A global ozone climatology from ozone soundings via trajectory mapping: a stratospheric perspective
Bibliographic record
Abstract
Abstract. This study explores a domain-filling trajectory approach to generate a global ozone climatology from relatively sparse ozonesonde data. Global ozone soundings comprising 51 898 profiles at 116 stations over 44 yr (1965–2008) are used, from which forward and backward trajectories are calculated from meteorological reanalysis data, to map ozone measurements to other locations and so fill in the spatial domain. The resulting global ozone climatology is archived monthly for five decades from the 1960s to the 2000s on a~grid of 5° × 5° × 1 km (latitude, longitude, and altitude), from the surface to 26 km altitude. It is also archived yearly from 1965 to 2008. The climatology is validated at 20 selected ozonesonde stations by comparing the actual ozone sounding profile with that derived through trajectory mapping of ozone sounding data from all stations except the one being compared. The two sets of profiles are in good agreement, both individually with correlation coefficients (r) between 0.975 and 0.998 and root mean square (RMS) differences of 87 to 482 ppbv, and overall with r = 0.991 and an RMS of 224 ppbv. The ozone climatology is also compared with two sets of satellite data, from the Satellite Aerosol and Gas Experiment (SAGE) and the Optical Spectrography and InfraRed Imager System (OSIRIS). The ozone climatology compares well with SAGE and OSIRIS data in both seasonal and zonal means. The mean differences are generally quite small, with maximum differences of 20% above 15 km. The agreement is better in the Northern Hemisphere, where there are more ozonesonde stations, than in the Southern Hemisphere; it is also better in the middle and high latitudes than in the tropics where reanalysis winds are less accurate. This ozone climatology captures known features in the stratosphere, as well as seasonal and decadal variations of these features. Compared to current satellite data, it offers more complete high latitude coverage as well as a much longer record. The climatology shows clearly the depletion of ozone from the 1970s to the mid 1990s and ozone recovery in the lower stratosphere in the 2000s. When this climatology is used as the upper boundary condition in an Environment Canada operational chemical forecast model, the forecast is improved in the vicinity of the upper troposphere–lower stratosphere (UTLS) region. As this ozone climatology is neither dependent on a priori data nor photochemical modeling, it provides independent information and insight that can supplement satellite data and model simulations of stratospheric ozone.
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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".