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Record W4221069593 · doi:10.1029/2021gl096076

Distinct North American Cooling Signatures Following the Zonally Symmetric and Asymmetric Modes of Winter Stratospheric Variability

2022· article· en· W4221069593 on OpenAlexaboutno aff
Xiuyuan Ding, Gang Chen, Lantao Sun, Pengfei Zhang

Bibliographic record

VenueGeophysical Research Letters · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsnot available
FundersNational Aeronautics and Space AdministrationNational Science Foundation
KeywordsPolar vortexStratosphereSudden stratospheric warmingArctic oscillationVortexPolarAtmospheric sciencesArcticClimatologyGeologyOscillation (cell signaling)PhysicsMeteorologyNorthern HemisphereChemistryAstronomyOceanography

Abstract

fetched live from OpenAlex

Abstract This paper compares surface signatures of the zonally symmetric and asymmetric modes of stratospheric variability, which describe the strength of the polar vortex and a planetary wave‐1 pattern, respectively. Unlike a weak polar vortex followed by negative Arctic Oscillation–like anomalies, strong stratospheric wave activity features a polar vortex displacement with a deep planetary wave‐1 structure, resulting in positive North Atlantic Oscillation–like North American cooling in about 10 days. Moreover, the linkage between the stratosphere and surface is examined in two reanalyzes and four models of different configurations, which show more robust North American cooling following the displacement of the polar vortex due to strong stratospheric wave activity than the zonally symmetric weakening of the polar vortex. This suggests strong stratospheric wave activity acts as a better predictor for cold spells in the northern U.S. and Canada compared with a weak polar vortex.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.018
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.022
GPT teacher head0.282
Teacher spread0.259 · 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 source (direct Gemma or distilled Codex), 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

Citations27
Published2022
Admission routes1
Has abstractyes

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