Real‐time estimation of Arctic sea ice thickness through maximum covariance analysis
Bibliographic record
Abstract
Abstract A challenge for model‐based seasonal predictions of sea ice is an accurate representation of sea ice initial conditions, particularly sparsely observed sea ice thickness (SIT). The Canadian Seasonal to Interannual Prediction System (CanSIPS) currently initializes SIT by nudging simulated values toward a model‐based climatology. To improve on this, we use sea ice data from Pan‐Arctic Ice Ocean Modeling and Assimilation System to investigate how accurately SIT can be estimated in real time using better observed and physically relevant predictors. We (1) test the skill of several predictors using maximum covariance analysis (MCA), (2) apply an approach which blends sea ice concentration and lagged (4 month averaged) sea level pressure, and (3) compare this method against the current CanSIPS initialization scheme over 1981–2012. The MCA‐based statistical model reduces SIT areal mean and temporal mean absolute errors by 48% relative to the current CanSIPS initialization and shows consistent skill estimating ice volume in all months (r = 0.95).
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".