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Record W1966462928 · doi:10.1029/2006gl028756

Response of the Atlantic meridional overturning circulation to increasing atmospheric CO<sub>2</sub>: Sensitivity to mean climate state

2007· article· en· W1966462928 on OpenAlexafffund
Andrew J. Weaver, Michael Eby, Markus Kienast, Oleg A. Saenko

Bibliographic record

VenueGeophysical Research Letters · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsDalhousie UniversityUniversity of Victoria
FundersCanada Research ChairsCanadian Foundation for Climate and Atmospheric Sciences
KeywordsClimatologyEnvironmental scienceClimate modelAtmospheric sciencesTransient (computer programming)Climate changeGreenhouse gasZonal and meridionalSnowAtmospheric circulationClimate sensitivityClimate stateGlobal warmingGeologyOceanographyEffects of global warming

Abstract

fetched live from OpenAlex

The dependence on the mean climate state of the response of the Atlantic meridional overturning circulation (AMOC) is investigated in 17 increasing greenhouse gas experiments with different initial conditions. The AMOC declines in all experiments by 15% to 31%, with typically the largest declines in those experiments with the strongest initial AMOC. In all cases, changes in surface heat fluxes, rather than changes in surface freshwater fluxes, are the dominant cause for the transient AMOC decrease. Surface freshwater fluxes actually switch from reducing the transient AMOC decrease, for low values of atmospheric CO2, to reinforcing the transient AMOC decrease, for higher values of atmospheric CO2. In addition, we find that due to changes in the strengths of feedbacks associated with water vapour and snow/sea ice, the climate sensitivity and transient climate response of the UVic model strongly depends on the mean climate state.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.024
GPT teacher head0.295
Teacher spread0.271 · 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

Citations62
Published2007
Admission routes2
Has abstractyes

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