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Record W2522245011 · doi:10.1175/jcli-d-16-0440.1

Tropical Pacific SST Drivers of Recent Antarctic Sea Ice Trends

2016· article· en· W2522245011 on OpenAlexaff
Ariaan Purich, Matthew H. England, Wenju Cai, Yoshimitsu Chikamoto, Axel Timmermann, John C. Fyfe, Leela M. Frankcombe, Gerald A. Meehl, Julie M. Arblaster

Bibliographic record

VenueJournal of Climate · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsClimatologyTeleconnectionSea icePacific decadal oscillationArctic ice packSea surface temperatureSea ice concentrationEnvironmental scienceOceanographyAntarctic sea iceGeologyLatitudeClimate changeLead (geology)El Niño Southern OscillationSea ice thickness

Abstract

fetched live from OpenAlex

Abstract A strengthening of the Amundsen Sea low from 1979 to 2013 has been shown to largely explain the observed increase in Antarctic sea ice concentration in the eastern Ross Sea and decrease in the Bellingshausen Sea. Here it is shown that while these changes are not generally seen in freely running coupled climate model simulations, they are reproduced in simulations of two independent coupled climate models: one constrained by observed sea surface temperature anomalies in the tropical Pacific and the other by observed surface wind stress in the tropics. This analysis confirms previous results and strengthens the conclusion that the phase change in the interdecadal Pacific oscillation from positive to negative over 1979–2013 contributed to the observed strengthening of the Amundsen Sea low and the associated pattern of Antarctic sea ice change during this period. New support for this conclusion is provided by simulated trends in spatial patterns of sea ice concentrations that are similar to those observed. These results highlight the importance of accounting for teleconnections from low to high latitudes in both model simulations and observations of Antarctic sea ice variability and change.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.127
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.021
GPT teacher head0.259
Teacher spread0.238 · 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.

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

Citations113
Published2016
Admission routes1
Has abstractyes

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