Wavelet analysis of precipitation extremes over Canadian ecoregions and teleconnections to large‐scale climate anomalies
Bibliographic record
Abstract
Abstract To detect significant interannual and interdecadal oscillations and their teleconnections to large‐scale climate anomalies such as El Niño–Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), and North Atlantic Oscillation (NAO), monthly and seasonal maximum daily precipitation (MMDP and SMDP) from 131 stations across Canada were analyzed by using variants of wavelet analysis. Interannual (1–8 years) oscillations were found to be more significant than interdecadal (8–30 years) oscillations for all selected stations, and the oscillations are both spatial and time‐dependent. Similarly, the significant wavelet coherence and the phase difference between leading principal components of monthly precipitation extremes and climate indices were highly variable in time and in periodicity, and a single climate index explains less than 40% of the total variability. Partial wavelet coherence analysis shows that both ENSO and PDO modulated the interannual variability and PDO modulated the interdecadal variability, of MMDP over Canada. NAO is correlated with the western MMDP at interdecadal scale and the eastern MMDP at interannual scale. The composite analysis shows that precipitation extremes at about three fourths of the stations have been significantly influenced by ENSO and PDO patterns, while about one half of the stations by the NAO patterns. The magnitude of SMDP in extreme El Niño years, and extreme PDO event of positive phase, was mostly lower (higher) over the Canadian Prairies in summer and winter (spring and autumn) than in extreme La Niña years. Overall, the degree of influence of large‐scale climate patterns on Canadian precipitation extremes varies by season and by region.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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".