Exact timing, sulfur spread and global climate footprint of the caldera-forming Mt. Mazama eruption, the largest volcanic eruption of the Holocene.
Bibliographic record
Abstract
Large volcanic eruptions are key time markers in paleoclimatology because they inject large quantities of volcanic fallout (such as sulfuric acids and tephra) into the atmosphere which is then widely distributed and deposited in environmental archives such as ice cores, lakes and peat bogs. They also produce strong climate effects, imprinted in climate archives such as tree-rings. The caldera-forming eruption of Mount Mazama (Crater Lake, Oregon, USA) some 7700 years ago ranks among the largest eruptions of the Holocene but little is known about its exact timing and global-scale climate impacts. Here we use new high-resolution ice-core analyses of volatiles (S, Cl), particle-size distribution, crypto-tephra and sulfur isotopes (33S, 34S), from ice cores in Greenland and Antarctica, to constrain the date, stratospheric sulfur injection, global aerosol distribution and climate forcing of this eruption. We further demonstrate that the climatic effects left distinctive fingerprints in ultra-long tree-ring chronologies from North America and Europe allowing the date of this eruption to be pinned to a specific year, thereby aligning climate proxy records in North America, Greenland and Europe on a common timeline. Using an ensemble of fully-coupled Earth System Model simulations we identify some key regions experiencing large anomalies in temperature and hydro-climate following the Mt. Mazama eruption. These extreme conditions were not only relevant for hunter-gatherer communities and early agricultural societies emerging in Eurasia, that experienced these compounding effects, but they also help us in identifying a global existential risk arising from comparable eruptions in the future.
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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 | 0.000 |
| 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".