Observed Changes in Canada’s Snowfall as Inferred from Precipitation and Daily Mean Temperatures
Bibliographic record
Abstract
Limited long-term snowfall observations make it difficult to document how snowfall is changing across Canada. Proxy snowfall measures derived from more plentiful temperature and precipitation may therefore be helpful. We consider simple partitioning of daily precipitation into rainfall and snowfall based on whether temperature is above or below either 0°C or a station specific threshold. Using daily mean temperature and the fixed 0°C threshold resulted in more accurate estimates of annual and seasonal snow-day number and water equivalent snowfall amount than using daily maximum or daily minimum temperature. Using station-specific thresholds further improved estimation accuracy. Trends estimated from these proxy snowfall indices well match those estimated from observed snowfall data for periods and locations when both are available. The median annual proxy snowfall amount in Canada derived from homogenized daily precipitation and temperature data decreased 2.5% per decade over 1949–2023 south of 60°N and increased 0.5% per decade north of 60°N. Seasonally, annual proxy snowfall amount has changed most rapidly in winter, declining 2.6% per decade in southern Canada and increasing 3.6% per decade in northern Canada. This simple approach improves prospects for the continuation of long-term snowfall monitoring in Canada by exploiting long-term daily precipitation and temperature data.
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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) | 0.000 | 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 | 0.000 | 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".