Changing Intrasynoptic Type Characteristics and Interannual Frequencies of Circulation Patterns Conducive to Lake-Effect Snowfall
Bibliographic record
Abstract
Abstract Using a temporal synoptic index, synoptic-scale atmospheric patterns suitable for lake-effect snow downwind of Lakes Erie and Ontario are identified and analyzed from 1950 to 2009. In response to prior research noting a trend reversal of snowfall in this region, changes in the inherent meteorological characteristics and winter-season frequencies of lake-effect synoptic weather types are evaluated as possible forcing mechanisms. Four atmospheric patterns are identified during the December–February winter season as lake-effect synoptic types. Changes in inherent meteorological characteristics and winter frequencies of these types are attributed to between 88% and 95% of the observed snowfall changes during the study period. Decreasing air temperatures and surface pressures, increasing boundary layer instability, and changes toward stronger zonal flow are noted for multiple lake-effect synoptic types from 1950 to 1979 as likely forcing mechanisms of observed snowfall increases, on the order of nearly 1.0 cm yr−1 downwind of Lake Ontario per individual synoptic type. Similarly, the significant increases in the winter frequency of multiple lake-effect synoptic types also are attributed to some of the increases in snowfall. From 1980 to 2009, however, the lake-effect synoptic types remained relatively unchanged or decreased in frequency, as did snowfall totals. The results of this study indicate that changes in the synoptic-scale environment are a viable mechanism forcing snowfall trends, in addition to the more commonly considered seasonal temperature and lake-ice considerations, and should be incorporated into future discussions of lake-effect snowfall projections.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".