A closer look at Arctic ozone loss and polar stratospheric clouds
Bibliographic record
Abstract
Abstract. The empirical relationship found between column-integrated Arctic ozone loss and the potential volume of polar stratospheric clouds inferred from meteorological analyses is recalculated in a self-consistent manner using the ERA Interim reanalyses. The relationship is found to hold at different altitudes as well as in the column. The use of a PSC formation threshold based on temperature dependent cold aerosol formation makes little difference to the original, empirical relationship. Analysis of the photochemistry leading to the ozone loss shows that activation is limited by the photolysis of nitric acid. This step produces nitrogen dioxide which is converted to chlorine nitrate which in turn reacts with hydrogen chloride on any polar stratospheric clouds to form active chlorine. The rate-limiting step is the photolysis of nitric acid: this occurs at the same rate every year and so the interannual variation in the ozone loss is caused by the extent and persistence of the polar stratospheric clouds. In early spring the ozone loss rate increases as the solar insolation increases the photolysis of the chlorine monoxide dimer in the near ultraviolet. However the length of the ozone loss period is determined by the photolysis of nitric acid which also occurs in the near ultraviolet. As a result of these compensating effects, the amount of the ozone loss is principally limited by the extent of original activation rather than its timing. In addition a number of factors, including the vertical changes in pressure and total inorganic chlorine as well as denitrification and renitrification, offset each other. As a result the extent of original activation is the most important factor influencing ozone loss. These results indicate that relatively simple parameterisations of Arctic ozone loss could be developed for use in coupled chemistry climate models.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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 teacher head, 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".