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Record W2096175486 · doi:10.1029/2001jd001251

Large‐scale mass balance effects of blowing snow and surface sublimation

2002· article· en· W2096175486 on OpenAlexaffabout
Stephen J. Déry, M. K. Yau

Bibliographic record

VenueJournal of Geophysical Research Atmospheres · 2002
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsMcGill University
FundersCold Regions Research and Engineering Laboratory
KeywordsSnowSublimation (psychology)Environmental scienceArcticAtmospheric sciencesSnow fieldClimatologyGlacier mass balanceGeologySnow coverOceanographyGlacierGeomorphology

Abstract

fetched live from OpenAlex

This study examines the effects of surface sublimation and blowing snow on the surface mass balance on a global and basin scale using the European Centre for Medium‐Range Weather Forecasts (ECMWF) Re‐Analysis (ERA15) data at a resolution of 2.5° that span the years 1979–1993. The combined processes of surface and blowing snow sublimation are estimated to remove 29 mm yr −1 snow‐water equivalent (swe) over Antarctica, disposing about 17 to 20% of its annual precipitation. In the Northern Hemisphere, these processes are generally less important in continental areas than over the frozen Arctic Ocean, where surface and blowing snow sublimation deplete upward of 100 mm yr −1 swe. Areas with frequent blowing snow episodes, such as the coastal regions of Antarctica and the Arctic Ocean, are prone to a mass transport >100 Mg m −1 yr −1 . Although important locally, values of the divergence of mass through wind redistribution are generally 2 orders of magnitude less than surface and blowing snow sublimation when evaluated over large areas. For the entire Mackenzie River Basin of Canada, surface sublimation remains the dominant sink of mass as it removes 29 mm yr −1 swe, or about 7% of the watershed's annual precipitation. Although the first of its kind, this study provides only a first‐order estimate of the contribution of surface sublimation and blowing snow to the surface mass balance because of limitations with the data set and some uncertainties in the blowing snow process.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score0.627

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 head0.027
GPT teacher head0.273
Teacher spread0.246 · 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.

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

Citations182
Published2002
Admission routes2
Has abstractyes

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