The Role of Pacific Climate on Low-Frequency Hydroclimatic Variability and Predictability in Southern Alberta, Canada
Bibliographic record
Abstract
Abstract Wavelet and rank correlation analysis were used to identify the links between primary Pacific climate variability modes and low-frequency hydroclimatic variability in the South Saskatchewan River basin (SSRB) of southern Alberta. The April–September average streamflow shows strong interdecadal oscillations with dominant scales of 19–22, 41–42, and 62 yr whereas statistically significant wavelet power in the interannual scale was organized on a background scale of approximately 20–25 yr. At interannual scales, strong coherency is observed between streamflow and the Niño-3 index prior to the 1940s, and in the 1950s, 1970s, and 1980s. However, a change in the phase difference from near 0° in the 1950s to near 180° in the 1980s indicates that the relationship between streamflow and the El Niño–Southern Oscillation (ENSO) is not consistent. Streamflow–Pacific–North America pattern (PNA) and streamflow–Pacific decadal oscillation (PDO) relationships at interannual scales also exhibit similar inconsistencies in phase difference. At interdecadal scales, PDO and streamflow exhibited consistently strong coherence with a stable phase difference of 180° for scales >20 yr. From the period of 1913–2001, the median partial correlation between streamflow and PDO|Niño-3 (read as PDO given Niño-3) was −0.36, whereas it was zero between streamflow and Niño-3|PDO, suggesting that PDO is the primary mode of importance in streamflow variability and predictability in the SSRB. Precipitation variability was also dominated by interdecadal oscillations; however, there is less spatial coherence for dominant scales. Correlations between the basin’s winter precipitation and climate indices are also weaker than with streamflow.
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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".