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Record W2921644530 · doi:10.1175/jcli-d-18-0410.1

Recent Strengthening of Greenland Blocking Drives Summertime Surface Warming over Northern Canada and Eastern Siberia

2019· article· en· W2921644530 on OpenAlexaboutno aff
Sai Wang, Debashis Nath, Wen Chen, Lin Wang

Bibliographic record

VenueJournal of Climate · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsnot available
FundersCentrum fÖr Personcentrerad VårdMinistry of Science and Technology of the People's Republic of ChinaChinese Academy of SciencesNational Natural Science Foundation of China
KeywordsClimatologyGeopotential heightSubarctic climateNorthern HemisphereEnvironmental scienceAtmospheric circulationGlobal warmingAtmospheric sciencesClimate changeGeologyGeographyPrecipitationOceanographyMeteorology

Abstract

fetched live from OpenAlex

Abstract In the last three decades, rapid surface warming is observed in the land areas of northern high latitudes during boreal summer months. Although the warming trend is thought to be driven by early snowmelt, the exact causes, especially its relationship with atmospheric circulation changes, remain a subject of debate. By analyzing ERA-Interim data, this study examines the possible factors for rapid subarctic warming. It is found that more than half of the warming trend over the entire subarctic and 80% over northern Canada and eastern Siberia (regions with maximum amplification) can be explained by enhanced downward infrared radiation (IR). Downward IR is largely driven by horizontal atmospheric moisture flux convergence and warm-air advection. The positive trend in geopotential height over the Greenland region is key for moisture flux convergence over northern Canada and eastern Siberia through changes in the storm tracks. An enhanced summertime blocking activity in the Greenland region seems responsible for the positive trend in geopotential heights.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.189
Threshold uncertainty score0.380

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0020.000

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 head0.010
GPT teacher head0.222
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations11
Published2019
Admission routes1
Has abstractyes

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