Stratospheric Injection Lifetimes
Bibliographic record
Abstract
Abstract Material injected to the stratosphere by volcanoes and pyrocumulonimbus clouds (pyroCbs) is observed to have different lifetimes depending on the altitude, latitude, season of the injection, and removal processes. We adopt a framework that describes the stratospheric lifetime of injected material as the sum of lag and decay timescales and compute these quantities in tracer simulations by injecting trajectory parcels and tracking them over 8 years. We simulate the evolution of the water vapor plume from the January 2022 Hunga eruption. The simulation suggests a lag time of 1.4 years and the decay time 2.35 ± 0.05 years, producing a stratospheric lifetime of 3.75 ± 0.05 years. From Microwave Limb Sounder observations, we estimate the Hunga water vapor plume decay time to be 2.6 ± 0.75 years and the lifetime to be 4.0 ± 0.75 years which is in good agreement with our model calculations. Overall, we find that injected material lifetime increases with altitude and decreases with the latitude. If polar stratospheric cloud formation is a loss process, the lifetime is shortened. Aerosol gravitational settling also shortens the lifetime and should be included especially for aerosols with >0.5 μm radius. We use the observed lifetime of the Hunga aerosol plume and gravitational settling rate to estimate a particle median radius of ∼0.3 µm in agreement with other estimates. Our calculations are also relevant to geoengineering plans for modifying the stratospheric albedo, where sustained stratospheric aerosol concentrations are envisioned.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".