Dynamic winter climate response to large tropical volcanic eruptions since 1600
Bibliographic record
Abstract
We have analyzed the mean climate response pattern following large tropical volcanic eruptions back to the beginning of the 17th century using a combination of proxy‐based reconstructions and modern instrumental records of cold‐season surface air temperature. Warm anomalies occur throughout northern Eurasia, while cool anomalies cover northern Africa and the Middle East, extending all the way to China. In North America, the northern portion of the continent cools, with the anomalies extending out over the Labrador Sea and southern Greenland. The analyses confirm that for two years following eruptions the anomalies strongly resemble the Arctic Oscillation/Northern Annular Mode (AO/NAM) or the North Atlantic Oscillation (NAO) in the Atlantic‐Eurasian sector. With our four‐century record, the mean response is statistically significant at the 95% confidence level over much of the Northern Hemisphere land area. However, the standard deviation of the response is larger than the mean signal nearly everywhere, indicating that the anomaly following a single eruption is unlikely to be representative of the mean. Both the mean response and the variability can be successfully reproduced in general circulation model simulations. Driven by the solar heating induced by the stratospheric aerosols, these models produce enhanced westerlies from the lower stratosphere down to the surface. The climate response to volcanic eruptions thus strongly suggests that stratospheric temperature and wind anomalies can affect surface climate by forcing a shift in the AO/NAM or NAO.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".