Regional Changes to Lake Effect Snow Levels in New York State Under Projected Future Climate Conditions
Bibliographic record
Abstract
Lake-effect snowstorms are an important element of climate and weather in the Great Lakes region of North America. Here, I investigate how lake-effect snow levels could change in the future with anthropogenic climate change as predicted by a regional climate model (RegCM3) driven by two different sets of global climate model output (from GFDL CM2.1 and CGCM3, experiments run as part of the North American Regional Climate Change Assessment Program, Phase II). I analyze a subset of the domain focused on the Great Lakes area, paying particular attention to the southeastern Lake Ontario Snowbelt and the southeastern Lake Erie Snowbelt, both of which are mainly within New York State. My results show a decrease in lake-effect snow cover in the future (2040-2070) compared to the recent past (1970-2000) for this region. Total precipitation levels are shown to not change significantly, so it is likely that lake-effect snowstorms will be replaced largely by rain in the future. Both magnitudes of values as well as trends for snow levels in specific cities varied significantly depending on which global climate model output was used to drive RegCM3, pointing to a possibly serious source of uncertainty and error in regional climate modeling studies that do not utilize multiple global climate model output.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".