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Record W4300862427 · doi:10.1175/jas-d-22-0013.1

Origin of the Warm Arctic–Cold North American Pattern on the Intraseasonal Time Scale

2022· article· en· W4300862427 on OpenAlexafffundabout
Hai Lin, Bin Yu, Nicholas M. J. Hall

Bibliographic record

VenueJournal of the Atmospheric Sciences · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsEnvironment and Climate Change Canada
FundersMcGill University
KeywordsClimatologyAtmospheric circulationArcticRossby waveMiddle latitudesGeologyArctic dipole anomalySea iceTroposphereSiberian HighEddyAtmospheric sciencesArctic ice packOceanographyDrift iceGeographyMeteorologyTurbulence

Abstract

fetched live from OpenAlex

Abstract The warm Arctic–cold continent pattern (WACC) of near-surface air temperature variability has often been associated with the connection between Arctic sea ice reduction and cold weather over the midlatitude continents. Whether the existence of this pattern is due to variability of sea ice or is caused by atmospheric internal dynamics is subject to debate. Based on a long integration of a primitive equation atmospheric model (SGCM), this study examines the origin of the warm Arctic–cold North American pattern (WACNA), which is characterized by a pair of opposite surface air temperature anomalies over the high-latitude Chukchi–Bering Sea region and the North American continent, in boreal winter on the intraseasonal time scale. The model atmosphere is maintained by a time-independent forcing, so that atmospheric internal dynamics is the only source of variability. It is found that the SGCM model simulates well the behavior of the observed WACNA pattern. The WACNA pattern develops by interacting with the time-mean flow and synoptic-scale transient eddies. Two pathways of Rossby wave propagation are associated with WACNA. The northern pathway originates from eastern Siberia moving eastward across the Bering Strait to Canada, and the southern pathway is rooted in the subtropical waveguide propagating across the eastern North Pacific. Our simulation of this pattern implies that tropospheric dynamics alone can generate the WACNA, and the predictability associated with this pattern is likely limited by its internal dynamics nature. Significance Statement The warm Arctic–cold continent pattern of temperature variability has often been associated with the connection between Arctic sea ice reduction and cold weather over the midlatitude continents, which implies possible impacts of polar warming on midlatitude climate. There has been debate on whether the existence of this pattern depends on variability of sea ice or can be caused by processes within the atmosphere. In this study, we use a simple atmospheric model, which has a constant forcing; thus, atmospheric internal dynamics is the only source of variability. We show that atmospheric internal dynamics alone can generate the warm Arctic–cold North American pattern. The result has implications for our understanding of the impact of global warming.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.976
Threshold uncertainty score0.047

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 head0.014
GPT teacher head0.222
Teacher spread0.208 · 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 designSimulation or modeling
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

Citations9
Published2022
Admission routes3
Has abstractyes

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