Global and zonal total ozone variations estimated from ground‐based and satellite measurements: 1964–2000
Bibliographic record
Abstract
Six data sets of monthly average zonal total ozone were intercompared and then used to estimate latitudinal and global total ozone temporal variations and trends. The data sets were prepared by different groups and are based on TOMS, SBUV‐SBUV/2, GOME, and ground‐based measurements. Different approaches have been used to homogenize the records over the period 1979–2000. Systematic differences of up to 3% were found between different data sets for zonal and global total ozone area weighted average values. However, when these systematic differences were removed by deseasonalizing the data, the residuals agreed to within ±0.5% of the long‐term mean ozone values. All data sets show changes in the rate of the total ozone decline in recent years. While global ozone was fairly constant during the 1990s, the average values of the 1990s are about 2–3% lower than those of the late 1970s. About 38% of the global ozone is located between 25°S and 25°N where the data show no decline. The strongest decline and the largest variability occur over the 35°N–60°N zone during the winter‐spring season with the largest negative deviations occurring in 1993 and 1995. The decline in autumn is much smaller at these latitudes. Over the 35°S–60°S zone the ozone decline shows less seasonal dependence, and the largest deviations there were observed in 1985 and 1997. Sliding 11‐year trends were calculated to estimate ozone changes over different time intervals. The first interval was from 1964 to 1974, and the last interval was from 1990 to 2000. The steepest year‐round trends, of up to −5% per decade, occurred in the 11‐year periods ending between 1992 and 1997 over the 35°–60°N zone and between 1985 and 1993 over the 35°–55°S zone. More recent 11‐year trends have smaller declines.
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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 | 0.002 | 0.002 |
| 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 | 0.000 |
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".