Atmospheric drivers of snowfall and snow cover ablation variability within the Great Lakes Basin of North America
Bibliographic record
Abstract
This dissertation examines the relationships between snow and synoptic-scale atmospheric circulation in the Great Lakes region of North America in a series of three journal articles. The first assesses the variability and long-term trends of lake-effect snowfall along the eastern shores of Lakes Erie and Ontario, and determines the particular synoptic-scale weather types that drive the variability in snowfall. These weather type frequencies explain over 68% of inter-annual lake-effect snowfall variability, and between 89-95% of the observed linear changes in snowfall can be explained by long-term changes in the frequency and snowfall rates of these synoptic patterns. ☐ The second article builds a climatology of snow ablation events within the Great Lakes basin by isolating ablation from a daily gridded snow depth product. Ablation events are latitudinally-dependent, with peak probability of an event shifting northwards during the spring months in conjunction with enhanced incoming solar radiation, surface air temperatures, and atmospheric moisture. No long-term changes in the seasonal timing of ablation events are detected within the basin, however two spatially coherent regions corresponding to the northern Lake Superior and the eastern Lake Huron/Georgian Bay drainage basins did experience significant decreases and increases in inter-annual ablation event frequency from 1960-2009, respectively. Such changes are hypothesized to be driven by changes in the frequency of particular mid-latitude cyclones influencing the region and long-term trends in lake-effect snowfall. ☐ The third article employs a synoptic-classification procedure that identifies and analyzes the atmospheric conditions that lead to snow ablation events across the Great Lakes basin. Three primary categories of synoptic weather types lead to ablation, corresponding to ‘southerly flow’, ‘rain-on-snow’, and ‘high-pressure overhead’ patterns. Each pattern influences the meteorological conditions forcing ablation at the surface, and exhibits substantial inter-annual variability. The second and third most common ablation-inducing synoptic weather type categorizes, ‘high-pressure overhead’ and ‘rain-on-snow’, are respectively increasing and decreasing in inter-annual frequency from 1960-2009. Together, these three articles showcase the variable forcings of snow in the Great Lakes basin, and highlight the importance of understanding the links between atmospheric circulation and cryospheric water resources.
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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 | 0.000 | 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 | 0.001 |
| 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".