Synoptically classified lake-effect snowfall trends to the lee of Lakes Erie and Ontario
Bibliographic record
Abstract
CR Climate Research Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsSpecials CR 74:1-13 (2017) - DOI: https://doi.org/10.3354/cr01480 Synoptically classified lake-effect snowfall trends to the lee of Lakes Erie and Ontario Zachary J. Suriano*, Daniel J. Leathers Department of Geography, University of Delaware, Newark, DE 19716, USA *Corresponding author: zsuriano@udel.edu ABSTRACT: Recent research has indicated that snowfall in portions of the North American Great Lakes region subject to lake-effect snow has undergone a trend reversal, with snowfall declining in recent decades. This study examines the seasonal variability and trends specifically in synoptically classified lake-effect snow across the eastern Great Lakes region, and investigates the mechanisms responsible for observed changes. Using a synoptic climatological approach, days are identified where the synoptic-scale conditions are conducive to lake-effect snowfall and the associated snowfall is analyzed. Seven synoptic types over the November to March snowfall season are identified with characteristics of lake-effect conditions. Snowfall from these 7 lake-effect synoptic types represents between 45 and 53% of the seasonal snowfall total along the eastern shores of Lakes Erie and Ontario, with snowfall totals being highest during January. Lake-effect snowfall exhibits a 60 yr increasing trend downwind of Lakes Erie and Ontario; however, through examination over shorter 30 yr periods, a change in the trend of snowfall is observed around 1980. While a true trend reversal is not detected, lake-effect snowfall significantly increases from 1950-1979 before exhibiting no significant trend from 1980-2009. The inter-annual variability of seasonal lake-effect snowfall is highly related to the frequency of lake-effect synoptic types where an increase (decrease) in synoptic type occurrence leads to enhanced (diminished) lake-effect snowfall totals. Depending on the period examined, long-term changes in the frequency of lake-effect synoptic types and snowfall rates represent between 89 and 95% of the observed changes in lake-effect snow. KEY WORDS: Great Lakes · Synoptic classification · Snowfall variability · Climate change · Lake effect Full text in pdf format NextCite this article as: Suriano ZJ, Leathers DJ (2017) Synoptically classified lake-effect snowfall trends to the lee of Lakes Erie and Ontario. Clim Res 74:1-13. https://doi.org/10.3354/cr01480 Export citation RSS - Facebook - Tweet - linkedIn Cited by Published in CR Vol. 74, No. 1. Online publication date: October 16, 2017 Print ISSN: 0936-577X; Online ISSN: 1616-1572 Copyright © 2017 Inter-Research.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".