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Record W4408473244 · doi:10.5194/egusphere-egu25-45

Improvement of the CLASSIC Snow Model to Better Simulate Arctic Snowpacks

2025· preprint· en· W4408473244 on OpenAlexaffabout
Mickaël Lalande, Christophe Kinnard, Alexandre Roy

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsSnowpackSnowArcticEnvironmental scienceWind speedAtmospheric sciencesClimatologyMeteorologyGeologyGeographyOceanography

Abstract

fetched live from OpenAlex

Current snow models – including the most sophisticated ones, such as CROCUS and SNOWPACK – struggle to properly simulate Arctic snowpack characteristics such as density profiles. Indeed, those models have been developed and designed for Alpine snowpacks, which evolve differently from Arctic ones due to higher wind speeds, increasing the compaction of the upper snowpack layers, and stronger temperature gradients, inducing upward water vapor fluxes within the snowpack and influencing the compaction and metamorphism. Both phenomena – combined with complex interactions with the vegetation – are at the origin of the wind-slab and depth hoar formation in Arctic snowpacks. The Canadian Land Surface Scheme including Biogeochemical Cycles (CLASSIC) – being the Canadian Earth System Model (CanESM) land surface component – uses a medium-complexity single-layer snow scheme. Whether correctly representing Arctic snowpack bulk characteristics requires a multilayer approach over a single-layer snow scheme is still an open question. To assess the model skills, 1D simulations were performed at ten sites – including three Arctic sites. Improvements in the snow model scheme were carried out, including three new parameterizations to better represent Arctic snow: (1) blowing snow sublimation losses, (2) wind inclusion in the computation of fresh snow density, and (3) increased wind compaction. Those improvements allow most of the current model skills to be improved at the Arctic sites. Uncertainties related to the meteorological forcing, variable measurements, snow drift, and model bias compensations are a perpetual challenge in those model assessments. Future studies will involve spatial evaluation of those model developments in addition to implementing new snow cover fraction parameterization in CLASSIC. The influence of these new developments will be assessed against the ESA Snow CCI variables for different land types and for the simulated surface energy and carbon fluxes.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.125
Threshold uncertainty score0.249

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.030
GPT teacher head0.246
Teacher spread0.216 · 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
GenreMethods

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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