Influence of North Pacific Decadal Variability on the Western Canadian Arctic over the past 700 years
Bibliographic record
Abstract
Abstract. It is well established that the Arctic strongly influences global climate through positive feedback processes (Cohen et al., 2014), one of the most effective being the sea-ice – albedo feedback (Screen et al., 2010). Understanding the region’s sensitivity to both internal and external forcings is a prerequisite to better forecast future global climate variations. Here, sedimentological evidence from an annually laminated (varved) record highlights that North Pacific climate variability has been a persistent regulator of the regional climate in the western Canadian Arctic. The varved record is negatively correlated with both the instrumental and reconstructed Pacific Decadal Oscillation (PDO) (D'arrigo et al., 2001; Gedalof et al., 2001; Macdonald et al., 2005; Mantua et al., 1997) throughout most of the last 700 years, suggesting drier conditions during high PDO phases, and vice-versa. This is in agreement with known regional teleconnections whereby the PDO is negatively and positively correlated with summer precipitation and mean sea level pressure, respectively. This pattern is also seen during the positive phase of the North Pacific Index (NPI) (Trenberth et al., 1994) in autumn. A reduced sea-ice cover during summer is observed in the region during PDO- (NPI+), as has been found during winter (Screen et al., 2016). Strongest during the autumn season, low-level southerly winds extend from the northernmost Pacific across the Bering Strait and can reach as far as the Western Canadian Arctic. These climate anomalies projecting onto the PDO- (NPI+) phase are key factors in enhancing evaporation and subsequent precipitation in this region. As projected sea-ice loss will contribute to enhanced future warming in the Arctic, future negative phases of the PDO (or NPI+) will likely act as amplifiers of this positive feedback (Screen et al., 2016).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".