MétaCan
Menu
Back to cohort
Record W2222153457 · doi:10.1175/jas-d-15-0099.1

Simulating the QBO in an Atmospheric General Circulation Model: Sensitivity to Resolved and Parameterized Forcing

2016· article· en· W2222153457 on OpenAlexaff
James Anstey, John Scinocca, Martin Keller

Bibliographic record

VenueJournal of the Atmospheric Sciences · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsUniversity of TorontoUniversity of Victoria
Fundersnot available
KeywordsStratosphereForcing (mathematics)Quasi-biennial oscillationWave dragGravity waveAtmospheric sciencesOscillation (cell signaling)ClimatologyDragPhysicsAtmospheric circulationGravitational waveDissipationAtmospheric modelWavenumberEnvironmental scienceGeologyMeteorologyMechanicsDrag coefficientAstrophysics

Abstract

fetched live from OpenAlex

Abstract The quasi-biennial oscillation (QBO) of tropical stratospheric zonal winds is simulated in an atmospheric general circulation model and its sensitivity to model parameters is explored. Vertical resolution in the lower tropical stratosphere finer than ≈1 km and sufficiently strong forcing by parameterized nonorographic gravity wave drag are both required for the model to exhibit a QBO-like oscillation. Coarser vertical resolution yields oscillations that are seasonally synchronized and driven mainly by gravity wave drag. As vertical resolution increases, wave forcing in the tropical lower stratosphere increases and seasonal synchronization is disrupted, allowing quasi-biennial periodicity to emerge. Seasonal synchronization could result from the form of wave dissipation assumed in the gravity wave parameterization, which allows downward influence by semiannual oscillation (SAO) winds, whereas dissipation of resolved waves is consistent with radiative damping and no downward influence. Parameterized wave drag is nevertheless required to generate a realistic QBO, effectively acting to amplify the relatively weaker mean-flow forcing by resolved waves.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.084
Threshold uncertainty score0.367

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 communication0.0000.001
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 head0.028
GPT teacher head0.259
Teacher spread0.231 · 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

Citations66
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the Atmospheric SciencesSame topicAtmospheric Ozone and ClimateFrench-language works237,207