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Record W2084981456 · doi:10.1175/jcli-d-13-00590.1

The Antarctic Sea Ice Response to the Ozone Hole in Climate Models

2013· article· en· W2084981456 on OpenAlexaff
Michael Sigmond, John C. Fyfe

Bibliographic record

VenueJournal of Climate · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsSea iceOzone depletionClimatologyEnvironmental scienceOzoneOzone layerSouthern HemisphereForcing (mathematics)CryosphereAtmospheric sciencesCoupled model intercomparison projectNorthern HemisphereStratosphereClimate modelArctic ice packClimate changeOceanographyGeologyMeteorologyGeography

Abstract

fetched live from OpenAlex

Abstract It has been suggested that the increase of Southern Hemisphere sea ice extent since the 1970s can be explained by ozone depletion in the Southern Hemisphere stratosphere. In a previous study, the authors have shown that in a coupled atmosphere–ocean–sea ice model the ozone hole does not lead to an increase but to a decrease in sea ice extent. Here, the robustness of this result is established through the analysis of models from phases 3 and 5 of the Coupled Model Intercomparison Project (CMIP3 and CMIP5). Comparison of the mean sea ice trends in CMIP3 models with and without time-varying stratospheric ozone suggests that ozone depletion is associated with decreased sea ice extent, and ozone recovery acts to mitigate the future sea ice decrease associated with increasing greenhouse gases. All available historical simulations with CMIP5 models that were designed to isolate the effect of time-varying ozone concentrations show decreased sea ice extent in response to historical ozone trends. In most models, the historical sea ice extent trends are mainly driven by historical greenhouse gas forcing, with ozone forcing playing a secondary role.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.507
Threshold uncertainty score0.887

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.019
GPT teacher head0.258
Teacher spread0.239 · 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 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

Citations70
Published2013
Admission routes1
Has abstractyes

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