Global Ground-based Tropospheric Ozone Measurements: Reference Data and Individual Site Trends (2000–2022) from the TOAR-II/HEGIFTOM Project
Bibliographic record
Abstract
Abstract. Tropospheric ozone trends from models and satellites are found to diverge. Ground-based (GB) observations are used to reference models and satellites but GB data themselves might display station biases and discontinuities. Re-processing with uniform procedures, the TOAR-II Working Group Harmonization and Evaluation of Ground-based Instruments for Free-Tropospheric Ozone Measurements (HEGIFTOM) homogenized public data from 5 networks: ozonesondes, In-service Aircraft for a Global Observing System (IAGOS) profiles, solar absorption Fourier-Transform Infrared (FTIR) spectrometer measurements, Lidar observations, and Dobson Umkehr data. Amounts and uncertainties for total tropospheric ozone (“TrOC”, surface to 300 hPa), free and lower tropospheric ozone, are calculated for each network. We report trends (2000 to 2022) for these segments using Quantile Regression (QR) and Multiple Linear Regression (MLR) for 55 datasets, including 6 multi-instrument stations. The findings: (1) Median TrOC trends computed with QR and MLR trends are essentially the same; (2) Pole-to-pole, across all longitudes, TrOC trends fall within +3 ppbv/decade to -3 ppbv/decade, equivalent to (-4 % to + 8 %)/decade depending on site. (3) The greatest fractional increases occur over most tropical/subtropical sites with decreases at northern high latitudes but these patterns are not uniform. (4) Post-COVID trends are smaller than pre-COVID trends for Northern Hemisphere mid-latitude sites. In summary, this analysis conducted in the frame of TOAR-II/HEGIFTOM shows that high-quality, multi-instrument, harmonized data over a wide range of ground sites provide clear standard references for TOAR-II models and evolving tropospheric ozone satellite products for 2000–2022.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".