Evaluating the skill of seasonal forecasts of sea ice in the Southern Ocean: insights from the SIPN South project 2017-2022
Bibliographic record
Abstract
The SIPN South project is an international, coordinated initiative endorsed by the Year Of Polar Prediction (YOPP), that aims at identifying the skill of current seasonal predictions of sea ice around Antarctica. Here, we review and analyze the results of five years of predictions of summer sea ice conducted by 20 groups since 2017, having contributed together more than 1000 forecasts. A wide range of approaches is considered, ranging from statistical data-driven to dynamical process-based models. We evaluate the ability of the forecasts to reproduce observed sea ice area at the circumpolar and regional levels and their skill relative to trivial forecasts (climatology, persistence). We find that a substantial spread exists already at day one in the dynamical forecasts, pointing at problems with the initialization. We also find that the forecasts based on statistical modeling perform generally better than forecasts based on dynamical modeling.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".