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Record W7024828542

Synoptiska variationer vid extrema snofall i S:t Eliasbergen, Yukon, Kanada

2015· article· en· W7024828542 on OpenAlexaboutno aff

Bibliographic record

VenueKTH Publication Database DiVA (KTH Royal Institute of Technology) · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsnot available
Fundersnot available
KeywordsSnowPeninsulaAir mass (solar energy)Automatic weather stationGlacierSynoptic scale meteorologyAir temperatureSea levelWarm frontWeather station
DOInot available

Abstract

fetched live from OpenAlex

Glaciers of southwestern Yukon (Canada) and southeastern Alaska (USA) are presently experiencing high rates of annual mass loss. These high melt rates have mainly been investigated with respect to regional temperature trends, but comparatively little is known about how climate variations regulate snow accumulation on these glaciers. This study examines the synoptic weather patterns and air flow trajectories associated with extreme snowfall events in the central St. Elias Mountains (Yukon). The analyses are based on data retrieved from an automated weather station (AWS) between 2003 and 2012, which provide the longest continuous records of surface meteorological data ever obtained from this remote region. The AWS data reveal that 47 extreme snowfall events (> 27 cm per 12 hours) occurred during this period, of which 79 % took place during the cold season months. Air flow trajectories associated with these events indicate that a vast majority had their origin in the North Pacific south of 50°N. Less frequent were air masses with a source in the Aleutian Arc/Bering Sea region and the Gulf of Alaska, and in a few rare cases precipitating air was traced to continental source regions in Western Canada and Alaska. Composite maps of sea-level pressure and upper-level winds associated with extreme snowfall events revealed a frequent synoptic pattern with a low-pressure area centered over the Kenai Peninsula (Alaska), which drives strong southerly winds over the Gulf of Alaska towards the St. Elias Mountains. This pattern is consistent with AWS data wind recordings during snow storms. The most typical synoptic configurations of the North Pacific low-pressure area during extreme snowfall events are either elongated, split, or single-centered, and these situations represent possible seasonal analogues for the different states of the Aleutian Low in the subarctic North Pacific. However, neither the geographical position or intensity of negative sea-level pressure anomalies, nor surface pressure gradients associated with extreme snowfall events are good predictors of the actual snowfall SWE amounts recorded in the central St. Elias Mountains. Estimated snowfall and total precipitation gradients with altitude were confirmed to be much steeper (by up to ~30 %) on the continental side (Yukon), than on the coastal side (Alaska) of the St. Elias Mountains, reflecting the strong orographic division between the continental and coastal marine climatic regimes. Finally, patterns of 500-mb geopotential height anomalies associated with extreme snowfall events at Divide were compared with those associated with unusually high accumulation years in an ice core from the nearby Eclipse Icefield. Results confirm previous findings that associate high snow accumulation winters in this region with the presence of a strong dipole pressure structure between western North America and the Aleutian Low region, a structure which resembles the positive phase of the Pacific North American atmospheric circulation pattern.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.821
Threshold uncertainty score0.357

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 science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0140.004

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 head0.038
GPT teacher head0.239
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".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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