Seasonal Predictions of Regional and Pan-Antarctic Sea Ice With a Dynamical Forecast System
Bibliographic record
Abstract
Operational seasonal to interannual forecasting systems are in continued development around the world. Various studies have applied models to the dynamical forecasting of sea ice, particularly in the Arctic. The Antarctic, however, has received relatively little attention, with few previous endeavours to quantify operational forecast skill of sea ice. This study assesses sea ice extent prediction skill of the Canadian Seasonal to Interannual Prediction System version 2 (CanSIPSv2) in the Pan-Antarctic domain as well as in various sectors of the Southern Ocean. The forecast skill of GEM-NEMO, one of two constituent models that together comprise CanSIPSv2, is found to generally exceed that of the other, CanCM4i. This difference is potentially due to substantial model drift of sea ice extent away from observations in CanCM4i, in addition to their different initializations of sea ice thickness. Both models show significant forecast skill exceeding that of an anomaly persistence forecast. Prediction skill was found to vary substantially across different sectors of the Southern Ocean. Moreover, our analysis also finds that CanSIPSv2 forecast skill in the Antarctic shows a dependence on time period, demonstrating generally lower skill than seen in the Arctic over the years 1980–2010, in contrast to generally higher skill than in the Arctic over the years 1980–2019.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".