Stratospheric composition feedbacks in a changing climate: a review
Bibliographic record
Abstract
The important role of stratospheric feedbacks for the climate system – most notably how the ozone layer responds to anthropogenic forcings, and how that response then feeds back on the climate itself – remains largely unexplored, apart from the effects associated with gases regulated by the Montreal Protocol. This is because, to date, most models participating to CMIP inter-comparisons do not account for the complex interplay between stratospheric composition, dynamics and radiation. Here, we are providing a review of recent work highlighting the importance of such interplay on a broad range of time-scales, encompassing short-term variability to long-term climate change. First, we will show that increasing carbon dioxide levels lead to substantial changes in the ozone layer, and that these changes have a substantial effect on the circulation response to that forcing in both hemispheres (Chiodo & Polvani 2017; 2019). Then, we will review recent work on stratospheric water vapor (SWV) feedbacks under global warming, showing contrasting results concerning the effects on surface climate. Lastly, we will explore the connection between Arctic ozone and surface climate, highlighting the impacts of springtime ozone depletion on surface climate, and the sizable contribution of ozone feedbacks. Such findings demonstrate that stratospheric composition feedbacks play a key role in shaping climate response to anthropogenic forcings and stratosphere-troposphere coupling, both via radiative and dynamical processes. However, the coupling between ozone, SWV and climate is still subject to large uncertainties. We will discuss sources of uncertainty and model limitations, and implications for CMIP6.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".