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 impacts on climate. Due to their unpredictable nature, when a major volcanic eruption occurs, decadal forecasts issued prior to the eruption will be inaccurate. Consequently, new decadal forecasts including updated estimates of the stratospheric sulfate aerosol evolution must be produced. To rapidly generate such volcanic forcing once the initial eruption characteristics are known, the Easy Volcanic Aerosol (EVA) forcing generator, and its updated version EVA_H, can be used. Comparing the volcanic forcings generated with these tools and the one from Coupled Model Intercomparison Project phase 6 for the recent eruptions of Mount Agung (1963), El Chichón (1982) and Mount Pinatubo (1991), we identify some differences in the magnitude and latitudinal structure, particularly for the eruptions of Mount Agung and El Chichón. Using these forcings, we conduct a set of retrospective prediction experiments for these eruptions with the Barcelona Supercomputing Center decadal forecast system, following a specifically designed protocol. The predictions driven by the three forcing datasets show similar post-eruption radiative responses, with particularly good agreement for the eruption of Mount Pinatubo. The global mean top-of-atmosphere flux and global mean surface temperature responses in the hindcast experiments are indistinguishable across the three forcing sets and three eruptions. However, we find differences in the zonal mean and regional responses due to the latitudinally-varying structure of the volcanic forcings, particularly for the eruptions of Mount Agung and El Chichón. Significant differences among the datasets are found in the global mean lower stratospheric warming, where the responses are strongest. Comparing the predicted anomalies in these hindcasts with observations we show that overall there is better agreement when volcanic forcing is included, highlighting its importance to accurate predictions. Our study suggests that either EVA and EVA_H forcings can be used for predicting the post-volcanic radiative response, although the generated forcing datasets and simulations should be interpreted with care given the limitations of these reduced-complexity empirical models.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".