Simulation of Snow on Arctic Sea Ice Using a Coupled Snow–Ice Model
Bibliographic record
Abstract
Abstract The new Recherche Prévision Numérique (NEW-RPN) model, a coupled system including a multilayer snow thermal model (SNTHERM) and the sea ice model currently used in the Meteorological Service of Canada (MSC) operational forecasting system, was evaluated in a one-dimensional mode using meteorological observations from the Surface Heat Budget of the Arctic Ocean (SHEBA)’s Pittsburgh site in the Arctic Ocean collected during 1997/98. Two parameters simulated by NEW-RPN (i.e., snow depth and ice thickness) are compared with SHEBA’s observations and with simulations from RPN, MSC’s current coupled system (the same sea ice model and a single-layer snow model). Results show that NEW-RPN exhibits better agreement for the timing of snow depletion and for ice thickness. The profiles of snow thermal conductivity in NEW-RPN show considerable variability across the snow layers, but the mean value (0.39 W m−1 K−1) is within the range of reported observations for SHEBA. This value is larger than 0.31 W m−1 K−1, which is commonly used in single-layer snow models. Of particular interest in NEW-RPN’s simulation is the strong temperature stratification of the snowpack, which indicates that a multilayer snow model is needed in the SHEBA scenario. A sensitivity analysis indicates that snow compaction is also a crucial process for a realistic representation of the snowpack within the snow/sea ice system. NEW-RPN’s overestimation of snow depth may be related to other processes not included in the study, such as small-scale horizontal variability of snow depth and blowing snow processes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".