Nonuniform Contribution of Internal Variability to Recent Arctic Sea Ice Loss
Bibliographic record
Abstract
Abstract Over the last half century, the Arctic sea ice cover has declined dramatically. Current estimates suggest that, for the Arctic as a whole, nearly one-half of the observed loss of summer sea ice cover is not due to anthropogenic forcing but rather is due to internal variability. Using the 40 members of the Community Earth System Model Large Ensemble (CESM-LE), our analysis provides the first regional assessment of the role of internal variability on the observed sea ice loss. The CESM-LE is one of the best available models for such an analysis, because it performs better than other CMIP5 models for many metrics of importance. Our study reveals that the local contribution of internal variability has a large range and strongly depends on the month and region in question. We find that the pattern of internal variability is highly nonuniform over the Arctic, with internal variability accounting for less than 10% of late summer (August–September) East Siberian Sea sea ice loss but more than 60% of the Kara Sea sea ice loss. In contrast, spring (April–May) sea ice loss, notably in the Barents Sea, has so far been dominated by internal variability.
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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 | 0.001 | 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) | 0.002 | 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".