 90 days. The use of an attention-based convolution offered the additional benefit of interpretability by highlighting the primary direction and magnitude of information flow that aligned with the direction of freezing and melting. The study lays the groundwork for future exploration into dynamic graph-based forecasting, and future work forecasting ice-ocean phenomena.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".