Radiatively Driven Convection: A Comparison Between Two- and Three-Dimensional Simulations
Bibliographic record
Abstract
At the end of winter as solar radiation and increasing air temperatures melt the snow layer above the ice, significant radiation from the sun is able to enter the water column. In the cold water regime (T<4∘C, where 4∘C is the freshwater temperature of maximum density) increasing the temperature also increases the density. Therefore, adding heat near the surface results in radiatively driven convection (RDC). This process under ice has received attention recently due to both its uniqueness (solar radiation driving convection that is shielded from wind), and its sensitivity to climate change (i.e. the narrow temperature range over which it can occur). We report on direct numerical simulations designed to compare RDC in 2D and 3D. Our work demonstrates that a volumetric forcing term leads to a Rayleigh-Taylor-like instability whereby heat is exchanged with a motionless ambient below. 2D simulations have significantly less viscous dissipation and larger convective velocities, compared to 3D simulations, but the depth of the convective layer grows at a similar rate. Upwelling plumes are largely irrotational, contributing to most – but not all – of the difference in viscous dissipation between 2D and 3D. In 3D, large convective plumes persist, but the features are significantly smaller scale and upwelling plumes are rotational and dissipative. The research shows that if broad features like the evolution of the depth of a convective layer are of interest, 2D simulations may be sufficient. However, modellers should be cautious about using 2D simulations to accurately describe turbulent motions and transport under ice, and should opt for 3D for a more complete description where possible.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".