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Record W4321490779 · doi:10.5194/egusphere-egu23-2883

The Atlantic meridional overturning circulation at 35N from deep moorings, floats, and satellite altimeter

2023· preprint· en· W4321490779 on OpenAlexaboutno aff
Isabela Le Bras, J. L. Willis, Ian Fenty

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsnot available
Fundersnot available
KeywordsAltimeterGeologyMooringSubmarine pipelineBoundary currentClimatologyOceanographyOcean currentThermohaline circulationGeodesy

Abstract

fetched live from OpenAlex

From 2004 to 2014, the Line W moorings measured a 0.7 Sv/yr slowing of the Deep Western Boundary Current (DWBC) offshore of Cape Cod, Massachusetts. Here, we combine these deep mooring observations with float and satellite altimeter data and find that this DWBC change corresponded to a slowing of the cross-basin Atlantic Meridional Overturning Circulation (AMOC) of about 0.3 Sv/yr. Our AMOC transport time series corresponds well with the ECCO state estimate, particularly when the Line W mooring data influences our reconstruction of upper ocean volume fluxes. We compare our 35N time series with a similar time series at 41N as well as with the 26N RAPID AMOC, and find AMOC declines across datasets during this time period. The relative magnitudes of these declines are consistent with interdecadal variability originating in the Labrador Sea. We find that though our integrated overturning estimate agrees well with ECCO, the structure of the deep flow differs substantially. While we cannot rule out a decreasing AMOC trend during the 20th century, we find that natural variability is too large to detect a net AMOC decrease in direct observations or the ECCO ocean model since 2004.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.100
Threshold uncertainty score0.199

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.019
GPT teacher head0.203
Teacher spread0.184 · 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 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

Citations1
Published2023
Admission routes1
Has abstractyes

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