Hydroclimatic Analysis of Snowfall Trends Associated with the North American Great Lakes
Bibliographic record
Abstract
Research over the past several decades has indicated that snowfall has increased dramatically over portions of the past century across those areas of the Great Lakes region of North America that are subject to lake-effect snowfall. Within this study, time series of annual midwinter snowfall within lake-effect areas show evidence of a clear increase in both snowfall and snowfall frequency through a 40-yr period beginning in the early 1930s and ending in the early 1970s. The goal of the work presented here is to determine to what extent the apparent increases in lake-effect snowfall actually modified the winter hydroclimate of the areas. Simple hydroclimatic analysis of midwinter precipitation to the lee of Lakes Erie and Ontario for the period of significant snowfall increases suggests that the changes were a product of 1) a shift toward more precipitation events that were snowfall rather than rainfall, 2) an associated decrease in midwinter rainfall, 3) an increase in the intensity of individual snowfall events, and 4) an increase in the snowfall/snow water equivalence ratio. The balance was a small increase in total precipitation confined to areas in close proximity to the lakes across northeastern Ohio and western New York, while areas outside the regions generally experienced an overall decrease in midwinter precipitation. While the cause(s) of the snowfall trends remains elusive, the results of the work presented here suggest that no great long-term regional change occurred in the true wintertime seasonal hydroclimate of the lake-effect areas. Rather, much of the touted snowfall increase simply came at the expense of rainfall events to produce only small changes in total precipitation over the time period of significant snowfall increase.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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 teacher head, 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".