Climatological trends of snowfall over the Laurentian Great Lakes Basin
Bibliographic record
Abstract
The leewards shores of the Laurentian Great Lakes are highly susceptible to lake‐induced snowfall. During the late autumn and winter season, cold air advection over relatively warm lakes can induce instability in the lower planetary boundary layer (PBL), facilitating the exchange of moisture and energy fluxes and fuelling the development of snowfall. Snowfall in this region can have disastrous impacts on local communities such as the November 2014 Buffalo storm that caused 13 fatalities. This paper discusses historical snowfall trends along the Canadian leewards shores of Lakes Superior and Huron‐Georgian Bay and explores several lake‐induced predictor variables that may influence the snowfall trends. Spatio‐temporal snowfall and total precipitation trends were computed for the 1980–2015 period over the Great Lakes Basin (GLB) using the Daymet (version 3) gridded estimated data set. Results show a significant decrease in snowfall, at a rate of 40 cm/36 years, and a significant decrease in total precipitation of 20 mm/36 years, along the Ontario snowbelts of Lake Superior and partially along that of Lake Huron‐Georgian Bay at the 95% confidence level during the cold season. Attributions to these negative spatio‐temporal trends are explored using data from the North American Regional Reanalysis (NARR) and the Canadian Ice Service (CIS) data sets. Predictor variables show significant warming in lake surface temperature (LST) at a rate of over 6 K/36 years for Lake Superior, significant decrease in ice cover fraction for both lakes, and an increase in the vertical temperature gradient (VTG) between the LST and the 850 mb level. While the behavioural trends of these variables are believed to enhance snowfall through increased evaporation, there are other complex processes involved, such as inefficient moisture recycling and increased moisture storage in warmer air masses that may inhibit the development of snowfall along the immediate leewards shores of Lake Superior.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".