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. This study uses data from six on-ice weather stations, calibrated MODIS-derived albedo and proglacial river gauging measurements to drive and validate an energy balance model. We aim to quantify the record-setting positive temperature anomaly in 2010 and its effect on mass balance and runoff from the Kangerlussuaq sector of the Greenland ice sheet. In 2010, the average temperature was 4.9 °C (2.7 standard deviations) above the 1974–2010 average in Kangerlussuaq. High temperatures were also observed over the ice sheet, with the magnitude of the positive anomaly increasing with altitude, particularly in August. Simultaneously, surface albedo was anomalously low in 2010, predominantly in the upper ablation zone. The low albedo was caused by high ablation, which in turn profited from high temperatures and low winter snowfall. Surface energy balance calculations show that the largest melt excess (∼170%) occurred in the upper ablation zone (above 1000 m), where higher temperatures and lower albedo contributed equally to the melt anomaly. At lower elevations the melt excess can be attributed to high atmospheric temperatures alone. In total, we calculate that 6.6 ± 1.0 km3 of surface meltwater ran off the ice sheet in the Kangerlussuaq catchment in 2010, exceeding the reference year 2009 (based on atmospheric temperature measurements) by ∼150%. During future warm episodes we can expect a melt response of at least the same magnitude, unless a larger wintertime snow accumulation delays and moderates the melt-albedo feedback. Due to the hypsometry of the ice sheet, yielding an increasing surface area with elevation, meltwater runoff will be further amplified by increases in melt forcings such as atmospheric heat.
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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".