Role of observable nonlinearities in solar cycle modulation
Bibliographic record
Abstract
Context. Two candidate mechanisms have recently been considered with regard to the nonlinear modulation of solar cycle amplitudes. Tilt quenching (TQ) comprises the negative feedback between the cycle amplitude and the mean tilt angle of bipolar active regions relative to the azimuthal direction. Latitude quenching (LQ) consists of a positive correlation between the cycle amplitude and average emergence latitude of active regions. Aims. Here, we explore the relative importance and the determining factors behind the LQ and TQ effects. Methods. We systematically probed the degree of nonlinearity induced by TQ and LQ, as well as a combination of both using a grid based on surface flux transport (SFT) models. The roles played by TQ and LQ are also explored in the successful 2×2D dynamo model, which has been optimized to reproduce the statistical behaviour of real solar cycles. Results. The relative importance of LQ versus TQ is found to correlate with the ratio u 0 / η in the SFT model grid, where u 0 is the meridional flow amplitude and η is the diffusivity. An analytical interpretation of this result is given, further demonstrating that the main underlying parameter is the dynamo effectivity range, λ R , which is, in turn, determined by the ratio of equatorial flow divergence to diffusivity. The relative importance of LQ versus TQ is shown to scale as $ C_1+C_2/\lambda_R^2 $. The presence of a latitude quenching effect is seen in the 2×2D dynamo, contributing to the nonlinear modulation by an amount that is comparable to TQ. For other dynamo and SFT models considered in the literature, the contribution of LQ to the modulation covers a broad range – from entirely insignificant to serving as a dominant source of feedback. On the other hand, the contribution of a TQ effect (with the usually assumed amplitude) is never shown to be negligible.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".