Stratospheric residence time and the lifetime of volcanic stratospheric aerosols
Bibliographic record
Abstract
The amount of time that volcanic aerosols spend in the stratosphere is one of the primary factors influencing the climate impact of volcanic eruptions. Descriptions of stratospheric aerosol persistence vary, with many works quoting an approximately 1-year residence time for aerosol from large tropical eruptions, but other references to 1-2 year “lifetimes”. We introduce a framework for describing the evolution of global stratospheric aerosol after major volcanic eruptions and assess its persistence, based on analysis of global satellite-based aerosol observations, tracer transport simulations and simple conceptual modeling. We show that stratospheric residence time, which is estimated through passive tracer pulse experiments and is one factor influencing the lifetime of stratospheric aerosols, is strongly dependent on the injection latitude and height, with an especially strong sensitivity to injection height in the first four kilometers above the tropical tropopause. Time series of simulated stratospheric tracer fraction are best described by a simple model which includes a lag between the injection and initiation of removal from the stratosphere. A simple model including lagged decay, as well as a timescale for sulfate aerosol production, produces a best fit to global observations of stratospheric aerosol after the 1991 eruption of Mt. Pinatubo consistent with a stratospheric lifetime of about 24 months. We estimate the potential impact of observational uncertainties on this lifetime estimate and find it likely that the lifetime of Pinatubo stratospheric aerosol is 20 months or greater.
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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 | 0.000 |
| 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) | 0.001 | 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".