Climate Feedback to Stratospheric Aerosol Forcing: The Key Role of the Pattern Effect
Bibliographic record
Abstract
Abstract Volcanic aerosol forcing has previously been found to cause a weak global mean temperature response, as compared with CO2 radiative forcing of equal magnitude: its efficacy is supposedly low, but for reasons that are not fully understood. To investigate this, we perform idealized, time-invariant stratospheric sulfate aerosol forcing simulations with the MPI-ESM-1.2 and compare them with 0.5 × CO2 and 2 × CO2 runs. While the early decades of the aerosol forcing simulations are characterized by strong negative feedback (i.e., low efficacy), the feedback weakens on the decadal to centennial time scale. Although this effect is qualitatively also found in CO2-warming simulations, it is more pronounced for stratospheric aerosol forcing. The strong early and weak late cooling feedbacks compensate, leading to an equilibrium efficacy of approximately 1 in all simulations. The 0.5 × CO2 cooling simulations also exhibit strong feedback changes over time, albeit less than in the idealized aerosol forcing simulations. This suggests that the underlying cause for the feedback change is not exclusively specific to aerosol forcing. One critical region for the feedback differences between simulations with negative and positive radiative forcing is the tropical Indo-Pacific warm-pool region (30°S–30°N, 50°E–160°W). In the first decades of cooling, the temperature change in this region is stronger than the global average, whereas it is stronger outside it for 2 × CO2 warming. In cooling scenarios, this leads to an enhanced activation of the warm-pool region’s strongly negative lapse-rate feedback. Significance Statement Large volcanic eruptions can enhance the scattering aerosol layer in the stratosphere, which leads to a global cooling for a few years. Surprisingly, Earth has been found to cool less from radiative flux perturbations from stratospheric aerosol forcing, in comparison with how much it warms as a result of increases in CO2 concentration. We find that specific surface temperature change patterns after volcanic eruptions cause this effect. The temperature change in the tropical Indian and western Pacific Ocean determines how much global temperature change is needed to regain radiative equilibrium. Our findings contribute to understanding the climate response to volcanic eruptions and are relevant for understanding the mechanisms of climate change due to changes in CO2 concentration.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".