Total ozone trends and variability during 1979–2012 from merged datasets of various satellites
Bibliographic record
Abstract
Abstract. The study presents a~long term statistical trend analysis of total ozone datasets obtained from various satellites. A multi-variate linear regression was applied to annual mean zonal mean data using various natural and anthropogenic explanatory variables that represent dynamical and chemical processes which modify global ozone distributions in a changing climate. The study investigated the magnitude and zonal distribution of the different atmospheric chemical and dynamical factors to long-term total ozone changes. The regression model included the equivalent effective stratospheric chlorine (EESC), the 11 yr solar cycle, the Quasi-Biennial Oscillation (QBO), stratospheric aerosol loading describing the effects from major volcanic eruptions, the El Niño/Southern Oscillation (ENSO), the Arctic and Antarctic Oscillation (AO/AAO), and accumulated eddy heat flux (EHF), the latter representing changes due to the Brewer–Dobson circulation. The total ozone column dataset used here comprises the SBUV/TOMS/OMI merged data (1979–2012) MOD V8.0, the SBUV/SBUV-2 merged V8.6 and the merged GOME/SCIAMACHY/GOME-2 (GSG) WFDOAS merged data (1995–2012). The trend analysis was performed for twenty six 5° wide latitude bands from 65° S to 65° N, the analysis explained most of the ozone variability. The results show that QBO dominates the ozone variability in the tropics (±7 DU) while at higher latitudes, the dynamical indices, AO/AAO and eddy heat flux, have substantial influence on total ozone variations by up to ±10 DU. Volcanic aerosols are only prominent during the eruption periods and these together with the ENSO signal are more evident in the Northern Hemisphere. The signature of the solar cycle is evident over all latitudes and contributes about 10 DU from solar maximum to solar minimum. EESC is found to be a main contributor to the long-term ozone decline and the trend changes after the end of 1990s. A positive significant trend in total ozone columns is found after 1997 (between 1 and 8.2 DU decade−1) which points at the slowing of ozone decline and the onset of ozone recovery. The EESC based trends are compared with the trends obtained from the statistical piecewise linear trend (PWLT or hockey stick) model with a turnaround in 1997 to examine the differences between both approaches. Similar and significant pre-turnaround trends are observed. On the other hand, our results do indicate that the positive PWLT turnaround trends are larger than indicated by the EESC trends, however, they agree within 2-sigma, thus demonstrating the success of the Montreal Protocol phasing out of the ozone depleting substances (ODS). A sensitivity study is carried out by comparing the regression results, using SBUV MOD 8.0 merged time series (1979–2012) and a merged dataset combining TOMS/SBUV (1979–June 1995) and GOME/SCIAMACHY/GOME-2 ("GSG") WFDOAS (Weighting Function DOAS) (July 1995–2012) as well as SBUV/SBUV-2 MOD 8.6 (1979–2012) in the regression analysis in order to investigate the uncertainty in the long-term trends due to different ozone datasets and data versions. Replacing the late SBUV merged data record with GSG data (unscaled and adjusted) leads to very similar results demonstrating the high consistency between satellite datasets. However, the comparison of the new SBUV merged Mod V8.6 with the V8.0 data showed somewhat smaller sensitivities with regard to several proxies, however, the EESC and PWLT trends are very similar. On the other hand, the new MOD 8.6 data in the PWLT model revealed a~reduced ODS related upward trend after 1997.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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".