Analysis and attribution of total column ozone changes over the Tibetan Plateau during 1979–2017
Bibliographic record
Abstract
Abstract. Various observation-based datasets have confirmed positive zonal mean column ozone trends at midlatitudes as a result of the successful implementation of the Montreal Protocol. However, there is still uncertainty about the longitudinal variation of these trends and the direction and magnitude of ozone changes at low latitudes. Here, we use the extended Copernicus Climate Change Service (C3S) dataset (1979–2017) to investigate the long-term variations in total column ozone (TCO) over the Tibetan Plateau (TP) for different seasons. We use piecewise linear trend (PWLT) and equivalent effective stratospheric chlorine loading (EESC)-based multivariate regression models with various proxies to attribute the influence of dynamical and chemical processes on the TCO variability. We also compare the seasonal behaviour of the relative total ozone low (TOL) over the TP with the zonal mean at the same latitude. Both regression models show that the TP column ozone trends change from negative trends from 1979 to 1996 to small positive trends from 1997 to 2017, although the later positive trend based on PWLT is not statistically significant. The wintertime positive trend starting from 1997 is larger than that in summer, but both seasonal TP recovery rates are smaller than the zonal means over the same latitude band. For TP column ozone, both regression models suggest that the geopotential height at 150 hPa (GH150) is a more suitable and realistic dynamical proxy compared to a surface temperature proxy used in some previous studies. Our analysis also shows that the wintertime GH150 plays an important role in determining summertime TCO over the TP through persistence of the ozone signal. For the zonal mean column ozone at this latitude, the quasi-biennial oscillation (QBO) is nonetheless the dominant dynamical proxy. We also use a 3-D chemical transport model to diagnose the contributions of different proxies for the TP region. The role of GH150 variability is illustrated by using two sensitivity experiments with repeating dynamics of 2004 and 2008. The simulated ozone profiles clearly show that wintertime TP ozone concentrations are largely controlled by tropics to midlatitude pathways, whereas in summer variations associated with tropical processes play an important role. These model results confirm that the long-term trends of TCO over the TP are dominated by different processes in winter and summer. The different TP recovery rates relative to the zonal means at the same latitude band are largely determined by wintertime dynamical processes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".