Regional glacier melt modeling: insights from surface energy balance and positive degree-day comparisons
Bibliographic record
Abstract
Abstract Large-scale glacier mass-balance models often rely on positive degree-day (PDD) melt models, which have known limitations. This study evaluates a relatively simple, elevation-dependent surface energy balance (SEB) model that requires minimal downscaling of climate input data to simulate glacier melt. Using ECMWF Reanalysis v5 (ERA5) reanalysis data and multi-year mass-balance observations from 23 glaciers across Canada, we compare mass-balance models incorporating SEB and PDD components under various calibration scenarios. Initial tests with the uncalibrated SEB model highlight the importance of accurate ERA5 inputs, particularly lapse-rate corrections for 2 m air temperature. Mass-balance simulations with the SEB model that includes calibrated corrections for precipitation and albedo match or outperform those with the PDD model, especially when using a machine learning-derived albedo trained on remote sensing data, which tends to underestimate summer albedo in accumulation zones. Seasonal calibration further improves accuracy of the mass-balance simulations by addressing biases in summer melt and winter accumulation. Despite its simplicity, the SEB model provides a good balance of performance and computational efficiency, emphasizing its utility for regional-scale applications when calibrated appropriately.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".