Revisiting the Relationship between Polar Lows and Weather Regimes
Bibliographic record
Abstract
Abstract The link between weather regimes (WRs) and polar low (PL) activity is examined over the North Atlantic and North Pacific basins. Compared to earlier studies based on limited, regional PL datasets, our study conducts a more complete evaluation of regional WR–PL relationships using an expanded PL climatology. Our findings show that PL activity is increased over the Norwegian and Barents Seas during the Atlantic ridge regime and decreased over the former region during the Scandinavian blocking regime, with negative impacts also stretching to the Irminger Sea. Over the Labrador and Irminger Seas, PL activity is modulated strongest by the North Atlantic Oscillation (NAO), with positive impacts during the positive phase and vice versa. Over the North Pacific, the Arctic low contributes to increased PL activity over most regions, while the opposite is true for the Pacific wave train regime. The variability of PL activity associated with WRs is strongly related to changes in key environmental conditions. In general, regions of enhanced (reduced) PL activity are coincident with anomalous low-level northerly (southerly) flow and reduced (increased) static stability. Further analysis shows that certain persistent WRs can strongly modulate PL activity over some regions, either due to the amplification or propagation of favorable or unfavorable conditions, which cautions the limitation of regarding WRs as stationary patterns. A previously developed PL genesis potential index is shown to represent the observed impacts well, which serves to confirm the robustness of our findings and suggests the potential applicability of WRs to the subseasonal prediction of PLs. Significance Statement Polar lows are intense small-scale (∼300 km) cyclones that form over high-latitude oceanic regions. The hazardous impacts they pose to coastal communities and maritime and air operations in the Arctic motivate their skillful prediction, which remains a major challenge at lead times beyond a few days. In this study, we relate PLs and the key environmental conditions that favor their development to weather regimes, which are recurrent large-scale circulation patterns that can persist for weeks at a time. We find that weather regimes have strong impacts on polar low activity through the modulation of key environmental conditions. These relationships can potentially be utilized in the extended-range prediction of polar lows.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".