Sensitivity of stratospheric inorganic chlorine to differences in transport
Bibliographic record
Abstract
Abstract. Correctly modeling stratospheric inorganic chlorine (Cly) is crucial for modeling the past and future evolution of stratospheric ozone. However, comparisons of the chemistry climate models used in the latest international assessment of stratospheric ozone depletion have shown large differences in the modeled Cly, with these differences explaining many of the differences in the simulated evolution of ozone over the next century. Here in, we examine the role of transport in determining the simulated Cly using three simulations from the same off-line chemical transport model that have the same lower tropospheric boundary conditions and the same chemical solver, but differing resolution and/or meteorological fields. These simulations show that transport plays a key role in determining the Cly distribution, and that Cly depends on both the time scales and pathways of transport. The time air spends in the stratosphere (e.g., the mean age) is an important transport factor determining stratospheric Cly, but the relationship between mean age and Cly is not simple. Lower stratospheric Cly depends on the fraction of air that has been in the upper stratosphere, and transport differences between models having the same mean age can result in differences in the fraction of organic chlorine converted into Cly. Differences in transport pathways result in differences in vertical profiles of CFCs, and comparisons of observed and modeled CFC profiles provide a stringent test of transport pathways in models.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".