What historical landfast ice observations tell us about projected ice conditions in Arctic archipelagoes and marginal seas under anthropogenic forcing
Bibliographic record
Abstract
Arctic landfast ice extent and duration are examined from observations, ice assimilations, ocean reanalyses and coupled models. From observations and assimilations, it is shown that in areas where landfast ice conditions last more than 5 months the first-year ice typically grows to more than 2 m and is rarely less than 1 m. The observed spatial distribution of landfast ice closely matches assimilation products but less so for ocean reanalyses and coupled models. Although models generally struggle to represent the landfast ice necessary to emulate the observed import/export of sea ice in regions favourable to landfast ice conditions, some do exhibit both a realistic climatology and a realistic decline of landfast ice extent under an anthropogenic forcing scenario. In these more realistic simulations, projections show that an extensive landfast ice cover should remain for at least 5 months of the year, well into the end of the 21st century. This is in stark contrast with the simulations that have an unrealistic emulation of landfast ice conditions. In these simulations, slow and packed ice conditions shrink markedly over the same period. In all simulations and in areas with landfast ice that lasts more than 5 months, the end-of-winter sea ice thickness remains between 1 and 2 m, well beyond the second half of the century. It is concluded that in the current generation of climate models, projections of winter sea ice conditions in the Canadian Arctic Archipelago and the Laptev Sea are overly sensitive to the representation of landfast ice conditions and that ongoing development in landfast ice parameterization will likely better constrain these projections.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".