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Record W2167856909 · doi:10.1111/cag.12155

An analysis of recent observed climate trends and variability in Labrador

2015· article· en· W2167856909 on OpenAlexafffundvenueabout
Joel Finnis, Trevor Bell

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsMemorial University of Newfoundland
FundersArcticNet
KeywordsClimatic variabilityClimatologyClimate changeGeographyNorth Atlantic oscillationEnvironmental scienceEl Niño Southern OscillationPhysical geographyOceanographyGeology

Abstract

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Recent warm winters have negatively impacted many rural Labrador residents, raising concerns over regional climate change and variability. To better understand these events, the recent temperature record was examined in detail using atmospheric reanalyses and relevant station data. In addition to quantifying seasonal and annual trends, the influence of major climate drivers is explored and their contributions to recent anomalies are estimated. The North Atlantic Oscillation (NAO) and Atlantic Multidecadal Oscillation (AMO) are identified as the dominant sources of variability, with the winter NAO producing the largest and most predictable anomalies. These drivers are associated with decadal‐scale variability in the region, including unusually cool conditions from the 1980 s through late 1990 s and a subsequent shift to warmer conditions. Removing the influence of the NAO and other climate drivers greatly reduces the magnitude of recent warm anomalies. However, temperature trends in the residual data are amplified in most seasons, with winter residual trends four times larger than the raw winter data. This suggests that climate change, previously obscured by natural variability, is exerting a significant influence on Labrador. Although climate change likely contributed relatively little to recent extreme events, it is gradually raising the probability of similar future events. Une analyse des dernières tendances climatiques observées et de leur variabilité au Labrador Les hivers chauds des dernières années ont entraîné des effets négatifs sur de nombreux résidents des régions rurales du Labrador et ont suscité des préoccupations sur les changements climatiques et la variabilité au niveau régional. Pour mieux comprendre ces événements, les relevés de température les plus récents ont fait l'objet d'un examen détaillé en tirant parti des réanalyses atmosphériques et des données des stations pertinentes. En plus de quantifier les tendances saisonnières et annuelles, une exploration de l'influence des principaux facteurs climatiques déterminants a été menée au moyen d'une estimation de leurs contributions aux anomalies récentes. Il est établi que l'oscillation nord‐atlantique (ONA) et l'oscillation atlantique multi‐décennale (OAM) constituent les principales sources de variabilité, et que c'est l'OAN de l'hiver qui est responsable des anomalies les plus importantes et prévisibles. Ces facteurs déterminants vont de pair avec la variabilité décennale dans la région, y compris les conditions anormalement fraîches enregistrées à partir des années 1980 jusqu'à la fin des années 1990, suivi d'un changement abrupt vers des conditions plus clémentes. Écarter l'influence de l'ONA et des autres facteurs climatiques déterminants réduit considérablement l'étendue des anomalies chaudes observées récemment. Il ressort des données résiduelles, une amplification des tendances de la température au cours de la plupart des saisons, avec des tendances hivernales quatre fois plus accentuées que les données de base pour l'hiver. Cela laisse entendre que les changements climatiques, masqués auparavant par la variabilité naturelle, exercent une influence notable sur le Labrador. Bien que les changements climatiques aient vraisemblablement peu contribué aux événements extrêmes récents, ils augmentent peu à peu la probabilité que de tels événements se produisent à l'avenir.

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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)0.001
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 score0.913
Threshold uncertainty score0.173

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.025
GPT teacher head0.226
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 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".

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Citations23
Published2015
Admission routes4
Has abstractyes

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