Interannual variability in air temperature and snow drive differences in ice formation and growth
Bibliographic record
Abstract
Abstract. Recent warming of northern, high-latitude regions has raised alarms for the safe and efficient use of frozen lakes for winter transportation and recreation. This concern is significant in Canada’s Northwest Territories (NWT), where seasonally constructed roads over lakes, rivers, and land (winter roads) span thousands of kilometers and act as vital links to isolated communities and resource development projects. Current climate change and weather variability is altering the evolution of lake ice, challenging predictions of freeze-up, ice growth, and ice decay. The accurate simulation of ice evolution is imperative for safe and efficient planning, operation, and maintenance of winter roads under a changing climate and heightened weather variability. This is particularly significant in the early winter period when ice road planning and design is undertaken. Here, we investigate the effects of weather variability on ice formation, growth, and evolution in a small lake near Yellowknife, NWT, Canada. High-resolution measurements of air, snow, ice, and water temperatures were collected continuously from a floating research station between October and December in 2021, 2022, and 2023 and variability in ice evolution and weather examined. Combinations of above and below average snowfall and winter air temperatures resulted in variability of up to 17 days in freeze-up dates (FUD) and 8 days in freeze-up durations. End of December ice thicknesses (hi) varied up to 12 cm, while the duration between the FUD and hi=30 cm varied up to 10 days. hi were effectively simulated (RMSE=1.11–2.33 cm) using empirical relationships developed using cumulative freezing degree days (CFDD) and seasonally cumulative snowfall (ST), while snow-ice thicknesses simulated (RMSE=0.83–1.21 cm) using CFDD and daily snowfall. Developed relationships between air temperatures, snow, and ice thicknesses can be used for predicting minimum ice thicknesses required for commencing ice road construction, and to assist in the effective management of construction activities.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".