Simulation of monsoon intraseasonal oscillations in a coarse‐resolution aquaplanet GCM
Bibliographic record
Abstract
Abstract The skill of the global climate models (GCMs) to realistically simulate the monsoon intraseasonal oscillations (MISOs) is related to the sensitivity of their convective parameterization schemes. Here we show that by coupling a simple multicloud parameterization to a coarse‐resolution aquaplanet GCM, realistic MISOs can be simulated. We conduct three different simulations with a fixed nonhomogeneous sea surface temperature mimicking the Indian Ocean/western Pacific warm pool (WP) centered at the three latitudes 5°N, 10°N, and 15°N, respectively, to replicate the seasonal migration of the Tropical Convergence Zone (TCZ). This results in the generation of mean circulation resembling the monsoonal flow pattern in boreal summer. Succession of eastward propagating Madden‐Julian Oscillation (MJO) disturbances with phase speed, amplitude, and structure similar to summer MJOs are simulated when the WP is at 5°N. When the WP is located over 10°N, northward and eastward propagating MISOs are simulated. This case captures the meridional seesaw of convection between continental and oceanic TCZ observed during boreal summer over South Asia. Westward propagating Rossby wave‐like disturbances are simulated when the WP is over 15°N congruous with the synoptic disturbances seen over the monsoon trough. The initiation of intraseasonal oscillations in the model can occur internally through organization of convective events above the WP associated with internal dynamics.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 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".