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Record W1930831509 · doi:10.1175/jcli-d-15-0317.1

The Role of Standing Waves in Driving Persistent Anomalies of Upward Wave Activity Flux

2015· article· en· W1930831509 on OpenAlexaff
Oliver Watt‐Meyer, Paul J. Kushner

Bibliographic record

VenueJournal of Climate · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPolar vortexFlux (metallurgy)Standing waveAtmospheric sciencesPhysicsVortexBreaking waveNorthern HemisphereAttenuationClimatologyStratosphereGeophysicsGeologyWave propagationMechanicsChemistryOptics

Abstract

fetched live from OpenAlex

Abstract Northern Hemisphere stratospheric polar vortex strength variability is known to be largely driven by persistent anomalies in upward wave activity flux. It has also been shown that attenuation and amplification of the stationary wave is the primary way in which wave activity flux varies. This study determines the structure of the wave anomalies that interfere with the climatological wave and drive this variability. Using a recently developed spectral decomposition it is shown that fixed-node standing waves are the primary drivers of the “linear interference” phenomenon. This is particularly true for the low-frequency component of the upward wave activity flux. The linear part of the flux is shown to be more persistent than the total flux and has significant tropospheric standing wave precursors that lead changes in the strength of the stratospheric polar vortex. Evidence is presented that current-generation high-top climate models are able to credibly simulate this variability in wave activity fluxes and the connection to polar vortex strength. Finally, the precursors to displacement and split sudden stratospheric warmings are examined. Displacement events are found to be preceded by about 25 days of anomalously high upward wave activity flux forced by standing waves amplifying the climatology. Split events have more short-lived wave activity flux precursors, which are dominated by the nonlinear part of the flux.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.135
Threshold uncertainty score0.207

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.032
GPT teacher head0.256
Teacher spread0.224 · 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

Citations14
Published2015
Admission routes1
Has abstractyes

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