Event‐Based Precipitation Isotopes in the Laurentian Great Lakes Region Reveal Spatiotemporal Patterns in Moisture Recycling
Bibliographic record
Abstract
Abstract Lake effect snowstorms influence climate, ecology, and agriculture in the Laurentian Great Lakes region and can be costly to surrounding communities in the United States and Canada. Stable isotopes of lake effect precipitation events throughout the year display a distinct signature that can be used to better understand how these storms may respond to a changing climate. Here we present event‐based δ 18 O and δ 2 H of precipitation from a site in Skaneateles, NY, downwind of Lakes Ontario and Erie, between April 2015 and February 2018. We find a seasonal isotopic cycle with a well‐defined signature of high deuterium excess ( d ‐excess) during National Weather Service‐defined lake effect snowstorms. Application of a previously developed moisture recycling model to this data set shows that up to 25% more moisture recycling takes place when the lake water and air temperature difference is large, air temperature is below freezing, and wind direction permits storms to move over Lake Ontario or Lake Erie. Moisture recycling occurs less frequently during spring, summer, and early fall due to meteorological and lake parameters that are less conducive to moisture recycling. Comparison of annual mean precipitation d ‐excess at sites both upwind and downwind of the Laurentian Great Lakes provides evidence that this high d ‐excess signature is characteristic of mean annual precipitation isotopic composition at downwind sites and therefore may be used to quantify changes in moisture recycling that occur on event to annual time scales in response to past and future climate changes.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".