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Record W4321500635 · doi:10.5194/egusphere-egu23-2545

Surface cooling as an internal wave generator in high latitudes.

2023· preprint· en· W4321500635 on OpenAlexaff
Matheus Ferreira Azevedo, Yujiro Kitade

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsConvectionMechanicsDiurnal cycleForcing (mathematics)Hydrostatic equilibriumInternal waveMixed layerInternal heatingGeologyClimatologyAtmospheric sciencesPhysicsGeophysicsMeteorology

Abstract

fetched live from OpenAlex

Heat-forced convection is a phenomenon observed frequently in high-latitude oceans. An inherent part of the Global Ocean Conveyor, it affects the global climate state over a wide range of spatial and temporal scales while being fundamentally tied to the diurnal cycle. Despite the importance of convective phenomena, most ocean general circulation models do not fully resolve it, instead parametrizing convection with adjustment schemes that remove static instability in the water by mixing vertically adjacent grid cells. However, the mixed layer response to daily-averaged fluxes is not necessarily the same as the average response to the diurnal cycle. Neglecting the diurnal cycle replaces periodic nightly convective pulses with chronic mixing that does not reach as deep. (Soloviev and Klinger, 2008).Furthermore, the current understanding of upper-layer processes does not elucidate the consequences of the oscillatory behavior of the diurnal convection at the boundaries of the mixed layer. To address this issue, we devised a numerical experiment to investigate whether an upward heat flux is enough to generate internal waves capable of propagation despite their original forcing having a non-propagating period (~24 hours). To reproduce the surface cooling-induced convection and the consequent internal wave generation, we formulated a 2-D model incorporating non-hydrostatic dynamics. Although pressure is the most computationally intensive term to calculate in such models, we could exclude it from our calculation by employing the Navier-Stokes equation with a rigid-lid, incompressible, and Boussinesq approximation, and cross-differentiating the equation system to reach a single equation defined in terms of vorticity and stream function. The model was set with a 60s time step, implemented using a leap-frog scheme, constant step Δx=200m for the horizontal and Δz=5m for the vertical axis, over a 40000 x 2000 m domain. The bottom and lateral boundaries were respectively set to a reflective non-slip and a cyclic boundary. The inertial period for the domain was set at 13.81h, simulating the 60°S latitude. The experiment started from a stratified condition and was forced using a sinusoidal heat-flux function at the middle of the domain with a diurnal period and varying amplitudes.Our experiment indicates that internal waves are generated at the boundary of the mixed layer by nonlinear wave-wave interactions of the diurnal and inertial periods. The enhancement of the near-inertial period was observed as well as the generation of higher frequency waves of 8, 6 and 4 hours. These waves travel far beyond their generation site and propagate down to 2000 m deep, as deep as the vertical domain allows.The internal waves observed in the numerical experiment might play an important role in enhancing mixing in the ocean interior at high latitudes, especially during the winter. This mechanism could also help to explain deep and bottom ocean variability and establish a pathway for the upper layer and deep ocean interaction.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

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 communication0.0000.000
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 head0.033
GPT teacher head0.243
Teacher spread0.210 · 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 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

Citations0
Published2023
Admission routes1
Has abstractyes

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