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Record W2801026045 · doi:10.1175/jcli-d-17-0635.1

Mechanisms Governing the Development of the North Atlantic Warming Hole in the CESM-LE Future Climate Simulations

2018· article· en· W2801026045 on OpenAlexaboutno aff
Melissa Gervais, Jeffrey Shaman, Yochanan Kushnir

Bibliographic record

VenueJournal of Climate · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsnot available
FundersU.S. Department of EnergyOffice of ScienceNational Science Foundation
KeywordsClimatologyClimate modelEnvironmental scienceGeneral Circulation ModelClimate changeOceanographyGeology

Abstract

fetched live from OpenAlex

Abstract A warming deficit in North Atlantic sea surface temperatures is a striking feature in global climate model future projections. This North Atlantic warming hole has been related to a slowing of the Atlantic meridional overturning circulation (AMOC); however, the detailed mechanisms involved in its generation remain an open question. An analysis of the Community Earth System Model Large Ensemble simulations is conducted to obtain further insight into the development of the warming hole and its relationship to the AMOC. It is shown that increasing freshwater fluxes through the Arctic gates lead to surface freshening and reduced Labrador Sea deep convection, which in turn act to cool Labrador Sea sea surface temperatures. Furthermore, the resulting changes in surface ocean circulation lead to enhanced transport of cooled Labrador Sea surface waters into the interior of the subpolar gyre and a more zonal orientation of the North Atlantic Current. As a result, there is an increase in ocean advective heat flux divergence within the center of the subpolar gyre, causing this warming deficit in North Atlantic sea surface temperatures. These local changes to the ocean circulation affect the AMOC and lead to its slowdown.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.017
GPT teacher head0.247
Teacher spread0.230 · 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 source (direct Gemma or distilled Codex), 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

Citations86
Published2018
Admission routes1
Has abstractyes

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