Associations between low frequency variability modes and winter temperature extremes in Canada
Bibliographic record
Abstract
Relationships between patterns of low frequency climate variability including the El Niño‐Southern Oscillation (ENSO), the Arctic Oscillation (AO) and the Quasi‐Biennial Oscillation (QBO), and the occurrence of Canadian winter temperature extremes are examined for the 1950–98 period. Composite analyses reveal that ENSO plays a dominant role in the frequency and duration of both cold and warm spells. In particular, the warm phase of ENSO is associated with a significant increase (decrease) in the occurrence of warm (cold) spells and the number of extreme warm (cold) days across most of Canada. The findings are generally the opposite for the cold ENSO phase. The singular value decomposition (SVD) technique further shows that ENSO‐like interdecadal sea surface temperature variability plays a significant role in the variability of winter cold and warm spells over Canada. Differences between the high and low index phases of the AO show a significantly higher frequency and duration of cold spells over eastern Canada during positive AO winters, while the frequency of winter warm spells increases over the Canadian Prairies. These results are also confirmed by the SVD analysis using northern hemisphere 1000‐hPa circulation. The QBO, as determined by the phase of the stratospheric winds, has an effect on the spatial pattern of Canadian extreme temperatures that is similar to that of the AO. The most significant result involves increases in the frequency of warm spells and the number of extreme warm days over the southern Prairies during the westerly phase of the QBO. The diagnostic results from this study improve the understanding of the imapact of the low frequency variability modes on temperature extremes over Canada. They may also assist in the attribution of past trends and variability in extremes, and potentially, provide insight into future changes to extreme temperature events.
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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 | 0.002 |
| Science and technology studies | 0.001 | 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".