Application of a Three‐Dimensional Coupled Hydrodynamic‐Ice Model to Assess Spatiotemporal Variations in Ice Cover and Underlying Mechanisms in Lake Nam Co, Tibetan Plateau, 2007–2017
Bibliographic record
Abstract
Abstract A three‐dimensional lake‐ice coupled model is used to investigate the space‐time variations of ice and underlying mechanisms in Lake Nam Co (LNC), the third largest lake over Tibetan Plateau (TP), during 2007–2017. The model reasonably reproduces the in situ measured ice thickness and water temperature profile, and satellite retrieved ice coverage and lake surface temperature. Seasonally, the lake ice first forms in the eastern basin during early January, expands from east to west during January and February, covers nearly the entire LNC in March, starts melting from west to east in April, and eventually disappears in May. The eastward drift of thin ice throughout the ice‐covered phase and the eastward water heat transport during the ice melting phase are key factors to determine the spatial variation of ice and freeze‐thaw processes. A multiple linear regression analysis confirms that the eastward drift of thin ice can be mostly attributed to the prevailing westerly. During 2007–2017, ice volume, duration, ice‐on and ice‐off dates show significant interannual variations, and they are highly correlated with the surface air temperature (T2m) averaged over January‐March, from the preceding December to May, in December and over March–May, respectively, suggesting the “cumulative effects” of T2m. Seasonal and interannual variations of ice drift are attributed to the combined effects of wind and ice volume variations. Sensitivity analysis further points out the important impacts of ice on the lake temperature and circulation structure in winter and spring, hence the necessity of hydrodynamic‐ice coupled models in large TP lakes.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".