Review of 'Surface energy balance sensitivity to meteorological variability on Haig Glacier, Canadian Rocky Mountains'
Bibliographic record
Abstract
In this paper, theoretical considerations as well as an energy balance model are employed to assess the surface energy balance sensitivity to variations in meteorological variables.The methods are applied at an automatic weather station (AWS) site on a mid-latitude glacier in the Canadian Rocky Mountains.In addition to the in situ AWS observations over the period 2002-2012, meteorological data from a reanalysis product are used to force the model.Only the main melt season (June-August or May-September) is considered.The paper reads well and is written in good English.However, the methods used are not always described in enough detail, in particular regarding the energy balance C1 TCD Interactive commentPrinter-friendly version Discussion paper model.Some model elements are not introduced at all, others are mentioned at a too late point in the manuscript.See the specific comments below for an overview.Apart from model parts not being described, I do not think the model and approach used are suitable for the sensitivity analysis performed in this paper.The surface energy balance contains important feedback mechanisms, which are pointed out by the authors at places in the manuscript.Although they account for albedo changes associated with increased surface melt, they do not seem to include the opposite effect of summer snowfalls on the albedo.More importantly, they do not calculate surface temperature internally in the model, while this variable is easily affected by changing atmospheric conditions.In its turn, it changes the outgoing longwave radiation and the turbulent fluxes.The authors do mention that surface temperature is generally at the melting point in the summer months, but not in the early and late melt season.Still, most of their results are presented for the entire melt season.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".