The Impact of Climate on Surging at Donjek Glacier, Yukon, Canada
Bibliographic record
Abstract
Abstract. Links between climate and glacier surges are not well understood, but are required to enable prediction of glacier surges and mitigation of associated hazards. Here, we investigate the role of snow accumulation and temperature on surge periodicity, glacier area changes, and timing of surge initiation since the 1930s for Donjek Glacier, Yukon, Canada. Snow accumulation measured in three ice cores collected at Eclipse Icefield, at the head of the glacier, indicate that a cumulative accumulation of 13.1–17.7 m w.e. of snow occurred in the 10–12 years between each of its last eight surges. This suggests that a cumulative accumulation threshold must be passed before the initiation of a surge event, although it remains unclear whether the relationship between cumulative snowfall and surging is due to the consistency in repeat surge interval and decadal average precipitation, or if it is indeed a prerequisite to surging. We also examined the 1968 to 2017 climate record from Burwash Landing, 30 km from the glacier, to determine whether a relationship exists between surge periodicity and an increase of 2.5 °C in mean annual air temperature over this period. No such relationship was found, although each of the past 8 surge events has been less extensive than the previous, with the terminus area approximately 7.96 km2 smaller after the 2012–2014 surge event compared to the ~ 1947 surge event. This study shows that the impacts of climate and surging is not yet understood and suggests that internal glacier processes may play a more important role in controlling glacier surge events.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| 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".