Near-Surface Temperature Lapse Rates over Arctic Glaciers and Their Implications for Temperature Downscaling
Bibliographic record
Abstract
Distributed glacier surface melt models are often forced using air temperature fields that are either downscaled from climate models or reanalysis, or extrapolated from station measurements. Typically, the downscaling and/or extrapolation are performed using a constant temperature lapse rate, which is often taken to be the free-air moist adiabatic lapse rate (MALR: 68–78C km21). To explore the validity of this approach, the authors examined altitudinal gradients in daily mean air temperature along six transects across four glaciers in the Canadian high Arctic. The dataset includes over 58 000 daily averaged temperature measure-ments from 69 sensors covering the period 1988–2007. Temperature lapse rates near glacier surfaces vary on both daily and seasonal time scales, are consistently lower than theMALR (ablation seasonmean: 4.98Ckm21), and exhibit strong regional covariance. A significant fraction of the daily variability in lapse rates is associated with changes in free-atmospheric temperatures (higher temperatures 5 lower lapse rates). The temperature fields generated by downscaling point location summit elevation temperatures to the glacier surface using temporally variable lapse rates are a substantial improvement over those generated using the static MALR. These findings suggest that lower near-surface temperature lapse rates can be expected under awarming climate and that the air temperature near the glacier surface is less sensitive to changes in the temperature of the free atmosphere than is generally assumed. 1.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".