The historical ozone trends simulated with the SOCOLv4 and their comparison with observations and reanalysis
Bibliographic record
Abstract
Abstract. There is evidence that the ozone layer has begun to recover owing to the ban on the production of halogen-containing ozone-depleting substances (hODS) accomplished by the Montreal Protocol and its Amendments (MPA). However, recent studies, while reporting an increase in tropospheric ozone and confirming the ozone recovery in the upper stratosphere, also indicate a continuing decline in the lower tropical and mid-latitudinal stratospheric ozone. While these are indications derived from observations, they are not reproduced by current global chemistry-climate models (CCMs), which show positive or near-zero trends for ozone in the lower stratosphere. This makes it difficult to robustly establish ozone recovery and has sparked debate about the ability of contemporary CCMs to simulate future ozone trends. We applied the new Earth system model SOCOLv4 to calculate long-term ozone trends and compare them with trends derived from observations and reanalyses. The analysis is performed separately for the ozone depletion [1985–1997] and the ozone recovery [1998–2018] periods. Within the 1998–2018 period, SOCOLv4 shows clear ozone recovery in the mesosphere, upper and middle stratosphere; no significant ozone trend in the extra-polar lower stratosphere; and a steady increase in the tropospheric ozone. However, the lower stratospheric ozone trends remain controversial because the reanalysis datasets and SOCOLv4 results suggest slightly negative but insignificant trends which do not agree with some observation composite analysis. The obtained pattern of ozone trends is in general agreement with observations and reanalysis data sets, confirming that modern chemistry-climate models such as SOCOLv4 are generally capable of simulating the observed ozone changes, justifying their use to project the future evolution of the ozone layer.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".