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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".