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Record W4389977483 · doi:10.1029/2023gl106492

Reduced Deep Convection and Bottom Water Formation Due To Antarctic Meltwater in a Multi‐Model Ensemble

2023· article· en· W4389977483 on OpenAlexaff
Jiajia Chen, Neil C. Swart, Rebecca L. Beadling, Xuhua Cheng, Tore Hattermann, André Jüling, Qian Li, John Marshall, Torge Martin, Morven Muilwijk, Andrew G. Pauling, Ariaan Purich, Inga J. Smith, Max Thomas

Bibliographic record

VenueGeophysical Research Letters · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsEnvironment and Climate Change CanadaUniversity of Victoria
FundersGoddard Institute for Space StudiesMunich-Centre for Advanced PhotonicsAustralian Research CouncilUniversity of OtagoNederlandse Organisatie voor Wetenschappelijk OnderzoekNational Natural Science Foundation of ChinaAntarctica New ZealandNational Aeronautics and Space Administration
KeywordsMeltwaterClimatologyGeologyClimate modelAntarctic Bottom WaterConvectionAntarctic ice sheetDownscalingSea iceEnvironmental scienceCryosphereClimate changeOceanographyMeteorologyThermohaline circulationGlacierGeomorphology

Abstract

fetched live from OpenAlex

Abstract The additional water from the Antarctic ice sheet and ice shelves due to climate‐induced melt can impact ocean circulation and global climate. However, the major processes driving melt are not adequately represented in Coupled Model Intercomparison Project phase 6 (CMIP6) models. Here, we analyze a novel multi‐model ensemble of CMIP6 models with consistent meltwater addition to examine the robustness of the modeled response to meltwater, which has not been possible in previous single‐model studies. Antarctic meltwater addition induces a substantial weakening of open‐ocean deep convection. Additionally, Antarctic Bottom Water warms, its volume contracts, and the sea surface cools. However, the magnitude of the reduction varies greatly across models, with differing anomalies correlated with their respective mean‐state climatology, indicating the state‐dependency of the climate response to meltwater. A better representation of the Southern Ocean mean state is necessary for narrowing the inter‐model spread of response to Antarctic meltwater.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.060
GPT teacher head0.321
Teacher spread0.261 · 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

Citations27
Published2023
Admission routes1
Has abstractyes

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