The term “intermittency” was originally coined to characterize signals measured in turbulent fluids, but has now come to refer more generally to systems undergoing apparently random, rapid switching from quiescent to bursting behaviors, as measured by the magnitude of some suitable system variable (see, e.g., Platt et al., 1993). Intermittency thus requires at least two distinct dynamical states available to the system, and a means of transiting from one to the other.
In the context of solar cycle model, intermittency refers to the existence of quiescent epochs of strongly suppressed activity randomly interspersed within periods of “normal” cyclic activity. Observationally, the Maunder Minimum is usually taken as the exemplar for such quiescent epochs. It should be noted, however, that dearth of sunspots does not necessarily mean a halted cycle; as noted earlier, flux ropes of strengths inferior to will not survive their rise through the convective envelope, and the process of flux rope formation from the dynamo-generated mean magnetic field may itself be subjected to a threshold in field strength. The same basic magnetic cycle may well have continued unabated all the way through the Maunder Minimum, but at an amplitude just below one of these thresholds. This idea finds support in the radioisotope record, which shows a clear and uninterrupted cyclic signal through the Maunder Minimum (see Panels B and C of Figure 19; also Beer et al., 1998). Strictly speaking, thresholding a variable controlled by a single dynamical state subject to amplitude modulation is not intermittency, although the resulting time series for the variable may well look quite intermittent.
Ossendrijver and Covas (2003) list 21 papers reporting empirical observation of intermittency-like behavior in one or another dynamo model, a bibliographical list that would not be particularly useful to replicate here. Research is also underway to categorize the observed behavior in terms of the various types of intermittency known to characterize dynamical systems (see, e.g., Covas and Tavakol, 1999; Brooke et al., 2002; Ossendrijver and Covas, 2003). In what follows, we attempt to pin down the physical origin of intermittent behavior in the various types of solar cycle models discussed earlier.
Intermittency has been shown to occur through stochastic fluctuations of the dynamo number in mean-field dynamo models operating at criticality (see, e.g., Hoyng, 1993). Such models also exhibit a solar-like anticorrelation between cycle amplitude and phase. This mechanism for “on-off intermittency” is not as gratuitous a procedure as it may first appear. As argued in Section 5.5, important stochastic fluctuations are indeed likely in the turbulent environment in which the solar dynamo operates. However there is no strong reason to believe that the solar dynamo is running just at criticality, so that it is not clear how good an explanation this is of Maunder-type Grand Minima.
Mininni and Gómez (2004) have presented a stochastically-forced 1D (in latitude) mean-field model, including algebraic -quenching as the amplitude-limiting nonlinearity, that exhibits a form of intermittency arising from the interaction of dynamo modes of opposite parity. The solution aperiodically produces episodes of markedly reduced cycle amplitude, and often showing strong hemispheric asymmetry. This superficially resembles the behavior associated with the type I amplitude nonlinear modulation discussed in Section 5.3.1 (compare the top panel in Figure 20 herein to Figure 7 in Mininni and Gómez (2004)). However, here it is the stochastic forcing that occasionally excites the higher-order modes that perturb the normal operation of the otherwise dominant dynamo mode.
Another way to trigger intermittency in a dynamo model is to let nonlinear dynamical effects, for example a reduction of the differential rotation amplitude, push the effective dynamo number below its critical value; dynamo action then ceases during the subsequent time interval needed to reestablish differential rotation following the diffusive decay of the magnetic field; in the low regime, this time interval can amount to many cycle periods, but must not be too small, otherwise Grand Minima become too rare (see, e.g., Küker et al., 1999). Values seem to work best. Such intermittency is most readily produced when the dynamo is operating close to criticality. For representative models exhibiting intermittency of this type, see Tobias (1996b, 1997); Brooke et al. (1998); Küker et al. (1999); Brooke et al. (2002).
Intermittency of this nature has some attractive properties as a Maunder Minimum scenario. First, the strong hemispheric asymmetry in sunspots distributions in the final decades of the Maunder Minimum (Ribes and Nesme-Ribes, 1993) can occur naturally via parity modulation (see Figure 20 herein). Second, because the same cycle is operating at all times, cyclic activity in indicators other than sunspots (such as radioisotopes, see Beer et al., 1998) is easier to explain; the dynamo is still operating and the solar magnetic field is still undergoing polarity reversal, but simply fails to reach the amplitude threshold above which the sunspot-forming flux ropes can be generated from the mean magnetic field, or survive their buoyant rise through the envelope.
There are also important difficulties with this explanatory scheme. Grand Minima tend to have similar durations and recur in periodic or quasi-periodic fashion, while the sunspot and radioisotope records, taken at face value, suggest a pattern far more irregular. Moreover, the dynamo solutions in the small regime are characterized by large, non-solar angular velocity fluctuations. In such models, solar-like, low-amplitude torsional oscillations do occur, but for . Unfortunately, in this regime the solutions then lack the separation of timescales needed for Maunder-like Grand Minima episodes. One is stuck here with two conflicting requirements, neither of which easily evaded.
Intermittency has also been observed in strongly supercritical model including -quenching as the sole amplitude-limiting nonlinearity. Such solutions can enter Grand Minima-like epochs of reduced activity when the dynamo-generated magnetic field completely quenches the -effect. The dynamo cycle restarts when the magnetic field resistively decays back to the level where the -effect becomes operational once again. The physical origin of the “long” timescale governing the length of the “typical” time interval between successive Grand Minima episodes is unclear, and the physical underpinning of intermittency harder to identify. For representative models exhibiting intermittency of this type, see Tworkowski et al. (1998).
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 23 shows one representative solution of the latter variety, where intermittency is driven by a weak -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.
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 21). 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 , 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).
With its strong polar branch often characteristic of dynamo models with meridional circulation, Figure 24 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 19), and residual pseudo-cyclic variations during quiescent phases, as suggested by data, cf. Panel B of Figure 19. 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 24 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 23.
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