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Record W2093034670 · doi:10.1175/jcli-d-13-00750.1

Winter Climate Extremes over the Northeastern United States and Southeastern Canada and Teleconnections with Large-Scale Modes of Climate Variability*

2014· article· en· W2093034670 on OpenAlexaboutno aff
Liang Ning, Raymond S. Bradley

Bibliographic record

VenueJournal of Climate · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsnot available
FundersU.S. Geological SurveySanta Clara UniversityNational Science Foundation
KeywordsTeleconnectionClimatologyGeopotential heightPrecipitationEnvironmental sciencePacific decadal oscillationNorth Atlantic oscillationClimate changeAtmospheric circulationClimate modelSea surface temperatureEl Niño Southern OscillationGeographyGeologyOceanographyMeteorology

Abstract

fetched live from OpenAlex

Abstract The relationship between winter climate extremes across the northeastern United States and adjacent parts of Canada and some important modes of climate variability are examined to determine how these circulation patterns are related to extreme events. Linear correlations between 15 extreme climate indices related to winter daily precipitation, maximum and minimum temperature, and three dominant large-scale patterns of climate variability [the North Atlantic Oscillation (NAO), Pacific–North American (PNA) pattern, and El Niño–Southern Oscillation (ENSO)] were analyzed for the period 1950–99. The mechanisms behind these teleconnections are analyzed by applying composite analysis to the geopotential height, sea level pressure (SLP), moisture flux, and wind fields. Pressure anomalies and associated airflow patterns related with the different modes of climate variability explain the patterns of temperature and precipitation extremes across the region. The responses of the daily scale climate extremes to the seasonally averaged large-scale circulation patterns are achieved through shifts in the probability distributions.

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)0.002
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 score0.321
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.206
Teacher spread0.199 · 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

Citations82
Published2014
Admission routes1
Has abstractyes

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