Albedo feedback enhanced by smoother Arctic sea ice
Bibliographic record
Abstract
Abstract The ICESat operational period 2003–2008 coincided with a dramatic decline in Arctic sea ice—linked to prolonged melt season duration and enhanced melt pond coverage. Although melt ponds evolve in stages, sea ice with smoother surface topography typically allows the pond water to spread over a wider area, reducing the ice‐albedo and accelerating further melt. Here we develop this theory into a quantitative relationship between premelt sea ice surface roughness and summer melt pond coverage. Our method, applied to ICESat observations of the end‐of‐winter sea ice roughness, can account for 85% of the variance in advanced very high resolution radiometer (AVHRR) observations of the summer ice‐albedo. An Arctic‐wide reduction in sea ice roughness from 2003 to 2008 explains a drop in ice‐albedo that resulted in a 16% increase in solar heat input to the sea ice cover, which represents ten times the heat input contributed by earlier melt onset timing over the same period.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".