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Record W1975799413 · doi:10.3402/tellusa.v55i3.12092

The atmospheric response to North Atlantic SST anomalies in seasonal prediction experiments

2003· article· en· W1975799413 on OpenAlexafffund
Hai Lin, Jacques Derome

Bibliographic record

VenueTellus A Dynamic Meteorology and Oceanography · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaNational Center for Atmospheric Research
KeywordsAnomaly (physics)ClimatologySea surface temperatureEnvironmental scienceForcing (mathematics)North Atlantic oscillationSeasonalityAtlantic multidecadal oscillationGeneral Circulation ModelAtmospheric circulationAtmospheric sciencesOceanographyClimate changeGeology

Abstract

fetched live from OpenAlex

Seasonal forecasts performed over a 26 yr period as part of the Historical Seasonal Forecasting Project (HFP) are used to analyze the influence of North Atlantic sea surface temperature (SST) anomalies on the atmospheric circulation, its seasonality, and model dependence. The signals related to the El Nino events are first removed from both the SST and the atmospheric data. The North Atlantic SST and the ensemble mean forecast are then correlated over the 26 yr to identify the model response to the SST forcing. The signal-to-noise ratio shows that in spring there is a significant forecast signal that is related to the SST anomaly in the North Atlantic. In that season the two models used in the HFP yield responses to the SST anomaly that are both similar to each other and to the observed response. For the other seasons the agreement between the responses and the observed atmospheric anomalies is poor. In winter the response is very sensitive to the model used.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.490

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.007
GPT teacher head0.221
Teacher spread0.213 · 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 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

Citations9
Published2003
Admission routes2
Has abstractyes

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