Effects of stratosphere‐troposphere chemistry coupling on tropospheric ozone
Bibliographic record
Abstract
A new, computationally efficient coupled stratosphere‐troposphere chemistry‐climate model (S/T‐CCM) has been developed based on three well‐documented components: a 64‐level general circulation model from the UK Met Office Unified Model, the tropospheric chemistry transport model (STOCHEM), and the UMSLIMCAT stratospheric chemistry module. This newly developed S/T‐CCM has been evaluated with various observations, and it shows good performance in simulating important chemical species and their interdependence in both the troposphere and stratosphere. The modeled total column ozone agrees well with Total Ozone Mapping Spectrometer observations. Modeled ozone profiles in the upper troposphere and lower stratosphere are significantly improved compared to runs with the stratospheric chemistry and tropospheric chemistry models alone, and they are in good agreement with Michelson Interferometer for Passive Atmospheric Sounding satellite ozone profiles. The observed CO tape recorder is also successfully captured by the new CCM, and ozone‐CO correlations are in accordance with Atmospheric Chemistry Experiment observations. However, because of limitations in vertical resolution, intrusion of CO‐rich air in the stratosphere from the mesosphere could not be simulated in the current version of S/T‐CCM. Additionally, the simulated stratosphere‐to‐troposphere ozone flux, which controls upper tropospheric OH and O3 concentrations, is found to be more realistic in the new coupled model compared to STOCHEM.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".