Bibliographic record
Abstract
In recent decades, the Arctic minimum sea ice extent has transitioned from a predominantly thick multiyear ice cover to a thinner seasonal ice cover. We partition the total (observed) Arctic summer area loss into thermodynamic (lateral and basal melt) and dynamic (compaction, ridging and export) sea ice area loss during the satellite era from 1979 to 2021 using a Lagrangian sea ice tracking model driven by satellite-derived sea ice velocities. Results show that the thermodynamic signal dominates the total summer ice area loss and the dynamic signal remains small (~20%), even in years when the export is largest. Except in the 1980's and early 1990's when the summer sea ice concentration inside the marginal ice zone remains high and resists convergent motion and in the 2010's when ridging north of the Canadian Arctic Archipelago and export become more important, sea ice loss by compaction dominates the dynamic area loss. Results from a simple (Ekman) free-drift sea ice model, supported by results from the Lagrangian model, suggest that non-linear effects between dynamic and thermodynamic area loss are key for all-time record minimum sea ice extent years. A detailed analysis of two all-time record minimum years (2007 and 2012) — one with a semi-permanent high in the southern Beaufort Sea and the other with a short-lived but extreme storm in the Pacific sector of the Arctic in late summer — shows that compaction by Ekman convergence together with large thermodynamic melt in the marginal ice zone dominated the sea ice area loss in 2007 while, in 2012, it was dominated by Ekman divergence amplified by sea-ice albedo feedback — together with an early melt onset. We argue that Ekman divergence from more numerous and earlier intense summer storms when the sun is high above the horizon is a more likely mechanism for a "first time" ice-free Arctic
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".