Melt Onset Variability in the Northern Canadian Arctic Archipelago From Synthetic Aperture Radar and Passive Microwave Satellite Observations, 1979–2024
Bibliographic record
Abstract
The timing of sea ice melt onset (MO) in the Arctic is important because it controls the surface energy balance and influences the amount of sea ice that survives the melt season. Here, we investigate the timing of sea ice MO in the Queen Elizabeth Islands (QEI), located within the Last Ice Area of the Canadian Arctic Archipelago, using Sentinel-1 synthetic aperture radar (SAR) imagery from 2015 to 2024. The Last Ice Area is the most stable ice region in the Arctic, and changes within this region provide insight into the overall health of Arctic sea ice. We used a threshold-based approach to detect MO for every SAR pixel using time-series backscatter. We contextualized the MO record within the framework of the earlier SAR record and the long-term passive microwave (PMW) record. The QEI region exhibited considerable interannual variability in the timing of MO, as detected by SAR, with a mean MO date occurring on day of year (DOY) 157 (± 6 days). From 2015 to 2024, no significant trend toward earlier MO was observed; instead, SAR and PMW data showed a slight delay. Extending the analysis back to 1997, utilizing RADARSAT-1/2 data revealed a 5-day per decade earlier MO trend, whereas PMW data (1979 – 2022) indicated a 1.31 to 1.49-day per decade decrease. Sentinel-1 SAR provided superior spatial detail relative to PMW, particularly near coastlines and in narrow channels and showed greater sensitivity to melt detection, with discrepancies in trends attributed to methodological and data variations.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".