Uncertainties in modelling heterogeneous chemistry and Arctic ozone depletion in the winter 2009/2010
Bibliographic record
Abstract
Abstract. Stratospheric chemistry and denitrification are simulated for the Arctic winter 2009/2010 with the Lagrangian Chemistry and Transport Model ATLAS. A number of sensitivity runs is used to explore the impact of uncertainties in chlorine activation and denitrification on the model results. In particular, the efficiency of chlorine activation on different types of liquid aerosol versus activation on nitric acid trihydrate clouds is examined. Additionally, the impact of changes in reaction rate coefficients, in the particle number density of polar stratospheric clouds, in supersaturation, temperature or the extent of denitrification is investigated. Results are compared to satellite measurements of MLS and ACE-FTS and to in-situ measurements onboard the Geophysica aircraft during the RECONCILE measurement campaign. It is shown that even large changes in the underlying assumptions have only a small impact on the modelled ozone loss, even though they can cause considerable differences in chemical evolution of other species and in denitrification. Differences in column ozone between the sensitivity runs stay below 10% at the end of the winter. Chlorine activation on liquid aerosols alone is able to explain the observed magnitude and morphology of the mixing ratios of active chlorine, reservoir gases and ozone. This is even true for binary aerosols (no uptake of HNO3 from the gas-phase allowed in the model). Differences in chlorine activation between sensitivity runs are within 30%. Current estimates of nitric acid trihydrate (NAT) number density and supersaturation imply that, at least for this winter, NAT clouds play a relatively small role compared to liquid clouds in chlorine activation. The change between different reaction rate coefficients for liquid or solid clouds has only a minor impact on ozone loss and chlorine activation in our sensitivity runs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".