Bibliographic record
Abstract
Abstract. Previous assessments on modelling Arctic tropospheric ozone (O3) have shown that most atmospheric models continue to experience difficulties in simulating tropospheric O3 in the Arctic, particularly in capturing the seasonal variations at coastal sites, primarily attributed to the lack of representation of surface bromine chemistry in the Arctic. In this study, two independent chemical transport models (CTMs), DEHM (Danish Eulerian Hemispheric Model) and GEM-MACH (Global Environmental Multi-scale – Modelling Air quality and Chemistry), were used to simulate Arctic lower tropospheric O3 for the year 2015 at considerably higher horizontal resolutions (25-km and 15-km, respectively) than the large-scale models in the previous assessments. Both models include bromine chemistry and a representation of snow-sourced bromine mechanism: a blowing-snow bromine source mechanism in DEHM and a snowpack bromine source mechanism in GEM-MACH. Model results were compared with a suite of observations in the Arctic, including hourly observations from surface sites and mobile platforms (buoys and ship) and ozonesonde profiles, to evaluate models’ ability to simulate Arctic lower tropospheric O3, particularly in capturing the seasonal variations and the key processes controlling these variations. The study found that both models behave quite similarly outside the spring period and are able to capture the observed overall surface O3 seasonal cycle and synoptic scale variabilities, as well as the O3 vertical profiles in the Arctic. GEM-MACH (with the snowpack bromine source mechanism) was able to simulate most of the observed springtime Ozone Depletion Events (ODEs) at the coastal and buoy sites well, while DEHM (with the blowing-snow bromine source mechanism) simulated much fewer ODEs. The study showed that the springtime O3 depletion process plays a central role in driving the surface O3 seasonal cycle in Central Arctic, and that the bromine-mediated ODEs, while occurring most notably within the lowest few hundred metres of air above the Arctic Ocean, can induce a 5–7 % of loss in the total pan-Arctic tropospheric O3 burden during springtime. The model simulations also showed an overall enhancement in the pan-Arctic O3 concentration due to northern boreal wildfire emissions in summer 2015; the enhancement is more significant at higher altitudes. Higher O3 excess ratios (ΔO3/ΔCO) found aloft compared to near the surface indicate greater photochemical O3 production efficiency at higher altitudes in fire-impacted air masses. The model simulations further indicated an enhancement in NOy in the Arctic due to wildfires; a large portion of NOy produced from the wildfire emissions is found in the form of PAN that is transported to the Arctic, particularly at higher altitudes, potentially contributing to O3 production there.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.014 | 0.007 |
| Insufficient payload (model declined to judge) | 0.558 | 0.405 |
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".