Sub-kilometer Scale Snow Depth Distribution on Sea Ice of Different Ages and Thickness
Bibliographic record
Abstract
Abstract. Accurately representing the snow depth (SND) distribution on sea ice is essential for sea ice thickness (SIT) retrievals, ecological studies, and climate modeling. Using co-located SND and SIT measurements from multiple Arctic and Antarctic campaigns, this study examines sub-kilometer-scale SND variability, considering both ice type and SIT, and identifies the most suitable statistical distributions to represent SND across different ice ages and thicknesses. First, we examine the statistical properties of SND and their dependence on SIT, finding a linear increase of SND with SIT for new and first-year ice, reflecting concurrent seasonal growth. The ratio between the standard deviation and the mean SND is referred to as the coefficient of variation (CV). A consistent CV ≈ 0.50 is observed to be independent of SIT, allowing variability to be estimated directly from the mean SND. Notably, flooded snow exhibits a lower CV. Furthermore, we investigate four probability density functions (Normal, Log-normal, Gamma, and Skew) and find that the best-fit distribution depends on ice ages, SIT, deformation, and meteorological events such as snow fall and drift. Finally, SND correlation lengths derived from semi-variograms show a positive relation with SIT and are enhanced by snow drift events. The results reveal substantial differences in SND distributions across ice types and SIT during winter and summer, underscoring the importance of ice-condition-dependent parameterizations for representing sub-kilometer SND variability. These findings support improved parameterizations of SND variability at sub-grid scale in remote sensing and climate models.
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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 | 0.000 |
| 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) | 0.001 | 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".