Small-scale variability of snow properties on sea ice: from snow pits to the SnowMicroPen
Bibliographic record
Abstract
Snow on sea ice alters the properties of the underlying ice cover as well as associated exchange processes at the interfaces between atmosphere, sea ice, and ocean. It contributes significantly to the sea-ice mass and energy budgets due to comprehensive seasonal transition processes within the snowpack. Therefore, several studies have shown the importance of comprehensive understanding of snow properties for large-scale estimates in the ice-covered oceans. However, field studies reveal not only a strong seasonality but especially spatial variations on floe-size scales. It is therefore necessary to locate and quantify seasonal snow processes, such as internal snowmelt, snow metamorphism, and snow-ice formation in the Arctic and Antarctic snowpack on small scales. \nDoing so, we present here in-situ observations of physical snow properties from point measurements (snow pits) and transect lines (SnowMicroPen, SMP) during recent expeditions in the Weddell Sea and off the northeastern coast of Ellesmere Island, Canada, from 2013 to 2019, covering summer and winter conditions. \nResults from a case study of snow pit analyses in the Weddell Sea during austral winter reveal a high variability of snow parameters throughout the snowpack. It is shown that snow grain size dominates the spatial variability of the snow pack while snow density variability can be neglected. The additional use of the SMP allows to even quantify length-scale variabilities of snow properties in different ice regimes in both hemispheres. \nOverall, results will improve our understanding of seasonal processes in the snowpack and will guide us towards upscaling approaches of vertical snow layers on Arctic and Antarctic sea ice.
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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 | 0.000 | 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".