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Record W3109745803 · doi:10.1088/1748-9326/abc215

North American cold events following sudden stratospheric warming in the presence of low Barents-Kara Sea sea ice

2020· article· en· W3109745803 on OpenAlexaboutno aff
Pengfei Zhang, Yutian Wu, Gang Chen, Yueyue Yu

Bibliographic record

VenueEnvironmental Research Letters · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsnot available
FundersDivision of Atmospheric and Geospace SciencesNational Natural Science Foundation of China
KeywordsClimatologySea iceArctic sea ice declineArctic geoengineeringArctic ice packPredictabilityEnvironmental sciencePolar vortexSea surface temperatureArcticCryosphereOceanographyPolar nightStratosphereAntarctic sea iceGeology

Abstract

fetched live from OpenAlex

Abstract While the relationship between the Arctic sea ice loss and midlatitude winter climate has been well discussed, especially on the seasonal mean scale, it remains unclear whether the Arctic sea ice condition affects the predictability of North American cold weather on the subseasonal time scale. Here we find that, in the presence of low Barents-Kara Sea (BKS) sea ice, sudden stratospheric warmings (SSWs) can favor surface cold spells over North America at the subseasonal timescale based on observations and model experiments. A persistent ridge of wave-2 pattern emerges over the Bering Sea-Gulf of Alaska several weeks after the SSW onset, with a coherent structure from the stratosphere to the surface, which, in turn, is conducive to synoptic cold air outbreaks in Canada and midwestern USA. This highlights a planetary wave pathway relating to BKS sea ice changes, by which the stratospheric polar vortex impacts the regional surface temperature on the subseasonal scale. In contrast, this mechanism does not occur with positive BKS sea ice anomaly. These findings help to improve the subseasonal predictability over North America, especially under the background of rapid change of Arctic sea ice in a warming world.

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 score0.009
Threshold uncertainty score0.461

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.019
GPT teacher head0.244
Teacher spread0.226 · 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

Citations40
Published2020
Admission routes1
Has abstractyes

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