On the sensitivity of deep‐convection initiation to horizontal grid resolution
Bibliographic record
Abstract
Abstract Idealized numerical simulations are used to study the sensitivity of deep‐convection initiation to horizontal grid spacing ( ). In a conditionally unstable but strongly inhibited environment, a localized surface heating function gives rise to low‐level convergence and a vigorous subcloud updraught that breaches the level of free convection (LFC). The vertical cloud development is sensitive to : the clouds reach 8–9 km for m (CTRL) and m (DX500) but only 6–7 km for m (DX125). This trend is not associated with major differences in midlevel cloud vigour (e.g., buoyancy and vertical velocity), but can be explained on the basis of cloud‐core mass flux ( ). The profile is regulated by its value at the LFC, largely set by subcloud processes, and its core‐layer gradient, set by entrainment and detrainment. The former is the largest in CTRL and weakens as the grid is coarsened (DX500) or refined (DX125), both due to a widening and weakening of the subcloud updraught. Whereas in DX500 this widening stems from a failure to adequately resolve the 1‐km‐wide updraught on the model grid, in DX125 it results from stronger total (subgrid plus resolved) turbulent mixing, which increases monotonically as is decreased. The wider and more diffuse updraughts in DX125 and DX500 are also more hydrostatic and generate weaker buoyancy‐driven accelerations. Within the cloud layer, the entrainment is very similar in the three cases, as is the detrainment for the higher‐resolution cases (DX125 and CTRL). However, the midlevel detrainment is relatively weak in DX500, which facilitates slightly deeper ascent than in CTRL. By contrast, the small core‐base and strong midlevel detrainment yields the shallowest clouds in DX125.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".