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Record W3184541416 · doi:10.5194/egusphere-egu21-10382

Evolving air-sea interaction due to sea-ice retreat points to a re-organisation of water mass transformation in the Nordic and Barents Seas

2021· article· en· W3184541416 on OpenAlexaff
G. W. K. Moore, Kjetil Våge, Ian A. Renfrew, Bob Pickart

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsWater massConvectionClimatologyClimate changeEnvironmental scienceOceanographyCurrent (fluid)Zonal and meridionalSea iceLatent heatGeologyGeographyMeteorology

Abstract

fetched live from OpenAlex

The Nordic and Barents Seas play a critical role in the climate system resulting from water mass transformation, triggered by intense air-sea heat fluxes, that is an integral component of the Atlantic Meridional Overturning Circulation (AMOC). These seas are undergoing rapid warming, associated with a retreat in ice cover. Here we present a novel analysis, covering the period 1950-2020, of the spatiotemporal variability of the air-sea heat fluxes along the region’s boundary currents, where the impacts on the water mass transformation are large. We find that the variability is a function of the relative orientation of the current and the axis of sea-ice change that can result in up to a doubling of the heat fluxes over the period of interest. This implies enhanced water mass transformation is occurring along these currents. In contrast, previous work has shown a reduction in fluxes in the interior sites of the Nordic Seas, where ocean convection is also observed, suggesting that a reorganization may be underway in the nature of the water mass transformation, that needs to be considered when ascertaining how the AMOC will respond to a warming climate.

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)0.000
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 score0.027
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.010
GPT teacher head0.216
Teacher spread0.206 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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