Atmospheric drivers of melt-related ice speed-up events on the Russell Glacier in Southwest Greenland
Bibliographic record
Abstract
Abstract. The Greenland ice sheet is a major contributor to current and projected sea level rise in the warming climate. However, uncertainties in Greenland’s contribution to future sea level rise remain, partly due to challenges in constraining the role of ice dynamics. One process that has the potential to indirectly affect the mass budget of the ice sheet are transient ice accelerations, or ice speed-up events, lasting from one day to a week and triggered by overloading the subglacial drainage system with an increase in water supply. In this study, we identify melt-induced ice speed-up events at the Russell Glacier, Southwest Greenland, in order to analyse synoptic patterns driving these events. The short-term speed-up events are identified from daily ice velocity time series collected from six GPS stations along the glacier, for each summer (May–September) from 2009 to 2012. In total, 45 ice speed-up events are identified, of which 36 are considered melt-induced events where melt is derived from two in-situ observational datasets and one regional climate model forced by ERA5 reanalysis. 16 out of the 45 speed-up events co-occur with lake drainage events, and only four are linked with extreme rainfall events. The 36 melt-induced speed-up events occur during synoptic patterns that can be grouped into three main clusters: (1) patterns that resemble atmospheric rivers with a landfall in Southwest Greenland, (2) patterns with anticyclonic blockings centred over Southwest Greenland, and (3) patterns that show low pressure systems centred either south or southeast of Greenland. Out of these clusters, the one resembling atmospheric river patterns is linked to the strongest speed-up events induced by a 2–3 day continuously increasing surface melt driven by anomalously high sensible heat flux and incoming longwave radiation. In the other two clusters, the net shortwave radiation dominates the contribution to the melt energy. As the frequency and intensity of these weather patterns may change in the warming climate, so may the frequency and intensity of ice speed-up events, ultimately altering the mass loss of the ice sheet.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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".