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. Meteorological and glaciological observations are not always available to measure glacier energy and mass balance directly, so models of energy balance processes are often necessary to understand glacier response to meteorological variability and climate change. This paper explores the theoretical and empirical response of a mid-latitude glacier in the Canadian Rocky Mountains to the daily and interannual variations in the meteorological parameters that govern the surface energy balance. The model's reference conditions are based on 11 years of in situ observations from an automatic weather station at an elevation of 2660 m, in the upper ablation area of Haig Glacier. We use an energy balance model to run sensitivity tests to perturbations in temperature, specific humidity, wind speed, incoming shortwave radiation, and glacier surface albedo. The variables were perturbed one at a time for the duration of the glacier melt season, May to September, for the years 2002–2012. The experiments indicate that summer melt has the strongest sensitivity to interannual variations in incoming shortwave radiation, albedo, and temperature, in that order. To explore more realistic scenarios where meteorological variables and internal feedbacks such as the surface albedo co-evolve, we use the same perturbation approach using meteorological forcing from the North American Regional Reanalysis (NARR) over the period 1979–2014. These experiments provide an estimate of historical variability in Haig Glacier surface energy balance an d melt for years prior to our observational study. The methods introduced in this paper provide a methodology that can be employed in distributed energy balance modelling at regional scales. They also provide the foundation for theoretical framework that can be adapted to compare the climatic sensitivity of glaciers in different climate regimes, e.g., polar, maritime, or tropical environments.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".