Inconsistencies between chemistry–climate models and observed lower stratospheric ozone trends since 1998
Bibliographic record
Abstract
Abstract. The stratospheric ozone layer shields surface life from harmful ultraviolet radiation. Following the Montreal Protocol ban on long-lived ozone-depleting substances (ODSs), rapid depletion of total column ozone (TCO) ceased in the late 1990s, and ozone above 32 km is now clearly recovering. However, there is still no confirmation of TCO recovery, and evidence has emerged that ongoing quasi-global (60∘ S–60∘ N) lower stratospheric ozone decreases may be responsible, dominated by low latitudes (30∘ S–30∘ N). Chemistry–climate models (CCMs) used to project future changes predict that lower stratospheric ozone will decrease in the tropics by 2100 but not at mid-latitudes (30–60∘). Here, we show that CCMs display an ozone decline similar to that observed in the tropics over 1998–2016, likely driven by an increase in tropical upwelling. On the other hand, mid-latitude lower stratospheric ozone is observed to decrease, while CCMs that specify real-world historical meteorological fields instead show an increase up to present day. However, these cannot be used to simulate future changes; we demonstrate here that free-running CCMs used for projections also show increases. Despite opposing lower stratospheric ozone changes, which should induce opposite temperature trends, CCMs and observed temperature trends agree; we demonstrate that opposing model–observation stratospheric water vapour (SWV) trends, and their associated radiative effects, explain why temperature changes agree in spite of opposing ozone trends. We provide new evidence that the observed mid-latitude trends can be explained by enhanced mixing between the tropics and extratropics. We further show that the temperature trends are consistent with the observed mid-latitude ozone decrease. Together, our results suggest that large-scale circulation changes expected in the future from increased greenhouse gases (GHGs) may now already be underway but that most CCMs do not simulate mid-latitude ozone layer changes well. However, it is important to emphasise that the periods considered here are short, and internal variability that is both intrinsic to each CCM and different to observed historical variability is not well-characterised and can influence trend estimates. Nevertheless, the reason CCMs do not exhibit the observed changes needs to be identified to allow models to be improved in order to build confidence in future projections of the ozone layer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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) | 0.002 | 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".