Sensitivity of net mass-balance estimates to near-surface temperature lapse rates when employing the degree-day method to estimate glacier melt
Bibliographic record
Abstract
Abstract Glacier mass-balance models that employ the degree-day method of melt modeling are most commonly driven by surface air temperatures that have been downscaled over the area of interest, using digital elevation models and assuming a constant free air lapse rate that is often taken to be the moist adiabatic lapse rate (MALR: –6.5°Ckm–1). Air-temperature lapse rates measured over melting glacier surface are, however, consistently less steep than free air values and have been shown to vary systematically with lower-tropospheric temperatures. In this study, the implications of including a variable near-surface lapse rate in a 26 year (1980–2006) degree-day model simulation of the surface mass balance of Devon Ice Cap, Nunavut, Canada, are examined and compared with estimates derived from surface air temperatures downscaled using a constant near-surface lapse rate equal to the measured summer mean (–4.9°Ckm–1) and the MALR. Our results show that degree-day models are highly sensitive to the choice of lapse rate. When compared with 23 years of surface mass-balance measurements from the northwest sector of the ice cap, model estimates are significantly better when surface air temperatures are downscaled using a modeled daily lapse rate rather than a constant lapse equal to either the summer mean or the MALR.
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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 | 0.002 | 0.007 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".