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Record W4322006380 · doi:10.5194/egusphere-egu23-7296

Revisiting the volcanic forcing of high-latitude Northern Hemisphere eruptions

2023· preprint· en· W4322006380 on OpenAlexaff
Herman Fuglestvedt, Zhihong Zhuo, Matthew Toohey, Kirstin Krüger

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsVolcanoNorthern HemisphereGeologyExplosive eruptionExtratropical cycloneAtmospheric sciencesPlumeForcing (mathematics)LatitudeClimatologyVolcanismMiddle latitudesSulfate aerosolStratosphereEnvironmental scienceMeteorologyGeochemistryMagmaGeographySeismology

Abstract

fetched live from OpenAlex

Large high-latitude explosive volcanic eruptions remain less well understood than their low-latitude counterparts, despite their potential for strong hemispheric climate impacts. Using the high-top coupled Earth system model CESM2-WACCM6 with prognostic stratospheric aerosols and chemistry, we simulate Pinatubo-magnitude Northern Hemisphere (NH) volcanic eruptions at 64° N. We show how the SO2 lifetime and growth of volcanic sulphate aerosols are strongly modulated by the initial state of the NH polar vortex for eruptions at this latitude. The resulting variability of the volcanic forcing is of comparable magnitude to its sensitivity to varying the plume composition, eruption season, and plume height. We compare the modelled volcanic sulphate deposition over the Greenland ice sheet to that assumed in current forcing reconstructions of past NH extratropical eruptions and provide a new model-based estimate of the magnitude and uncertainty of the transfer function used to reconstruct sulphur injections from such eruptions. Our results demonstrate the great potential for improvement in understanding and reconstructing the climatic impacts of NH high-latitude eruptions.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score0.138

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.027
GPT teacher head0.240
Teacher spread0.213 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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