Variabilities in climate sensitivities and mass balance of four High Mountain Asian glaciers
Bibliographic record
Abstract
We analyse the mass balance evolution and climate sensitivities of four glaciers of High Mountain Asia (HMA) over the last five decades: Tuyuksu and Sary Tor glaciers (Northern and Central Tien Shan, Central Asia), Chhota Shigri Glacier (West-Central Himalaya), and an unnamed glacier (Glacier No. 4) (Eastern Himalaya). Using declassified Corona and Hexagon imageries (1960–1980) and recent high-resolution optical satellite data, we estimate mass loss rates of −0.3 ± 0.1 m w.e a −1 (Tuyuksu and Chhota Shigri, 1971–2020), −0.6 ± 0.1 m w.e a −1 (Sary Tor, 1973–2023) and − 0.4 ± 0.1 m w.e. a −1 (Glacier No. 4, 1969–2022). A calibrated mass balance model (SnowModel) coupled with an ice dynamics model was used to simulate long-term annual and seasonal mass balance. Sensitivity analysis indicates temperature has a much stronger influence than precipitation, with Glacier No. 4 being most climate sensitive. Even in the most optimistic future climate scenario (SSP126), temperatures are projected to rise by ~2 °C with a ~ 10 % increase in precipitation, leading to continuous mass loss. Sensitivity based projections suggest losses within a range of 0.8 m w.e. a −1 (Chhota Shigri) and 1.8 m w.e. a −1 (Glacier No. 4), with higher losses under more extreme scenarios. Glacier No. 4 faces the greatest threat due to insufficient precipitation to counterbalance warming. These findings highlight the vulnerability of HMA glaciers and the challenge of maintaining equilibrium under future climate scenarios.
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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 | 0.001 | 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".