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Record W2029488249 · doi:10.1029/2002jd002504

Thermal effects of saturating gravity waves in the atmosphere

2003· article· en· W2029488249 on OpenAlexaffabout
Alexander S. Medvedev, G. P. Klaassen

Bibliographic record

VenueJournal of Geophysical Research Atmospheres · 2003
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsYork UniversityDalhousie University
Fundersnot available
KeywordsPhysicsGravity waveMechanicsMean flowZonal flow (plasma)Heat fluxAtmosphere (unit)DissipationEnergy fluxThermalAtmospheric waveGravitational waveFlow (mathematics)Computational physicsHeat transferThermodynamicsTurbulenceAstrophysicsQuantum mechanics

Abstract

fetched live from OpenAlex

Breaking/saturating gravity waves (GWs) not only exert drag on the mean flow due to their momentum deposition but also affect the background thermally because of the associated energy flux divergence. We present a rigorous derivation of terms describing the thermal effects of GWs on the mean flow, based on the corresponding energy cycle for wave/mean flow interactions. The combined effect of saturating GWs is to produce both differential heating and cooling by inducing a downward wave heat flux, and an irreversible conversion of wave energy into heat. The former effect can also be represented as thermal diffusion acting on the mean potential temperature gradient. This rigorous theory for the thermal exchange between waves and the mean flow can be closed once the mechanism for GW dissipation is parameterized. To illustrate the procedure, we employ our recent nonlinear theory of GW spectra to derive expressions for the wave‐induced heating rates. This yields a parameterization of the thermal effects of GWs, which is suitable for use in general circulation models and requires the source GW spectrum as the only tunable parameter. We present results of numerical calculations of wave heating terms for typical wind and temperature profiles as well as simulations with the full‐scale Canadian Middle Atmosphere Model (CMAM).

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)0.002
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.308
Threshold uncertainty score0.551

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 integrity0.0000.001
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 head0.012
GPT teacher head0.290
Teacher spread0.278 · 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 designTheoretical or conceptual
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

Citations89
Published2003
Admission routes2
Has abstractyes

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