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Record W2141297979 · doi:10.1175/jcli-d-11-00055.1

The Role of Linear Interference in Northern Annular Mode Variability Associated with Eurasian Snow Cover Extent

2011· article· en· W2141297979 on OpenAlexafffund
Karen L. Smith, Paul J. Kushner, Judah Cohen

Bibliographic record

VenueJournal of Climate · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Foundation for Climate and Atmospheric SciencesNational Oceanic and Atmospheric AdministrationNational Science Foundation
KeywordsClimatologyRossby waveTroposphereNorthern HemisphereStratosphereArctic oscillationAtmospheric sciencesGeologyAnomaly (physics)Environmental scienceSnowPhysics

Abstract

fetched live from OpenAlex

Abstract One of the outstanding questions regarding the observed relationship between October Eurasian snow cover anomalies and the boreal winter northern annular mode (NAM) is what causes the multiple-week lag between positive Eurasian snow cover anomalies in October and the associated peak in Rossby wave activity flux from the troposphere to the stratosphere in December. This study explores the following hypothesis about this lag: in order to achieve amplification of the wave activity, the vertically propagating Rossby wave train associated with the snow cover anomaly must reinforce the climatological stationary wave, which corresponds to constructive linear interference between the anomalous wave and the climatological wave. It is shown that the lag in peak wave activity flux arises because the Rossby wave train associated with the snow cover is in quadrature or out of phase with the climatological stationary wave from October to mid-November. Beginning in mid-November the associated wave anomaly migrates into a position that is in phase with the climatological wave, leading to constructive interference and anomalously positive upward wave activity fluxes until mid-January. Climate models from the Coupled Model Intercomparison Project 3 (CMIP3) do not capture this behavior. This linear interference effect is not only associated with stratospheric variability related to Eurasian snow cover anomalies but is a general feature of Northern Hemisphere troposphere–stratosphere interactions and, in particular, dominated the negative NAM events of the fall–winter of 2009/10.

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 score0.049
Threshold uncertainty score0.315

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 head0.016
GPT teacher head0.235
Teacher spread0.219 · 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

Citations70
Published2011
Admission routes2
Has abstractyes

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