Surface energy balance sensitivity to meteorological variability on Haig Glacier, Canadian Rocky Mountains
Bibliographic record
Abstract
Abstract. Energy exchanges between the atmosphere and the glacier surface control the net energy available for snow and ice melt. This paper explores the response of a midlatitude glacier in the Canadian Rocky Mountains to daily and interannual variations in the meteorological parameters that govern the surface energy balance. We use an energy balance model to run sensitivity tests to perturbations in temperature, specific humidity, wind speed, incoming shortwave radiation, glacier surface albedo, and winter snowpack depth. Variables are perturbed (i) in isolation, (ii) including internal feedbacks, and (iii) with co-evolution of meteorological perturbations, derived from the North American regional climate reanalysis (NARR) over the period 1979–2014. Summer melt at this site has the strongest sensitivity to interannual variations in temperature, albedo, and specific humidity, while fluctuations in cloud cover, wind speed, and winter snowpack depth have less influence. Feedbacks to temperature forcing, in particular summer albedo evolution, double the melt sensitivity to a temperature change. When meteorological perturbations covary through the NARR forcing, summer temperature anomalies remain important in driving interannual summer energy balance and melt variability, but they are reduced in importance relative to an isolated temperature forcing. Covariation of other variables (e.g., clear skies, giving reduced incoming longwave radiation) may be partially compensating for the increase in temperature. The methods introduced in this paper provide a framework that can be extended to compare the sensitivity of glaciers in different climate regimes, e.g., polar, maritime, or tropical environments, and to assess the importance of different meteorological parameters in different regions.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".