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A weaker Atlantic Meridional Overturning Circulation at the Last Glacial Maximum led to a greater deep ocean carbon content

2020· preprint· en· W3086277696 on OpenAlexaboutno aff
Laurie Menviel, Paul Spence, Luke C Skinner, Kazuyo Tachikawa, Tobias Friedrich, Lise Missiaen, Jimin Yu

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsnot available
Fundersnot available
KeywordsNorth Atlantic Deep WaterOceanographyThermohaline circulationGeologyLast Glacial MaximumOcean currentGlacial periodClimatologyHoloceneGeomorphology

Abstract

fetched live from OpenAlex

While paleoproxy records and modelling studies consistently suggest that North Atlantic Deep Water (NADW) was shallower at the Last Glacial Maximum (LGM) than during pre-industrial times, its strength is still subject to debate partly due to different signals across the North Atlantic. Here, using a series of LGM experiments performed with a carbon isotopes enabled Earth system model, we show that proxy records are consistent with a shallower and weaker NADW. A significant equatorward advance of sea-ice over the Labrador Sea and the Nordic Seas shifts the NADW convection sites to the south of the Norwegian Sea. While the deep western boundary current in the Northwest Atlantic weakens with NADW, a change in density gradients strengthens the deep southward flow in the Northeast Atlantic. A shoaling and weakening of NADW further allow penetration of Antarctic Bottom Water in the North Atlantic despite its transport being reduced. This resultant globally weaker oceanic circulation leads to an increase in deep ocean carbon of ~500 GtC, thus significantly contributing to the lower LGM atmospheric CO2 concentration.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0050.001

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 head0.045
GPT teacher head0.243
Teacher spread0.198 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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