Chemical depletion of Arctic ozone in winter 1999/2000
Bibliographic record
Abstract
During Arctic winters with a cold, stable stratospheric circulation, reactions on the surface of polar stratospheric clouds (PSCs) lead to elevated abundances of chlorine monoxide (ClO) that, in the presence of sunlight, destroy ozone. Here we show that PSCs were more widespread during the 1999/2000 Arctic winter than for any other Arctic winter in the past two decades. We have used three fundamentally different approaches to derive the degree of chemical ozone loss from ozonesonde, balloon, aircraft, and satellite instruments. We show that the ozone losses derived from these different instruments and approaches agree very well, resulting in a high level of confidence in the results. Chemical processes led to a 70% reduction of ozone for a region ∼1 km thick of the lower stratosphere, the largest degree of local loss ever reported for the Arctic. The Match analysis of ozonesonde data shows that the accumulated chemical loss of ozone inside the Arctic vortex totaled 117 ± 14 Dobson units (DU) by the end of winter. This loss, combined with dynamical redistribution of air parcels, resulted in a 88 ± 13 DU reduction in total column ozone compared to the amount that would have been present in the absence of any chemical loss. The chemical loss of ozone throughout the winter was nearly balanced by dynamical resupply of ozone to the vortex, resulting in a relatively constant value of total ozone of 340 ± 50 DU between early January and late March. This observation of nearly constant total ozone in the Arctic vortex is in contrast to the increase of total column ozone between January and March that is observed during most years.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".