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Idealized Models of Slantwise Convection in a Baroclinic Flow

2002· article· en· W1980322098 on OpenAlexaboutno aff
Fiammetta Straneo, Mitsuhiro Kawase, Stephen C. Riser

Bibliographic record

VenueJournal of Physical Oceanography · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsnot available
FundersEli Lilly and Company
KeywordsBaroclinityStratification (seeds)GeologyConvectionMechanicsInviscid flowPotential vorticityMomentum (technical analysis)MesocycloneVorticityPhysicsMeteorologyVortex

Abstract

fetched live from OpenAlex

Intermediate, or deep, convection in a baroclinic flow occurs along slanted paths parallel to the alongflow absolute momentum surfaces. These surfaces are principally tilted due to the vertical shear in velocity but can be further modified by a nonvertical axis of rotation. An inviscid Lagrangian parcel model, using realistic parameters, is utilized to illustrate, qualitatively, the different scenarios resulting from the combined action of inertial and gravitational forces acting on sinking parcels of dense fluid. More quantitative results are derived from a series of numerical experiments using a zonally invariant, high-resolution, nonhydrostatic model. Convection occuring in a flow with tilted absolute momentum surfaces will mix properties along these slanted surfaces. This implies that the fluid can retain a weak vertical stratification while overturning and also, more importantly, that the evolution of the convective layer cannot be described in terms of one-dimensional, vertical mixing. The authors show, for conditions typical of the Labrador Sea, that the convective layer depth difference between that estimated by mixing vertically and one obtained allowing for slantwise mixing can be greater than 100 m; slantwise convection reaches deeper because of the reduced stratification along the slanted paths. An alternative slantwise mixing scheme, based on the assumption of zero potential vorticity of the convected fluid, is proposed.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.265
Threshold uncertainty score0.371

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.026
GPT teacher head0.247
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 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

Citations56
Published2002
Admission routes1
Has abstractyes

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