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Record W2736540001 · doi:10.1002/2017gl074327

Remarkable separability of circulation response to Arctic sea ice loss and greenhouse gas forcing

2017· article· en· W2736540001 on OpenAlexafffund
Kelly E. McCusker, Paul J. Kushner, John C. Fyfe, Michael Sigmond, V. V. Kharin, Cecilia M. Bitz

Bibliographic record

VenueGeophysical Research Letters · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsEnvironment and Climate Change CanadaUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsClimatologyArctic ice packSea iceEnvironmental scienceArctic sea ice declineArcticForcing (mathematics)Climate changeGreenhouse gasArctic geoengineeringClimate modelIce-albedo feedbackAtmospheric sciencesArctic oscillationAtmospheric circulationDrift iceGeologyOceanography

Abstract

fetched live from OpenAlex

Abstract Arctic sea ice loss may influence midlatitude climate by changing large‐scale circulation. The extent to which climate change can be understood as greenhouse gas‐induced changes that are modulated by this loss depends on how additive the responses to the separate influences are. A novel sea ice nudging methodology in a fully coupled climate model reveals that the separate effects of doubled atmospheric carbon dioxide (CO 2 ) concentrations and associated Arctic sea ice loss are remarkably additive and insensitive to the mean climate state. This separability is evident in several fields throughout most of the year, from hemispheric to synoptic scales. The extent to which the regional response to sea ice loss sometimes agrees with and sometimes cancels the response to CO 2 is quantified. The separability of the responses might provide a means to better interpret the diverse array of modeling and observational studies of Arctic change and influence.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.018
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.031
GPT teacher head0.301
Teacher spread0.271 · 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

Citations76
Published2017
Admission routes2
Has abstractyes

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