Satellite microwave observations of the interannual variability of snowmelt on sea ice in the Southern Ocean
Bibliographic record
Abstract
Snowmelt processes on Antarctic sea ice are examined. We present a simple snowmelt indicator based on diurnal brightness temperature variations from microwave satellite data. The method is validated through extensive field data from the western Weddell Sea and lends itself to the investigation of interannual and spatial variations of the typical snowmelt on Antarctic sea ice. We use in‐situ measurements of physical snow properties to show that despite the absence of strong melting, the summer period is distinct from all other seasons with enhanced diurnal variations of snow wetness. A microwave emission model reveals that repeated thawing and refreezing cause the typical microwave emissivity signatures that are found on perennial Antarctic sea ice during summer. The proposed melt indicator accounts for the characteristic phenomenological stages of snowmelt in the Southern Ocean and detects the onset of diurnal snow wetting. An algorithm is presented to map large‐scale snowmelt onset based on satellite data from the period between 1988 and 2006. The results indicate strong meridional gradients of snowmelt onset with the Weddell, Amundsen, and Ross Seas showing earliest (beginning of October) and most frequent snowmelt. Moreover, a distinct interannual variability of melt onset dates and large areas of first‐year ice where no diurnal freeze thawing occurs at the surface are determined.
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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 | 0.000 |
| 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".