The Magnitude of Decadal and Multidecadal Variability in North American Precipitation*,+
Bibliographic record
Abstract
Abstract The authors use singular spectrum analysis to investigate the relative magnitude of decadal to multidecadal (D2M) variability in annual and seasonal precipitation anomalies across North America. In most places, decadal (10–20 yr) or multidecadal (20–50 yr) variability makes up less than 10% of the total variance in either annual or seasonal precipitation, with interannual variability or secular trends having much greater importance. Decadal variability is most prominent (contributing 25%–30% of the total variance) in Minnesota and northern California during winter, and the central Rocky Mountains in autumn. Eastern Québec is the only major region where precipitation exhibits significant variance in the multidecadal band. Precipitation across much of Canada exhibits significant variance at extremely low frequencies (greater than 50 yr), but variability at these time scales cannot be separated from secular trends because of the limited length of instrumental climate records. Decadal signals in the discharge of the Sacramento River and, to a lesser degree, the Colorado River are coherent and in phase with similar signals in regional precipitation. Prominent D2M signals do not resemble the low-frequency components of major climate modes such as ENSO or the PDO, which suggests that this behavior is not a product of a simple linear translation of a single climate forcing.
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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".