Periodicity in Extreme Weather in the ‘Maritime Region’ of Eastern North America
Bibliographic record
Abstract
Abstract Spectral and wavelet analysis were used to identify trends and cycles in extreme temperature and precipitation events based on historical data (~100-150 years) from six climate stations within the “Maritime Region” of eastern North America. Many statistically significant climate cycles were identified using both spectral and Morlet wavelet analyses at each of these locations for both extreme high and low temperature and precipitation (rain, snow) data, with periodicities typically ranging from ~ 2–30 years. To assess potential drivers of these cyclical extreme weather events, the records of these events were compared, using cross wavelet analysis, to the climate indices of several teleconnections, including the 11-year Schwabe solar cycle, Atlantic Multidecadal Oscillation, North Atlantic Oscillation, Arctic Oscillation, El Niño Southern Oscillation and the Quasi–Biennial Oscillation. It was found that the 11-year solar cycle had the strongest influence over extreme temperature and precipitation in this region, whereas the remaining oscillations, with the exception the Quasi–Biennial Oscillation, exhibited complex interactions with one another, characterized a variety of both positive and negative modulating effects. The Quasi–Biennial Oscillation was found to drive high–frequency oscillations in extreme weather, particularly extreme precipitation. Overall, the findings of this study indicate that extreme weather events in this region have not substantially increased or decreased in number over time, but have been predominantly influenced by several cyclic climate phenomena.
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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 | 0.000 | 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) | 0.000 | 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 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".