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Record W2921871218 · doi:10.1029/2018gl080961

Deciphering Patterns and Drivers of Heat and Carbon Storage in the Southern Ocean

2019· article· en· W2921871218 on OpenAlexaff
Haidi Chen, Adele K. Morrison, Carolina O. Dufour, Jorge L. Sarmiento

Bibliographic record

VenueGeophysical Research Letters · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsMcGill University
FundersAustralian Research CouncilNational Aeronautics and Space AdministrationNational Oceanic and Atmospheric AdministrationNational Science Foundation
KeywordsThermoclineEnvironmental scienceHydrographyClimatologyGreenhouse gasOcean heat contentClimate changeCarbon cycleAtmospheric sciencesOcean currentOceanographyGeologyEcosystemEcology

Abstract

fetched live from OpenAlex

Abstract The storage of anomalous heat and carbon in the Southern Ocean in response to increasing greenhouse gases greatly mitigates atmospheric warming and exerts a large impact on the marine ecosystem. However, the mechanisms driving the ocean storage patterns are uncertain. Here using recent hydrographic observations, we compare for the first time the spatial patterns of heat and carbon storage, which show substantial differences in the Southern Ocean, in contrast with the conventional view of simple passive subduction into the thermocline. Using an eddy‐rich global climate model, we demonstrate that redistribution of the preindustrial temperature field is the dominant control on the heat storage pattern, whereas carbon storage largely results from passive transport of anthropogenic carbon uptake at the surface. Lastly, this study highlights the importance of realistic representation of wind and surface buoyancy flux in climate models to improve future projection of circulation change and thus heat and carbon storage.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.014
GPT teacher head0.235
Teacher spread0.221 · 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 teacher head, 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

Citations24
Published2019
Admission routes1
Has abstractyes

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