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Record W1970232808 · doi:10.1029/2006gl026815

Will Greenland melting halt the thermohaline circulation?

2006· article· en· W1970232808 on OpenAlexaboutno aff
Johann Jungclaus, Helmuth Haak, M. Esch, E. Roeckner, Jochem Marotzke

Bibliographic record

VenueGeophysical Research Letters · 2006
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsnot available
FundersBundesministerium für Bildung und ForschungDeutsches Klimarechenzentrum
KeywordsMeltwaterGreenland ice sheetThermohaline circulationShutdown of thermohaline circulationClimatologyGeologyFuture sea levelRidgeIce sheetOceanographyClimate changeOcean currentEnvironmental scienceGlacierArctic ice packNorth Atlantic Deep WaterSea iceAntarctic sea iceGeomorphologyPaleontology

Abstract

fetched live from OpenAlex

Climate projections for the 21st century indicate a gradual decrease of the Atlantic Meridional Overturning Circulation (AMOC). The weakening could be accelerated substantially by meltwater input from the Greenland Ice Sheet (GIS). Here we repeat recent experiments conducted for the Intergovernmental Panel of Climate Change, providing an idealized additional source of freshwater along Greenland's coast. For conservative and high melting estimates, the AMOC reduction is 35% and 42%, respectively, compared to a weakening of 30% for the original A1B scenario. Even for the high meltwater estimate the AMOC recovers in the 22nd century. The impact of the additional fresh water is limited to further enhancing the static stability in the Irminger and Labrador Seas, whereas the backbone of the overturning is maintained by the overflows across the Greenland‐Scotland Ridge. Our results suggest that abrupt climate change initiated by GIS melting is not a realistic scenario for the 21st century.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.040
GPT teacher head0.281
Teacher spread0.241 · 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 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

Citations142
Published2006
Admission routes1
Has abstractyes

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