Responses of large volcanic eruptions in the instrumental and documentary climatic data over Central Europe
Bibliographic record
Abstract
Abstract Responses of large volcanic eruptions in selected long temperature series from Austria, the Czech Republic and Germany as well as in three global radiation series in Central Europe are studied. In the example of seven large tropical eruptions (Krakatau 1883; Pelée, Soufriére and Santa María 1902; Agung, 1963; El Chichón, 1982; Mt Pinatubo, 1991) it has been demonstrated that volcanic signal in regional series is not so strongly expressed as in the hemispheric scale owing to different local effects and circulation patterns. This is also valid in the case of two further discussed eruptions of Tambora (1815) and Katmai (1912). The responses of eruptions in areas closer to Central Europe such as Iceland or Italy are more important. In nine analysed cases with VEI = 4–5 with a single exception of the Hekla eruption (1917), cold seasons were observed to follow the eruption. Responses to the Lakagígar eruption (1783) of Iceland with important impacts are also discussed in detail. Moreover, correlation between temperatures (annual and winter half‐year series) and NAOI is prevailingly smaller for the period following eruptions than in the period preceding eruptions. The importance of documentary evidence as a valuable source of the information about the impacts of volcanic eruptions is demonstrated. Copyright © 2006 Royal Meteorological Society.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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".