Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-25623
Publication type: Article in scientific journal
Type of review: Peer review (publication)
Title: Can stochastic resonance explain recurrence of Grand Minima?
Authors: Albert, Carlo
Ferriz-Mas, Antonio
Gaia, Filippo
Ulzega, Simone
et. al: No
DOI: 10.3847/2041-8213/ac0fd6
10.21256/zhaw-25623
Published in: The Astrophysical Journal Letters
Volume(Issue): 916
Issue: 2
Page(s): L9
Issue Date: 2021
Publisher / Ed. Institution: IOP Publishing
ISSN: 2041-8205
2041-8213
Language: English
Subjects: Solar physics; Stochastic resonance; Stochastic model; Stochastic delayed ODE
Subject (DDC): 500: Natural sciences
510: Mathematics
Abstract: The amplitude of the 11 yr solar cycle is well known to be subject to long-term modulation, including sustained periods of very low activity known as Grand Minima. Stable long-period cycles found in proxies of solar activity have given new momentum to the debate about a possible influence of the tiny planetary tidal forcing. Here, we study the solar cycle by means of a simple zero-dimensional dynamo model, which includes a delay caused by meridional circulation as well as a quenching of the α-effect at toroidal magnetic fields exceeding an upper threshold. Fitting this model to the sunspot record, we find a set of parameters close to the bifurcation point at which two stable oscillatory modes emerge. One mode is a limit cycle resembling normal solar activity including a characteristic kink in the decaying limb of the cycle. The other mode is a weak sub-threshold cycle that could be interpreted as Grand Minimum activity. Adding noise to the model, we show that it exhibits Stochastic Resonance, which means that a weak external modulation can toss the dynamo back and forth between these two modes, whereby the periodicities of the modulation get strongly amplified.
URI: https://digitalcollection.zhaw.ch/handle/11475/25623
Fulltext version: Published version
License (according to publishing contract): CC BY 4.0: Attribution 4.0 International
Departement: Life Sciences and Facility Management
Organisational Unit: Institute of Computational Life Sciences (ICLS)
Published as part of the ZHAW project: BISTOM - Bayesian Inference with Stochastic Models
Appears in collections:Publikationen Life Sciences und Facility Management

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