Modeling 2 m air temperatures over mountain glaciers: Exploring the influence of katabatic cooling and external warming
Bibliographic record
Abstract
Abstract Air temperature is one of the most relevant input variables for snow and ice melt calculations. However, local meteorological conditions, complex topography, and logistical concerns in glacierized regions make the measuring and modeling of air temperature a difficult task. In this study, we investigate the spatial distribution of 2 m air temperature over mountain glaciers and propose a modification to an existing model to improve its representation. Spatially distributed meteorological data from Haut Glacier d'Arolla (Switzerland), Place (Canada), and Juncal Norte (Chile) Glaciers are used to examine approximate flow line temperatures during their respective ablation seasons. During warm conditions (off‐glacier temperatures well above 0°C), observed air temperatures in the upper reaches of Place Glacier and Haut Glacier d'Arolla decrease down glacier along the approximate flow line. At Juncal Norte and Haut Glacier d'Arolla, an increase in air temperature is observed over the glacier tongue. While the temperature behavior over the upper part can be explained by the cooling effect of the glacier surface, the temperature increase over the glacier tongue may be caused by several processes induced by the surrounding warm atmosphere. In order to capture the latter effect, we add an additional term to the Greuell and Böhm (GB) thermodynamic glacier wind model. For high off‐glacier temperatures, the modified GB model reduces root‐mean‐square error up to 32% and provides a new approach for distributing air temperature over mountain glaciers as a function of off‐glacier temperatures and approximate glacier flow lines.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".