Dynamics, Variability, and Change in Seasonal Precipitation Reconstructions for North America
Bibliographic record
Abstract
Abstract Cool- and warm-season precipitation totals have been reconstructed on a gridded basis for North America using 439 tree-ring chronologies correlated with December–April totals and 547 different chronologies correlated with May–July totals. These discrete seasonal chronologies are not significantly correlated with the alternate season; the December–April reconstructions are skillful over most of the southern and western United States and north-central Mexico, and the May–July estimates have skill over most of the United States, southwestern Canada, and northeastern Mexico. Both the strong continent-wide El Niño–Southern Oscillation (ENSO) signal embedded in the cool-season reconstructions and the Arctic Oscillation signal registered by the warm-season estimates faithfully reproduce the sign, intensity, and spatial patterns of these ocean–atmospheric influences on North American precipitation as recorded with instrumental data. The reconstructions are included in the North American Seasonal Precipitation Atlas (NASPA) and provide insight into decadal droughts and pluvials. They indicate that the sixteenth-century megadrought, the most severe and sustained North American drought of the past 500 years, was the combined result of three distinct seasonal droughts, each bearing unique spatial patterns potentially associated with seasonal forcing from ENSO, the Arctic Oscillation, and the Atlantic multidecadal oscillation. Significant 200–500-yr-long trends toward increased precipitation have been detected in the cool- and warm-season reconstructions for eastern North America. These seasonal precipitation changes appear to be part of the positive moisture trend measured in other paleoclimate proxies for the eastern area that began as a result of natural forcing before the industrial revolution and may have recently been enhanced by anthropogenic climate change.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".