Near‐surface‐temperature lapse rates on the Prince of Wales Icefield, Ellesmere Island, Canada: implications for regional downscaling of temperature
Bibliographic record
Abstract
Abstract Screen temperatures were monitored from May 2001 to April 2003 in an array of 25 sites on the Prince of Wales Icefield, Ellesmere Island, Canada. The observational network covered an area of ca 15 650 km2 and spanned an altitude ranging from 130 to 2010 m above sea level. The spatial array provides a record of near‐surface‐temperature lapse rates and mesoscale temperature variability on the icefield. The mean daily lapse rate in the 2‐year record is − 4.1° C km−1, with an average summer lapse rate of − 4.3° C km−1. Surface‐temperature lapse rates in the region are therefore systematically less than the free‐air lapse rates that are typically adopted for extrapolations of sea‐level temperature to higher altitudes. Steep lapse rates, resembling moist adiabatic rates in the free air (−6 to − 7° C km−1), are more common in summer at our site and are associated with enhanced cyclonic activity (low‐pressure and high relative vorticity) and southerly flow aloft. In contrast, northerly, anticyclonic flow prevails when summer lapse rates are weak (above − 2° C km−1). The low surface‐temperature lapse rates and their systematic synoptic variability have important implications for applications that require downscaling or extrapolation of surface‐ or boundary‐layer temperatures, such as modelling of glacier mass balance. We illustrate this in an analysis of observed versus modelled snowmelt on the icefield. Copyright © 2006 Royal Meteorological Society.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".