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Record W1840015033 · doi:10.1002/2015gl064314

Inability of stratospheric sulfate aerosol injections to preserve the West Antarctic Ice Sheet

2015· article· en· W1840015033 on OpenAlexaff
Kelly E. McCusker, David S. Battisti, Cecilia M. Bitz

Bibliographic record

VenueGeophysical Research Letters · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsOcean Networks Canada SocietyUniversity of Victoria
Fundersnot available
KeywordsEnvironmental scienceSulfate aerosolFuture sea levelCryosphereClimatologyGreenhouse gasAntarctic ice sheetIce sheetAerosolIce shelfGlobal warmingAtmospheric sciencesClimate changeStratosphereOceanographyGeologySea iceMeteorologyGeography

Abstract

fetched live from OpenAlex

Abstract Injection of sulfate aerosols into the stratosphere has the potential to reduce the climate impacts of global warming, including sea level rise (SLR). However, changes in atmospheric and oceanic circulation that can significantly influence the rate of basal melting of Antarctic marine ice shelves and the associated SLR have not previously been considered. Here we use a fully coupled global climate model to investigate whether rapidly increasing stratospheric sulfate aerosol concentrations after a period of global warming could preserve Antarctic ice sheets by cooling subsurface ocean temperatures. We contrast this climate engineering method with an alternative strategy in which all greenhouse gases (GHG) are returned to preindustrial levels. We find that the rapid addition of a stratospheric aerosol layer does not effectively counteract surface and upper level atmospheric circulation changes caused by increasing GHGs, resulting in continued upwelling of warm water in proximity of ice shelves, especially in the vicinity of the already unstable Pine Island Glacier in West Antarctica. By contrast, removal of GHGs restores the circulation, yielding relatively cooler subsurface ocean temperatures to better preserve West Antarctica.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.161
Threshold uncertainty score0.945

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.075
GPT teacher head0.309
Teacher spread0.234 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations51
Published2015
Admission routes1
Has abstractyes

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