Impacts of midlatitude precursor emissions and local photochemistry on ozone abundances in the Arctic
Bibliographic record
Abstract
We assess the impact of transport of pollution from midlatitudes on the abundance of ozone in the Arctic in summer 2006 using the GEOS‐Chem global chemical transport model and its adjoint. We find that although the impact of midlatitude emissions on ozone abundances in the Arctic is at a maximum in fall and winter, in July transport from North America, Asia, and Europe together contributed about 25% of surface ozone abundances in the Arctic. Throughout the summer, the dominant source of ozone in the Arctic troposphere was photochemical production within the Arctic, which accounted for more than 50% of the ozone in the Arctic boundary layer and as much as 30%–40% of the ozone in the middle troposphere. An adjoint sensitivity analysis of the impact of NOx emissions on ozone at Alert shows that on synoptic time scales in both the lower and middle troposphere, ozone abundances are more sensitive to emissions between 50°N and 70°N, with important influences from anthropogenic, biomass burning, soil, and lightning sources. Although local surface NOx emissions contribute to ozone formation, transport of NOx in the form of peroxyacetyl nitrate (PAN) from outside the Arctic and from the upper troposphere also contributed to ozone production in the lower troposphere. We find that in late May and June the release of NOx from PAN decomposition accounted for 93% and 55% of ozone production at the Arctic surface, respectively.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".