Mesoscale Circulations and Surface Energy Balance During Snowmelt in a Regional Climate Model
Bibliographic record
Abstract
The Canadian Regional Climate Model has recently been coupled with an advanced, second generation land surface model - the Canadian Land Surface Scheme. In addition, high resolution land cover and soils data sets have been assembled on a 1 km horizontal resolution grid over North America. These data sets, along with the coupled model, provide a powerful tool for the examination of regional climate processes in complex, heterogeneous terrain. In a first application of the new modelling system, simulations of progressively higher horizontal resolution are performed over the Mackenzie Basin for the spring of 1995 in order to determine if turbulent fluxes associated with a heterogeneous land surface can generate mesoscale atmospheric circulations of relevance to the surface energy budget during the critical snow melt period. We have found that shallow, diurnally forced mesoscale circulations associated with surface flux heterogeneity developed regularly throughout the snow melt period in the higher resolution experiments. In one case, a localized downdraft associated with a low level isothermal layer was found to enhance turbulent heat exchange with the underlying snow. The net importance of this process to the overall energy balance of the snowpack did increase with resolution, but remained relatively small for the resolutions considered here.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".