Seasonal and Regional Antarctic Sea Ice Biases: A Closer Look at CMIP6
Bibliographic record
Abstract
Antarctic sea ice is poorly reproduced by most global coupled climate models, hindering understanding of historical sea ice behaviour and its broader climate interactions. In this study, a set of satellite-based observations are compared to pre-industrial control (piControl) sea ice concentration in 60 models from the sixth phase of the Coupled Model Intercomparison Project (CMIP6), to identify common and disparate mean state biases. Insufficient summer sea ice is a well-known deficiency; however, it is noted here that most models also underestimate maximum winter ice extent. Delayed onset of autumnal ice advance is common, and seasonal evolution of sea ice in early autumn is strongly correlated to maximum winter ice cover, indicating that deficient autumn ice gain impedes adequate winter expansion. Nearly a third of models overshoot the austral sea ice maximum, exaggerating the existing seasonal asymmetry, while several others omit the asymmetry entirely and instead have two equal-sized seasons. Summer and autumn ice tends to be less compact than observed, though winter compactness is better captured. Regional sea ice biases are most common in the Weddell and Haakon VII seas, as well as in East Antarctica, where winter ice is widely underestimated. These key temporal and spatial deficiencies highlight potential priorities for the improvement of simulated sea ice in future model development.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".