The sensitivity of EC-Earth3 decadal predictions to the choice of volcanic forcing dataset: Insights for the next major eruption
Bibliographic record
Abstract
Abstract. Large volcanic eruptions can have significant climatic impacts. Due to their unpredictable nature, such eruptions can render operational decadal forecasts inaccurate. To benefit from the strong climate signals they exert, which enhance climate predictability, decadal forecasts must be rerun with updated estimates of the stratospheric sulfate aerosol evolution. Two tools to rapidly generate the volcanic forcings are the Easy Volcanic Aerosol (EVA, Toohey et al., 2016) and its updated version, EVA_H (Aubry et al., 2020). In order to validate the use of the volcanic forcings generated with these simple models in decadal forecasts, we compare the volcanic forcings generated with EVA and EVA_H with CMIP6 for the recent eruptions of Mount Agung (1963), El Chichón (1982) and Mount Pinatubo (1991) and investigate the consistency in their associated climate responses in decadal predictions produced with the BSC decadal forecast system. Our findings reveal differences in the magnitude and latitudinal structure of the forcings generated by EVA and EVA_H compared to the official CMIP6 forcings, particularly for the eruptions of Mount Agung and El Chichón. These differences in the volcanic forcings lead to some global and regional quantitative differences in the predicted radiative responses, as evidenced in variables like the top-of-atmosphere (TOA) net radiative fluxes, surface temperature, and lower stratospheric temperature. Despite these differences, comparing the predicted anomalies in those variables with observations, we show that either of the forcings considered allows to make skillful predictions after the major volcanic eruptions. Our study thus supports both EVA and EVA_H generated forcings as reasonable choices for predicting the post-volcanic radiative responses.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".