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Record W2102115685 · doi:10.1175/jcli-d-13-00721.1

Midlatitude Moisture Contribution to Recent Arctic Tropospheric Summertime Variability*

2014· article· en· W2102115685 on OpenAlexafffund
Frédéric Laliberté, Paul J. Kushner

Bibliographic record

VenueJournal of Climate · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaNatural Environment Research CouncilSight Research UK
KeywordsTroposphereClimatologyMiddle latitudesArcticArctic dipole anomalyEnvironmental scienceZonal and meridionalAtmospheric sciencesLatitudeClimate modelClimate changeGeologyArctic ice packOceanographySea ice

Abstract

fetched live from OpenAlex

Abstract The dynamics of late summer Arctic tropospheric heat content variability is studied using reanalyses. In both trends and interannual variability, much of the August heat content variability in the Arctic midtroposphere can be explained by the total—sensible plus latent—heat content variability at the midlatitude near surface in July. Climate models suggest that this connection is part of the global warming signal in September–November, but in reanalyses the connection is most strongly present in July–August variability and trends. It is argued that heat content signals are propagated from the midlatitude near surface to the Arctic midtroposphere approximately along climatological moist isentropes. High-frequency data reveal that the propagating signal is primarily driven by a few strong meridional heat flux events each summer season. Composite analysis on these events shows that August meridional heat fluxes into the Arctic midtroposphere are succeeded by positive heat content anomalies in the lower troposphere a few days later. This second connection between the Arctic midtroposphere and the Arctic lower troposphere could be sufficient to explain some of the recent Arctic 850-hPa temperature variability north of 75°N.

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)0.001
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 score0.045
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0010.000

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 head0.006
GPT teacher head0.218
Teacher spread0.212 · 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

Citations24
Published2014
Admission routes2
Has abstractyes

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