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Record W2970692350 · doi:10.1029/2019gl083975

Impacts of Climate Change on Volcanic Stratospheric Injections: Comparison of 1‐D and 3‐D Plume Model Projections

2019· article· en· W2970692350 on OpenAlexafffund
Thomas J. Aubry, Matteo Cerminara, M. Jellinek

Bibliographic record

VenueGeophysical Research Letters · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsUniversity of British Columbia
FundersHorizon 2020 Framework ProgrammeIstituto Nazionale di Geofisica e VulcanologiaCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of CanadaRoyal SocietyNational Science Foundation
KeywordsStratosphereVolcanoTropospherePlumeAtmospheric sciencesEnvironmental scienceClimatologyClimate changeClimate modelExplosive eruptionGeologyMeteorologyGeographyMagmaSeismology

Abstract

fetched live from OpenAlex

Abstract Explosive volcanic eruptions are one of the most important driver of climate variability. Yet, we still lack a fundamental understanding of how climate change may affect future eruptions. Here, we use an ensemble of simulations by 1‐D and 3‐D volcanic plume models spanning a large range of eruption source and atmospheric conditions to assess changes in the dynamics of future eruptive columns. Our results shed new light on differences between the predictions of 1‐D and 3‐D plume models. Furthermore, both models suggest that as a result of ongoing climate change, for tropical eruptions, (i) higher eruption intensities will be required for plumes to reach the upper troposphere/lower stratosphere and (ii) the height of plumes currently reaching the upper troposphere/lower stratosphere or above will increase. We discuss the implications of these results for the climatic impacts of future eruptions. Our simulations can directly inform climate model experiments on climate‐volcano feedback.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score0.122

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.080
GPT teacher head0.358
Teacher spread0.278 · 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 designSimulation or modeling
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

Citations16
Published2019
Admission routes2
Has abstractyes

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