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Record W3082479116 · doi:10.3390/atmos11090943

Quasi-Biennial Oscillation and Sudden Stratospheric Warmings during the Last Glacial Maximum

2020· article· en· W3082479116 on OpenAlexaff
Qiang Fu, Mingcheng Wang, Rachel H. White, Hamid A. Pahlavan, Becky Alexander, John M. Wallace

Bibliographic record

VenueAtmosphere · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsUniversity of British Columbia
FundersNuclear Safety and Security CommissionEuropean CommissionNational Aeronautics and Space AdministrationNational Science Foundation
KeywordsClimatologyLast Glacial MaximumQuasi-biennial oscillationForcing (mathematics)Environmental scienceClimate modelAtmospheric sciencesProxy (statistics)Climate systemPaleoclimatologyGlacial periodClimate changeGeologyStratosphereOceanography

Abstract

fetched live from OpenAlex

The quasi-biennial oscillation (QBO) and sudden stratospheric warmings (SSWs) during the Last Glacial Maximum (LGM) are investigated in simulations using the Whole Atmosphere Community Climate Model version 6 (WACCM6). We find that the period of QBO, which is 27 months in the preindustrial and modern climate simulations, was 33 months in the LGM simulation using the proxy sea surface temperatures (SSTs) and 41 months using the model-based LGM SSTs. We show that the longer QBO period in the LGM is due to weaker wave forcing. The WACCM6 simulations of the LGM, preindustrial, and modern climates do not support previous modeling work that suggests that the QBO amplitude is smaller (larger) in a warmer (colder) climate. We find that SSWs in the LGM occurred later in the year, as compared to the preindustrial and modern climate, but that time of the final warming was similar. The difference in SSW frequency is inconclusive.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.998

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.013
GPT teacher head0.214
Teacher spread0.201 · 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.

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

Citations8
Published2020
Admission routes1
Has abstractyes

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