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Record W2156019889 · doi:10.1002/2014gl059255

Variability and trends in anticyclonic circulation over the Greenland ice sheet, 1948–2013

2014· article· en· W2156019889 on OpenAlexafffund
Jill Rajewicz, Shawn J. Marshall

Bibliographic record

VenueGeophysical Research Letters · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGreenland ice sheetAnticycloneClimatologyAtmospheric circulationGeologyGeopotentialGeopotential heightIce sheetGroenlandiaAtmospheric sciencesEnvironmental sciencePrecipitationOceanographyMeteorologyGeography

Abstract

fetched live from OpenAlex

Abstract Melting and mass loss on the Greenland ice sheet have accelerated in recent years. Possible causes include broader Arctic warming, reduced snow/ice cover, lowered ice sheet albedo, and changes in atmospheric circulation. Anticyclonic ridging over Greenland leads to southerly advection of warm air and clear‐sky conditions, promoting high melt rates. To examine the relative importance of ridging, we examine direct measures of summer 500 hPa anticyclonic circulation strength and frequency in Greenland from 1948 to 2013, isolating the atmospheric circulation signal from 500 hPa geopotential height variations. Anticyclonic circulation anomalies account for 38–49% of the interannual variability and trend in summer air temperature and melt indices. Vorticity‐detrended geopotential height anomalies explain an additional 13–27% of the variance. We identify an abrupt, persistent shift to strong anticyclonic circulation in central Greenland beginning in 2001, which has contributed to recent increases in Greenland ice sheet melt.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.110
Threshold uncertainty score0.218

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.033
GPT teacher head0.283
Teacher spread0.250 · 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 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
Published2014
Admission routes2
Has abstractyes

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