Western North American and Switzerland glaciers experience unprecedented mass loss over the last three years
Bibliographic record
Abstract
Glaciers in western North America (WNA) and Switzerland represent important sources of freshwater, especially during times of drought. We employ extensive airborne laser altimetry campaigns in WNA coupled with in-situ surface mass balance measurements for both regions to quantify recent mass change. Over the last three years glaciers within these regions respectively lost mass at rates of -22.8±7.4 and -1.7±0.3 Gt yr-1 which, for both regions, represents an approximate twofold increase in mass loss compared to the period 2010-2020. Based on the estimated glacier volume in the year 2020, total volume change in these regions was depleted by 9% (WNA) and 10% (Switzerland) over the last three years. The year 2023 represents the year of greatest common mass loss for both regions where glaciers in both WNA and Switzerland respectively lost -37.1±10.4 and -1.8±0.3 Gt yr-1. Meteorological conditions that favored high rates of mass loss included low winter snow accumulation, early-season heat waves, and prolonged warm, dry conditions. Loss of firn, high transient snow lines, and potential impurity loading due to wildfires (WNA) or Saharan dust (Switzerland) darkened glaciers and thereby accelerated melt via an increase in absorbed shortwave radiation available for melt. This ice-albedo feedback will lead to continued accelerated loss unless recently exposed dark firn and ice at high elevation can be buried by subsequent snowfall. Rates of mass loss for the years 2021-2023 exceed even those projected for unabated global emissions though the twenty-first century, signaling the need to rapidly mitigate greenhouse gas emissions if glaciers in both regions are to survive.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".