Simulating vortices and jets in deep atmospheres of gas giant planets
Bibliographic record
Abstract
Decades of observations have painted a dynamic and rich picture of the atmosphere on Saturn and Jupiter. Both planets have a dominant prograde equatorial jet, and strong zonal flows that alternate in direction at higher latitudes, with Saturn also having a mysterious hexagon shape embedded in one of the polar jets. Both planets also have numerous vortices or storms of different sizes scattered on their surface. All these features are striking examples of turbulent self-organization. While observations abound, the physics behind the formation of these dynamical features is still uncertain. Two interpretations have emerged over time: In one, the surface features are shallow, extending to depths ranging from 10s to 100s of kilometers, while, in the other, they extend to 1000s of kilometers. Here we utilize the deep interpretation and investigate the properties of rotating convection in deep spherical shells. We present three cases: In the first case a giant polar cyclone, alternating zonal flows, and a high latitude eastward jet having polygonal patterns form simultaneously; The second case generates alternating zonal flows as well as numerous cyclones and anticyclones on various latitudes; And, the third case exclusively generates anticyclones with few being as large as Jupiter's great red spot. We discuss what drives these features in these turbulent simulations, and what can we learn from these cases about the interior and surface dynamics of Saturn and Jupiter.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".