The Role of Interactive SST in the Cloud‐Resolving Simulations of Aggregated Convection
Bibliographic record
Abstract
Abstract This study investigates the role of interactive sea surface temperature (SST) in the early development of aggregated convection using a vector vorticity equation cloud‐resolving model coupled to a slab ocean. The simulations are initialized by a mock Walker circulation driven by initial SST gradient in the elongated x axis, with an average of 300 K and sinusoidal variation of amplitude ranging from 1.5 to 3 K. According to large‐scale perturbation strength, which is caused by SST variation, the results can be divided into two groups. Under weak perturbation, convection‐SST feedback efficiently eliminates SST gradient and moisture anomaly. The large‐scale environment is homogenized within 2 days. Even though SST in the group with stronger perturbation undergoes a similar process, significant moist static energy (MSE) advection in the boundary produces enough moisture difference to introduce virtual temperature effect and aggregation is triggered. Once dry zone starts to expand, radiative and convective effects regenerate SST gradient, which intensifies circulation and accelerates the process. We further show that the evolution of aggregation or not is captured by the trend of MSE‐EIS (estimated inversion strength) variance. The results highlight the boundary layer processes on the formation of aggregated convection in the tropics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".