The surface impacts of Arctic stratospheric ozone anomalies
Bibliographic record
Abstract
In the Arctic stratosphere, total column ozone in the spring can vary, from year to year, by as much as 30%. This large interannual variability, however, is absent from many present-generation climate models, in which the prescribed seasonal cycle of stratospheric ozone includes, at best, smooth multi-decadal trends. We here investigate the extent to which interannual variability in Arctic stratospheric ozone is able to affect the surface climate of the Northern Hemisphere extratropics. We do this by contrasting pairs of model integrations with positive and negative springtime ozone anomalies, using a simple yet widely used climate model. For ozone anomaly amplitudes somewhat larger than the recent observed variability, we find a significant influence on the tropospheric circulation, and the surface temperatures and precipitation patterns. More interestingly, these impacts have very clear regional patterns—they are largest over the North Atlantic sector—even though the prescribed ozone anomalies are zonally symmetric. However, confirming other studies, for ozone anomaly amplitudes within the observed range of the last three decades, our model experiments do not show statistically significant impacts at the surface.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".