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Record W4308915401 · doi:10.1126/sciadv.abn3112

Climate models fail to capture strengthening wintertime North Atlantic jet and impacts on Europe

2022· article· en· W4308915401 on OpenAlexaff
Russell Blackport, John C. Fyfe

Bibliographic record

VenueScience Advances · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsClimatologyPrecipitationClimate modelAtmospheric circulationEnvironmental scienceDivergence (linguistics)Atlantic multidecadal oscillationJet (fluid)Jet streamGeneral Circulation ModelCirculation (fluid dynamics)Coupled model intercomparison projectClimate systemClimate changeCentennialNorth Atlantic oscillationAtmospheric sciencesGeographyGeologyMeteorologyOceanography

Abstract

fetched live from OpenAlex

Projections of wintertime surface climate over Europe depend on reliable simulations of the North Atlantic atmospheric circulation from climate models. However, it is unclear whether these models capture the long-term observed trends in the North Atlantic circulation. Here, we show that over the period from 1951 to 2020, the wintertime North Atlantic jet has strengthened, while model trends are, on average, only very weakly positive. The observed strengthening is greater than in any one of the 303 simulations from 44 climate models considered in our study. This divergence between models and observations is now much more apparent because of a very strong jet observed over the past decade. The models similarly have difficulty capturing the observed precipitation trends over Europe. Our results suggest that projections of winter atmospheric circulation and associated precipitation over Europe may be unreliable because they fail to capture the response to human emissions or underestimate the magnitude of multidecadal-to-centennial time scale internal variability.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.242
Threshold uncertainty score0.621

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 studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
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 head0.015
GPT teacher head0.246
Teacher spread0.231 · 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 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

Citations83
Published2022
Admission routes1
Has abstractyes

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