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Record W4403907448 · doi:10.1029/2024gl112412

Projected Changes of the Warm Arctic‐Cold North American Pattern

2024· article· en· W4403907448 on OpenAlexaffabout
Bin Yu, Hai Lin

Bibliographic record

VenueGeophysical Research Letters · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsClimatologyArcticThe arcticGeologyEnvironmental scienceOceanography

Abstract

fetched live from OpenAlex

Abstract The Warm Arctic‐Cold North American (WACNA) pattern features opposing surface temperature anomalies, with centers over the Chukchi‐Bering Seas (CBS) and the North American Great Plains. The pattern is mainly driven by meridional heat transport and damped by the generation of available potential energy through diabatic heating. The Canadian Earth System Model CanESM5, part of the Coupled Model Intercomparison Project Phase 6 (CMIP6), reasonably reproduces this pattern and its formation mechanisms. Future projections under the Shared Socioeconomic Pathway 8.5 (SSP5‐8.5) suggest a significant weakening of WACNA with global warming. Notable changes in pattern intensity and spatial structure are anticipated, particularly a decrease in intensity over the CBS. WACNA changes are also found in 31 CMIP6 multi‐model ensemble simulations. These changes are attributed to Arctic amplification associated with global warming, which diminishes the equator‐to‐pole temperature gradient and consequently reduces meridional heat transport, particularly over the CBS region.

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 score0.214
Threshold uncertainty score0.426

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.043
GPT teacher head0.310
Teacher spread0.267 · 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

Citations1
Published2024
Admission routes2
Has abstractyes

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