Pacific–North American Teleconnection Controls on Precipitation Isotopes (δ18O) across the Contiguous United States and Adjacent Regions: A GCM-Based Analysis
Bibliographic record
Abstract
Abstract The Pacific–North American (PNA) teleconnection pattern has a strong influence on North America’s winter climate, but much less is known about how the PNA pattern controls precipitation isotopes (e.g., δ18O) across the United States. In this study, an isotopically equipped atmospheric general circulation model (isoGSM) is used to investigate how divergent phases of the PNA affect precipitation δ18O values across the United States. A simulation using observational climate and isotope data over the United States is evaluated first. The simulation explains 84% of the spatial variability of winter precipitation δ18O, with an overestimation in the northern Rocky Mountains and the Great Lakes. Temporally, the simulation explains 29%–81% of the interannual variability of winter precipitation δ18O, with typically a higher explained variance in the east than the west. The modeled winter precipitation δ18O exhibits a clear northwest–southeast (NW–SE) dipolelike pattern in response to shifts in the PNA pattern, with the center of positive polarity in the northwestern United States and the Canadian prairies and the center of negative polarity over the Ohio River valley. This dipolelike spatial pattern is a result of the difference in atmospheric circulation and moisture sources associated with the PNA pattern. These results highlight the importance of the PNA-associated circulation dynamics in governing precipitation isotope patterns across the United States. This understanding improves our ability to interpret paleoclimate records of water isotope/hydrologic change across the United States with a much greater appreciation of regional traits. The robust antiphase oscillation in precipitation isotopes in response to shifting the PNA pattern provides a promising opportunity to reconstruct the past variability in the PNA pattern that may be recorded in ice cores, tree rings, lake sediments, and speleothems.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".