Understanding the <scp>W</scp>est <scp>A</scp>frican <scp>M</scp>onsoon from the analysis of diabatic heating distributions as simulated by climate models
Bibliographic record
Abstract
Abstract Vertical and horizontal distributions of diabatic heating in the West African monsoon (WAM) region as simulated by four model families are analyzed in order to assess the physical processes that affect the WAM circulation. For each model family, atmosphere‐only runs of their CMIP5 configurations are compared with more recent configurations which are on the development path toward CMIP6. The various configurations of these models exhibit significant differences in their heating/moistening profiles, related to the different representation of physical processes such as boundary layer mixing, convection, large‐scale condensation and radiative heating/cooling. There are also significant differences in the models' simulation of WAM rainfall patterns and circulations. The weaker the radiative cooling in the Saharan region, the larger the ascent in the rainband and the more intense the monsoon flow, while the latitude of the rainband is related to heating in the Gulf of Guinea region and on the northern side of the Saharan heat low. Overall, this work illustrates the difficulty experienced by current climate models in representing the characteristics of monsoon systems, but also that we can still use them to understand the interactions between local subgrid physical processes and the WAM circulation. Moreover, our conclusions regarding the relationship between errors in the large‐scale circulation of the WAM and the structure of the heating by small‐scale processes will motivate future studies and model development.
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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 | 0.000 | 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 | 0.000 | 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 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".