Assessment of decadal variability in sea ice in the Community Earth System Model against a long-term regional observational record: implications for the predictability of an ice-free Arctic
Bibliographic record
Abstract
Abstract Uncertainties in the timing of a seasonal ice cover in the Arctic Ocean depend on model physics and parameterizations, natural variability at decadal timescales and uncertainties in climate scenarios and forcings. We use the Gridded Monthly Sea-Ice Extent and Concentration, 1850 Onward product to assess the simulated decadal variability from the Community Earth System Model – Large Ensemble (CESM-LE) in the Pacific, Eurasian and Atlantic sector of the Arctic where a longer observational record exists. Results show that sea-ice decadal (8-16 years) variability in CESM-LE is in agreement with the observational record in the Pacific sector of the Arctic, underestimated in the Eurasian sector of the Arctic, specifically in the East-Siberian Sea, and slightly overestimated in the Atlantic sector of the Arctic, specifically in the Greenland Sea. Results also show an increase in variability at decadal timescales in the Eurasian and Pacific sectors during the transition to a seasonally ice-free Arctic, in agreement with the observational record although this increase is delayed by 10-20 years. If the current sea-ice retreat in the Arctic continues to be Pacific-centric, results from the CESM-LE suggest that uncertainty in the timing of an ice-free Arctic associated with natural variability is realistic, but that a seasonal ice cover may occur earlier than projected.
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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 | 0.006 |
| 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 | 0.001 | 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".