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Record W2902983894 · doi:10.1029/2018jc014525

The Role of Ocean Heat Transport in Rapid Sea Ice Declines in the Community Earth System Model Large Ensemble

2018· article· en· W2902983894 on OpenAlexaff
Gabriel Auclair, Bruno Tremblay

Bibliographic record

VenueJournal of Geophysical Research Oceans · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsMcGill University
Fundersnot available
KeywordsSea iceArctic sea ice declineArctic ice packOceanographyArcticGeologyClimatologyContinental shelfDrift ice

Abstract

fetched live from OpenAlex

Abstract Many climate models, including the Community Earth System Model Large Ensemble (CESM‐LE), predict future rapid sea ice declines in the Arctic linked with anomalies in northward Ocean Heat Transport (OHT). Using CESM‐LE, we find that the partitioning of the poleward OHT between the different Arctic gates (Barents Sea Opening, BSO; Bering Strait; and Fram Strait) is key to this link with the rapid declines. Sixty‐four of the 79 rapid declines in CESM‐LE are correlated with the OHT anomalies through one of the gates. Rapid declines that happen earlier in the simulations when the sea ice covers the continental shelves are correlated with OHT anomalies. The interaction between OHT and sea ice happens mainly over continental shelves since most rapid declines are correlated with the BSO or Bering Strait OHTs and only a few with the Fram Strait OHT (often also correlated with BSO or Bering Strait OHTs). In most rapid declines not correlated with OHT, the September Sea Ice Extent (SIE) prior to the decline is smaller than the area covered by the deep basins. Those are associated with surface heat flux since the ice‐atmosphere heat fluxes are more strongly correlated with the sea ice concentrations over the deep basins than the ice‐ocean heat fluxes. Our results suggest that OHTs are causing rapid sea ice declines when the SIE is large enough to cover the continental shelves and that the atmosphere is the main driver when the initial SIE is located only over the deep basins.

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)0.007
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.446
Threshold uncertainty score0.688

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.032
GPT teacher head0.293
Teacher spread0.261 · 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

Citations60
Published2018
Admission routes1
Has abstractyes

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