Large Eddy Simulations of the Dusty Martian Convective Boundary Layer With MarsWRF
Bibliographic record
Abstract
Abstract Large eddy simulation (LES) of the Martian convective boundary layer (CBL) with a Mars‐adapted version of the Weather Research and Forecasting model is used to examine the impact of aerosol dust radiative‐dynamical feedbacks on turbulent mixing. The LES is validated against spacecraft observations and prior modeling. To study dust redistribution by coherent dynamical structures within the CBL, two radiatively active dust distribution scenarios are used: one in which the dust distribution remains fixed and another in which dust is freely transported by CBL motions. In the fixed dust scenario, increasing atmospheric dust loading shades the surface from sunlight and weakens convection. However, a competing effect emerges in the free dust scenario, resulting from the lateral concentration of dust in updrafts. The resulting enhancement of dust radiative heating in upwelling plumes both generates horizontal thermal contrasts in the CBL and increases buoyancy production, jointly enhancing CBL convection. We define a dust inhomogeneity index (DII) to quantify how much dust is concentrated in upwelling plumes. If the DII is large enough, the destabilizing effect of lateral heating contrasts can exceed the stabilizing effect of surface shading such that the CBL depth increases with increasing dust optical depth. Thus, under certain combinations of total dust optical depth and the lateral inhomogeneity of dust, a positive feedback exists between dust optical depth, the vigor and depth of CBL mixing, and—to the extent that dust lifting is controlled by the depth and vigor of CBL mixing—the further lifting of dust from the surface.
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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 | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".