Large surface meltwater discharge from the Kangerlussuaq sector of the Greenland ice sheet during the record-warm year 2010 explained by detailed energy balance observations
Bibliographic record
Abstract
Abstract. The year 2010 has been anomalously warm in most of Greenland, most notably in the south and along the western coast. Our study targets the Kangerlussuaq region around 67° N in Southwest Greenland, where the temperature anomalies were record setting. In 2010, the average temperature was 5 °C (2.7 standard deviations) above the 1974–2010 average in the town of Kangerlussuaq. High temperatures were also observed over the ice sheet, with the positive anomaly increasing with altitude. Also surface albedo, from calibrated MODIS measurements, was anomalously low in 2010, chiefly in the upper ablation zone. The low albedo was caused by the high ablation in 2010, which profited in turn from high temperatures, low albedo, and of low wintertime accumulation. The largest melt excess (166%) was found in the upper ablation zone, where higher temperatures and lower albedo contributed equally to the melt anomaly. In total, we estimate that 6.6 km3 of surface meltwater ran off the ice sheet in the Kangerlussuaq catchment area in 2010, exceeding "normal" year 2009 by 145%. When compared to discharge estimated from discharge measurements in the proglacial river we find good agreement. The time lag between the records is caused by storage within and underneath the ice sheet, and suggests adaption of the subglacial drainage system to meltwater availability, with more efficient drainage occurring after the peak of the melt season.
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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".