Glaciers in western North America experience exceptional transient snowline rise over satellite era
Bibliographic record
Abstract
Abstract We analyze changes in the maximum annual transient snowline elevation (MATSL) of glaciers for two regions in western North America from 1984 to 2024 using five satellite remote sensing datasets. MATSL reached its highest elevations in 2019 (155 m above the long-term average) for Alaska (Region 1) and in 2023 (148 m) for Western Canada and USA (Region 2). The rate of MATSL rise accelerated fourfold, increasing from 2.1 ± 0.8 m a−1 in 1984–2010 (r 2 = 0.1, p < 0.01) to 8.9 ± 1.7 m a−1 in 2010–2024 (r 2 = 0.5, p < 0.01). In 2019, 91 glaciers exceeded the 95th percentile MATSL elevation, a threshold indicative of complete loss of the accumulation area, in Region 1. In Region 2, 149 glaciers exceeded this threshold in 2023. Year-to-year variability in MATSL was strongly influenced by mean summer air temperature with sensitivities of +46 m °C−1 (r 2 = 0.54) and +23 m °C−1 (r 2 = 0.30) for Regions 1 and 2, respectively. Mean spring snow water equivalent also played an important role with sensitivities of −351 mm w.e.−1 (r 2 = 0.48) and −155 mm w.e.−1 (r 2 = 0.37), respectively. Per-glacier analysis revealed that south-facing slopes experienced the largest MATSL increases. Terrain attributes, including slope, aspect, hypsometry, and elevation, enhanced MATSL prediction models compared to those using only climate variables. The pronounced rise in MATSL underscores a critical glacier melt feedback mechanism, warranting further investigation. This study highlights the utility of automated MATSL time-series mapping for regional-scale analyses and identifies key limitations and opportunities for future research.
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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 | 0.001 | 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".