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Record W2024667166 · doi:10.1175/jcli-d-14-00228.1

Mechanisms of Multidecadal Atlantic Meridional Overturning Circulation Variability Diagnosed in Depth versus Density Space

2014· article· en· W2024667166 on OpenAlexaboutno aff
Young‐Oh Kwon, Claude Frankignoul

Bibliographic record

VenueJournal of Climate · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsnot available
FundersClimate Program OfficeNational Oceanic and Atmospheric AdministrationNational Science Foundation
KeywordsOcean gyreShutdown of thermohaline circulationThermohaline circulationClimatologyAtlantic multidecadal oscillationGeologyNorth Atlantic Deep WaterOceanographyOcean currentSubtropics

Abstract

fetched live from OpenAlex

Abstract Multidecadal variability of the Atlantic meridional overturning circulation (AMOC) is examined based on a comparison of the AMOC streamfunctions in depth and in density space, in a 700-yr present-day control integration of the fully coupled Community Climate System Model, version 3. The commonly used depth-coordinate AMOC primarily exhibits the variability associated with the deep equatorward transport that follows the changes in the Labrador Sea deep water formation. On the other hand, the density-based AMOC emphasizes the variability associated with the subpolar gyre circulation in the upper ocean leading to the changes in the Labrador Sea convection. Combining the two representations indicates that the ~20-yr periodicity of the AMOC variability in the first half of the simulation is primarily due to an ocean-only mode resulting from the coupling of the deep equatorward flow and the upper ocean gyre circulation near the Gulf Stream and North Atlantic Current. In addition, the density-based AMOC reveals a gradual change in the deep ocean associated with cooling and increased density, which is likely responsible for the transition of AMOC variability from strong ~20-yr oscillations to a weaker red noise–like multidecadal variability.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.022
Threshold uncertainty score0.295

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0000.000

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 head0.010
GPT teacher head0.222
Teacher spread0.212 · 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

Citations49
Published2014
Admission routes1
Has abstractyes

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