Lake Ice and Climate Perturbation: Numerical Experiments on a Small Boreal Lake
Bibliographic record
Abstract
Changes in lake ice cover resulting from systematic perturbations to individual meteorological forcing variables are examined here by way of numerical experimentation with a 1-dimensional thermodynamic lake model. Examination of a simplified vertical energy budget suggests that wind speed, air temperature, precipitation, and incoming shortwave radiation are key variables governing the creation and evolution of ice. Synthetic 30-year meteorological forcing datasets over a small boreal lake are generated by replicating 1 year of detailed observations with added Gaussian noise or by a scaling factor to each of these forcing variables in turn, and the impact on lake ice phenology, quality, and maximum thickness analysed. For the wind speed experiments, changes in phenology were nonlinear and asymmetric. For the largest wind speed reductions ice-on was delayed but for increasing mean wind speed perturbations, the ice-on date was essentially unchanged. For large wind speed perturbations of either sign the ice-off date was early, but smaller changes in mean wind speed, of either sign, had no effect. Thus, any significant change in mean wind speed would lead to a reduction in ice cover duration. Ice-on dates were only weakly affected by perturbations to any of the other forcing variables considered, including air temperature. Thus, observational studies that link increasing air temperatures to delays in ice-on should also consider the impacts of wind speed if data are available. Changes in mean air temperature led to changes in ice thickness and duration. Increasing precipitation was found to increase ice thickness as well as the fraction of white ice, while changing mean insolation had a significant impact on ice-off.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".