Arctic sea ice response to the eruptions of Agung, El Chichón, and Pinatubo
Bibliographic record
Abstract
Abstract Using a large initial condition ensemble of climate model simulations, we examine the impact of volcanic activity on Arctic sea ice cover from 1960 to 2005, a period that includes three very large tropical eruptions. Ensemble averaging across simulations with natural (volcanic and solar) forcings alone reduces noise due to internal variability to show a decade of increased Arctic sea extent (of up to half a million square kilometers) following each of the Mount Agung (1963), Mount El Chichón (1982), and Mount Pinatubo (1991) eruptions. A similar impact is seen when averaging over a large ensemble of simulations with natural and all‐known anthropogenic forcings. We show that the volcanic response in sea ice cover is sensitive to preeruption temperature, with warmer conditions before an eruption being associated with a larger than average response. Finally, a detection and attribution analysis using second‐generation Canadian Earth System Model (CanESM2) did not identify a significant response in the observations, while finding no evidence of inconsistency between observations and CanESM2 since regression coefficients were consistent with unity. A similar detection and attribution analysis using the somewhat stronger volcanic response from the simulations in the average of the CMIP5 models did identify a detectable natural forcing response in four observational sea ice extent data sets.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".