The effect of volcanic eruptions on global precipitation
Bibliographic record
Abstract
We examine robust features of the global precipitation response to 18 large low‐latitude volcanic eruptions using an ensemble of last millennium simulations from the climate model HadCM3. We then test whether these features can be detected in observational land precipitation data following five twentieth century eruptions. The millennium simulations show a significant reduction in global mean precipitation following eruptions, in agreement with previous studies. Further, we find that the response over ocean remains significant for around 5 years and matches the timescale of the near‐surface air temperature response. In contrast, the land precipitation response remains significant for 3 years and reacts faster than land temperature, correlating with aerosol optical depth and a reduction in land‐ocean temperature contrast. In the tropics, areas experiencing posteruption drying coincide well with climatologically wet regions, while dry regions get wetter on average, but there changes are spatially heterogeneous. This pattern is of opposite sign to, but physically consistent with, projections under global warming. A significant reduction in global mean and wet tropical land regions precipitation is also found in response to twentieth century eruptions in both the observations and model masked to replicate observational coverage, although this is not significant for the observed wet regions response in boreal summer. In boreal winter, the magnitude of this global response is significantly underestimated by the model; the discrepancy originating from the wet tropical regions although removing the influence of ENSO improves agreement. The modeled precipitation response is detectable in the observations in boreal winter but marginal in summer.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".