Spring melt pond fraction in the Canadian Arctic Archipelago predicted from RADARSAT-2
Bibliographic record
Abstract
Abstract. Melt ponds form on the surface of Arctic sea ice during spring, influencing how much solar radiation is absorbed into the sea ice–ocean system, which in turn impacts the ablation of sea ice during the melt season. Accordingly, melt pond fraction (fp) has been shown to be a useful predictor of sea ice area during the summer months. Sea ice dynamic and thermodynamic processes operating within the narrow channels and inlets of the Canadian Arctic Archipelago (CAA) during the summer months are difficult for model simulations to accurately resolve. Additional information on fp variability in advance of the melt season within the CAA could help constrain model simulations and/or provide useful information in advance of the shipping season. Here, we use RADARSAT-2 imagery to predict and analyze peak melt pond fraction (fpk) and evaluate its utility to provide predictive information with respect to sea ice area during the melt season within the CAA from 2009–2018. The temporal variability of RADARSAT-2 fpk over the 10-year record was found to be strongly linked to the variability of mean April multi-year ice area with a statistically significant detrended correlation (R) of R=-0.89. The spatial distribution of RADARSAT-2 fpk was found to be in excellent agreement with the sea ice stage of development prior to the melt season. RADARSAT-2 fpk values were in good agreement with fpk observed from in situ observations but were found to be ∼ 0.05 larger compared to MODIS fpk observations. Dynamically stable sea ice regions within the CAA exhibited higher detrended correlations between RADARSAT-2 fpk and summer sea ice area. Our results show that RADARSAT-2 fpk can be used to provide predictive information about summer sea ice area for a key shipping region of the Northwest Passage.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".