Variational Estimation of the Large-scale Time-dependent Meridional Circulation in the Sun: Proofs of Concept with a Solar Mean Field Dynamo Model

Hung, Ching Pui; Brun, Allan Sacha; Fournier, Alexandre; Jouve, L.; Talagrand, O.; Zakari, Mustapha

doi:10.3847/1538-4357/aa91d1

Cited by 16 publications

(16 citation statements)

References 60 publications

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“…Under the assumption that meridional flow modulations are the main factor controlling the buildup of the poloidal field from AR sources, Hung et al (2015), Hung et al (2017) suggest an inverse approach to derive flow variations from magnetic data. As, however, we will see in the next subsection, the validity of the underlying assumption is open to question.…”

Section: Photospheric Flow Variationsmentioning

confidence: 99%

Solar cycle prediction

Petrovay

2020

Living Rev Sol Phys

200

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A review of solar cycle prediction methods and their performance is given, including early forecasts for Cycle 25. The review focuses on those aspects of the solar cycle prediction problem that have a bearing on dynamo theory. The scope of the review is further restricted to the issue of predicting the amplitude (and optionally the epoch) of an upcoming solar maximum no later than right after the start of the given cycle. Prediction methods form three main groups. Precursor methods rely on the value of some measure of solar activity or magnetism at a specified time to predict the amplitude of the following solar maximum. The choice of a good precursor often implies considerable physical insight: indeed, it has become increasingly clear that the transition from purely empirical precursors to model-based methods is continuous. Model-based approaches can be further divided into two groups: predictions based on surface flux transport models and on consistent dynamo models. The implicit assumption of precursor methods is that each numbered solar cycle is a consistent unit in itself, while solar activity seems to consist of a series of much less tightly intercorrelated individual cycles. Extrapolation methods, in contrast, are based on the premise that the physical process giving rise to the sunspot number record is statistically homogeneous, i.e., the mathematical regularities underlying its variations are the same at any point of time, and therefore it lends itself to analysis and forecasting by time series methods. In their overall performance during the course of the last few solar cycles, precursor methods have clearly been superior to extrapolation methods. One method that has This article is a revised version of

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Section: Photospheric Flow Variationsmentioning

confidence: 99%

Solar cycle prediction

Petrovay

2020

Living Rev Sol Phys

200

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“…In recent years, dynamo models based on the Babcock-Leighton mechanism have been successful in explaining different observational aspects regarding solar activity (Dikpati & Charbonneau 1999;Nandy & Choudhuri 2002;Choudhuri et al 2004;Nandy et al 2011;Choudhuri & Karak 2012;Bhowmik & Nandy 2018;Hazra & Nandy 2019;Bhowmik 2019). Recently, data-driven 2.5D kinematic dynamo models and 3D kinematic solar dynamo models have also been developed to study different observational aspects regarding solar activity (Brun 2007;Jouve et al 2011;Yeates & Muñoz-Jaramillo 2013;Hung et al 2017;Hazra et al 2017;Karak & Miesch 2017;Hazra & Miesch 2018;Kumar et al 2019). For reviews of the solar and stellar dynamo model, see Charbonneau (2005), Brun et al (2015), and Brun & Browning (2017).…”

Section: Introductionmentioning

confidence: 99%

Does the mean-fieldαeffect have any impact on the memory of the solar cycle?

Hazra

Brun

Nandy

2020

A&A

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Context. Predictions of solar cycle 24 obtained from advection-dominated and diffusion-dominated kinematic dynamo models are different if the Babcock–Leighton mechanism is the only source of the poloidal field. Some previous studies argue that the discrepancy arises due to different memories of the solar dynamo for advection- and diffusion-dominated solar convection zones. Aims. We aim to investigate the differences in solar cycle memory obtained from advection-dominated and diffusion-dominated kinematic solar dynamo models. Specifically, we explore whether inclusion of Parker’s mean-field α effect, in addition to the Babcock–Leighton mechanism, has any impact on the memory of the solar cycle. Methods. We used a kinematic flux transport solar dynamo model where poloidal field generation takes place due to both the Babcock–Leighton mechanism and the mean-field α effect. We additionally considered stochastic fluctuations in this model and explored cycle-to-cycle correlations between the polar field at minima and toroidal field at cycle maxima. Results. Solar dynamo memory is always limited to only one cycle in diffusion-dominated dynamo regimes while in advection-dominated regimes the memory is distributed over a few solar cycles. However, the addition of a mean-field α effect reduces the memory of the solar dynamo to within one cycle in the advection-dominated dynamo regime when there are no fluctuations in the mean-field α effect. When fluctuations are introduced in the mean-field poloidal source a more complex scenario is evident, with very weak but significant correlations emerging across a few cycles. Conclusions. Our results imply that inclusion of a mean-field α effect in the framework of a flux transport Babcock–Leighton dynamo model leads to additional complexities that may impact memory and predictability of predictive dynamo models of the solar cycle.

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“…There are some caveats in analyzing the raw results of our solar cycle 25 predictions. For instance, our current dynamo model does not yet include large asymmetries between rising and declining phases of the cycle nor a time derivative of the meridional circulation state (we assume constant flow for the time being but can perform time dependent inversions as demonstrated in (Hung et al 2017)). Further the Waldmeier effect is only captured by adapting the diffusivity profile at the base of the convection zone as done by (Karak & Choudhuri 2011).…”

Section: Discussion Of Our Predictions and Caveatsmentioning

confidence: 99%

“…Here, we focus on predicting solar cycle 25, using solar cycle 24 data (so-called "recent climatology"). An extensive description of Solar Predict can be found in (Jouve et al 2011;Hung et al 2015Hung et al , 2017 and an image summarizing the pipeline is shown in Figure 1.…”

Section: Brief Presentation Of the Solar Predict Data Assimilation Pimentioning

confidence: 99%

A solar cycle 25 prediction based on 4D-var data assimilation approach

Brun¹,

Hung

Fournier

et al. 2019

Proc. IAU

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Based on our modern 4D-var data assimilation pipeline Solar Predict we present in this short proceeding paper our prediction for the next solar cycle 25. As requested by the Solar Cycle 25 panel call issued on January 2019 by NOAA/SWPC and NASA, we predict the timing of next minimum and maximum as well as their amplitude. Our results are the following: the minimum should have occured within the first semester of year 2019. The maximum should occur in year 2024.4 ± 6 months, with a value of the sunspot number equal to 92±10. This is in agreement with the NOAA/NASA consensus published in April 2019. Note that our prediction errors are based on 1-σ measure and do not consider all the systematics, so they are likely underestimated. We will update our prediction and error analysis regularly as more data becomes available and we improve our prediction pipeline.

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Variational Estimation of the Large-scale Time-dependent Meridional Circulation in the Sun: Proofs of Concept with a Solar Mean Field Dynamo Model

Cited by 16 publications

References 60 publications

Solar cycle prediction

Solar cycle prediction

Does the mean-fieldαeffect have any impact on the memory of the solar cycle?

A solar cycle 25 prediction based on 4D-var data assimilation approach

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