Evaluation of different methods to model near-surface turbulent fluxes for a mountain 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 four aerodynamic bulk methods (labeled as C methods), commonly used to estimate turbulent heat fluxes for a sloped glacier surface, and two less commonly used bulk methods developed from katabatic flow models. The C methods differ in their parameterizations of the bulk exchange coefficient that relates the fluxes to the near-surface measurements of mean wind speed, air temperature, and humidity. The methods' performance 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 a mountain glacier, using one-level meteorological and OPEC observations from multi-day periods in the 2010 and 2012 summer seasons. The analysis of the two independent seasons yielded the same key findings, which include the following: first, 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 a substantial overestimation of the friction velocity. This overestimation is most pronounced during the katabatic flow conditions, corroborating the previous findings that the M–O theory works poorly in the presence of a low wind speed maximum. Second, the method based on a katabatic flow model (labeled as the KInt method) outperforms any C method in simulating the friction velocity; however, the C methods outperform the KInt method in simulating the sensible-heat fluxes. Third, the best overall performance is given by a hybrid method, which combines the KInt approach with the C method; i.e., it parameterizes eddy viscosity differently than eddy diffusivity. An error analysis reveals that the uncertainties in the measured meteorological variables and the roughness lengths produce errors in the modeled fluxes that are smaller than the differences between the modeled and observed fluxes. This implies that further advances will require improvement to model theory rather than better measurements of input variables. 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".