Simulation of Heavy Lake-Effect Snowstorms across the Great Lakes Basin by RegCM4: Synoptic Climatology and Variability
Bibliographic record
Abstract
Abstract A historical simulation (1976–2002) of the Abdus Salam International Centre for Theoretical Physics Regional Climate Model, version 4 (ICTP RegCM4), coupled to a one-dimensional lake model, is validated against observed lake ice cover and snowfall across the Great Lakes Basin. The model reproduces the broad temporal and spatial features of both variables in terms of spatial distribution, seasonal cycle, and interannual variability, including climatological characteristics of lake-effect snowfall, although the simulated ice cover is overly extensive largely due to the absence of lake circulations. A definition is introduced for identifying heavy lake-effect snowstorms in regional climate model output for all grid cells in the Great Lakes Basin, using criteria based on location, wind direction, lake ice cover, and snowfall. Simulated heavy lake-effect snowstorms occur most frequently downwind of the Great Lakes, particularly to the east of Lake Ontario and to the east and south of Lake Superior, and are most frequent in December–January. The mechanism for these events is attributed to an anticyclone over the central United States and related cold-air outbreak for areas downwind of Lakes Ontario and Erie, in contrast to a nearby cyclone over the Great Lakes Basin and associated cold front for areas downwind of Lakes Superior, Huron, and Michigan.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".