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On the Potential Vorticity Balance on an Isentropic Surface during Normal and Anomalous Winters

2001· article· en· W2179382047 on OpenAlexaffabout
Jacques Derome, Gilbert Brunet, Yuhui Wang

Bibliographic record

VenueMonthly Weather Review · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsMcGill University
Fundersnot available
KeywordsAdvectionDiabaticPotential vorticityClimatologyEnvironmental scienceNorthern HemisphereAtmospheric sciencesVorticityIsentropic processPositive vorticity advectionMeteorologyGeologyMechanicsPhysics

Abstract

fetched live from OpenAlex

Data for 39 winters are used to compute the potential vorticity (PV) budget on the θ = 315 K isentropic surface over the Northern Hemisphere. The object is to compare the mechanisms that maintain the PV balance during normal winters with those that maintain the balance during winters with anomalies of the North Atlantic Oscillation (NAO) and Pacific–North American (PNA) types. On an isentropic surface that does not intersect the ground, which is usually the case for the 315 K surface, the mean seasonal flow must be such as to maintain a simple local balance between the diabatic and frictional sources/sinks of PV, the isentropic advection of PV by the mean seasonal flow, and the mean seasonal PV advection by the subseasonal transients. The climatology over the 39 winters shows that the main positive PV centers over the east coasts of Asia and Canada are maintained through a three-way balance among the upstream diabatic/frictional sources of PV, the PV advection by the mean seasonal flow, and that by the subseasonal transients. The transients with periods between 2 and 10 days and those with periods between 10 and 90 days are found to contribute about equally to the PV balance. The PV balance of NAO and PNA winter anomalies reveals that the PV advection by the subseasonal transients more systematically opposes the advection by the seasonal mean flow, so that the local PV source term is proportionately much less important than it is in the maintenance of climatological PV centers. The calculations were also made on the θ = 350 K and 450 K isentropes. The results are presented only briefly to highlight the main similarities and differences with those obtained at 315 K.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.113
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

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 head0.012
GPT teacher head0.226
Teacher spread0.214 · 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.

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

Citations6
Published2001
Admission routes2
Has abstractyes

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