High-latitude lake influence on highly concentrated precipitation from cold-season storms in western Canada
Bibliographic record
Abstract
: Cold-season (October–March) storms, particularly severe snowstorms, are responsible for significant economic losses and have crucial impacts on freshwater availability and ecosystems in high-latitude North America. These snowstorms also contribute to destructive floods during rapid snowmelt. Thus, ecosystems and water infrastructure in Canada are highly sensitive to changes in cold-season storms under global warming. This study employs an object-based approach, specifically utilizing a storm-tracking algorithm, to investigate how cold-season storm precipitation in western Canada responds to climate change under a worst-case warming scenario. In the entire study area, peak daily precipitation greater than 50 mm day −1 within storms significantly increases in both warm and cold seasons. The most extreme storms with highly concentrated precipitation (that is, storms with the precipitation intensity 5 times greater at the storm center compared to the area-averaged intensity), are expected to become more frequent in the future, particularly in the coastal regions and inland lake regions. More importantly, by analyzing the top 20 storms with the highest peak daily precipitation, we found that in the future, lakes will contribute more moisture to the atmosphere through increased evaporation, thereby intensifying the moisture supply and enhancing storm precipitation. Additionally, our findings indicate that future cold-season storms with highly concentrated precipitation may not increase evenly across each month. Warmer lakes in autumn, due to their high thermal inertia, will continue to provide significant local moisture to the atmosphere, which is crucial for the formation of highly concentrated precipitation. These findings suggest significant implications for understanding and predicting the impacts of climate change on storm dynamics and precipitation patterns over inland lakes.
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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 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".