Ground-based tropospheric ozone measurements: regional tropospheric ozone column trends from the TOAR-II/HEGIFTOM homogenized datasets
Bibliographic record
Abstract
Quantifying long-term free-tropospheric ozone trends is essential for understanding the impact of human activities and climate change on atmospheric chemistry. However, this is complicated by two key challenges: the differences among existing satellite-derived tropospheric ozone products, which are not yet fully understood or reconciled, and the limited temporal and spatial coverage of ground-based reference measurements. Here, we explore if a more consistent understanding of the geographical distribution of tropospheric ozone column (TrOC) trends can be obtained by focusing on regional trends from ground-based measurements. Regions were determined with a correlation analysis between modeled TrOCs at the site locations. For those regions, TrOC trends were estimated with quantile regression for the Trajectory-mapped Ozonesonde dataset for the Stratosphere and Troposphere (TOST) and with a linear mixed-effects modeling (LMM) approach to calculate synthesized trends from homogenized HEGIFTOM (Harmonization and Evaluation of Ground-based Instruments for Free-Tropospheric Ozone Measurements) individual site trends. For different periods (1990–2021/22, 1995–2021/22, 2000–2021/22), both approaches give increasing (partial) tropospheric ozone column amounts over almost all Asian regions (median confidence) and negative trends over Arctic regions (very high confidence). Trends over Europe and North America are mostly weakly positive (LMM) or negative (TOST). For both approaches, the 2000–2021/22 trends decreased in magnitude compared to 1995–2021/22 for most regions; and for all time periods and regions, the pre-COVID trends are larger than the post-COVID trends. Our results enable the validation of global satellite TrOC trends and assessment of the performance of atmospheric chemistry models to represent the distribution and variation of TrOC.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".