A simple way to improve the diurnal cycle in convective rainfall over land in climate models
Bibliographic record
Abstract
Within the tropics, and during the summer months in midlatitudes, most of the rainfall reaching the surface is generated by moist convection. Over land, the diurnal cycle in moist convective rainfall usually peaks in the late afternoon. In most climate models, the diurnal peak in convective rainfall occurs several hours too early and is often near local solar noon. We argue that this bias partly originates from the methods used in convective parameterizations to calculate the cloud base mass flux. In most convective parameterizations, the initial convective mass flux is determined from the convective available potential energy (CAPE) of an updraft parcel originating from the model layer closest to the surface. In models, the rapid increase in the CAPE of this near‐surface layer following sunrise contributes to a rapid increase in convective precipitation. However, the mass‐weighted CAPE of the boundary layer as a whole responds much more slowly to the increase in downward solar radiation at the surface. Using a recently developed convective parameterization in version 4 of the Community Atmosphere Model (CAM4), we show that the overall accuracy in the diurnal simulation of convective precipitation increases as the number of near‐surface layers from which convective air parcels are permitted to originate increases from one to four. We also show that the simulation of the diurnal cycle in convective precipitation over land can be improved through the introduction of variables which attempt to represent the persistence of subgrid‐scale convective organization within a grid column across model time steps.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".