Temperature and Ozone Response to Different Forcing in the Lower Troposphere and Stratosphere
Bibliographic record
Abstract
To evaluate the contributions of different forcings to the temperature and atmospheric composition changes between 1980 and 2020, we exploited the chemistry-climate model (CCM) SOCOLv3. The study examined ozone content and atmospheric temperature response to (1) ozone-depleting substances; (2) greenhouse gas concentrations, ocean surface temperature, and sea ice coverage; (3) solar irradiance; and (4) stratospheric aerosol loading and, separately, (5) greenhouse gas concentrations, (6) ocean surface temperature and sea ice coverage, and (7) NOx surface emissions. To evaluate the impacts of specific factors, we performed model runs driven by each factor (1–7) variability as well as a reference experiment that accounted for the influence of all factors simultaneously. We identified the relative contribution of different factors to the evolution of the temperature and ozone content of the lower troposphere and stratosphere from 1980 to 2020. The model results were in good agreement with the reanalyses (MERRA2 and ERA5). We showed that stratospheric ozone depletion before the Montreal Protocol introduction and partial recovery after that were chiefly driven by ODS. Stratospheric aerosol from major volcanic eruptions caused only short-term (up to 5 years) ozone decline. Increased greenhouse gas emissions dominate the ongoing long-term stratospheric cooling as well as tropospheric and surface warming. Solar irradiance contributed to short-term fluctuations but had a minimal long-term impact. Furthermore, our analysis of the solar signal in the tropical stratosphere underscores the complex interplay of solar radiation with volcanic, oceanic, and atmospheric factors, revealing significant altitudinal distributions of temperature and ozone responses to solar activity. Our findings advocate further innovative methodologies to take into account the nonlinearity of the atmospheric processes.
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) | 0.000 | 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) | 0.002 | 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".