Do Lake Effect Precipitation Events have a Unique Stable Isotope Fingerprint? An Analysis of Isotope Data from Western New York
Bibliographic record
Abstract
Lake effect snow, which occurs when cold, dry air passes over a relatively warmer lake, is a major weather hazard in upstate New York due to the influence of Lake Erie and Lake Ontario. However, the National Weather Service’s (NWS) definition of lake effect snow only considers systems that produced over 7 inches of snow, meaning some lake effect events may not be cataloged. This prevents us from fully understanding how the lake contributes to precipitation. While climate change could cause some of this lake effect snow to fall as rain due to warmer temperatures, it also has the potential to cause more intense snow storms due to higher evaporation rates. To understand the future of lake effect precipitation, we can turn to the past and see how lake effect precipitation has changed throughout Earth’s history. To do so, we would need to establish a proxy for lake effect precipitation that is preserved in geological archives. In this study, we analyzed the stable isotopic values (δ 2 H, δ 18 O, and d-excess) of daily precipitation samples collected from 2014-2025 at two sites in upstate New York, Amherst and Skaneateles. We find that lake effect events, as defined by the NWS, have different isotopic values than non-lake effect events, with d-excess being higher and both δ 2 H and δ 18 O being more depleted. Because NWS-defined lake effect events have a different isotopic signature than the non-lake effect events, our findings suggest that the isotopic values measured in precipitation can provide an alternative method of identifying lake effect events, due to higher d-excess values. This includes events with small snowfall totals or rain. However, there is a large amount of variability within our data, which could be attributed to meteorological factors, such as relative humidity, the temperature lapse rate, air temperature, and the amount of moisture evaporated from the lake. Our preliminary results could be an input for moisture recycling models to determine how much evaporation comes from the lake during all precipitation events, which may identify events not formally classified as lake effect snow events. They can also be compared to paleoclimate records to determine how moisture recycling from the lake varied over time and how it could possibly change in the future, which provides valuable insights for the future of climate in upstate New YorkAbstract content goes here
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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) | 0.001 | 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".