Evaluation of different methods to model near-surface turbulent fluxes for an alpine glacier in the Cariboo Mountains, BC, Canada
Bibliographic record
Abstract
Abstract. As part of surface energy balance models used to simulate glacier melting, choosing parameterizations to adequately estimate turbulent heat fluxes is extremely challenging. This study aims to evaluate a set of bulk methods commonly used to estimate turbulent heat fluxes for a sloped glacier surface. The methods differ in their parameterizations of the bulk exchange coefficient that relates the fluxes to the mean meteorological variables measured 2 m above a glacier surface. The performance of 23 bulk approaches in simulating 30-min sensible and latent heat fluxes is evaluated against the measured fluxes from an open path eddy-covariance (OPEC) method. The evaluation is performed at a point scale of an alpine glacier, using one-level meteorological and OPEC observations from a multi-day periods in the 2010 and 2012 summer season. The analysis of the two independent seasons yielded similar findings, listed as following. The bulk method, with or without the commonly used Monin–Obukhov (M–O) stability functions, overestimates the turbulent heat fluxes over the observational period, mainly due to an overestimation of the momentum flux. In the absence of OPEC-derived M–O stability parameter, no method can successfully predict this parameter, which results in poor performances of the M–O stability corrections and consequently the bulk method. The OPEC-derived 30-min momentum flux is linearly related to the measured wind speed, contrary to the proposed quadratic relation by the commonly used bulk methods. An approach based on a katabatic flow model, which assumes a linear relation between the shear stress and the wind speed, outperforms any other bulk approach that we tested in simulating the momentum flux. In agreement with the katabatic flow model, we show that in a more stable atmosphere the bulk exchange coefficient for momentum is smaller. The sensible heat flux can be more successfully modeled if the bulk exchange coefficients for momentum and heat are allowed to follow different parametrization schemes, rather than assuming equal schemes as is the case in the common bulk methods. Further data from different glaciers are needed to investigate any universality of these findings.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".