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Record W2153443072 · doi:10.1002/2015gl064156

Changes in ocean vertical heat transport with global warming

2015· article· en· W2153443072 on OpenAlexaff
Jan D. Zika, Frédéric Laliberté, Lawrence Mudryk, Willem P. Sijp, A. J. George Nurser

Bibliographic record

VenueGeophysical Research Letters · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsUniversity of Toronto
FundersAustralian Research CouncilNatural Environment Research CouncilSight Research UK
KeywordsDownwellingEffects of global warming on oceansClimatologyUpwellingGlobal warmingEnvironmental scienceOcean heat contentDeep ocean waterOcean currentOceanographyDeep seaThermohaline circulationOcean general circulation modelForcing (mathematics)Southern HemisphereClimate changeSea surface temperatureLead (geology)GeologyGeneral Circulation Model

Abstract

fetched live from OpenAlex

Abstract Heat transport between the surface and deep ocean strongly influences transient climate change. Mechanisms setting this transport are investigated using coupled climate models and by projecting ocean circulation into the temperature‐depth diagram. In this diagram, a “cold cell” cools the deep ocean through the downwelling of Antarctic waters and upwelling of warmer waters and is balanced by warming due to a “warm cell,” coincident with the interhemispheric overturning and previously linked to wind and haline forcing. With anthropogenic warming, the cold cell collapses while the warm cell continues to warm the deep ocean. Simulations with increasingly strong warm cells, set by their mean Southern Hemisphere winds, exhibit increasing deep‐ocean warming in response to the same anthropogenic forcing. It is argued that the partition between components of the circulation which cool and warm the deep ocean in the preindustrial climate is a key determinant of ocean vertical heat transport with global warming.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.036
GPT teacher head0.276
Teacher spread0.240 · 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

Citations17
Published2015
Admission routes1
Has abstractyes

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