Tropospheric Ozone Trends in the Arctic
Bibliographic record
Abstract
Tropospheric ozone, an important air pollutant and short-lived climate forcer, is changing globally with reported increases over emission regions that can influence ozone downwind. Here, ozone trends are examined in the Arctic troposphere, where surface warming is around four times faster than the global mean. Trends at the surface and in the free troposphere are estimated for 1993-2019 using available surface and ozonesonde data. Observed trends are also compared to modelled trends from the Arctic Monitoring Assessment Project (AMAP) multi-model evaluation, where models were run with the same anthropogenic emissions from 1990 to 2015 (Whaley et al., 2022, ACP). Findings include observed increases in annual mean surface ozone at Arctic coastal sites notably driven by increases during winter that are concurrent with decreasing surface carbon monoxide trends. Positive trends are also diagnosed at most high-Arctic ozonesonde sites in the wintertime free troposphere (up to 400 hPa). These ozone increases, which tend to be overestimated by the multi-model median (MMM) trends, are likely to be due to reductions in anthropogenic emission of nitrogen oxides at mid-latitudes leading to less ozone titration and influencing northern hemispheric ozone. Springtime increases are also found at the surface in northern coastal Alaska/Greenland but not in the MMMs. Causes are unknown but may be related to changing Arctic sea-ice or weather patterns affecting ozone sources or sinks. In contrast, surface ozone trends in northern Scandinavia are negative during spring, likely a response to decreasing ozone precursor emissions in Europe. MMM trends are also negative but generally overestimated. Springtime trends in the free troposphere also tend to be negative while summer trends are positive. Changes in ozone precursor emissions, the downward stratospheric ozone flux or general circulation may be contributing to these seasonal variations in the trends. The implications of these reported trends and model behaviour are discussed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".