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Record W2028740842 · doi:10.1175/jcli-d-12-00524.1

On the Relationship between Winter Sea Ice and Summer Atmospheric Circulation over Eurasia

2013· article· en· W2028740842 on OpenAlexaboutno aff
Bingyi Wu, Renhe Zhang, Rosanne D’Arrigo, Jingzhi Su

Bibliographic record

VenueJournal of Climate · 2013
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsClimatologyAtmospheric circulationForcing (mathematics)BaySea surface temperatureLatitudeGeologyGlobal wind patternsEnvironmental scienceOceanographyAtmospheric sciences

Abstract

fetched live from OpenAlex

Abstract Using NCEP–NCAR reanalysis and Japanese 25-yr Reanalysis (JRA-25) data, this paper investigates the association between winter sea ice concentration (SIC) in Baffin Bay southward to the eastern coast of Newfoundland, and the ensuing summer atmospheric circulation over the mid- to high latitudes of Eurasia. It is found that winter SIC anomalies are significantly correlated with the ensuing summer 500-hPa height anomalies that dynamically correspond to the Eurasian pattern of 850-hPa wind variability and significantly influence summer rainfall variability over northern Eurasia. Spring atmospheric circulation anomalies south of Newfoundland, associated with persistent winter–spring SIC and a horseshoe-like pattern of sea surface temperature (SST) anomalies in the North Atlantic, act as a bridge linking winter SIC and the ensuing summer atmospheric circulation anomalies over northern Eurasia. Indeed, this study only reveals the association based on observations and simple simulation experiments with SIC forcing. The more precise mechanism for this linkage needs to be addressed in future work using numerical simulations with SIC and SST as the external forcings. The results herein have the following implication: Winter SIC west of Greenland is a possible precursor for summer atmospheric circulation and rainfall anomalies over northern Eurasia.

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 score0.006
Threshold uncertainty score1.000

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)0.0010.000

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 head0.025
GPT teacher head0.245
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.

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

Citations96
Published2013
Admission routes1
Has abstractyes

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