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Record W4387398196 · doi:10.1007/s00382-023-06957-7

The weakening AMOC under extreme climate change

2023· article· en· W4387398196 on OpenAlexaboutno aff
Gaurav Madan, Ada Gjermundsen, Silje Christine Iversen, J. H. LaCasce

Bibliographic record

VenueClimate Dynamics · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsnot available
FundersNorges ForskningsrådUniversitetet i Oslo
KeywordsOcean gyreClimatologyOceanographyCurrent (fluid)UpwellingShutdown of thermohaline circulationCoupled model intercomparison projectClimate changeGeologyEnvironmental scienceClimate modelForcing (mathematics)North Atlantic Deep WaterThermohaline circulationSubtropicsFishery

Abstract

fetched live from OpenAlex

Abstract Changes in the Atlantic Meridional Overturning Circulation (AMOC) in the quadrupled CO 2 experiments conducted under the sixth Coupled Model Intercomparison Project (CMIP6) are examined. Increased CO 2 triggers extensive Arctic warming, causing widespread melting of sea ice. The resulting freshwater spreads southward, first from the Labrador Sea and then the Nordic Seas, and proceeds along the eastern coast of North America. The freshwater enters the subpolar gyre north of the separated Gulf Stream, the North Atlantic Current. This decreases the density gradient across the current and the current weakens in response, reducing the inflow to the deepwater production regions. The AMOC cell weakens in tandem, first near the North Atlantic Current and then spreading to higher and lower latitudes. This contrasts with the common perception that freshwater caps the convection regions, stifling deepwater production; rather, it is the inflow to the subpolar gyre that is suppressed. Changes in surface temperature have a much weaker effect, and there are no consistent changes in local or remote wind forcing among the models. Thus an increase in freshwater discharge, primarily from the Labrador Sea, is the precursor to AMOC weakening in these simulations.

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 categoriesInsufficient payload (model declined to judge)
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 score0.838
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.003

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 head0.060
GPT teacher head0.264
Teacher spread0.204 · 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.

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

Citations33
Published2023
Admission routes1
Has abstractyes

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