Review of "Stratospheric influence on marine cold air outbreaks in the Barents Sea"
Bibliographic record
Abstract
In this manuscript, the authors evaluate whether there is a relationship between marine cold air outbreaks (MCAOs) and Sudden Stratospheric Warmings (SSWs) in the Barents and Norwegian Seas.The authors make the conclusion that 33% of SSWs are associated with an enhanced MCAO response in the Barents Sea.They furthermore conclude that a positive zonal dipole pattern in the large-scale atmospheric flow accounts for 44% of the MCAO variance in the Barents Sea.This manuscript fits within the scope of WCD in that it addresses stratosphere-troposphere coupling, and prediction on subseasonal to seasonal time scales.The authors present convincing evidence that MCAOs in the North Atlantic are most frequent over the Barents, Norwegian, and Labrador Seas, while MCAOs are more frequent in the Barents and Norwegian Seas C1 WCDD Interactive commentPrinter-friendly version Discussion paper in a 30-day period following SSW events.However, I do not think this is strictly a new result (e.g., Fletcher et al. 2016).There is also a convincing case that the Zonal Dipole Index (ZDI) and MCAO are more correlated in the 30-days after an SSW.A key here though is that it is 'more' correlated, and it is not clear what threshold needs to be met in order for there to be a meaningful relationship.Furthermore, the composite patterns after SSWs (Fig. 3) and with MCAOs (Fig. 5) are only roughly similar.Overall, it is my opinion that while this manuscript has some promise, the results are far too premature for publication in WCD at this time.In particular:Printer-friendly version Discussion paper
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".