MétaCan
Menu
Back to cohort
Record W2471264687 · doi:10.1088/1748-9326/11/7/074001

Relationship between North American winter temperature and large-scale atmospheric circulation anomalies and its decadal variation

2016· article· en· W2471264687 on OpenAlexaff
Bin Yu, Hai Lin, Zhiwei Wu, William J. Merryfield

Bibliographic record

VenueEnvironmental Research Letters · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsTeleconnectionGeopotential heightClimatologyNorthern HemisphereNorth Atlantic oscillationAtmospheric circulationForcing (mathematics)Pacific decadal oscillationEnvironmental scienceAnomaly (physics)Atmospheric sciencesAtmospheric temperatureGeologySea surface temperatureEl Niño Southern OscillationGeographyPrecipitationMeteorologyPhysics

Abstract

fetched live from OpenAlex

The interannual relationship between North American (NA) winter temperature and large-scale atmospheric circulation anomalies and its decadal variation are analyzed. NA temperature anomalies are dominated by two leading maximum covariance analysis (MCA) modes of NA surface temperature and Northern Hemisphere 500 hPa geopotential anomalies. A new teleconnection index, termed the Asian-Bering-North American (ABNA) pattern, is constructed from the normalized geopotential field after linearly removing the contribution of the Pacific-North American (PNA) pattern. The ABNA pattern is sustained by synoptic eddy forcing. The first MCA mode of NA surface temperature is highly correlated with the PNA and ABNA teleconnections, and the second mode with the North Atlantic Oscillation (NAO). This indicates that NA temperature is largely controlled by these three large-scale atmospheric patterns, i.e., the PNA, ABNA and NAO. These temperature-circulation relationships appear stationary in the 20th century.

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 score0.045
Threshold uncertainty score0.439

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 studies0.0000.001
Scholarly communication0.0000.001
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 head0.025
GPT teacher head0.271
Teacher spread0.245 · 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

Citations34
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueEnvironmental Research LettersSame topicClimate variability and modelsFrench-language works237,207