Blowing Snow on Arctic Sea Ice: Results from an Improved Sea Ice–Snow–Blowing Snow Coupled System
Bibliographic record
Abstract
Abstract A one-dimensional (1D) version of a blowing snow model, called PIEKTUK-D, has been incorporated into a snow–sea ice coupled system. Blowing snow results in sublimation of approximately 12 mm of snow water equivalent (SWE), which is equal to approximately 6% of the annual precipitation over 324 days from 1997 to 1998. This effect leads to an average decrease of 9 cm in snow depth for an 11-month simulation of the Surface Heat Budget of the Arctic Ocean (SHEBA) dataset (from 31 October 1997 to 1 October 1998). Inclusion of blowing snow has a significant impact on snow evolution between February and June, during which it is responsible for a decrease in snow depth error by about 30%. Between November and January, however, other factors such as regional surface topography or horizontal wind transport may have had a greater influence on the evolution of the snowpack and sea ice. During these few months the new system does not perform as well, with a snow depth percentage error of 39%—much larger than the 12% error found between February and June. The results also indicate a slight increase of 4 cm on average for ice thickness, and a decrease of 0.4 K for the temperature at the snow–ice interface. One of the main effects of blowing snow is to shorten the duration of snow cover above sea ice by approximately 4 days and to lead to earlier ice melt by approximately 6 days. Blowing snow also has a very small impact on internal characteristics of the snowpack, such as grain size and density, leading to a weaker snowpack.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".