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Record W2132960716 · doi:10.1007/s00382-007-0297-y

Future changes in internal variability of the Atlantic Meridional Overturning Circulation

2007· article· en· W2132960716 on OpenAlexaboutno aff
Sybren Drijfhout, Wilco Hazeleger, Frank Selten, Rein Haarsma

Bibliographic record

VenueClimate Dynamics · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsnot available
Fundersnot available
KeywordsOcean gyreClimatologyConvectionForcing (mathematics)GeologyShutdown of thermohaline circulationThermohaline circulationFlux (metallurgy)Sea iceHeat fluxAtmospheric sciencesEnvironmental scienceNorth Atlantic Deep WaterHeat transferMechanicsMeteorologyGeography

Abstract

fetched live from OpenAlex

The response of the internal variability of the Atlantic Meridional Overturning Circulation (MOC) to enhanced atmospheric greenhouse gas concentrations has been estimated from an ensemble of climate change scenario runs. In the model, enhanced greenhouse forcing results in a weaker and shallower MOC with reduced internal variability. At the same time at 55°N between 0 and 1,000 m the overturning increases as a result of a change in the area of convection. In a warmer world, new regions of deepwater formation form further north due to the poleward retreat of the sea-ice boundary. The dominant pattern of internal MOC-variability consists of a monopole centered around 35°N. Due to anthropogenic warming this monopole shifts poleward. The shift is associated with a stronger relation between MOC-variations and heat flux variations over the subpolar gyre. In old convective sites (Labrador Sea) convection becomes more irregular which leads to enhanced heat flux variability. In new convective sites heat flux variations initially are related to sea-ice variations. When the sea-ice coverage further decreases they become associated with (irregular) deepwater formation. Both processes act to tighten the relation between subpolar surface heat flux variability and MOC-variability, resulting in a poleward shift of the latter.

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)0.000
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 score0.180
Threshold uncertainty score0.413

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.008
GPT teacher head0.229
Teacher spread0.220 · 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

Citations28
Published2007
Admission routes1
Has abstractyes

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