MétaCan
Menu
Back to cohort
Record W4404008823 · doi:10.1038/s43247-024-01819-4

Fast-get-faster explains wavier upper-level jet stream under climate change

2024· article· en· W4404008823 on OpenAlexaff
Tiffany A. Shaw, Osamu Miyawaki, Hsing-Hung Chou, Russell Blackport

Bibliographic record

VenueCommunications Earth & Environment · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsEnvironment and Climate Change Canada
FundersU.S. Department of EnergyNational Science Foundation
KeywordsJet streamClimate changeJet (fluid)Environmental scienceAtmospheric sciencesGeologyMeteorologyPhysicsMechanicsOceanography

Abstract

fetched live from OpenAlex

Abstract Earth’s upper-level jet streams primarily flow in the eastward direction. They often exhibit a north-south component or waviness connected to extreme weather at the surface. Recently the upper-level eastward jet stream was found to exhibit a fast-get-faster response under climate change explained by the impact of the nonlinear Clausius-Clapeyron relation on the latitudinal density contrast. Here we show the fast-get-faster mechanism also applies to the upper-level north-south jet stream wind and the longitudinal density contrast, implying increased waviness under climate change. Arctic Sea ice loss, which has been proposed as a driver of increased waviness, cannot explain the response. It leads to a fast-get-slower waviness response at all vertical levels. We demonstrate the fast-get-faster waviness signal has emerged in reanalysis data in the Southern Hemisphere but not yet in the Northern Hemisphere. The results show the fast-get-faster mechanism explains upper-level waviness changes and highlights a tug of war between upper- and mid-level waviness under climate change.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.795
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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.010

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 head0.096
GPT teacher head0.282
Teacher spread0.186 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations5
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueCommunications Earth & EnvironmentSame topicClimate variability and modelsFrench-language works237,207