Evaluating Winter Turbulent Heat Fluxes in a Hydrodynamic‐Ice Model of the Great Lakes
Bibliographic record
Abstract
Abstract Turbulent heat fluxes are affected by and influence the temperature dynamics and ice conditions of lakes. Significant efforts have been made to develop operational hydrodynamic and ice models for large lakes such as the North American Great Lakes. However, the behavior of surface fluxes in these lakes has previously focused on the ice‐free season and has not yet been fully assessed during winter conditions in the presence of ice. Given the importance of navigation support and regional weather forecasting, we therefore analyze operational configurations of the Great Lakes for modeled fluxes to evaluate them for open water, ice‐covered, and partial ice conditions. We compare the modeled fluxes with eddy covariance‐based observed fluxes from the Great Lakes Evaporation Network. While observed latent heat fluxes have periods of high values both during ice‐free and ice‐covered periods, we find that elevated open water fluxes in early winter can be well modeled. However, the modeled fluxes during ice‐covered periods appear less accurate, where the errors are likely related to the simulated ice thickness. Thin ice has many small cracks, resulting in large fluxes nearly as high as over open water; very thick ice can reduce the latent fluxes to near zero, according to observations. Overall, the algorithms used in existing operational models show promise in resolving winter lake fluxes; however, further improvement may require adaptations to underlying ice and hydrodynamic model formulations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".