Easy Volcanic Aerosol version 2: progress toward an updated volcanic aerosol forcing generator
Bibliographic record
Abstract
The Easy Volcanic Aerosol (EVA) family of simple models offers an approach to the generation of stratospheric aerosol fields from estimates of volcanic emissions. EVA takes as input a time series of volcanic eruption data, including the mass of sulfur injected into the stratosphere and location of the eruptions, and outputs aerosol optical properties as a function of time, latitude, height and wavelength based on a simple box-model of stratospheric transport. These aerosol properties are tailored for use as volcanic aerosol forcing in climate models. They are also useful as general quantitative estimates of the impact of volcanic eruptions on climate. EVA version 1 was based on observations of the aerosol from the 1991 Mt. Pinatubo eruption, while EVA_H was parameterized to improve agreement with a range of smaller magnitude eruptions observed over the 1979-2015 period, taking account of the estimated injection height of the emitted sulfur. Here, we present progress in the development of EVA version 2, which improves the fidelity of its output based on various important updates. The model accounts for bi-modal particle size distributions, in line with in-situ observations of Pinatubo aerosol plume. It can also account for the uncertainty in aerosol forcing due to the uncertainty in measurements of the refractive index of sulfuric acid solution. Further updates include implementation of a new method for incorporating injection height and its impact on aerosol growth and evolution. Improvements in the fidelity of aerosol properties is balanced with the aim of simplicity, making EVA2 well-suited for idealized model experiments as well as reconstructions of past volcanic forcing. We compare the results of EVA2 with observational data sets and quantify the impact of updates on reconstructions of volcanic forcing over periods relevant to upcoming CMIP7 experiments.
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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) | 0.001 | 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 | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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; both teacher heads agree on what is shown here.
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".