Impact of volcanic eruptions in CMIP6 decadal prediction systems: a multi-model analysis
Bibliographic record
Abstract
In recent decades three major volcanic eruptions of different intensity have occurred: Mount Agung (1963), El Chichón (1982) and Mount Pinatubo (1991), with reported climate impacts on seasonal-to-decadal timescales and providing a high prediction potential. The Decadal Climate Prediction Project component C (DCPP-C) includes a protocol to investigate the impact of such volcanic eruptions on decadal prediction, which consists in performing initialised sets of predictions just before the three historical volcanic eruptions, but in which the volcanic aerosol forcing is excluded. The impact of the volcanic eruptions is therefore determined by comparing these new forecasts with those included in the corresponding retrospective prediction experiment DCPP-A, which include historical volcanic aerosol forcing. Here we present the results from six CMIP6 decadal prediction systems (CanESM5, CESM1, EC-Earth3, HadGEM3, IPSL-CM6A and CMCC-CM2-SR5). The global mean temperature cooling is comparable among models and consistent with previous studies. The surface temperature response pattern in the first years is similar across all the models and for the individual volcanic eruptions. At later forecast times (years 6-9), differences among the models and eruptions emerge. Preliminary results show that the volcanic eruptions impact the atmospheric and oceanic dynamics, as shown in previous studies, although some differences across models emerge, specifically on the ocean overturning and gyre circulation changes.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 | 0.000 |
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".