Distributed summer air temperatures across mountain glaciers in the south-east Tibetan Plateau: temperature sensitivity and comparison with existing glacier datasets
Bibliographic record
Abstract
Near-surface air temperature ( T a ) is highly important for modelling glacier ablation, though its spatio-temporal variability over melting glaciers still remains largely unknown. We present a new dataset of distributed T a for three glaciers of different size in the south-east Tibetan Plateau during two monsoon-dominated summer seasons. We compare on-glacier T a to ambient T a extrapolated from several local off-glacier stations. We parameterise the along-flowline sensitivity of T a on these glaciers to changes in off-glacier temperatures (referred to as “temperature sensitivity”) and present the results in the context of available distributed on-glacier datasets around the world. Temperature sensitivity decreases rapidly up to 2000–3000 m along the down-glacier flowline distance. Beyond this distance, both the T a on the Tibetan glaciers and global glacier datasets show little additional cooling relative to the off-glacier temperature. In general, T a on small glaciers (with flowline distances <1000 m) is highly sensitive to temperature changes outside the glacier boundary layer. The climatology of a given region can influence the general magnitude of this temperature sensitivity, though no strong relationships are found between along-flowline temperature sensitivity and mean summer temperatures or precipitation. The terminus of some glaciers is affected by other warm-air processes that increase temperature sensitivity (such as divergent boundary layer flow, warm up-valley winds or debris/valley heating effects) which are evident only beyond ∼70 % of the total glacier flowline distance. Our results therefore suggest a strong role of local effects in modulating temperature sensitivity close to the glacier terminus, although further work is still required to explain the variability of these effects for different glaciers.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".