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Record W2131201021 · doi:10.1175/2010jas3349.1

The Role of the Central Asian Mountains on the Midwinter Suppression of North Pacific Storminess

2010· article· en· W2131201021 on OpenAlexafffund
Hyo‐Seok Park, John C. H. Chiang, Seok‐Woo Son

Bibliographic record

VenueJournal of the Atmospheric Sciences · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaComer Science and Education Foundation
KeywordsBaroclinityBarotropic fluidClimatologyBorealMiddle latitudesGeologyOceanographyAtmospheric sciencesAtmospheric circulationGeneral Circulation ModelEnvironmental scienceClimate changePaleontology

Abstract

fetched live from OpenAlex

Abstract The role of the central Asian mountains on North Pacific storminess is examined using an atmospheric general circulation model by varying the height and the areas of the mountains. A series of model integrations show that the presence of the central Asian mountains suppresses the North Pacific storminess by 20%–30% during boreal winter. Their impact on storminess is found to be small during other seasons. The mountains amplify stationary waves and effectively weaken the high-frequency transient eddy kinetic energy in boreal winter. Two main causes of the reduced storminess are diagnosed. First, the decrease in storminess appears to be associated with a weakening of downstream eddy development. The mountains disorganize the zonal coherency of wave packets and refract them more equatorward. As the zonal traveling distance of wave packets gets substantially shorter, downstream eddy development gets weaker. Second, the central Asian mountains suppress the global baroclinic energy conversion. The decreased baroclinic energy conversion, particularly over the eastern Eurasian continent, decreases the number of eddy disturbances entering into the western North Pacific. The “barotropic governor” does not appear to explain the reduced storminess in our model simulations.

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 score0.060
Threshold uncertainty score0.626

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0020.000
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 head0.008
GPT teacher head0.212
Teacher spread0.205 · 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

Citations53
Published2010
Admission routes2
Has abstractyes

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