Stratospheric ozone depletion from future nitrous oxide increases
Bibliographic record
Abstract
Abstract. We have investigated the impact of the assumed nitrous oxide (N2O) increases on stratospheric chemistry and dynamics using a series of idealized simulations with a coupled chemistry-climate model (CCM). In a future cooler stratosphere the net yield of NOy from N2O is shown to decrease in a reference run following the IPCC A1B scenario, but NOy can still be significantly increased by extra increases of N2O over 2001–2050. Over the last decade of simulations, 50% increases in N2O result in a maximal 6% reduction in ozone mixing ratios in the middle stratosphere at around 10 hPa and an average 2% decrease in the total ozone column (TCO) compared with the control run. This enhanced destruction could cause an ozone decline in the first half of this century in the middle stratosphere around 10 hPa, while global TCO still shows an increase at the same time. The results from a multiple linear regression analysis and sensitivity simulations with different forcings show that the chemical effect of N2O increases dominates the N2O-induced ozone depletion in the stratosphere, while the dynamical and radiative effects of N2O increases are overall insignificant. The analysis of the results reveals that the ozone depleting potential of N2O varies with the time period and is influenced by the environmental conditions. For example, carbon dioxide (CO2) increases can strongly offset the ozone depletion effect of N2O.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".