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Record W4408422672 · doi:10.5194/egusphere-egu25-614

Evaluating a Parameterization for Sublimation of Blowing Snow with In-situ Observations in the Arctic

2025· preprint· en· W4408422672 on OpenAlexaffabout
Lukas Monrad-Krohn, Maximilian Maahn, M. M. Frey, Stephen J. Déry

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsSublimation (psychology)SnowArcticIn situThe arcticMeteorologyEnvironmental scienceClimatologyPhysical geographyAtmospheric sciencesGeologyGeographyOceanographyPsychology

Abstract

fetched live from OpenAlex

Surface albedo, sea ice growth and glacier mass balance in the Arctic are all heavily dependent on snow and thus also impacted by blowing snow through redistribution and increased sublimation. The sublimation of blowing snow is significantly higher than that of ground snow due to the larger surface area of the suspended snow crystals and the continuous entrainment of dry air. Thus, sublimation of blowing snow impacts the exchange of energy, moisture and particles between the snow and atmosphere in windy conditions.Because of the difficulty of modelling such a small-scale process for large areas, parameterizations of sublimation of blowing snow are necessary for snow mass balance and aerosol production studies. The widely used Déry and Yau (2001) parameterization has only been evaluated with model data from the Canadian Prairie, but never for other surface types, where it is applied, or with in-situ observations. Therefore, the goal of this work is to evaluate the parameterization by Déry and Yau (2001) with observations from the MOSAiC expedition in the central Arctic and the Intensive Observation Period for Water (IOP4H20) field measurements in Ny-Ålesund, Svalbard.Here we show observations of blowing snow events that were detected and characterized by a snow particle counter and the Video In-Situ Snowfall Sensor (VISSS). During these events, measurements of latent heat fluxes from eddy covariance systems are used to evaluate the parameterized sublimation rate. To address challenges with eddy covariance observations in snowy conditions and calculating column-integrated values the observations are complemented with the 1D-column PIEKTUK-D blowing snow model.In this way, comparing the parameterization with observations brings insights into its uncertainty or possible limitations for two different surface types and thereby improves the estimation of the accuracy of snow mass balance and aerosol production studies that apply this parameterization.

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)0.001
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.114
Threshold uncertainty score0.226

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.134
GPT teacher head0.319
Teacher spread0.185 · 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
Published2025
Admission routes2
Has abstractyes

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