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Record W2121908730 · doi:10.1175/jcli-d-14-00631.1

A Lagrangian Method to Isolate the Impacts of Mixed Layer Subduction on the Meridional Overturning Circulation in a Numerical Model

2015· article· en· W2121908730 on OpenAlexaboutno aff
Matthew Thomas, Anne‐Marie Tréguier, Bruno Blanke, Julie Deshayes, Aurore Voldoire

Bibliographic record

VenueJournal of Climate · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsnot available
Fundersnot available
KeywordsOcean gyreSubductionMixed layerGeologyLagrangianZonal and meridionalOceanographyOcean currentClimatologyOcean dynamicsStructural basinThermohaline circulationOceanic basinSubtropicsSeismologyPaleontologyTectonics

Abstract

fetched live from OpenAlex

Abstract Large differences in the Atlantic meridional overturning circulation (AMOC) exhibited between the available ocean models pose problems as to how they can be interpreted for climate policy. A novel Lagrangian methodology has been developed for use with ocean models that enables a decomposition of the AMOC according to its source waters of subduction from the mixed layer of different geographical regions. The method is described here and used to decompose the AMOC of the Centre National de Recherches Météorologiques (CNRM) ocean model, which is approximately 4.5 Sv (1 Sv = 106 m3 s−1) too weak at 26°N, compared to observations. Contributions from mixed layer subduction to the peak AMOC at 26°N in the model are dominated by the Labrador Sea, which contributes 7.51 Sv; but contributions from the Nordic seas, the Irminger Sea, and the Rockall basin are also important. These waters mostly originate where deep mixed layers border the topographic slopes of the Subpolar Gyre and Nordic seas. The too-weak model AMOC can be explained by weak model representations of the overflow and of Irminger Sea subduction. These are offset by the large Labrador Sea component, which is likely to be too strong as a result of unrealistically distributed and too-deep mixed layers near the shelf.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.309
Threshold uncertainty score0.152

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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)0.0000.000

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 head0.033
GPT teacher head0.278
Teacher spread0.245 · 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 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

Citations34
Published2015
Admission routes1
Has abstractyes

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