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Record W4225095208 · doi:10.1175/jpo-d-21-0145.1

Forcing Space: An Alternative to Regime Diagrams for Predicting Characteristics of Turbulence in the Ocean Surface Mixing Layer

2022· article· en· W4225095208 on OpenAlexaff
Ann E. Gargett

Bibliographic record

VenueJournal of Physical Oceanography · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsNorth Pacific Marine Science Organization
FundersNational Oceanic and Atmospheric AdministrationNational Science Foundation
KeywordsTurbulenceBuoyancyForcing (mathematics)Mixed layerMechanicsConvectionSurface layerAtmospheric sciencesPhysicsGeologyMeteorologyGeophysicsMaterials scienceLayer (electronics)

Abstract

fetched live from OpenAlex

Abstract Various forms of regime diagrams have become an accepted means of identifying the dominant type of forcing of turbulence in the ocean surface layer. However, all of the proposed forms share a number of issues, demonstrated here, that make them an imperfect tool for this purpose. Instead, I suggest a forcing space consisting of surface buoyancy flux (usually dominated by surface heat flux) and a growth rate defined as the inverse of a theoretical time scale for growth of Langmuir circulations in an unstratified water column. Using coastal data, it is demonstrated that, provided forcing conditions are roughly constant for several hours, location in the upper half-plane of this forcing space predicts organizational characteristics of observed turbulence that range in a systematic way between those of “pure” convection and those of full depth Langmuir circulations. In this upper half-plane, where a convective scale velocity exists and the surface Stokes drift velocity can be computed, allowing calculation of a Stokes scale velocity, a linear combination of the two scale velocities provides a consistent estimate of observed rms turbulent vertical velocity. Time dependence is nevertheless a frequent characteristic of ocean surface layer forcing, if only because of the (usually large) diurnal variation in surface heat flux. It is shown that the time scale of response of surface layer turbulence to time variable forcing depends on whether the major change is due to wind/wave or buoyancy forcing. Relevant modeling studies are suggested.

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)0.001
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 score0.157
Threshold uncertainty score0.455

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.015
GPT teacher head0.242
Teacher spread0.227 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

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