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Record W4302774290 · doi:10.1029/2022gl100202

Using CMIP6 Models to Assess the Significance of the Observed Trend in the Atlantic Meridional Overturning Circulation

2022· article· en· W4302774290 on OpenAlexafffund
Richard Kelson, David Straub, Carolina O. Dufour

Bibliographic record

VenueGeophysical Research Letters · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsClimatologyCoupled model intercomparison projectClimate modelZonal and meridionalEnvironmental scienceSeries (stratigraphy)Climate systemGeologyClimate changeAtmospheric sciencesOceanography

Abstract

fetched live from OpenAlex

Abstract Observations from the RAPID array at 26.5°N suggest a weakening in the Atlantic meridional overturning circulation between 2004 and 2020. The shortness of this time series raises the question of whether this weakening should be thought of as natural variability or as related to climate change. To estimate natural variability, preindustrial control runs of the Coupled Model Intercomparison Project Phase 6 are used and the observed weakening is found to fall within the distribution of naturally occurring trends. These trends result from model variability on periods longer than the observational window, and variation between models at these frequencies is especially pronounced. Finally, adding noise to inflate model variability also inflates model trends; however, the effect on trends is sensitive only to the low frequency portion of the noise spectrum.

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)0.002
metaresearch head score (Gemma)0.004
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.266
GPT teacher head0.351
Teacher spread0.085 · 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

Citations19
Published2022
Admission routes2
Has abstractyes

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