Winter Arctic Sea Ice Surface Form Drag During 1999-2021: Satellite Retrieval and Spatiotemporal Variability
Bibliographic record
Abstract
The neutral form drag coefficient is an important parameter when estimating surface turbulent fluxes over Arctic sea ice. The form drag caused by surface features (𝑪 𝒅𝒏,𝒇𝒓 ) dominates the total drag in the winter, but long-term pan-Arctic records of 𝑪 𝒅𝒏,𝒇𝒓 are still lacking for Arctic sea ice. In this study, we first developed an improved surface feature detection algorithm and characterized the surface features (including height and spacing) over Arctic sea ice during the late winter of 2009-2019 using the full-scan laser altimeter data obtained in the Operation IceBridge mission. 𝑪 𝒅𝒏,𝒇𝒓 was then estimated using an existing parameterization scheme. This was followed by applying a satellite-derived backscatter coefficient (𝝈 𝒐 𝒗𝒗 ) to 𝑪 𝒅𝒏,𝒇𝒓 regression model to extrapolate, for the first time, 𝑪 𝒅𝒏,𝒇𝒓 to the pan-Arctic scale for the entire winter season over two decades (from 1999 to 2021). We found that the surface features have a larger height and smaller spacing over multi-year ice (1.15 ± 0.21 m and 142 ± 49 m) than over first-year ice (0.90 ± 0.16 m and 241 ± 129 m). The monthly mean 𝑪 𝒅𝒏,𝒇𝒓 increases through the winter, from 0.2 × 10 −3 in November to 0.4-0.5 × 10 −3 in April. The central Arctic has the largest 𝑪 𝒅𝒏,𝒇𝒓 (up to 2 × 10 −3), but experienced a drop of ~50% in the period from 2001/2002 to 2008/2009. The interannual fluctuations in 𝑪 𝒅𝒏,𝒇𝒓 are strongly linked to the variability of sea ice thickness and deformation, and the latter has become increasingly important for 𝑪 𝒅𝒏,𝒇𝒓 since 2009.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".