Dynamo Models of the Solar Cycle

1.1 Scope of review

The cyclic regeneration of the Sun’s large-scale magnetic field is at the root of all phenomena collectively known as “solar activity”. A near-consensus now exists to the effect that this magnetic cycle is to be ascribed to the inductive action of fluid motions pervading the solar interior. However, at this writing nothing resembling consensus exists regarding the detailed nature and relative importance of various possible inductive flow contributions.

My assigned task, to review “dynamo models of the solar cycle”, is daunting. I will therefore interpret this task as narrowly as I can get away with. This review will not discuss in any detail solar magnetic field observations, the physics of magnetic flux tubes and ropes, the generation of small-scale magnetic field in the Sun’s near-surface layers, hydromagnetic oscillator models of the solar cycle, or magnetic field generation in stars other than the Sun. Most of these topics are all worthy of full-length reviews, and do have a lot to bear on “dynamo models of the solar cycle”, but a line needs to be drawn somewhere. With the exception of recent cycle prediction schemes based explicitly on dynamo models, I also chose to exclude from consideration the voluminous literature dealing with prediction of sunspot cycle amplitudes, including the related literature focusing exclusively on the mathematical modelling of the sunspot number time series, in manner largely or even sometimes entirely decoupled from the underlying physical mechanisms of magnetic field generation.

This review thus focuses on the cyclic regeneration of the large-scale solar magnetic field through the inductive action of fluid flows, as described by various approximations and simplifications of the partial differential equations of magnetohydrodynamics. Most current dynamo models of the solar cycle rely heavily on numerical solutions of these equations, and this computational emphasis is reflected throughout the following pages. Many of the mathematical and physical intricacies associated with the generation of magnetic fields in electrically conducting astrophysical fluids are well covered in a few recent reviews (see Hoyng, 2003; Ossendrijver, 2003), and so will not be addressed in detail in what follows. The focus is on models of the solar cycle, seeking primarily to describe the observed spatio-temporal variations of the Sun’s large-scale magnetic field.

1.2 What is a “model”?

The review’s very title demands an explanation of what is to be understood by “model”. A model is a theoretical construct used as thinking aid in the study of some physical system too complex to be understood by direct inferences from observed data. A model is usually designed with some specific scientific questions in mind, and asking different questions about a given physical system will, in all legitimacy, lead to distinct model designs. A well-designed model should be as complex as it needs to be to answer the questions having motivated its inception, but no more than that. Throwing everything into a model — usually in the name of “physical realism” — is likely to produce results as complicated as the data coming from the original physical system under study. Such model results are doubly damned, as they are usually as opaque as the original physical data, and, in addition, are not even real-world data!

Nearly all of the solar dynamo models discussed in this review rely on severe simplifications of the set of equations known to govern the dynamics of the Sun’s turbulent, magnetized fluid interior. Yet all of them are bona fide models, as defined here.

1.3 A brief historical survey

While regular observations of sunspots go back to the early seventeenth century, and discovery of the sunspot cycle to 1843, it is the landmark work of George Ellery Hale and collaborators that, in the opening decades of the twentieth century, demonstrated the magnetic nature of sunspots and of the solar activity cycle. In particular, Hale’s celebrated polarity laws established the existence of a well-organized toroidal magnetic flux system, residing somewhere in the solar interior, as the source of sunspots. In 1919, Larmor suggested the inductive action of fluid motions as one of a few possible explanations for the origin of this magnetic field, thus opening the path to contemporary solar cycle modelling. Larmor’s suggestion fitted nicely with Hale’s polarity laws, in that the inferred equatorial antisymmetry of the solar internal toroidal fields is precisely what one would expect from the shearing of a large-scale poloidal magnetic field by an axisymmetric and equatorially symmetric differential rotation pervading the solar interior. However, two decades later T.S. Cowling placed a major hurdle in Larmor’s path — so to speak — by demonstrating that even the most general purely axisymmetric flows could not, in themselves, sustain an axisymmetric magnetic field against Ohmic dissipation. This result became known as Cowling’s antidynamo theorem.

A way out of this quandary was only discovered in the mid-1950s, when E.N. Parker pointed out that the Coriolis force could impart a systematic cyclonic twist to rising turbulent fluid elements in the solar convection zone, and in doing so provide the break of axisymmetry needed to circumvent Cowling’s theorem (see Figure 1). This groundbreaking idea was put on firm quantitative footing by the subsequent development of mean-field electrodynamics, which rapidly became the theory of choice for solar dynamo modelling. By the late 1970s, concensus had almost emerged as to the fundamental nature of the solar dynamo, and the α-effect of mean-field electrodynamics was at the heart of it.

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Serious trouble soon appeared on the horizon, however, and from no less than four distinct directions. First, it was realized that because of buoyancy effects, magnetic fields strong enough to produce sunspots could not be stored in the solar convection zone for sufficient lengths of time to ensure adequate amplification. Second, numerical simulations of turbulent thermallydriven convection in a thick rotating spherical shell produced magnetic field migration patterns that looked nothing like what is observed on the Sun. Third, and perhaps most decisive, the nascent field of helioseismology succeeded in providing the first determinations of the solar internal differential rotation, which turned out markedly different from those needed to produce solar-like dynamo solutions in the context of mean-field electrodynamics. Fourth, the ability of the α-effect and magnetic diffusivity to operate as assumed in mean-field electrodynamics was also called into question by theoretical calculations and numerical simulations.

It is fair to say that solar dynamo modelling has not yet recovered from this four-way punch, in that nothing remotely resembling concensus currently exists as to the mode of operation of the solar dynamo. As with all major scientific crises, this situation provided impetus not only to drastically redesign existing models based on mean-field electrodynamics, but also to explore new physical mechanisms for magnetic field generation, and resuscitate older potential mechanisms that had fallen by the wayside in the wake of the α-effect — perhaps most notably the so-called Babcock-Leighton mechanism, dating back to the early 1960s (see Figure 2). These post-helioseismic developments, beginning in the mid to late 1980s, are the primary focus of this review.

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1.4 Sunspots and the butterfly diagram

Historically, next to cyclic polarity reversal the sunspot butterfly diagram has provided the most stringent observational constraints on solar dynamo models (see Figure 3). In addition to the obvious cyclic pattern, two features of the diagram are particularly noteworthy:

• Sunspots are restricted to latitudinal bands some ≃ 30° wide, symmetric about the equator.

• Sunspots emerge closer and closer to the equator in the course of a cycle, peaking in coverage at about ± 15° of latitude.

Sunspots appear when deep-seated toroidal flux ropes rise through the convective envelope and emerge at the photosphere. Assuming that they rise radially and are formed where the magnetic field is the strongest, the sunspot butterfly diagram can be interpreted as a spatio-temporal “map” of the Sun’s internal, large-scale toroidal magnetic field component. This interpretation is not unique, however, since the aforementioned assumptions may be questioned. In particular, we still lack even rudimentary understanding of the process through which the diffuse, large-scale solar magnetic field produces the concentrated toroidal flux ropes that will later, upon buoyant destabilisation, give rise to sunspots. This remains perhaps the most severe missing link between dynamo models and solar magnetic field observations. On the other hand, the stability and rise of toroidal flux ropes is now fairly well-understood (see, e.g., Fan, 2009, and references therein).

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Magnetographic mapping of the Sun’s surface magnetic field (see Figure 4) have also revealed that the Sun’s poloidal magnetic component undergoes cyclic variations, changing polarities at times of sunspot maximum. Note in Figure 4 the poleward drift of the surface fields, away from sunspot latitudes. This pattern is believed to originate from the transport of magnetic flux released by the decay of sunspots at low latitudes (see Petrovay and Szakály, 1999, for an alternate explanation). The surface polar cap flux amounts to about 10²² Mx, while the total unsigned flux emerging in active regions in the course of a typical cycle adds up to a few 10²⁵ Mx; this is usually taken to indicate that the solar internal magnetic field is dominated by its toroidal component.

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1.5 Organization of review

The remainder of this review is organized in five sections. In Section 2 the mathematical formulation of the solar dynamo problem is laid out in some detail, together with the various simplifications that are commonly used in modelling. Section 3 details various possible physical mechanisms of magnetic field generation. In Section 4, a selection of representative models relying on different such mechanisms are presented and critically discussed, with abundant references to the technical literature. Section 5 focuses on the origin of cycle amplitude fluctuations, again presenting some illustrative model results and reviewing recent literature on the topic. The concluding Section 6 offers a somewhat more personal discussion of current challenges and trends in solar dynamo modelling.

A great many review papers have been and continue to be written on dynamo models of the solar cycle, and the solar dynamo is discussed in most recent solar physics textbooks, notably Stix (2002), Foukal (2004), and Schrijver and Siscoe (2009). The series of review articles published in Proctor and Gilbert (1994) and Ferriz-Mas and Núñez (2003) are also essential reading for more in-depth reviews of some of the topics covered here. Among the most recent reviews, Petrovay (2000); Tobias (2002); Rüdiger and Arlt (2003); Usoskin and Mursula (2003); Ossendrijver (2003), and Brandenburg and Subramanian (2005) offer (in my opinion) particularly noteworthy alternate and/or complementary viewpoints to those expressed here.

2.1 Magnetized fluids and the MHD induction equation

2.2 The dynamo problem

The first term on right hand side of Equation (1) represents the inductive action of the flow field, and it can act as a source term for B; the second term, on the other hand, describes the resistive dissipation of the current systems supporting the magnetic field, and is thus always a global sink for B. The relative importances of these two terms is measured by the magnetic Reynolds number Rm = μL/η, obtained by dimensional analysis of Equation (1). Here η, u, and L are “typical” numerical values for the magnetic diffusivity, flow speed, and length scale over which B varies significantly. The latter, in particular, is not easy to estimate a priori, as even laminar MHD flows have a nasty habit of generating their own magnetic length scales (usually ∝ Rm^-1/2 at high Rm). Nonetheless, on length scales comparable to the sun itself, Rm is immense, and so is the usual viscous Reynolds number. This implies that energy dissipation will occur on length scales very much smaller than the solar radius.

Because of the great disparity of time- and length scales involved, and the fact that the outer 30% in radius of the Sun are the seat of vigorous, thermally-driven turbulent convective fluid motions, the solar dynamo problem is very hard to tackle as a direct numerical simulation of the full set of MHD equations (but do see Section 4.9 below). Most solar dynamo modelling work has thus relied on simplification — usually drastic — of the MHD equations, as well as assumptions on the structure of the Sun’s magnetic field and internal flows.

2.3 Kinematic models

A first drastic simplification of the MHD system of equations consists in dropping Equation (2) altogether by specifying a priori the form of the flow field u. This kinematic regime remained until relatively recently the workhorse of solar dynamo modelling. Note that with u given, the MHD induction equation becomes truly linear in B. Moreover, helioseismology (Christensen-Dalsgaard, 2002) has now pinned down with good accuracy two important solar large-scale flow components, namely differential rotation throughout the interior, and meridional circulation in the outer half of the solar convection zone (for reviews, see Gizon, 2004; Howe, 2009). Given the low amplitude of observed torsional oscillations in the solar convective envelope, and the lack of significant cycle-related changes in the internal solar differential rotation inferred by helioseismology to this date, the kinematic approximation is perhaps not as bad a working assumption as one may have thought, at least for the differential rotation part of the mean flow u.

2.4 Axisymmetric formulation

2.5 Boundary conditions and parity

The axisymmetric dynamo equations are to be solved in a meridional plane, i.e., R_i ≤ r ≤ R_⊙ and 0 ≤ θ ≤ π, where the inner radial extent of the domain (R_i) need not necessarily extend all the way to r = 0. It is usually assumed that the deep radiative interior can be treated as a perfect conductor, so that R_i is chosen a bit deeper than the lowest extent of the region where dynamo action is taking place; the boundary condition at this depth is then simply A = 0, ∂(rB)/∂r = 0.

3.1 Poloidal to toroidal

Let us begin by expressing the (steady) large-scale flow field u as the sum of an axisymmetric azimuthal component (differential rotation), and an axisymmetric “poloidal” component up u_p (≡ u_r(r, θ)ê_r + u_θ(r, θ)ê_θ), i.e., a flow confined to meridional planes:

$$u(r,\theta ) = u_p (r,\theta ) + \varpi \Omega (r,\theta )\hat e_\varphi$$

(10)

where \(\varpi = r\sin \theta and \Omega\) is the angular velocity (rad s^-1). Substituting this expression into Equation (5) and into the φ-components of Equation (1) yields

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(11)

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(12)

Advection means bodily transport of B by the flow; globally, this neither creates nor destroys magnetic flux. Resistive decay, on the other hand, destroys magnetic flux and therefore acts as a sink of magnetic field. Diamagnetic transport can increase B locally, but again this is neither a source nor sink of magnetic flux. The compression/dilation term is a direct consequence of toroidal flux conservation in a flow moving across a density gradient. The shearing term in Equation (12), however, is a true source term, as it amounts to converting rotational kinetic energy into magnetic energy. This is the needed P → T production mechanism.

However, there is no comparable source term in Equation (11). No matter what the toroidal component does, A will inexorably decay. Going back to Equation (12), notice now that once A is gone, the shearing term vanishes, which means that B will in turn inexorably decay. This is the essence of Cowling’s theorem: An axisymmetric flow cannot sustain an axisymmetric magnetic field against resistive decay².

3.2 Toroidal to poloidal

In view of Cowling’s theorem, we have no choice but to look for some fundamentally non-axisymmetric process to provide an additional source term in Equation (11). It turns out that under solar interior conditions, there exist various mechanisms that can act as a source of poloidal field. In what follows we introduce and briefly describe the four classes of such mechanisms that appear most promising, but defer discussion of their implementation in dynamo models to Section 4, where illustrative solutions are also presented.

4.1 Model ingredients

All kinematic solar dynamo models have some basic “ingredients” in common, most importantly (i) a solar structural model, (ii) a differential rotation profile, and (iii) a magnetic diffusivity profile (possibly depth-dependent).

Helioseismology has pinned down with great accuracy the internal solar structure, including the internal differential rotation, and the exact location of the core-envelope interface. Unless noted otherwise, all illustrative models discussed in this section were computed using the following analytic formulae for the angular velocity Ω(r, θ) and magnetic diffusivity η(r):

$$\frac{{\Omega (r,\theta )}} {{\Omega _E }} = \Omega _C + \frac{{\Omega _S (\theta ) - \Omega _C }} {2}\left[ {1 + erf\left( {\frac{{r - r_c }} {w}} \right)} \right],$$

(15)

with

$$\Omega _S (\theta ) = 1 - a_2 \cos ^2 \theta - a_4 \cos ^4 \theta ,$$

(16)

and

$$\frac{{\eta (r)}} {{\eta _T }} = \Delta \eta + \frac{{1 - \Delta \eta }} {2}\left[ {1 + erf\left( {\frac{{r - r_c }} {w}} \right)} \right].$$

(17)

With appropriately chosen parameter values, Equation (15) describes a solar-like differential rotation profile, namely a purely latitudinal differential rotation in the convective envelope, with equatorial acceleration and smoothly matching a core rotating rigidly at the angular speed of the surface mid-latitudes³. This rotational transition takes place across a spherical shear layer of half-thickness w coinciding with the core-envelope interface at r_c/R_⊙ = 0.7 (see Figure 5, with parameter values listed in caption). As per Equation (17), a similar transition takes place with the net diffusivity, falling from some large, “turbulent” value η_T in the envelope to a much smaller diffusivity η_c in the convection-free radiative core, the diffusivity contrast being given by Δ_η = η_c/η_T. Given helioseismic constraints, these represent minimal yet reasonably realistic choices.

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It should be noted already that such a solar-like differential rotation profile is quite complex from the point of view of dynamo modelling, in that it is characterized by three partially overlapping shear regions: a strong positive radial shear in the equatorial regions of the tachocline, an even stronger negative radial shear in its the polar regions, and a significant latitudinal shear throughout the convective envelope and extending partway into the tachocline. As shown in panel B of Figure 5, for a tachocline of half-thickness w/R_⊙ = 0.05, the mid-latitude latitudinal shear at ../... = 0.7 is comparable in magnitude to the equatorial radial shear; its potential contribution to dynamo action should not be casually dismissed.

4.2 αΩ mean-field models

4.2.3 The αΩ dynamo equations

Adding the mean-electromotive force given by Equation (18) to the MHD induction equation leads to the following form for the axisymmetric mean-field dynamo equations:

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(24)

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(25)

where, in general, η_T ≫ η. There are now source terms on both right hand sides, so that dynamo action becomes possible at least in principle. The relative importance of the α-effect and shearing terms in Equation (25) is measured by the ratio of the two dimensionless dynamo numbers

$$\begin{array}{*{20}c} {C_\alpha = \frac{{\alpha _0 R_ \odot }} {{\eta _0 }},} & {C_\Omega = \frac{{(\Delta \Omega )_0 R_ \odot }} {{\eta _0 }}} \\ \end{array} ,$$

(26)

where, in the spirit of dimensional analysis, α₀, η₀, and (ΔΩ)₀ are “typical” values for the α-effect, turbulent diffusivity, and angular velocity contrast. These quantities arise naturally in the non-dimensional formulation of the mean-field dynamo equations, when time is expressed in units of the magnetic diffusion time τ based on the envelope (turbulent) diffusivity:

$$\tau = \frac{{R_ \odot ^2 }} {{\eta _T }}.$$

(27)

In the solar case, it is usually estimated that C_α ≪ C_Ω, so that the α-term is neglected in Equation (25); this results in the class of dynamo models known as αΩ dynamos, which will be the only ones discussed here⁴.

4.2.6 Representative results

We first consider αΩ models without meridional circulation (u_p = 0 in Equations (24, 25)), with the α-term omitted in Equation (25), and using the diffusivity and angular velocity profiles of Figure 5. We will investigate the behavior of αΩ models with the α-effect concentrated just above the core-envelope interface (green line in Figure 6). We also consider two latitudinal dependencies, namely α ∝ cos θ, which is the “minimal” possible latitudinal dependency compatible with the required equatorial antisymmetry of the Coriolis force, and an α-effect concentrated towards the equator⁶ via an assumed latitudinal dependency α ∝ sin² θ cos θ.

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Figures 7 and 8 show a selection of such dynamo solutions, in the form of animations in meridional planes and time-latitude diagrams of the toroidal field extracted at the core-envelope interface, here r_c/R_⊙ = 0.7. If sunspot-producing toroidal flux ropes form in regions of peak toroidal field strength, and if those ropes rise radially to the surface, then such diagrams are directly comparable to the sunspot butterfly diagram of Figure 3. All models have C_Ω = 25000, |C_α| = 10, η_T/η_c = 10, and η_T = 5 × 10¹¹ cm² s^-1, which leads to τ ≃ 300 yr. To facilitate comparison between solutions, here antisymmetric parity was imposed via the boundary condition at the equator.

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Examination of these animations reveals that the dynamo is concentrated in the vicinity of the core-envelope interface, where the adopted radial profile for the α-effect is maximal (cf. Figure 6). In conjunction with a fairly thin tachocline, the radial shear therein then dominates the induction of the toroidal magnetic component. With an eye on Figure 5, notice also how the dynamo waves propagates along isocontours of angular velocity, in agreement with the Parker-Yoshimura sign rule (cf. Section 4.2.5). In the butterfly diagram, this translates a systematic tilt of the isocontours of toroidal magnetic field. Note that even for an equatorially-concentrated α-effect (Panels B and C), a strong polar branch is nonetheless apparent in the butterfly diagrams, a direct consequence of the stronger radial shear present at high latitudes in the tachocline (see also corresponding animations). Models using an α-effect operating throughout the whole convective envelope, on the other hand, would feed primarily on the latitudinal shear therein, so that for positive C_α the dynamo mode would propagate radially upward in the envelope (see Lerche and Parker, 1972).

It is noteworthy that co-existing dynamo branches, as in Panel B of Figure 8, can have distinct dynamo periods, which in nonlinearly saturated solutions leads to long-term amplitude modulation. This is typically not expected in dynamo models where the only nonlinearity present is a simple algebraic quenching formula such as Equation (23). Note that this does not occur for the C_α < 0 solution, where both branches propagate away from each other, but share a common latitude of origin and so are phased-locked at the onset (cf. Panel C of Figure 8).

A common property of all oscillatory αΩ solutions discussed so far is that their period, for given values of the dynamo numbers C_α, C_Ω, is inversely proportional to the numerical value adopted for the (turbulent) magnetic diffusivity η_T. The ratio of poloidal-to-toroidal field strength, in turn, is found to scale as some power (usually close to 1/2) of the ratio C_α/C_Ω, at a fixed value of the product C_α × C_Ω.

The models discussed above are based on rather minimalistics and partly ad hoc assumptions on the form of the α-effect. More elaborate models have been proposed, relying on calculations of the full α-tensor based on some underlying turbulence models (see, e.g., Kitchatinov and Rüdiger, 1993). While this approach usually displaces the ad hoc assumptions into the turbulence model, it has the definite merit of offering an internally consistent approach to the calculation of turbulent diffusivities and large-scale flows. Rüdiger and Brandenburg (1995) remain a good example of the current state-of-the-art in this area; see also Rüdiger and Arlt (2003), and references therein.

4.3 Interface dynamos

4.3.2 Representative results

The next obvious step is to construct an interface dynamo in spherical geometry, using a solarlike differential rotation profile. This was undertaken by Charbonneau and MacGregor (1997). Unfortunately, the numerical technique used to handle the discontinuous variation in η at the core-envelope interface turned out to be physically erroneous for the vector potential A describing the poloidal field⁷ (see Markiel and Thomas, 1999, for a discussion of this point), which led to spurious dynamo action in some parameter regimes. The matching problem is best avoided by using a continuous but rapidly varying diffusivity profile at the core-envelope interface, with the α-effect concentrated at the base of the envelope, and the radial shear immediately below, but without significant overlap between these two source regions (see Panel B of Figure 9). Such numerical models can be constructed as a variation on the αΩ models considered earlier.

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In spherical geometry, and especially in conjunction with a solar-like differential rotation profile, making a working interface dynamo model is markedly trickier than if only a radial shear is operating, as in the Cartesian models discussed earlier (see Charbonneau and MacGregor, 1997; Markiel and Thomas, 1999; Zhang et al., 2003a). Panel A of Figure 9 shows a butterfly diagram for a numerical interface solution with C_Ω = 2.5 × 10⁵, C_α = +10, and a core-to-envelope diffusivity contrast Δ_η = 10^-2. The poleward propagating equatorial branch is precisely what one would expect from the combination of positive radial shear and positive α-effect according to the Parker-Yoshimura sign rule⁸. Here the α-effect is (artificially) concentrated towards the equator, by imposing a latitudinal dependency α ∼ sin(4θ) for π/4 ≤ θ ≤ 3π/4, and zero otherwise.

The model does achieve the kind of toroidal field amplification one would like to see in interface dynamos. This can be seen in Panel B of Figure 9, which shows radial cuts of the toroidal field taken at latitude π/8, and spanning half a cycle. Notice how the toroidal field peaks below the core-envelope interface (vertical dotted line), well below the α-effect region and near the peak in radial shear. Panel C of Figure 9 shows how the ratio of peak toroidal field below and above r_c varies with the imposed diffusivity contrast Δ_η. The dashed line is the dependency expected from Equation (32). For relatively low diffusivity contrast, -1.5 ≤ log(Δ_η) ≲ 0, both the toroidal field ratio and dynamo period increase as ∼ (Δ_η)^-1/2. Below log(Δ_η) ∼ -1.5, the max(B)-ratio increases more slowly, and the cycle period falls, contrary to expectations for interface dynamos (see, e.g., MacGregor and Charbonneau, 1997). This is basically an electromagnetic skin-depth effect; the cycle period is such that the poloidal field cannot diffuse as deep as the peak in radial shear in the course of a half cycle. The dynamo then runs on a weaker shear, thus yielding a smaller field strength ratio and weaker overall cycle; on the energetics of interface dynamos (see Ossendrijver and Hoyng, 1997, also Steiner and Ferriz-Mas, 2005).

4.4 Mean-field models including meridional circulation

Meridional circulation is unavoidable in turbulent, compressible rotating convective shells. It basically results from an imbalance between Reynolds stresses and buoyancy forces. The ∼ 15 m s^-1 poleward flow observed at the surface (see, e.g., Hathaway, 1996; Ulrich and Boyden, 2005) has now been detected helioseismically, down to r/R_⊙ ≃ 0.85 (Schou and Bogart, 1998; Braun and Fan, 1998), without significant departure from the poleward direction except locally and very close to the surface, in the vicinity of active region belts (see Gizon, 2004; Gizon and Rempel, 2008, and references therein), and in polar latitudes at some phases of the solar cycle (Haber et al., 2002). Long considered unimportant from the dynamo point of view, meridional circulation has gained popularity in recent years, initially in the Babcock-Leighton context but now also in other classes of models.

Accordingly, we now add a steady meridional circulation to our basic αΩ models of Section 4.2. The convenient parametric form developed by van Ballegooijen and Choudhuri (1988) is used here and in all later illustrative models including meridional circulation (Sections 4.5 and 4.8). This parameterization defines a steady quadrupolar circulation pattern, with a single flow cell per quadrant extending from the surface down to a depth r_b. Circulation streamlines are shown in Figure 10, together with radial cuts of the latitudinal component at mid-latitudes (θ = π/4). The flow is poleward in the outer convection zone, with an equatorial return flow peaking slightly above the core-envelope interface, and rapidly vanishing below.

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4.4.1 Representative results

Meridional circulation can bodily transport the dynamo-generated magnetic field (terms labeled “advective transport” in Equations (11, 12)), and therefore, for a (presumably) solar-like equatorward return flow that is vigorous enough — in the sense of Rm being large enough — overpower the Parker-Yoshimura propagation rule embodied in Equation (30). This was nicely demonstrated by Choudhuri et al. (1995), in the context of a mean-field αΩ model with a positive α-effect concentrated near the surface, and a latitude-independent, purely radial shear at the core-envelope interface. The behavioral turnover from dynamo wave-like solutions to circulation-dominated magnetic field transport sets in when the circulation speed becomes comparable to the propagation speed of the dynamo wave. In the circulation-dominated regime, the cycle period loses sensitivity to the assumed turbulent diffusivity value, and becomes determined primarily by the circulation’s turnover time. Models achieving equatorward propagation of the deep toroidal magnetic component in this manner are now often called flux-transport dynamos.

With a solar-like differential rotation profile, however, once again the situation is far more complex. Starting from the most basic αΩ dynamo solution with α ∼ cos θ (Figure 8A), new solutions are now recomputed, this time including meridional circulation. An animation of a typical solution is shown in Figure 11, and a sequence of time-latitude diagrams for four increasing values of the circulation flow speed, as measured by Rm, are plotted in Figure 12.

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At Rm = 50, little difference is seen with the circulation-free solutions (cf. Figure 8A), except for an increase in the cycle frequency, due to the Doppler shift experienced by the equatorwardly propagating dynamo wave (see Roberts and Stix, 1972). At Rm = 100 (part B), the cycle frequency has further increased and the poloidal component produced in the high-latitude region of the tachocline is now advected to the equatorial regions on a timescale becoming comparable to the cycle period, so that a cyclic activity, albeit with a longer period, becomes apparent at low latitudes. At Rm = 10³ (panel C and animation in Figure 11) the dynamo mode now peaks at mid-latitude, a consequence of the inductive action of the latitudinal shear, favored by the significant stretching experienced by the poloidal fieldlines as they get advected equatorward. At Rm = 2000 the original high latitude dynamo mode has all but vanished, and the mid-latitude mode is dominant. The cycle period is now set primarily by the turnover time of the meridional flow; this is the telltale signature of flux-transport dynamos.

All this may look straightforward, but it must be emphasized that not all dynamo models with solar-like differential rotation behave in this (relatively) simple manner. For example, the C_α = -10 solution with α ∼ sin² θ cos θ (Figure 8C) transits to a steady mode as Rm increases above ∼ 10². Moreover, the sequence of α ∼ cos θ shown in Figure 12 actually presents a narrow window around Rm ∼ 200 where the dynamo is decaying, due to a form of destructive interference between the high-latitude αΩ mode and the mid-latitude advection-dominated dynamo mode that dominates at higher values of Rm. Qualitatively similar results were obtained by Küker et al. (2001) using different prescriptions for the α-effect and solar-like differential rotation (see in particular their Figure 11; see also Rüdiger and Elstner, 2002; Bonanno et al., 2003). When field transport by turbulent pumping are included (see Käpylä et al., 2006b), αΩ models including meridional circulation can provide time-latitude “butterfly” diagrams that are reasonably solar-like.

Even if the meridional flow is too slow — or the turbulent magnetic diffusivity too high — to force the dynamo model in the advection-dominated regime, being much faster at the surface the poleward flow can dominate the spatio-temporal evolution of the radial surface magnetic field, as shown in Figure 13, for the same sequence of αΩ solutions with α ∼ cos θ as in Figure 12, at Rm = 0, 50, 100, and 500 (panels A – D). For low circulation speeds (Rm ≲ 50), the equatorward drift of the surface radial field is simply a diffused imprint of the equatorward drift of the deep-seated toroidal field (cf. Figure 8A and 12A). At higher circulation speeds, however, the surface magnetic field is swept instead towards the pole (see Figure 13C), becoming strongly concentrated and amplified there for Rm exceeding a few hundreds (Figures 11 and 13D).

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4.5 Models based on shear instabilities

We now turn to a recently proposed class of flux transport dynamo models relying on the latitudinal shear instability of the angular velocity profiles in the upper radiative portion of the solar tachocline (Dikpati and Gilman, 2001; Dikpati et al., 2004). These authors work with what are effectively the mean field αΩ dynamo equations including meridional circulation. They design their “tachocline α-effect” in the form of a latitudinal parameterization of the longitudinally-averaged kinetic helicity associated with the planforms they obtain from a linear hydrodynamical stability analysis of the latitudinal differential rotation in the part of the tachocline coinciding with the overshoot region. The analysis is carried out in the framework of shallow-water theory (see Dikpati and Gilman, 2001). In analogy with mean-field theory, the resulting α-effect is assumed to be proportional to kinetic helicity but of opposite sign (see Equation (19)), and ends up predominantly positive at mid-latitudes in the Northern solar hemisphere. In their dynamo model, Dikpati and Gilman (2001) use a solar-like differential rotation, depth-dependent magnetic diffusivity and meridional circulation pattern much similar to those shown in Figures 5, 6, and 10 herein. The usual ad hoc α-quenching formula (cf. Equation (23)) is introduced as the sole amplitude-limiting nonlinearity.

4.5.1 Representative solutions

Many representative solutions for this class of dynamo models can be examined in Dikpati and Gilman (2001) and Dikpati et al. (2004), where their properties are discussed at some length. Figure 14 shows time-latitude diagrams of the toroidal field at the core-envelope interface, and surface radial field. This is a solar-like solution with a mid-latitude surface meridional (poleward) flow speed of 17 m s^-1, envelope diffusivity η_T = 5 ¢ 10¹¹ cm2 s^-1, and a core-to-envelope magnetic diffusivity contrast Δ_η = 10^-3. Note the equatorward migration of the deep toroidal field, set here by the meridional flow in the deep envelope, and the poleward migration and intensification of the surface poloidal field, again a direct consequence of advection by meridional circulation, as in the mean-field dynamo models discussed in Section 4.4, when operating in the advection-dominated, high Rm regime. The three-lobe structure of each spatio-temporal cycle in the butterfly diagram reflects the presence of three peaks in the latitudinal profile of kinetic helicity for this model.

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4.6 Models based on buoyant instabilities of sheared magnetic layers

Dynamo models relying on the buoyant instability of magnetized layers have been presented in Thelen (2000b), the layer being identified with the tachocline. Here also the resulting azimuthal electromotive force is parameterized as a mean-field-like α-effect, introduced into the standard αΩ dynamo equations. The model is nonlinear, in that it includes the magnetic backreaction on the large-scale, purely radial velocity shear within the layer. The analysis of Thelen (2000a) indicates that the α-effect is negative in the upper part of the shear layer. Cyclic solutions are found in substantial regions of parameter space, and, not surprisingly, the solutions exhibit migratory wave patterns compatible with the Parker-Yoshimura sign rule.

Representative solutions for this class of dynamo models can be examined in Thelen (2000b). These models are not yet at the stage where they can be meaningfully compared with the solar cycle. They do have a number of attractive features, including their ability to operate in the strong field regime.

4.7 Models based on flux tube instabilities

4.7.1 From instability to α-effect

To date, stability studies of toroidal flux ropes stored in the overshoot layer have been carried out in the framework of the thin-flux tube approximation (Spruit, 1981). It is possible to construct “stability diagrams” taking the form of growth rate contours in a parameter space comprised of flux tube strength, latitudinal location, depth in the overshoot layer, etc. One such diagram, taken from Ferriz-Mas et al. (1994), is reproduced in Figure 15. The key is now to identify regions in such stability diagrams where weak instability arises (growth rates ≳ 1 yr). In the case shown in Figure 15, these regions are restricted to flux tube strengths in the approximate range 60 – 150 kG. The correlation between the flow and field perturbations is such as to yield a mean azimuthal electromotive force equivalent to a positive α-effect in the N-hemisphere (Ferriz-Mas et al., 1994; Brandenburg and Schmitt, 1998).

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4.8 Babcock-Leighton models

Solar cycle models based on what is now called the Babcock-Leighton mechanism were first proposed by Babcock (1961) and further elaborated by Leighton (1964, 1969), yet they were all but eclipsed by the rise of mean-field electrodynamics in the mid- to late 1960s. Their revival was motivated not only by the mounting difficulties with mean-field models alluded to earlier, but also by the fact that synoptic magnetographic monitoring over solar cycles 21 and 22 has offered strong evidence that the surface polar field reversals are indeed triggered by the decay of active regions (see Wang et al., 1989; Wang and Sheeley Jr, 1991, and references therein). The crucial question is whether this is a mere side-effect of dynamo action taking place independently somewhere in the solar interior, or a dominant contribution to the dynamo process itself.

The mode of operation of a generic solar cycle model based on the Babcock-Leighton mechanism is illustrated in cartoon form in Figure 16. Let P_n represent the amplitude of the high-latitude, surface (“A”) poloidal magnetic field in the late phases of cycle n, i.e., after the polar field has reversed. The poloidal field P_n is advected downward by meridional circulation (A → B), where it then starts to be sheared by the differential rotation while being also advected equatorward (B → C). This leads to the growth of a new low-latitude (C) toroidal flux system T_n+1, which becomes buoyantly unstable (C→D) and starts producing sunspots (D) which subsequently decay and release the poloidal flux P_n+1 associated with the new cycle n + 1. Poleward advection and accumulation of this new flux at high latitudes (D→A) then obliterates the old poloidal flux P_n, and the above sequence of steps begins anew.

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Meridional circulation clearly plays a key role in this “conveyor belt” model of the solar cycle, by providing the needed link between the two spatially segregated source regions. Not surprisingly, topologically more complex multi-cells circulation patterns can lead to markedly different dynamo behavior (see, e.g., Bonanno et al., 2006; Jouve and Brun, 2007), and can also have a profound impact on the evolution of the surface magnetic field (Dikpati et al., 2004; Jiang et al., 2009).

4.8.2 Representative results

Figure 17 is a meridional plane animation of a representative Babcock-Leighton dynamo solution computed following the model implementation of Charbonneau et al. (2005). The equatorward advection of the deep toroidal field by meridional circulation is here clearly apparent. Note also how the surface poloidal field first builds up at low latitudes, and is subsequently advected poleward and concentrated near the pole.

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Figure 18 shows N-hemisphere time-latitude diagrams for the toroidal magnetic field at the core-envelope interface (Panel A), and the surface radial field (Panel B), for a Babcock-Leighton dynamo solution now computed following the closely similar model implementation of Dikpati and Charbonneau (1999). Note how the polar radial field changes from negative (blue) to positive (red) at just about the time of peak positive toroidal field at the core-envelope interface; this is the phase relationship inferred from synoptic magnetograms (see, e.g., Figure 4 herein) as well as observations of polar faculae (see Sheeley Jr, 1991).

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Although it exhibits the desired equatorward propagation, the toroidal field butterfly diagram in Panel A of Figure 18 peaks at much higher latitude (∼ 45°) than the sunspot butterfly diagram (∼ 15° – 20°, cf. Figure 3). This occurs because this is a solution with high magnetic diffusivity contrast, where meridional circulation closes at the core-envelope interface, so that the latitudinal component of differential rotation dominates the production of the toroidal field, a situation that persists in models using more realistic differential profiles taken from helioseismic inversions (see Muñoz-Jaramillo et al., 2009). This difficulty can be alleviated by letting the meridional circulation penetrate below the core-envelope interface. Solutions with such flows are presented, e.g., in Dikpati and Charbonneau (1999) and Nandy and Choudhuri (2001, 2002). These latter authors have argued that this is in fact essential for a solar-like butterfly diagram to materialize, but this conclusion appears to be model-dependent at least to some degree (Guerrero and Muñoz, 2004; Guerrero and de Gouveia Dal Pino, 2007; Muñoz-Jaramillo et al., 2009). From the hydrodynamical standpoint, the boundary layer analysis of Gilman and Miesch (2004) (see also Rüdiger et al., 2005) indicates no significant penetration below the base of the convective envelope, although this conclusion has not gone unchallenged (see Garaud and Brummell, 2008), leaving the whole issue somewhat muddled at this juncture. The present-day observed solar abundances of Lithium and Beryllium restrict the penetration depth to r/R ≃ 0.62 (Charbonneau, 2007b), which is unfortunately too deep to pose very useful constraints on dynamo models, so that the final word will likely come from helioseismology, hopefully in the not too distant future.

A noteworthy property of this class of model is the dependency of the cycle period on model parameters; over a wide portion of parameter space, the meridional flow speed is found to be the primary determinant of the cycle period P. For example, in the Dikpati and Charbonneau (1999) model, this quantity is found to scale as

$$P = 56.8 u_0^{ - 0.89} s_0^{ - 0.13} \eta _T^{0.22} [yr].$$

(34)

This behavior arises because, in these models, the two source regions are spatially segregated, and the time required for circulation to carry the poloidal field generated at the surface down to the tachocline is what effectively sets the cycle period. The corresponding time delay introduced in the dynamo process has rich dynamical consequences, to be discussed in Section 5.4 below. The weak dependency of P on η_T and on the magnitude ..0 of the poloidal source term is very much unlike the behavior typically found in mean-field models, where both these parameters usually play a dominant role in setting the cycle period. The analysis of Hathaway et al. (2003) supports the idea that the solar cycle period is indeed set by the meridional flow speed (but do see Schmitt and Schüssler, 2004, for an opposing viewpoint). As demonstrated by Jouve et al. (2010), interesting constraints can also be obtained from the observed dependence of stellar cycle periods on rotation rates.

An interesting variation on the above model follows from the inclusion of turbulent pumping. With the expected downward pumping throughout the bulk of the convective envelope, and with a significant equatorward latitudinal component at low latitudes, the Babcock-Leighton mechanism can lead to dynamo action even if the meridional flow is constrained to the upper portion of the convective envelope. Downward turbulent pumping then links the two sources regions, and latitudinal pumping provides the needed equatorward concentration of the deep-seated toroidal component. An example taken from Guerrero and de Gouveia Dal Pino (2008) is shown in Figure 19. In this specific solution the circulation penetrates only down to r/R = 0.8, and the radial and latitudinal peak pumping speed are γ_r0 = 0.3 m s^-1 and γ_θ0 = 0.9 m s^-1, respectively.

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With downward turbulent pumping now the primary mechanism linking the surface and tachocline, the dynamo period loses sensitivity to the meridional flow speeds, and becomes set primary by the radial pumping speed. Indeed the dynamo solutions presented Guerrero and de Gouveia Dal Pino (2008) are found to obey a scaling law of the form

$$P = 181.2 u_0^{ - 0.12} \gamma _{r0}^{ - 0.51} \gamma _{\theta 0}^{ - 0.05} [yr],$$

(35)

over a fairly wide range of parameter values. The radial pumping speed γ_r0 emerges here as the primary determinant of the cycle period. Finally, one can note in Figure 19 that the surface magnetic field no longer shows the strong concentration in the polar region that usually characterizes Babcock-Leighton dynamo solutions operating in the advection-dominate regime. This can be traced primarily to the efficient downward turbulent pumping that subducts the poloidal field as it is carried poleward by the meridional flow.

4.9 Numerical simulations of solar dynamo action

Ultimately, the solar dynamo problem should be tackled as a (numerical) solution of the complete set of MHD partial differential equations in a rotating, stratified spherical domain undergoing thermally-driven turbulent convection in its outer 30% in radius. The first full-fledged attempts to do so go back some some thirty years, to the simulations of Gilman and Miller (1981); Gilman (1983); Glatzmaier (1985a,b). These epoch-making simulations did produce cyclic dynamo action and latitudinal migratory patterns suggestive of the dynamo waves of mean-field theory. However, the associated differential rotation profile turned out non-solar, as did the magnetic field’s spatio-temporal evolution. In retrospect this is perhaps not surprising, as limitations in computing resources forced these simulations to be carried out in a parameter regime far removed from solar interior conditions. Later simulations taking advantages of massively parallel computing architectures did managed to produce tolerably solar-like mean internal differential rotation (see, e.g., Miesch and Toomre, 2009, and references therein), as well as copious small-scale magnetic field, but failed to generate a spatially well-organized large-scale magnetic component (see Brun et al., 2004). Towards this end the inclusion of a stably stratified fluid layer below the convecting layers is now believed to be advantageous (although not strictly necessary, see Brown et al., 2010) as it allows the development of a tachocline-like shear layer where magnetic field produced within the convection zone can accumulate in response to turbulent pumping from above, and be further amplified by the rotational shear (see Browning et al., 2006, also Tobias et al., 2001, 2008, and references therein, for related behavior in local cartesian simulations).

Some of these simulations are now beginning to yield regular polarity reversals of the large-scale magnetic components. Figures 20 and 21 present some sample results taken from Ghizaru et al. (2010), see also Brown et al. (2009) and Käpylä et al. (2010). Figure 20 is an animation in Mollweide latitude-longitude projection of the toroidal magnetic component 0.02R_⊙ below the nominal interface between the convecting layers and underlying stable layers in one of these simulations. This toroidal component reaches some 2.5 kG here, and shows a very clear global antisymmetry about the equator, despite strong spatiotemporal fluctuations produced by convective undershoot. The cyclic variation of this large-scale field is quite apparent on the animation, with polarity reversals approximately synchronous across hemispheres.

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Figure 21A shows, for the same simulation as in Figure 20, a time-latitude diagram of the zonally-averaged toroidal component, now constructed at a depth corresponding to the core-envelope interface in the model. This is again assumed to be the simulation’s equivalent to the sunspot butterfly diagram. This simulation was run for 255 yr, in the course of which eight polarity reversals have taken place, with a mean (half-)period of about 30 yr. Note the tendency for equatorward migration of the toroidal flux structures, and the good long-term synchrony between the Northern and Southern hemispheres, persisting despite significant fluctuations in the amplitude and duration of cycles in each hemiphere. Figure 21B shows the corresponding time-evolution of the zonaly-averaged radial surface magnetic component, again in a time-latitude diagram. The surface field is characterized by a well-defined dipole moment aligned with the rotational axis, with transport of surface fields taking place from lower latitudes and (presumably) contributing to the reversal of the dipole moment. Compare these time-latitude diagrams to the sunspot butterfly diagram of Figure 3 and synoptic magnetogram of Figure 4, and reflect upon the similarities and differences.

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Although much remains to be investigated regarding the mode of dynamo action in these simulations, some encouraging links to mean-field theory (Section 3.2.1) do emerge. The fact that a positive toroidal component breeds here a positive dipole moment is what one would expect from a turbulent α-effect (more precisely, the α_φφ tensor component) positive in the Northern hemisphere. A posteriori calculation of the mean electromotive force \(\mathcal{E} = \left\langle {u' \times B'} \right\rangle\) does reveal a clear hemispheric pattern, with ε_φ having the same sign in both hemisphere, but changing sign from one cycle to the next, again consistent with the idea that the turbulent ..-effect is the primary source of the large-scale poloidal component. Likewise, having a well-defined axisymmetric dipolar component being sheared by an axisymmetric differential rotation is consistent with the buildup of a large-scale toroidal component antisymmetric about the equatorial plane.

On the other hand, calculation of the r and θ-components of the mean electromotive force indicates that the latter contributes to the production of the toroidal field at a level comparable to shearing of the poloidal component by differential rotation, suggestive of what, in mean-field electrodynamics parlance, is known as an α²Ω dynamo. Calculation of the α-tensor components also reveals that the latter do not undergo significant variations between maximal and minimal phases of the cycle, suggesting that α-quenching is not the primary amplitude-limiting mechanism in this specific simulation run. Although it would premature to claim that these simulations vindicate the predictions of mean-field theory, to the level at which they have been analyzed thus far, they do not appear to present outstanding departures from the mean-field Weltanschau.

5.1 The observational evidence: An overview

Hathaway (2010) offers a comprehensive review of the observational phenomenology of the solar cycle, as viewed through the sunspot number and other activity indicators; what follows is restricted to feature having most direct bearing on dynamo modeling. Panel A of Figure 22 shows a time series of the so-called Zürich sunspot numbers, starting in the mid-eighteenth century and extending to the present. The 11-year sunspot cycle is the most obvious feature of this time series, although the period of the underlying magnetic cycle is in fact twice that (sunspot counts being insensitive to magnetic polarity). Cycle-to-cycle variations in sunspot counts are usually taken to indicate a corresponding variation in the amplitude of the Sun’s dynamo-generated internal magnetic field. As reasonable as this may sound, it remains a working assumption; at this writing, the process via which the dynamo-generated mean magnetic field produces sunspot-forming concentrated flux ropes is not understood. One should certainly not take for granted that a difference by a factor of two in sunspot count indicates a corresponding variation by a factor of two in the strength of the internal magnetic field.

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At any rate, the notion of a nicely regular 11/22-year cycle does not hold long upon even cursory scrutiny, as the amplitude of successive cycles is clearly not constant, and their overall shape often differs significantly from one cycle to another (cf. cycles 14 and 15 in Panel A of Figure 22). Closer examination of Figure 22 also reveals that even the cycle’s duration is not uniform, spanning in fact a range going from 9 yr (cycle 2) to nearly 14 yr (cycles 4 and 23). These amplitude and duration variations are not a sunspot-specific artefact; similar variations are in fact observed in other activity proxies with extended records, most notably the 10.7 cm radio flux (Tapping, 1987), polar faculae counts (Sheeley Jr, 1991), and the cosmogenic radioisotopes ¹⁴C and ¹⁰Be (Beer et al., 1991; Beer, 2000).

Equally striking is the pronounced dearth of sunspots in the interval 1645 – 1715 (see Panel C of Figure 22); this is not due to lack of observational data (see Ribes and Nesme-Ribes, 1993; Hoyt and Schatten, 1996), but represents instead a phase of strongly suppressed activity now known as the Maunder Minimum (Eddy, 1976, 1983, and references therein). Evidence from cosmogenic radioisotopes indicates that similar periods of suppressed activity have taken place in ca. 1282 – 1342 (Wolf Minimum) and ca. 1416 – 1534 (Spörer Minimum), as well as a period of enhanced activity in ca. 1100 – 1250 (the Medieval Maximum), and have recurred irregularly over the more distant past (Usoskin, 2008).

The various incarnations of the sunspot number time series (monthly SSN, 13-month smoothed SSN, yearly SSN, etc.) are arguably the most intensely studied time series in astrophysics, as measured by the number of published research paper pages per data points. Various correlations and statistical trends have been sought in these datasets. Panels D and E of Figure 22 present two such classical trends. The “Waldmeier Rule”, illustrated in Panel D of Figure 22, refers to a statistically significant anticorrelation between cycle amplitude and rise time (linear correlation coefficient r = -0.68). A similar anticorrelation exists between cycle amplitude and duration, but is statistically more dubious (r = -0.37). The “Gnevyshev-Ohl” rule, illustrated in Panel E of Figure 22, refers to a marked tendency for odd (even) numbered cycles to have amplitudes above (below) their running mean (blue line in Panel E of Figure 22), a pattern that seems to have held true without interruption between cycles 9 and 21 (see also Mursula et al., 2001). For more on these empirical sunspot “Rules”, see Hathaway (2010).

A number of long-timescale modulations have also been extracted from these data, most notably the so-called Gleissberg cycle (period = 88 yr), but the length of the sunspot number record is insufficient to firmly establish the reality of these periodicities. One must bring into the picture additional solar cycle proxies, primarily cosmogenic radioisotopes, but difficulties in establishing absolute amplitudes of production rates introduce additional uncertainties into what is already a complex endeavour (for more on these matters, see Beer, 2000; Usoskin and Mursula, 2003). Likewise, the search for chaotic modulation in the sunspot number time series has produced a massive literature (see, e.g., Feynman and Gabriel, 1990; Mundt et al., 1991; Carbonell et al., 1994; Rozelot, 1995, and references therein), but without really yielding firm, statistically convincing conclusions, again due to the insufficient lengths of the datasets.

The aim in this section is to examine in some detail the types of fluctuations that can be produced in the various dynamo models discussed in the preceding section⁹. After going briefly over the potential consequences of fossil fields (Section 5.2), dynamical nonlinearities are first considered (Section 5.3), followed by time-delay effects (Section 5.4). We then turn to stochastic forcing (Section 5.5), which leads naturally to the issue of intermittency (Section 5.6).

5.2 Fossil fields and the 22-yr cycle

The presence of a large-scale, quasi-steady magnetic field of fossil origin in the solar interior has long been recognized as a possible explanation of the Gnevyshev-Ohl rule (Panel E of Figure 22). The basic idea is quite simple: The slowly-decaying, deep fossil field being effectively steady on solar cycle timescales, its superposition with the 11-yr polarity reversal of the overlying dynamogenerated field will lead to a 22-yr modulation, whereby the cycle is stronger when the fossil and dynamo field have the same polarity, and weaker when these polarities are opposite (see, e.g., Boyer and Levy, 1984; Boruta, 1996). The magnitude of the effect is directly related to the strength of the fossil field, versus that of the dynamo-generated magnetic field. All of this, however, presumes that flows and dynamical effects within the tachocline still allow “coupling” between the deep fossil field below, and the cyclic dynamo-generated field above. However, models of the solar tachocline taking into account its interaction with an underlying fossil field (see, e.g., Kitchatinov and Rüdiger, 2006) suggest that it is unlikely for this coupling to take place in the simple manner implicitly assumed in dynamo models, that typically incorporate the effect of fossil fields via the lower boundary condition (see also Dikpati et al., 2005).

One strong prediction is associated with this explanation of the Gnevyshev-Ohl rule: While the pattern may become occasionally lost due to large amplitude fluctuations of other origin, whenever it is present even-numbered cycles should always be of lower amplitudes and odd-numbered cycles of higher amplitude (under Wolf’s cycle numbering convention). Evidently, this prediction can be tested observationally, provided one can establish a measure of sunspot cycle amplitude that is truly characteristic of the strength of the underlying dynamo magnetic field. Taken at face value, the analysis of Mursula et al. (2001), based on cycle-integrated group sunspot numbers, indicates that the odd/even pattern has reversed between the time periods 1700 – 1800 and 1850 – 1990 (see their Figure 1). This would then rule out the fossil field hypothesis unless, as argued by some authors (see Usoskin et al., 2009a, and references therein), a sunspot cycle has been “lost” around 1790, at the onset of the so-called Dalton minimum.

5.3 Dynamical nonlinearity

5.3.1 Backreaction on large-scale flows

The dynamo-generated magnetic field will, in general, produce a Lorentz force that will tend to oppose the driving fluid motions. This is a basic physical effect that should be included in any dynamo model. It is not at all trivial to do so, however, since in a turbulent environment both the fluctuating and mean components of the magnetic field can affect both the large-scale flow components, as well as the small-scale turbulent flow providing the Reynolds stresses powering the large-scale flows. One can thus distinguish a number of (related) amplitude-limiting mechanisms:

• Lorentz force associated with the mean magnetic field directly affecting large-scale flow (sometimes called the “Malkus-Proctor effect”, after the groudbreaking numerical investigations of Malkus and Proctor, 1975).

• Large-scale magnetic field indirectly affecting large-scale flow via effects on small-scale turbulence and associated Reynolds stresses (sometimes called “Λ-quenching”, see, e.g., Kitchatinov and Rüdiger, 1993).

• Maxwell stresses associated with small-scale magnetic field directly affecting flows at all scales.

The α-quenching formulae introduced in Section 4.2.1 is a particularly simple — some would say simplistic — way to model the backreaction of the magnetic field on the turbulent fluid motions producing the α-effect¹⁰. In the context of solar cycle models, one could also expect the Lorentz force to reduce the amplitude of differential rotation until the effective dynamo number falls back to its critical value, at which point the dynamo again saturates¹¹. The third class of quenching mechanism listed above has not yet been investigated in detail, but numerical simulations of MHD turbulence indicate that the effects of the small-scale turbulent magnetic field on the α-effect can be profound (see Pouquet et al., 1976; Durney et al., 1993; Brandenburg, 2009; Cattaneo and Hughes, 2009).

Introducing magnetic backreaction on differential rotation is a tricky business, because one must then also, in principle, provide a model for the Reynolds stresses powering the large-scale flows in the solar convective envelope (see, e.g., Kitchatinov and Rüdiger, 1993), as well as a procedure for computing magnetic backreaction on these. This rapidly leads into the unyielding realm of MHD turbulence, although algebraic “Λ-quenching” formulae akin to α-quenching have been proposed based on specific turbulence models (see, e.g., Kitchatinov et al., 1994). Alternately, one can add an ad hoc source term to the right hand side of Equation (2), designed in such a way that in the absence of the magnetic field, the desired solar-like large-scale flow is obtained. As a variation on this theme, one can simply divide the large-scale flow into two components, the first (U) corresponding to some prescribed, steady profile, and the second (U′) to a time-dependent flow field driven by the Lorentz force (see, e.g., Tobias, 1997; Moss and Brooke, 2000; Thelen, 2000b):

$$u = U\left( x \right) + U'\left( {x,t,\left\langle B \right\rangle } \right),$$

(36)

with the (non-dimensional) governing equation for U′ including only the Lorentz force and a viscous dissipation term on its right hand side. If u amounts only to differential rotation, then B′ must obey a (nondimensional) differential equation of the form

$$\frac{{\partial U'}} {{\partial t}} = \frac{\Lambda } {{4\pi \rho }}\left( {\nabla \times \left\langle B \right\rangle } \right) \times \left\langle B \right\rangle + P_m \nabla ^2 U,$$

(37)

where time has been scaled according to the magnetic diffusion time \(\tau = R_ \odot ^2 /\eta _T\) as before. Two dimensionless parameters appear in Equation (37). The first (Λ) is a numerical parameter measuring the influence of the Lorentz force, and which can be set to unity without loss of generality (cf. Tobias, 1997; Phillips et al., 2002). The second, P_m = v/η, is the magnetic Prandtl number. It measures the relative importance of viscous and Ohmic dissipation. When P_m ≪ 1, large velocity amplitudes in U′ can be produced by the dynamo-generated mean magnetic field. This effectively introduces an additional, long timescale in the model, associated with the evolution of the magnetically-driven flow; the smaller P_m, the longer that timescale (cf. Figures 4 and 10 in Brooke et al., 1998).

The majority of studies published thus far and using this approach have only considered the nonlinear magnetic backreaction on differential rotation. This has been shown to lead to a variety of behaviors, including amplitude and parity modulation, periodic or aperiodic, as well as intermittency (more on the latter in Section 5.6).

Figure 23 shows two butterfly diagrams produced by the nonlinear mean-field interface model of Tobias 1997 (see also Beer et al., 1998; Bushby, 2006). The model is defined on a Cartesian slab with a reference differential rotation varying only with depth, and includes backreaction on the differential rotation according to the procedure described above. The model exhibits strong, quasi-periodic modulation of the basic cycle, leading to epochs of strongly reduced amplitude, with the modulation period controlled by the magnetic Prandtl number. Note how the dynamo can emerge from such epochs with strong hemispheric asymmetries (top panel), or with a different parity (bottom panel).

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It is not clear, at this writing, to what degree these behaviors are truly generic, as opposed to model-dependent. The analysis of Knobloch et al. (1998) suggests that generic behaviors do exist. On the other hand, a number of counterexamples have been published, showing that even in a qualitative sense, the nonlinear behavior can be strongly dependent on what one would have hoped to be minor modelling details (see, e.g., Moss and Brooke, 2000; Phillips et al., 2002).

The differential rotation can also be suppressed indirectly by magnetic backreaction on the small-scale turbulent flows that produce the Reynolds stresses driving the large-scale mean flow. Inclusion of this so-called “Λ-quenching” in mean-field dynamo models, alone or in conjunction with other amplitude-limiting nonlinearities, has also been shown to lead to a variety of periodic and aperiodic amplitude modulations, provided the magnetic Prandtl number is small (see Küker et al., 1999; Pipin, 1999; Rempel, 2006b). This type of models stand or fall with the turbulence model used to compute the various mean-field coefficients, and it is not yet clear which aspects of the results are truly generic to Λ-quenching. Gizon and Rempel (2008) do show that information is present in subsurface measurements of the time-varying component of large-scale flows, which can be used to constrain the Λ-effect and its cycle-related variations.

To date, dynamical backreaction on large-scale flows has only been studied in detail in the context of dynamo models based on mean-field electrodynamics. Equivalent studies must be carried out in the other classes of solar cycle models discussed in Section 4. In particular, it is essential to model the effect of the Lorentz force on meridional circulation in models based on the Babcock- Leighton mechanism and/or hydrodynamical instabilities in the tachocline, since in these models the circulation is the primary determinant of the cycle period and enforces equatorward propagation in the butterfly diagram.

5.4 Time-delay dynamics

The introduction of ad hoc time-delays in dynamo models is long known to lead to pronounced cycle amplitude fluctuations (see, e.g., Yoshimura, 1978). Models including nonlinear backreaction on differential rotation can also exhibit what essentially amounts to time-delay dynamics in the low Prandtl number regime, with the large-scale flow perturbations lagging behind the Lorentz force because of inertial effects. Finally, time-delay effects can arise in dynamo models where the source regions for the poloidal and toroidal magnetic components are spatially segregated. This is a type of time delay we now turn to, in the context of dynamo models based on the Babcock-Leighton mechanism.

5.4.2 Reduction to an iterative map

The long time delay inherent in B-L models of the solar cycle allows a formulation of cycle-tocycle amplitude variations in terms of a simple one-dimensional iterative map (Durney, 2000; Charbonneau, 2001). Working in the kinematic regime, neglecting resistive dissipation, and in view of the conveyor belt argument of Section 4.8, the toroidal field strength T_n+1 at cycle n + 1 is assumed to be linearly proportional to the poloidal field strength P_n of cycle n, i.e.,

$$T_{n + 1} = aP_n .$$

(38)

Now, because flux eruption is a fast process, the strength of the poloidal field at cycle n + 1 is (nonlinearly) proportional to the toroidal field strength of the current cycle:

$$P_{n + 1} = f\left( {T_{n + 1} } \right)T_{n + 1} .$$

(39)

Here the “Babcock-Leighton” function ƒ(T_n+1) measures the efficiency of surface poloidal field production from the deep-seated toroidal field. Substitution of Equation (38) into Equation (39) leads immediately to a one-dimensional iterative map,

$$p_{n + 1} = \alpha f\left( {p_n } \right)p_n ,$$

(40)

where the p_n’s are normalized amplitudes, and the normalization constants as well as the constant a in Equation (38) have been absorbed into the definition of the map’s parameter a, here operationally equivalent to a dynamo number (see Charbonneau, 2001). We consider here the following nonlinear function,

$$f\left( p \right) = \frac{1} {4}\left[ {1 + erf\left( {\frac{{p - p_1 }} {{w_1 }}} \right)} \right]\left[ {1 - erf\left( {\frac{{p - p_2 }} {{w_2 }}} \right)} \right],$$

(41)

with p₁ = 0.6, w₁ = 0.2, p₂ = 1.0, and w₂ = 0.8. This catches an essential feature of the B-L mechanism, namely the fact that it can only operate in a finite range of toroidal field strength.

A bifurcation diagram for the resulting iterative map is presented in Panel A of Figure 24. For a given value of the map parameter α, the diagram gives the locus of the amplitude iterate p_n for successive n values. The “critical dynamo number” above which dynamo action becomes possible, is here α = 0.851 (p_n = 0 for smaller α values). For 0.851 ≤ α ≤ 1.283, the iterate is stable at some finite value of p_n, which increases gradually with α. This corresponds to a constant amplitude cycle. As α reaches 1.283, period doubling occurs, with the iterate p_n alternating between high and low values (e.g., p_n = 0.93 and p_n = 1.41 at α = 1.4). Further period doubling occurs at α = 1.488, then at α = 1.531, then again at α = 1.541, and ever faster until a point is reached beyond which the amplitude iterate seems to vary without any obvious pattern (although within a bounded range); this is in fact a chaotic regime.

As in any other dynamo model where the source regions for the poloidal and toroidal magnetic field components are spatially segregated, the type of time delay considered here is unavoidable. The B-L model is just a particularly clear-cut example of such a situation. One is then led to anticipate that the map’s rich dynamical behavior should find its counterpart in the original, arguably more realistic spatially-extended, diffusive axisymmetric model that inspired the map formulation. Remarkably, this is indeed the case.

Panel B of Figure 24 shows a bifurcation diagram, conceptually equivalent to that shown in Panel A, but now constructed from a sequence of numerical solutions of the Babcock-Leighton model of Charbonneau et al. (2005), for increasing values of the dynamo number. Time series of magnetic energy were calculated from the numerical solutions, and successive peaks found and plotted for each individual solution. The sequence of period doubling, eventually leading to a chaotic regime, is strikingly similar to the bifurcation diagram constructed from the corresponding iterative map, down to the narrow multiperiodic windows interspersed in the chaotic domain. This demonstrates that time delay effects are a robust feature, and represent a very powerful source of cycle amplitude fluctuation in Babcock-Leighton models, even in the kinematic regime (for further discussion see Charbonneau, 2001; Charbonneau et al., 2005; Wilmot-Smith et al., 2006).

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5.5 Stochastic forcing

Another means of producing amplitude fluctuations in dynamo models is to introduce stochastic forcing in the governing equations. Sources of stochastic “noise” certainly abound in the solar interior; large-scale flows in the convective envelope, such as differential rotation and meridional circulation, are observed to fluctuate, an unavoidable consequence of dynamical forcing by the surrounding, vigorous turbulent flow. Ample observational evidence now exists that a substantial portion of the Sun’s surface magnetic flux is continuously being reprocessed on a timescale commensurate with convective motions (see Schrijver et al., 1997; Hagenaar et al., 2003). The culprit is most likely the generation of small-scale magnetic fields by these turbulent fluid motions (see, e.g., Cattaneo, 1999; Cattaneo et al., 2003, and references therein). This amounts to a form of zero-mean “noise” superimposed on the slowly-evolving mean magnetic field. In addition, the azimuthal averaging implicit in all models of the solar cycle considered above will yield dynamo coefficients showing significant deviations about their mean values, as a consequence of the spatiotemporally discrete nature of the physical events (e.g., cyclonic updrafts, sunspot emergences, flux rope destabilizations, etc.) whose collective effects add up to produce a mean azimuthal electromotive force.

The (relative) geometrical and dynamical simplicity of the various types of dynamo models considered earlir severely restricts the manner in which such stochastic effects can be modeled. Perhaps the most straightforward is to let the dynamo number fluctuate randomly in time about some preset mean value. By most statistical estimates, the expected magnitude of these fluctuations is quite large, i.e., many times the mean value (Hoyng, 1988, 1993), a conclusion also supported by numerical simulations (see, e.g., Otmianowska-Mazur et al., 1997; Ossendrijver et al., 2001). One typically also introduces a coherence time during which the dynamo number retains a fixed value. At the end of this time interval, this value is randomly readjusted. Depending on the dynamo model at hand, the coherence time can be physically related to the lifetime of convective eddies (α-effect-based mean-field models), to the decay time of sunspots (Babcock-Leighton models), or to the growth rate of instabilities (hydrodynamical shear or buoyant MHD instability-based models).

Figure 25 shows some representative results for an αΩ dynamo solutions including meridional circulation and operating in the advection-dominated regime, similar to that of Figure 11, with imposed stochastic fluctuation at the ± 100% level in C_α, and coherence time amounting to 5% of the cycle period in the deterministic parent solution. The red curve is the total magnetic energy in the solution domain, used here as a measure of cycle amplitude and proxy for the sunspot number. The green curve is the absolute value of the N-hemisphere surface polar field strength. Perhaps the most striking feature of these curves is the fact that even with a coherence time much smaller than the cycle period, zero-mean stochastic forcing can induce patterns of amplitude modulation with characteristic timescales spanning many cycles (e.g., at 0.01 ≤ t/τ ≤ 0.11 and 0.49 ≤ t/τ ≤ 0.62 in Figure 25A). This can be traced to the buildup of strong magnetic fields in the low-diffusivity layers underlying the convective envelope.

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Stochastic forcing of the dynamo number can also produce a significant spread in cycle period, although in the model run used to produce Figure 25 the very weak positive correlation between cycle amplitude and rise time is anti-solar (the Waldmeier rule has r = -0.68, based on smoothed monthly SSN, cf. Figure 22D), and the positive correlation between rise time and cycle duration (r = +0.27, not shown) is comparable to solar (r = +0.4). It must be kept in mind that these inferences are all predicated on the use of total magnetic energy as a SSN proxy; different choices can lead to varying degrees of correlation.

The effect of noise has been investigated in most detail in the context of classical mean-field models (see Choudhuri, 1992; Hoyng, 1993; Ossendrijver and Hoyng, 1996; Ossendrijver et al., 1996; Mininni and Gómez, 2002, 2004; Moss et al., 2008). A particularly interesting consequence of random variations of the dynamo number, in mean-field models at or very close to criticality, is the coupling of the cycle’s duration and amplitude (Hoyng, 1993; Ossendrijver and Hoyng, 1996; Ossendrijver et al., 1996), leading to a pronounced anticorrelation between these two quantities that is reminiscent of the Waldmeier Rule (cf. Panel D of Figure 22), and hard to produce by purely nonlinear effects (cf. Ossendrijver and Hoyng, 1996). However, this behavior does not carry over to the supercritical regime, so it is not clear whether this can indeed be accepted as a robust explanation of the observed amplitude-duration anticorrelation. In the supercritical regime, α-quenched mean-field models are less sensitive to noise (Choudhuri, 1992), unless of course they happen to operate close to a bifurcation point, in which case large amplitude and/or parity fluctuations can be produced (see, e.g., Moss et al., 1992).

In the context of Babcock-Leighton models, introducing stochastic forcing of the dynamo numbers leads to amplitude fluctuation patterns qualitatively similar to those plotted in Figure 25: long timescale amplitude modulation, spread in cycle period, (non-solar) positive correlations between cycle amplitude and rise time, and (solar-like) positive correlation between duration and rise time, with the interesting addition that in some model formulations cycle-to-cycle amplitude variation patterns reminiscent of the Gnevyshev-Ohl Rule are also produced (see Charbonneau et al., 2007). Charbonneau and Dikpati (2000) have presented a series of dynamo simulations including stochastic fluctuations in the dynamo number as well as in the meridional circulation. Working in the supercritical regime with a form of algebraic α-quenching as the sole amplitude-limiting nonlinearity, they succeed in producing a solar-like weak anticorrelation between cycle amplitude and duration for fluctuations in the dynamo numbers in excess of 200% of its mean value, with coherence time of one month. However, these encouraging results did not prove very robust across the model’s parameter space.

A different approach is followed by Passos and Lopes (2008) and Lopes and Passos (2009), who used a low-order dynamo model resulting from truncation of the 2D axisymmetric mean-field dynamo equations, with flux loss due to magnetic buoyancy as the amplitude-limiting nonlinearity. Fitting equilibrium solutions to their low-order model to the smoothed SSN time series, one magnetic cycle at a time (Figure 26A), they can plausibly interpret variations in their fitting parameters as being due to systematic, persistent variations of the meridional flow speed on decadal timescales (Figure 26B). They then input these variations in the kinematic axisymmetric Babcock-Leighton model of Chatterjee et al. (2004), conceptually similar to that described in Section 4.8 but replacing the nonlinearity on the poloidal source term by a threshold function for magnetic flux loss through magnetic buoyancy. The resulting SSN-proxy time series reconstructed in this manner shows some remarkable similarities to the true SSN time series, including an epoch of strongly reduced cycle amplitude in the opening decades of the nineteenth century, and secular rise of cycle amplitudes from the mid-nineteenth to the mid-twentieth century (Figure 26C). This suggests that relatively small but persistent changes in the meridional flow, at the ∼ 5 – 30% level, could account for much of the variation in amplitude and duration observed in the solar cycle, and possibly even Grand Minima of activity (see Passos and Lopes, 2009), the topic to which we now turn.

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5.6 Intermittency

5.6.4 Intermittency from threshold effects

Intermittency can also arise naturally in dynamo models characterized by a lower operating threshold on the magnetic field. These include models where the regeneration of the poloidal field takes place via the MHD instability of toroidal flux tubes (Sections 4.7 and 3.2.3). In such models, the transition from quiescent to active phases requires an external mechanism to push the field strength back above threshold. This can be stochastic noise (see, e.g., Schmitt et al., 1996), or a secondary dynamo process normally overpowered by the “primary” dynamo during active phases (see Ossendrijver, 2000a). Figure 27 shows one representative solution of the latter variety, where intermittency is driven by a weak α-effect-based kinematic dynamo operating in the convective envelope, in conjunction with magnetic flux injection into the underlying region of primary dynamo action by randomly positioned downflows (see Ossendrijver, 2000a, for further details). The top panel shows a sample trace of the toroidal field, and the bottom panel a butterfly diagram constructed near the core-envelope interface in the model.

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The model does produce irregularly-spaced quiescent phases, as well as an occasional “failed minimum” (e.g., at t ≃ 11), in qualitative agreement with the solar record. Note here how the onset of a Grand Minimum is preceded by a gradual decrease in the cycle’s amplitude, while recovery to normal, cyclic behavior is quite abrupt. The fluctuating behavior of this promising class of dynamo models clearly requires further investigation.

5.6.5 Intermittency from time delays

Dynamo models exhibiting amplitude modulation through time-delay effects are also liable to show intermittency in the presence of stochastic noise. This was demonstrated in Charbonneau (2001) in the context of Babcock-Leighton models, using the iterative map formalism described in Section 5.4.2. The intermittency mechanism hinges on the fact that the map’s attractor has a finite basin of attraction (indicated by gray shading in Panel A of Figure 24). Stochastic noise acting simultaneously with the map’s dynamics can then knock the solution out of this basin of attraction, which then leads to a collapse onto the trivial solution p_n = 0, even if the map parameter remains supercritical. Stochastic noise eventually knocks the solution back into the attractor’s basin, which signals the onset of a new active phase (see Charbonneau, 2001, for details).

A corresponding behavior was subsequently found in a spatially-extended model similar to that described in Section 4.8 (see Charbonneau et al., 2004). Figure 28 shows one such representative solution, in the same format as Figure 27. This is a dynamo solution which, in the absence of noise, operates in the singly-periodic regime. Stochastic noise is added to the vector potential Aê_φ in the outermost layers, and the dynamo number is also allowed to fluctuate randomly about a pre-set mean value. The resulting solution exhibits both amplitude fluctuations and intermittency.

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With its strong polar branch often characteristic of dynamo models with meridional circulation, Figure 28 is not a particularly good fit to the solar butterfly diagram, yet its fluctuating behavior is solar-like in a number of ways, including epochs of alternating higher-than-average and lower-than-average cycle amplitudes (the Gnevyshev-Ohl rule, cf. Panel E of Figure 22), and residual pseudocyclic variations during quiescent phases, as suggested by ¹⁰Be data, cf. Panel B of Figure 22. This later property is due at least in part to meridional circulation, which continues to advect the (decaying) magnetic field after the dynamo has fallen below threshold (see Charbonneau et al., 2004, for further discussion). Note also in Figure 28 how the onset of Grand Minima is quite sudden, while recovery to normal activity is more gradual, which is the opposite behavior to the Grand Minima in Figure 27.

5.7 Solar cycle predictions based on dynamo models

The idea that measurements of the solar surface magnetic field in the descending phase of a cycle can be used to forecast the amplitude (and/or timing) of the next cycle goes back many decades, but it is Schatten et al. (1978) who explicitly justified this procedure on the basis of dynamo models, which led to a wide variety of dynamo-inspired precursor schemes (see Hathaway et al., 1999, for a review).

This dynamo logic has recently been pushed further, by using dynamo models to actually advance in time measurements of the solar surface magnetic field in order to produce a cycle forecast. This approach is justified if the surface magnetic field is indeed a significant source of the poloidal field to be sheared into a toroidal component in the upcoming cycle, so that using this approach to forecasting already amounts to a strong assumption on the mode of solar dynamo action. In the stochastically-forced flux-transport αΩ dynamo solution of Figure 25, a strong correlation materializes between the peak polar field at cycle minimum, and amplitude of the subsequent cycle (see panel C). This occurs because in this model the surface polar field is advected down by the meridional flow to the dynamo source region at the base of the convection, and ends up feeding back into the dynamo loop. In other types of dynamo models where this feedback of the surface field does not occur, no such correlation materializes. For more on these matters see Charbonneau and Barlet (2010).

It is particularly instructive to compare and contrast the forecast schemes (and cycle 24 predictions) of Dikpati et al. 2006 (see also Dikpati and Gilman, 2006) and Choudhuri et al. 2007 (see also Jiang et al., 2007). Both groups use a dynamo model of the Babcock-Leighton variety (Section 4.8), in conjunction with input of solar magnetic field observations in a manner often (and incorrectly) described as “data assimilation”. The model parameters are adjusted to reproduce the known amplitudes of previous sunspot cycles, and the model is then integrated forward in time beyond this calibration interval to provide a forecast.

Table 1 details the various modelling components associated with each forecasting scheme. Both are remarkably similar, differing at the level of what one would usually consider modelling details, and do about as well at reproducing amplitude of past cycles over their respective calibration intervals. Yet, they end up producing cycle 24 amplitude forecasts that stand at opposite ends of the very wide range of cycle 24 forecasts produced by other techniques. A cycle 24 with SSN = 80 would place it amongst the weakest of the past century (cycles 14 and 16), while SSN = 180 would rank it on par with the two highest cycle amplitude on record (cycles 4 and 19; see Figure 22).

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Much criticism has been leveled at these dynamo model-based cycle forecasting schemes, and sometimes unfairly so. To dismiss the whole idea on the grounds that the solar dynamo is a chaotic system is likely too extreme a stance, especially since (1) even chaotic systems can be amenable to prediction over a finite temporal window, and (2) input of data (even if not via true data assimilation) can in principle lead to some correction of the system’s trajectory in phase space. More relevant (in my opinion) has been the explicit demonstration that (1) very small changes in some unobservable and poorly constrained input parameters to the dynamo model used for the forecast can introduce significant errors already for next-cycle amplitude forecasts (see Bushby and Tobias, 2007, also Yeates et al., 2008); (2) the exact manner in which surface data drives the model can have a huge impact on the forecasting skill (Cameron and Schüssler, 2007). Consequently, the discrepant forecasts of Table 1 indicate mostly that current dynamo model-based predictive schemes still lack robustness. True data assimilation has been carried out using highly simplified dynamo models (Kitiashvili and Kosovichev, 2008), and clearly this must be carried over to more realistic dynamo models.

Finally, one must also keep in mind that other plausible explanations exist for the relatively good precursor potential of the solar surface magnetic field. In particular, Cameron and Schüssler (2008) have argued that the well-known spatiotemporal overlap of cycles in the butterfly diagram (see Figure 3), taken in conjunction with the empirical anticorrelation between cycle amplitude and rise time embodied in the Waldmeier Rule (Figure 22D; also Hathaway, 2010, Section 4.6), could in itself explain the precursor performance of the polar field strength at solar activity minimum. Given the unusually extended minimum phase between cycles 23 and 24, it will be very interesting to revisit all these model results once cycle 24 reaches its peak amplitude.

6.1 What is the primary poloidal field regeneration mechanism?

Given the amount of effort having gone into building detailed dynamo models of the solar cycle, it is quite sobering to reflect upon the fact that the physical mechanism responsible for the regeneration of the poloidal component of the solar magnetic field has not yet been identified with confidence. As discussed at some length in Section 4, current models relying on distinct mechanisms all have their strengths and weaknesses, in terms of physical underpinning as well as comparison with observations.

Something akin to the α-effect of mean-field electrodynamics has been measured in a number of local and global numerical simulations including rotation and stratification, so this certainly remains a favored magnetic field generation mechanism. Modelling of the evolution of the Sun’s surface magnetic flux has abundantly confirmed that the Babcock-Leighton mechanism is operating on the Sun, in the sense that magnetic flux liberated by the decay of tilted bipolar active regions does accumulate in the polar regions, where it triggers polarity reversal of the poloidal component (see Wang and Sheeley Jr, 1991; Schrijver et al., 2002; Wang et al., 2002; Baumann et al., 2004, and references therein). The key question is whether this is an active component of the dynamo cycle, or a mere side-effect of active region decay. Likewise, the buoyant instability of magnetic flux tubes (Section 4.7) is, in some sense, unavoidable; here again the question is whether or not the associated azimuthal mean electromotive force contributes significantly to dynamo action in the Sun.

6.2 What limits the amplitude of the solar magnetic field?

The amplitude of the dynamo-generated magnetic field is almost certainly restricted by the backreaction of Lorentz forces on the driving fluid motions. However, as outlined in Section 5.3.1, this backreaction can occur in many ways.

Helioseismology has revealed only small variations of the differential rotation profile in the course of the solar cycle. The observed variations amount primarily to an extension in depth of the pattern of low-amplitude torsional oscillations long known from surface Doppler measurements (but see also Basu and Antia, 2001; Toomre et al., 2003; Howe, 2009). Taken at face value, these results suggest that quenching of differential rotation is not the primary amplitude-limiting mechanism, unless the dynamo is operating very close to criticality. Once again the hope is that in the not-too-distant future, helioseismology will have mapped accurately enough cycle-induced variations of differential rotation in the convective envelope and tachocline, to settle this issue.

Algebraic quenching of the α-effect (or α-effect-like source terms) is the mechanism most often incorporated in dynamo models. However, this state of affairs usually has much more to do with computational convenience than commitment to a specific physical quenching mechanism. There is little doubt that the α-effect will be affected once the mean magnetic field reaches equipartition; the critical question is whether it becomes quenched long before that, for example by the small-scale component of the magnetic field. The issue hinges on helicity conservation and flux through boundaries, and subtleties of flow-field interaction in MHD turbulence. For recent entry points into this very active area of current research, see Cattaneo and Hughes (1996), Blackman and Field (2000), Brandenburg and Dobler (2001), and Brandenburg (2009).

Flux loss through magnetic buoyancy is the primary reason why most contemporary dynamo models of the solar cycle rely on the rotational shear in the tachocline to achieve toroidal field amplification. If the dynamo were to reside entirely in the convective envelope, then this would be an important, perhaps even dominant, amplitude limiting mechanism (see Schmitt and Schüssler, 1989; Moss et al., 1990). If, on the other hand, toroidal field amplification takes place primarily at or beneath the core-envelope interface, then it is less clear whether or not this mechanism plays a dominant role. In fact, it may even be that rising flux ropes amplify the deep-seated magnetic field, as nicely demonstrated by the numerical calculations of Rempel and Schüssler (2001). Magnetic flux loss through buoyancy can also have a large impact on the cycle period (see, e.g. Kitchatinov et al., 2000), and the model calculations of Lopes and Passos (2009) indicate that combined with fluctuations in the meridional flow speed, very solar-like cycle amplitude variations can be produced. The impact of this amplitude limiting mechanism clearly requires further investigation.

6.3 Flux tubes versus diffuse fields

The foregoing discussion has implicitly assumed that the dynamo process produces a mean, large-scale magnetic field that then concentrates itself into the flux ropes that subsequently give rise to sunspots. High-resolution observations of the photospheric magnetic field show that even outside of sunspots, the field is concentrated in flux tubes (see, e.g., Parker, 1982, and references therein), presumably as a consequence of convective collapse of magnetic flux concentrations too weak to block convection and form sunspots. In this picture, which is basically the framework of all dynamo models discussed above, the mean magnetic field is the dominant player in the cycle.

An alternate viewpoint is to assume that the solar magnetic field is a fibril state from beginning to end, throughout the convection zone and tachocline, and that whatever large-scale field there may be in the photosphere is a mere by-product of the decay of sunspots and other flux tube-like small-scale magnetic structures. The challenge is then to devise a dynamo process that operates entirely on flux tubes, rather than on a diffuse mean field. Some exploratory calculations have been made (e.g., DeLuca et al., 1993; Schatten, 2009), but this intriguing question has received far less attention than it deserves.

6.4 How constraining is the sunspot butterfly diagram?

The shape of the sunspot butterfly diagram (see Figure 3) continues to play a dominant constraining role in many dynamo models of the solar cycle. Yet caution is in order on this front. Calculations of the stability of toroidal flux ropes stored in the overshoot region immediately beneath the core-envelope interface indicate that instability is much harder to produce at high latitudes, primarily because of the stabilizing effect of the magnetic tension force; thus strong fields at high latitudes may well be there, but not produce sunspots. Likewise, the process of flux rope formation from the dynamo-generated mean magnetic field is currently not understood. Are flux ropes forming preferentially in regions of most intense magnetic fields, in regions of strongest magnetic helicity, or in regions of strongest hydrodynamical shear? Is a stronger diffuse toroidal field forming more strongly magnetized flux ropes, or a larger number of flux ropes always of the same strength?

These are all crucial questions from the point of view of comparing results from dynamo models to sunspot data. Until they have been answered, uncertainty remains as to the degree to which the sunspot butterfly diagram can be compared in all details to time-latitude diagrams of the toroidal field, as produced by this or that dynamo model.

6.5 Is meridional circulation crucial?

The main question regarding meridional circulation is not whether it is there or not, but rather what role it plays in the solar cycle. The answer hinges on the value of the turbulent diffusivity η_T, which is notoriously difficult to estimate with confidence. It is probably essential in mean-field and mean-field-like dynamo models characterized by positive α-effects in the Northern hemisphere, in order to ensure equatorward transport of the sunspot-forming, deep-seated toroidal magnetic field (see Sections 4.4, 4.5, and 4.7), unless the latitudinal turbulent pumping speeds turn out significantly larger than currently estimated (Käpylä et al., 2006a). It also appears to be a major determinant in the evolution of the surface magnetic field in the course of the solar cycle. Something like it is certainly needed in dynamo models based on the Babcock-Leighton mechanism, to carry the poloidal field generated at the surface down to the tachocline, where production of the toroidal field is taking place (see Section 4.8).

The primary unknown at this writing is the degree to which meridional circulation is affected by the Lorentz force associated with the dynamo-generated magnetic field. Recent calculations (Rempel, 2006a,b) suggest that the backreaction is limited to regions of strongest toroidal fields, so that the “conveyor belt” is still operating in the bulk of the convective envelope, but this issue requires further study. Another important related issue is the advective role of turbulent pumping, which may well compete and/or complement the advective effect of the meridional flow.

6.6 Is the mean solar magnetic field really axisymmetric?

While the large-scale solar magnetic field is axisymmetric about the Sun’s rotation axis to a good first approximation, various lines of observational evidence point to a persistent, low-level non-axisymmetric component; such evidence includes the so-called active longitudes (see Henney and Harvey, 2002, and references therein), rotationally-based periodicity in cycle-related eruptive phenomena (Bai, 1987), and the shape of the white-light corona in the descending phase of the cycle.

Various mean-field-based dynamo models are known to support non-axisymmetric modes over a substantial portion of their parameter space (see, e.g., Moss et al., 1991; Moss, 1999; Bigazzi and Ruzmaikin, 2004, and references therein). At high Rm, strong differential rotation (in the sense that C_Ω ≪ C_α) is known to favor axisymmetric modes, because it efficiently destroys any non-axisymmetric component on a timescale much faster than diffusive (∝ Rm1/3 at high Rm, instead of ∝ Rm). Although it is not entirely clear that the Sun’s differential rotation is strong enough to place it in this regime (see, e.g., Rüdiger and Elstner, 1994), some 3D models do show this symmetrizing effect of differential rotation (see, e.g., Zhang et al., 2003a). Likewise, the recent numerical 3D MHD simulations of solar-like cycles by Ghizaru et al. (2010) do produce a large-scale magnetic field with a dominant axisymmetric component. These types of simulations will probably offer the best handle on this question.

6.7 What causes Maunder-type Grand Minima?

The origin of Grand Minima in solar activity also remains a question subjected to intense scrutiny. Broadly speaking, Grand Minima can occur either through amplitude modulation of a basic underlying dynamo cycle, or through intermittency. In this latter case, the transition from one state to another can take place via the system’s internal dynamics, or through the influence of external stochastic noise, or both. Not surprisingly, a large number of plausible Grand Minima models can now be found in the extant literature (cf. Section 5.6).

Historical researches have shown that the Sun climbed out of the Maunder Minimum gradually, and showing strongly asymmetric activity, with nearly all sunspots observed between 1670 and 1715 located in the Southern solar hemisphere (see Ribes and Nesme-Ribes, 1993). Some historical reconstructions of the butterfly diagram in the pre-photographic era also suggest the presence of what could be interpreted as a quadrupolar component (Arlt, 2009). These are the kind of pattern that can be readily produced by nonlinear parity modulation (cf. Figure 23 herein; see also Beer et al., 1998; Sokoloff and Nesme-Ribes, 1994; Usoskin et al., 2009b). Then again, in the context of an intermittency-based model, it is quite conceivable that one hemisphere can pull out of a quiescent epoch before the other, thus yielding sunspot distributions compatible with the aforecited observations in the late Maunder Minimum. Such scenarios, relying on cross-hemispheric coupling, have hardly begun to be explored (Charbonneau, 2005, 2007a; Chatterjee and Choudhuri, 2006).

Another possible avenue for distinguishing between these various scenarios is the persistence of the primary cycle’s phase through Grand Minima. Generally speaking, models relying on amplitude modulation can be expected to exhibit good phase persistence across such minima, because the same basic cycle is operating at all times (cf. Figure 23). Intermittency, on the other hand, should not necessarily lead to phase persistence, since the active and quiescent phases are governed by distinct dynamics. One can but hope that careful analysis of cosmogenic radioisotope data may soon indicate the degree to which the solar cycle’s phase persisted through the Maunder, Spörer, and Wolf Grand Minima, in order to narrow down the range of possibilities.

6.8 Where do we go from here?

Recent years have witnessed a number of significant advances in solar cycle modelling. Local magnetohydrodynamical simulations of thermally-driven convection have now allowed measurements of the α-tensor, and of its variation with depth and latitude in the solar interior, and with rotation rate; and global magnetohydrodynamical simulation of solar convection are now producing large-scale magnetic fields, in some cases even undergoing polarity reversals on decadal timescales. Such simulations are ideally suited for investigating a number of important issues, such as the mechanism(s) responsible for regulating the amplitude of the solar cycle, the magnetically-driven temporal variations of the large-scale flows important for the solar cycle, and the possible impact of a cycling large-scale magnetic field on convective energy transport, to mention but a few.

Despites continuing advances in computing power, global MHD simulations remain extremely demanding, and proper capture of important solar cycle elements — most notably the formation, emergence and surface decay sunspots and active regions— are certainly not forthcoming. Nonetheless, comparison between cyclic solutions arising in full numerical simulations and those characterizing simpler mean-field-like models should also allow to test the validity limit of the kinematic approximation and of the simple algebraic amplitude-limiting nonlinearities still so prevalent in the latter class of solar cycle models. It appears likely that in the foreseeable future, the simpler, mean-field and mean-field-like solar cycle models reviewed here will remain the workhorses of research on long timescale phenomena such as grand activity minima and maxima, on the evolution of surface magnetic flux, on dynamo-model-based solar cycle prediction, and on the modelling and interpretation of stellar activity cycles.