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Record W1982077027 · doi:10.1175/2007jcli1583.1

On the Extreme Variability and Change of Cold-Season Temperatures in Northwest Canada

2008· article· en· W1982077027 on OpenAlexaffabout
Kit K. Szeto

Bibliographic record

VenueJournal of Climate · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsClimatologyStructural basinEnvironmental scienceAtmospheric circulationClimate changeZonal flow (plasma)Atmospheric sciencesGeologyOceanography

Abstract

fetched live from OpenAlex

Abstract The Mackenzie River basin (MRB) in northwestern Canada is a climatologically important region that exerts significant influences on the weather and climate of North America. The region exhibits the largest cold-season temperature variability in the world on both the intraseasonal and interannual time scales. In addition, some of the strongest recent warming signals have been observed over the basin. To understand the nature of these profound and intriguing observed thermal characteristics of the region, its atmospheric heat budget is assessed by using the NCEP–NCAR reanalysis dataset. The composite heat budgets and large-scale atmospheric conditions that are representative of anomalous winters in the region are examined in unison to study the processes that are responsible for the development of extreme warm/cold winters in the MRB. It is shown that the large winter temperature variability of the region is largely a result of the strong variability of atmospheric circulations over the North Pacific, the selective enhancement/weakening of latent heating of the cross-barrier flow for various onshore flow configurations, and synoptic-scale feedback processes that accentuate the thermal response of the basin to the changes in upwind conditions. The improved understanding of mechanisms that govern the thermal response of the basin to changes in the upstream environment provides a theoretical basis to interpret the climate change and modeling results for the region. In particular, the large recent warming trend observed for the region can be understood as the enhanced response of the basin to the shift in North Pacific circulation regime during the mid-1970s. The strong cold bias that affected the region in some climate model results can be attributed to the underprediction of orographic precipitation and associate latent heating of the cross-barrier flow, and the subsequent weakening of mean subsidence and warming over the basin in the models.

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)0.001
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 score0.010
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
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 head0.032
GPT teacher head0.225
Teacher spread0.193 · 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

Citations24
Published2008
Admission routes2
Has abstractyes

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