Lake Ice in Canada: Improving one-dimensional lake ice modelling and investigating changing ice cover and trends
Bibliographic record
Abstract
Lakes are a dominant geographic feature in northern landscapes, and lake ice demonstrates a strong relationship with air temperature and large-scale atmospheric patterns, showing that lake ice is sensitive to climate variability and change. Changes to present-day lake ice regimes could result in major ecosystem changes and lead to substantial economic and environmental changes. The Canadian Lake Ice Model (CLIMo) was selected to study lake ice in Canada. The first study objective identified that regionally specific measurements of snow and ice albedo can be used to improve the accuracy of lake ice simulations, where field measurements of snow and ice albedo for two temperate region lakes were used to improve CLIMo. The simulated results presented an improvement to ice-off timing to within 0 to 7 days of observations. The second study objective examined the radiation balance of a High Arctic Lake during the open water period and found that CLIMo was able to simulate the radiation balance during this period (Index of Agreement of 0.74 for net radiation). This information was used to simulate future open water conditions using the Arctic CORDEX CMIP5 RCP 8.5 conditions. Future open water duration is expected to increase by 12 to 14 days per decade with both net radiation and net shortwave radiation decreasing and net longwave radiation increasing. Using the model derived in the first study, an exploration of lake size and its representation in CLIMo was assessed to determine if we could create an adjustment factor that could adjust many lakes at once (a batch adjustment) based on a specified range of lake sizes. It was determined based on lake size and simulated ice-off, that no batch adjustment factor could be derived. However, individual adjustments can be done using the mean absolute error to determine length and mean bias error to determine direction. Finally, lake ice phonology and snow trends were assessed and changepoints in the lake ice phenology and snow trends were examined, to determine the overall magnitude and direction of trends and changepoint years across Canada. The trends and changepoints show that ice cover duration is decreasing, through earlier ice-off and later ice-on, and snow cover across Canada is showing decreasing snow depth, snow density and snow water equivalent. The results show that there is regional variability which means that lake ice phenology and snow are not changing the same everywhere across Canada. The observed regional variability in lake ice phenology and snow across Canada underscores the need to understand these changing patterns under a changing climate so that we can better understand and predict how lake ice phenology and snow will change in the future.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".