Improvement of the CLASSIC Snow Model to Better Simulate Arctic Snowpacks
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".