Statistical properties of reversals and chrons in numerical dynamos and implications for the geodynamo

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    Article history:Received 14 December 2012Received in revised form 10 April 2013Accepted 18 April 2013Available online 25 April 2013Edited by Chris Jones


    such as polarity reversals (see, e.g., Christensen et al., 2007; Wichtet al., 2009, 2010 for reviews). Despite limitations in computingcapacities the explored parameter space is still very remote fromwhere the Earths dynamo lies and an excessive viscosity is oftenused to suppress small scales that cannot be resolved investiga-

    mechanism of reversals and excursions (e.g. Wicht and Olson,2004; Aubert et al., 2008a; Olson et al., 2011), while others stroveto assess the effect of boundary conditions (e.g. Glatzmaier et al.,1999; Olson and Christensen, 2002; Aubert et al., 2008b; Takahashiet al., 2008). Thanks to a permanent increase in computationalpower, it has also become possible to produce long dynamo solu-tions over a time equivalent to tens of millions of years (e.g. Wichtet al., 2009; Driscoll and Olson, 2011). These solutions have beenused to investigate factors possibly controlling the reversalfrequency, with some emphasis on the role played by theinhomogeneous heat ow pattern at the core-mantle boundary,

    Corresponding author at: Department of Earth and Environmental Sciences,Ludwig Maximilians University, Munich, Germany. Tel.: +49 (89) 2180 4236; fax:+49 (89) 2180 4205.

    E-mail addresses: (F. Lhuillier),

    Physics of the Earth and Planetary Interiors 220 (2013) 1936

    Contents lists available at

    Physics of the Earth an

    .e(G. Hulot), (Y. Gallet).times. 2013 Elsevier B.V. All rights reserved.

    1. Introduction

    Since the advent of the rst 3D numerical geodynamo simula-tions (Glatzmaier and Roberts, 1995a,b; Kageyama et al., 1995),numerous dynamo models have been published, gradually suc-ceeding in reproducing salient properties of the geomagnetic eld,

    tion of these dynamos have also already led to some understandingof the scaling laws controlling the evolution of key dynamo quan-tities (e.g. Christensen and Aubert, 2006; Olson and Christensen,2006; Aubert et al., 2009) and to the identication of parameter re-gimes hopefully relevant for geophysical investigations (Christen-sen et al., 2010). Some studies focused on describing theGeodynamoDipole-eld behaviourPolarity reversalsPolarity excursionsChron durationsStatistical distributions0031-9201/$ - see front matter 2013 Elsevier B.V. A b s t r a c t

    We analyse a series of very long runs (equivalent to up to 50 Myr) produced by chemically-driven dyna-mos. All runs assume homogeneous boundary conditions, an electrically conducting inner-core (exceptfor one run) and only differ by the choice of the Rayleigh number RaH. Introducing dynamo-based de-nitions of reversals, chrons and related concepts, such as failed reversals and segments (bounded byreversals or failed reversals), we investigate the distributions of chron and segment lengths, those ofreversal and failed reversal durations, the way dipole eld behaves through reversals and failed reversals,and the possible links between the axial dipole intensity and chron or segment lengths. We show thatchron and segment lengths are very well described in terms of a Poisson process (with no occurrenceof superchrons), while distributions of reversal and failed reversal durations are better tted by log-nor-mal distributions. We found that reversal rates generally increase in proportion to Rm Rmc ; Rm beingthe magnetic Reynolds number and Rmc a critical value. In contrast, reversal and failed reversal durationsappear to be mainly controlled by the cores magnetic diffusion timescale. More generally, we show thatmuch of the reversing behaviour of these dynamos can be understood by examining their signature in ag01; g11; h11 phase-space plot. This reveals that the run with an insulating inner-core is very different andhas only two distinct modes of opposite polarity, which we argue is the reason it displays less reversalsand failed reversals, and has a clear tendency to produce an intensity overshoot and some systematicpattern in the dipole pole behaviour through reversals and failed reversals. This contrasts with conduct-ing inner-core runs, which display an additional central unstable mode, the importance of whichincreases with Rm, and which is responsible for the more complex reversing behaviour of these dynamos.Available paleomagnetic data suggest that the current geodynamo could have such a (small) centralmode, which would thus imply a strong sensitivity of the frequency and complexity of reversals and ofthe likelihood of failed reversals, to changes in the geodynamos driving parameters through geologicala Institut de Physique du Globe de Paris, Sorbonne Paris Cit, Universit Paris Diderot, UMR 7154 CNRS, F-75005 Paris, FrancebDepartment of Earth and Environmental Sciences, Ludwig-Maximilians-Universitt, Munich, GermanyStatistical properties of reversals and chrand implications for the geodynamo

    Florian Lhuillier a,b,, Gauthier Hulot a, Yves Gallet a

    journal homepage: wwwll rights reserved.s in numerical dynamos

    SciVerse ScienceDirect

    d Planetary Interiors

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  • tribution of such segments.We next explore the properties of rever-sals and such failed reversals, particularly the distribution of their

    andduration and the way the dipole eld behaves through such events,looking for special features such as overshoots, similar to what thedata suggests occurs during geomagnetic reversals (e.g. Valetet al., 2005, 2012) and to what was found in experimental dynamosand simple models (e.g. Ptrelis et al., 2009). We also investigatepossible correlations between chron (or segment) length and eldintensity a question puzzling paleomagnetists and still essentiallyunsettled (see, e.g., Tauxe et al., 2007 for a review).

    To this purpose, we rely on a number of dynamo simulationswith homogeneous boundary conditions run over periods of timeequivalent to 4050 Myr. One simulation is with an insulating in-ner-core. This control run allows us to investigate the impact ofthis assumption, often used in recent analyses of long dynamo runs(e.g. Driscoll and Olson, 2009a,b; Olson et al., 2010). The other sim-ulations are with a conducting inner-core and only differ by theirforcing amplitude (i.e. the value of the Rayleigh number). They willallow us to investigate the impact of this parameter.

    The paper is organised as follows. We rst present the numeri-cal models and the various diagnostic parameters and statisticaltools we employ, placing emphasis on the denitions we use forthe concepts of reversal, failed reversal, stable low intensity event,chron and segment (Section 2). We next present the results of ourinvestigations of chron and segment lengths (Section 3.1), of rever-sal and failed reversal durations (Section 3.2), of dipole eld behav-iour through such events (Section 3.3), and of possible linksbetween the eld intensity and chron and segment lengths (Sec-tion 3.4), before putting these results in perspective with the helpof useful phase-space plots (Section 3.5). Finally, these results arediscussed (Section 4) and their implications for paleomagnetismand the geodynamo provided with concluding comments(Section 5).

    2. Models and tools

    2.1. Numerical models

    We use the code PARODY (Dormy et al., 1998; Aubert et al.,2008a) and consider simulations close to the chemically-driven dy-namo initially studied by Olson (2007) and extensively used to ten-tatively account for the geomagnetic polarity timescale (Driscolland Olson, 2009a,b, 2011; Olson et al., 2010, 2011) . The choiceof these authors was mainly motivated by the fact that, contraryto thermally-driven dynamos having the usual drawback of beingweakly dipolar when displaying polarity reversals (Kutzner andChristensen, 2002), chemically-driven dynamos are more stronglydipolar. These dynamos are also not far from the Earth-like pathin attempts to account for the observed sequence of reversals (e.g.Driscoll and Olson, 2009a,b, 2011; Olson et al., 2010, 2013).

    In this paper, we do not seek to reproduce the reversal sequenceof the geomagnetic eld. Rather, we aim at better characterisingreversal and chron properties in very long runs produced by suchmodels, and intend to investigate how these properties depend onparameters such as the conducting or non-conducting nature ofthe inner-core, or the strength of dynamo forcing. We rst focuson the distribution of chron lengths a topicwhich has already beeninvestigated in numerous studies, both in paleomagnetism andnumerical dynamo simulations (see, e.g., Amit et al., 2010 for a re-view. In the present context, however, we further introduce the con-cept of segments bounded by either a successful or failedreversal (dened in a way we later specify) and investigate the dis-

    20 F. Lhuillier et al. / Physics of the Earthdescribed by Christensen et al. (2010), and relatively cheap to com-pute. They are from these standpoints convenient candidates forcomparison with paleomagnetic data. As they suffer from the clas-sical shortcomings of numerical dynamos (e.g. Christensen, 2011),comparison with such data must naturally be done with great care.

    Following the conventions of Aubert et al. (2008a), the four con-trol parameters of the simulations are the modied Rayleigh num-ber RaH (a measure of the strength with which the dynamo isdriven), the Ekman number E (a measure of the relative importanceof viscous to Coriolis forces), the hydrodynamic Prandtl number Pr(ratio between kinematic and thermal diffusivities) and the mag-netic Prandtl number Pm (ratio between kinematic and magneticdiffusivities). Our numerical dynamos are all characterised by thexed parameters E 6:5 103; Pr 1 and Pm 20. Table 1 pro-vides the RaH used for each simulation, together with the followingrelevant output parameters: the magnetic Reynolds numberRm UD=g, where U is the time-averaged rms velocity over theouter-core shell, D the shell thickness (we assume an aspect ratioof 0.35 as in Aubert et al., 2008a) and g the magnetic diffusivity;the dipolarity f 13dip dened as the time-averaged rms amplitude ofthe dipole relative to the eld up to degree 13 at the core-mantleboundary; and f eqax , the ratio of the equatorial dipole to the axial di-pole at the core-mantle boundary.

    The rstmodel has an insulating inner-core and is virtually iden-tical to the dynamos investigated by Olson (2007) and Olson et al.(2009). The others have a conducting inner-core of the same con-ductivity as the outer-core and operate in the same parameter rangeas the dynamos investigated by Driscoll and Olson (2011) and Olsonet al. (2011); Olson et al., 2013. Note that the additional studies ofDriscoll and Olson (2009a,b) and Olson et al. (2010) are also in thesame parameter range, but assume an insulating inner-core.

    All our simulations operate with a lateral resolution of 44spherical harmonic degrees, which ensures that the ratio betweenthe minimum and maximum of the spatial power spectra (inte-grated over the outer-cores shell) is typically higher than250000 for the velocity eld and higher than 2500 for the magneticeld. Unless otherwise noted, all magnetic quantities are expressedin units of ql0gX1=2 (as in Christensen et al. (2001), where q isthe core density, l0 the magnetic permeability, and X the rotationrate assumed for the dynamo) and all temporal quantities are ex-pressed in units of the magnetic diffusion time sg D2=g. The timesteps, which are determined according to an adaptive criterion(Christensen et al., 1999), are on the order of 1:25 105sg forthe model with an insulating inner-core, on the order of8:75 106sg for the models with a conducting inner-core.

    2.2. Timescales of interest

    To describe the timescales of the Earths magnetic eld for a gi-ven spherical harmonic degree n, it is convenient to dene the cor-relation times with


    Pnm0 gmn 2 hmn 2

    h iD EPn

    m0 _gmn 2 _hmn 2h iD E

    vuuut ; 1in which fgmn ;hmn g and f _gmn ; _hmn g are respectively the Gauss coef-cients and their time derivatives of spherical harmonic degree nand order m, whereas the angle brackets denote time averaging(Hulot and Le Moul, 1994; Christensen and Tilgner, 2004). Theyprovide a statistical measure of how long it would take for the ob-served eld at a given spherical harmonic degree n to be com-pletely renewed, and decrease with increasing sphericalharmonic degree (meaning that, the smaller the length scales,the shorter the correlation times). Similar quantities sn can bedened for the eld produced by any dynamo. Considering both

    Planetary Interiors 220 (2013) 1936numerical geodynamo simulations and geomagnetic eld models,it was shown that this decrease is statistically compatible withan inverse linear law sn sSV=n nP 2, whose single parameter

  • remaining low intensity intervals I i ai; bi (of durationDt bi ai) during which the ADM is below l r but neverreaches zero will be referred to as Stable Low Intensity (SLI)events (see light grey zones in Fig. 1(a)).

    4. determination of chrons Ci bi1; ai as time intervals betweentwo consecutive successful reversals, and of segmentsSi bi1; ai as time intervals between two consecutive suc-cessful or failed reversals (see arrows in Fig. 1(a));

    5. facultative step: ltering of segments shorter than Dt (seebelow for details);

    r RaH is an input parameter. The magnetic Reynolds number Rm, the dipolarity f 13dip and theor-growth timescale). T is the total duration of each run. All times are expressed in units of

    Rm f 13dip feqax sSV=sg se=sg

    110 0.58 0.25 2:90 102 6:50 102108 0.57 0.28 3:05 102 8:50 102117 0.54 0.33 2:85 102 7:75 102124 0.54 0.33 2:65 102 7:00 102133 0.50 0.33 2:40 102 4:25 102

    and Planetary Interiors 220 (2013) 1936 21sSV is then known as the secular-variation timescale (Lhuillieret al., 2011a). This parameter sSV can thus conveniently be usedas an alternative to sg to scale time in dynamo outputs, and en-sure consistency between model and the Earths secular-variationtimescales, an important issue for geomagnetic data assimilationapproaches (Fournier et al., 2010, 2011; Aubert and Fournier,2011). Based on the observatory part (i.e. the period 18401990) of gufm1 (Jackson et al., 2000), sSV was found to be on theorder of 500 yr for the geodynamo. We will use this value whenconverting time in years (see, e.g. Fig. 3).

    Another timescale of interest in numerical geodynamo simula-tions is the error-growth timescale se, which corresponds to theinverse of the average growth rate at which small errors intro-duced in a reference solution are exponentially amplied. Thisgrowth rate is a robust quantity independent of the conditions(type, amplitude, time) under which the small errors are intro-duced, and thus gives an insight into the predictability of themodel (Hulot et al., 2010a; Lhuillier et al., 2011b). Contrary tothe correlation times, this timescale cannot be obtained as a di-rect output of the simulation, but needs the introduction of smallcontrolled perturbations in a reference solution and the study ofthe subsequent divergence between reference and perturbedsolutions.

    In this paper, both the secular-variation timescale sSV and theerror-growth timescale se will be of interest. Their non-dimen-sional values are provided in Table 1 for the numerical models un-der investigation. The timescale sSV is a temporal average over theentire solution, whereas se is a mean timescale deduced from tenindependent measurements of the growth rate of small errors ofrelative amplitude 1010 introduced at ten distinct times of thesimulation, within periods of stable polarity.

    2.3. Dening reversals, failed reversals, stable low intensity events,chrons and segments

    In order to dene and detect reversals, failed reversals andstable low intensity events in our dynamo solutions, we adoptthe following algorithm in six steps:

    Table 1Properties of the numerical models under investigation. The modied Rayleigh numberatio f eqax are all output parameters, just like sSV (secular-variation timescale) and se (errthe magnetic diffusion time sg (see main text for complete denitions).

    Model Inner-core T=sg RaH

    (1) Insulating 3790 5:5 103(2) Conducting 3150 5:1 103(3) Conducting 2400 5:5 103(4) Conducting 2690 5:9 103(5) Conducting 2490 6:3 103

    F. Lhuillier et al. / Physics of the Earth1. estimation of the mean l and the standard deviation r of theabsolute value of the axial dipole moment (ADM) over thewhole solution;

    2. determination of time intervals I i ai; bi of low intensity dur-ing which the ADM is lower than l r ;

    3. detection of reversals, failed reversals and stable low inten-sity events among the low-intensity zones I i: a reversal Ri ai; bi (of duration Dt bi ai) is identied if

    the Dipole Pole (DP, dened as the outgoing pole of thedipole eld) is in a different hemisphere at t ai and t bi(see blue zones in Fig. 1(a));

    a failed reversal Ei ai; bi (of duration Dt bi ai) is iden-tied if the Dipole Pole is in the same hemisphere at t aiand t bi, and has reached the equator at least once withinE i (see dark gray zones in Fig. 1(a));Fig. 1. Sketch of the algorithm employed: (a) detection of reversals, failed reversals(FR) and stable low intensity (SLI) events, according to a criterium based on bothintensity and polarity; (b) ltering process used to merge the shortest segmentswith reversals or failed reversals (see Section 2.3 for more details).

  • where m is the geometric mean (and also the median) of the distri-bution and r the scale parameter.

    3. Results

    For the ve numerical models dened in Table 1, we rstchecked that the secular-variation timescale sSV is to rst order in-versely proportional to the magnetic Reynolds number Rm(Fig. 2(a), in accordance with Christensen and Tilgner, 2004;Lhuillier et al., 2011b). We also checked that the error-growthtimescale se is roughly inversely proportional to Rm (Fig. 2(b), inaccordance with Hulot et al., 2010a; Lhuillier et al., 2011b). Notein particular that model (1) with an insulating inner core leads tosSV and se values quite consistent with the trends dened by thefour other models, all with a conducting inner core. Note also thatall these chemically-driven dynamos operate with low Rm and pro-duce a se=sSV ratio on the order of 2, which is much larger than thevalue of 0.05 expected for dynamos with low Ekman number, suchas the geodynamo (Hulot et al., 2010a).

    Having characterised the sSV and se timescales, we next appliedthe algorithm detailed in Section 2.3 to all numerical models. Por-tions of the temporal evolution of models (1), (2) and (5) are shownin Fig. 3 in the case of ltering (b) to illustrate the behaviour ofthese dynamos and provide examples of reversals, failed reversalsand SLI events identied by our algorithm. These are the events wenow wish to characterise in statistical terms. Parameters recoveredfrom such analysis are all reported in Table 2.

    3.1. Distribution of chron and segment lengths

    and Planetary Interiors 220 (2013) 19366. computation of l and r during segments only (i.e. excludingtime spent in either successful or failed reversals), and repeti-tion four times of steps 2-5 to ensure convergence of thealgorithm.

    The facultative step implying a ltering process is illustrated inFig. 1(b). If several successive segments are shorter than Dt, thesegments are merged to form a single reversal or failed reversal(depending on the polarity of the previous and next segmentslonger than Dt). In this paper, we alternatively take Dt 0 (casea, no ltering), Dt equal to the shortest duration of a reversal (caseb) or Dt equal to the likeliest duration of reversal (case c). Note thatthere is no universal way to dene reversals, failed reversals andSLI events, and that other choices are possible (e.g. Wicht, 2005;Driscoll and Olson, 2009a). Our choice is motivated by the fact thatwe want to have a simple way to dene events, only based onthreshold values coming from the numerical model itself, andnot from a priori values (such as the angle of departure from thegeomagnetic pole for the latitude criterium, or some pre-denedarbitrary minimal duration allowed for chrons).

    2.4. Statistical distributions of interest

    Given a sequence of reversals, it is possible to proceed to a sta-tistical characterisation of the chron length, which can indirectlyprovide information on the reversal process. The simplest charac-terisation involves considering a Poisson process (Cox, 1968,1969), according to which the probability density function (pdf)of the chron length x is

    px 1l

    exp xl

    ; 2

    where l is the expected mean chron length. Then, the dynamo isassumed to have no memory of the previous event, and the proba-bility that a reversal occurs does not depend on the time elapsedsince the last reversal (e.g., Merrill et al., 1996). A more generalway to account for the distribution of chron length involves consid-ering a Gamma process (Naidu, 1971; Phillips, 1977), according towhich the pdf of the chron length x is

    px kl

    k xk1Ck exp k


    ; 3

    where Ck is the Gamma function of real index k, and l the ex-pected mean chron length. The Poisson process is an end-membercase of the Gamma process when k 1. But, contrary to the Poissonevent, the Gamma process has a memory of the previous eventwhen k 1 (e.g. Merrill et al., 1996). A value of k > 1 inhibits theprobability to reverse just after the previous reversal, whereas a va-lue of k < 1 encourages the dynamo to reverse just after the previ-ous reversal. For the sake of clarity, we note hereinafter kchr and lchrthe estimated parameters of the Gamma distribution for the chronlength (i.e. characterising the process responsible for just reversals,independently of failed reversals that may occur meanwhile). Byextension, we also dene the estimated parameters kseg and lseg,still computed from Eq. (3) but for the segment length (i.e. charac-terising a possible common process responsible for both reversalsand failed reversals).

    Concerning the duration of reversals and failed reversals them-selves (i.e. the lengths of the Ri and Ei intervals as dened in theprevious section), we shall see in Section 3 that they can reason-ably be modelled by a log-normal distribution, according to whichthe pdf of the duration x is 2" #

    22 F. Lhuillier et al. / Physics of the Earthpx 1xr


    p exp 12


    ; 4100 110 120 130 1405In this section, we rst deal with the distribution of chron andsegment lengths. For each model and each of the three ltering lev-

    100 110 120 130 14025










    bFig. 2. sg=sSV (inverse of the secular-variation timescale) and sg=se (inverse of theerror-growth timescale) as a function of Rm for the ve numerical models underinvestigation (insert: best linear ts, also plotted as a line).

  • andF. Lhuillier et al. / Physics of the Earthels mentioned in Section 2.3, we search for Gamma distributionsthat best t the distributions of chron and segment lengths.Fig. 4 shows examples of such distributions and ts for models

    Fig. 3. Examples of the temporal evolution of the Dipole Pole (DP) latitude and of thedetailed in Section 2.3 for ltering (b) (i.e. by the shortest duration of reversals, the Dt vaof negative polarity in green, and reversals in blue. Failed reversals during chrons are furtin light grey. The time is expressed both in units of the magnetic diffusion time sg (blackvariation timescale sSV set to 500 yr (see Section 2.2). (For interpretation of the referencarticle.)Planetary Interiors 220 (2013) 1936 23(1), (2) and (5) in the case of ltering (b). As can be seen, Gammats always provide very decent representations of the distribu-tions. Table 2 lists the corresponding maxima of likelihood for

    ADM for models (1), (2) and (5), with events identied according to the algorithmlue of which is also provided): chrons of positive polarity are depicted in red, chronsher highlighted in dark grey, while Stable Low Intensity (SIL) events are highlightedaxis) and in units of equivalent Myr (red axis) using a scaling based on the secular-es to colour in this gure caption, the reader is referred to the web version of this

  • parameters k and l (as dened by Eq. (3), with their 95% dispersionbars) for all models and lterings.

    First focus on the distribution of chron lengths (Fig. 4(a), (c) and(e); and Table 2). One can see that, for any given numerical model,kchr always increases with the ltering level, as expected (McFad-den, 1984). For model (2) for instance, kchr evolves from 0.840:110:13in the absence of ltering to 1.280:190:22 for ltering (c), with anintermediate value of 1.100:150:18 for ltering (b). The kchr index,however, is always close to a unit value, implying that the distribu-tions are never far from being Poissonian. Interestingly, ltering bythe shortest duration of reversals (ltering (b)) always leads to amatch with a Poisson distribution. This strongly suggests, rst, thatchrons lasting less than the quickest of all reversals should indeedbe considered as part of an ongoing reversal, and second, thatchrons do end (and therefore reversals occur) as a result of a pro-cess with no memory beyond the length of a reversal.

    Considering the distribution of segment lengths is also of inter-est (Fig. 4(b), (d) and (f); and Table 2). Such segments differ fromchrons only by the fact that they start and end by either a reversalor a failed reversal. Just like for chrons, the distributions of seg-ment lengths can be well tted by Gamma distributions, with

    magnetic Reynolds number Rm, and models (1) and (3) are drivenby the same modied Rayleigh number RaH (recall Table 1).Clearly, the conducting or non-conducting character of the inner-core plays an important role in dening the proportion of failedand successful reversals.

    Other parameters worth investigating are the expected meanchron (lchr) and segment (lseg) lengths recovered from the Gammats (see Table 2). Plotting sg=lchr (i.e. the average reversal fre-quency scaled with respect to the diffusion time) as a function ofRm (Fig. 4(g)) again highlights the strong contrast between model(1) and all other models. Chrons in model (1) roughly last threetimes longer than in model (2), and six times longer than in model(3). Fig. 4(g) otherwise suggests a general increasing trend ofsg=lchr with Rm (and therefore Ra

    H) for all dynamos with a con-ducting inner-core, in line with the ndings of Driscoll and Olson(2011).

    Plotting sg=lseg as a function of Rm (Fig. 4(h)) brings further in-sight. Because so few failed reversals occur in the case of model (1),the contrast between segment lengths produced by this model andmodels (2) and (3) is even stronger (with factors on the order of 3.6and 8). But what best emerges from Fig. 4(h) is a much clearer lin-

    (resratid isg-nand















    24 F. Lhuillier et al. / Physics of the Earth and Planetary Interiors 220 (2013) 1936kseg again close to a unit value and ltering (b) always leading todistributions closest to Poissonian (see Table 2). This observationstrongly suggests that a common process is responsible for produc-ing both reversals and failed reversals, i.e., events characterised bythe DP latitude crossing zero, with no memory beyond the lengthof the events themselves. Once such an event has ended, the dyna-mo is back in a state where another similar event has a constantinstantaneous probability of occurrence, as if resulting from a Pois-son process. Note that these observations do not imply that allevents are equally likely to end up as reversals or failed reversals.As it turns out, this is generally not the case.

    The ratio Nfrev=Nrev of the number of failed reversals to the num-ber of reversals illustrates this point (see Table 2). If one considersltering (b) as most appropriate, Nfrev=Nrev is on the order of 0.20for model (1) with an insulating inner-core, and range from 0.44to 0.57 for models (2) to (5) with a conducting inner-core. Thisshows that once the DP latitude has reached zero, the dynamos un-der investigation are more likely to proceed to a full reversal thanto revert to their original polarity. This is particularly true for mod-el (1), which hardly produces any failed reversals, in contrast tomodels (2) and (3), though models (1) and (2) share very similar

    Table 2Chron, segment, and events statistics for the models under investigation: number Nrev(resp. lseg), Gamma index kchr (resp. kseg) for the distribution of chron (resp. segment) duparameter rrev (resp. rfrev) of the corresponding log-normal distributions. Also providespent in SLI events within segments. The estimated parameters of the Gamma (resp. loa,b,c refer to the three ltering levels mentioned in Section 2.3. Note also that Nrev;Nfrev

    Model Nrev Nfrev lchr=sg kchr lseg=sg kseg

    (1a) 123 25 29.9717:0413:23 1.040:250:20 24.8412:7210:10 1.070:240:19(2a) 298 140 9.723:442:91 0.840:130:11 6.301:821:58 0.790:100:09(3a) 464 192 4.551:261:11 0.880:100:09 2.960:690:61 0.880:090:08(4a) 547 223 4.131:050:93 0.970:110:10 2.600:550:50 1.030:100:09(5a) 457 253 4.221:181:03 0.880:110:09 2.070:460:41 0.870:080:08(1b) 123 25 29.9717:0413:23 1.040:250:20 24.8412:7210:10 1.070:240:19(2b) 270 130 10.733:953:33 1.100:180:15 6.892:041:78 1.190:160:14(3b) 432 192 4.911:411:23 0.980:120:11 3.100:730:65 1.080:110:10(4b) 515 229 4.401:151:01 1.100:130:11 2.690:570:52 1.220:120:11(5b) 421 239 4.531:321:15 1.010:130:11 2.220:510:45 1.190:120:11(1c) 121 25 30.4617:4613:53 1.060:260:21 25.1712:9510:28 1.140:260:21(2c) 248 132 11.724:493:76 1.280:220:19 7.232:201:90 1.430:200:17(3c) 360 194 5.931:861:61 1.240:170:15 3.470:860:77 1.520:170:15

    (4c) 447 227 5.091:421:24 1.370:170:15 2.940:660:59 1.580:160:15 0(5c) 331 216 5.821:911:64 1.150:170:15 2.610:650:58 1.800:210:19 1ear trend for sg=lseg as a function of Rm than for sg=lchr. This resultcan be understood in view of our previous ndings. If both rever-sals and failed reversals are initially triggered as a result of a com-mon Poisson process, time spent in failed reversals should not becounted in the duration of the chron within which the failed rever-sal occurred. Chron lengths thus happen to be slightly biased to-wards larger values, especially when the duration of reversalsand failed reversals becomes signicant compared to the averagechron or segment lengths. As we shall later see, this is the casefor models (4) and particularly (5), both of which thus tend to pro-duce sg=lchr biased towards weaker values. Using the more reliablelinear trend inferred from Fig. 4(h), we nally note that this trendsuggests that reversal and failed reversal frequencies could drop tozero when reaching Rm Rmc 90. This illustrates how sensitivethe reversing properties of these dynamos are to relatively minorchanges in their driving parameters.

    3.2. Duration of reversals and failed reversals

    We now turn to the distribution of reversal durations. As can beseen in Fig. 5 (for the same cases as in Fig. 4), these distributions

    p. Nfrev) of reversals (resp. failed reversals), mean chron (resp. segment) duration lchrons, geometric meanmrev (resp.mfrev) of reversal (resp. failed reversal) durations, scalethe number NSLI of SLI events, their average duration mSLI and the fraction of time rSLIormal) distributions are provided with their rounded 95% dispersion bars. The lettersNSLI refer to the entire runs, the length of which depends on the model (recall Table 1).

    rev=sg rrev mfrev=sg rfrev NSLI mSLI=sg rSLI

    .580:030:02 0.24 0.03 0.620:060:05 0.21 0.05 3727 0.14 0.14

    .690:050:04 0.56 0.04 0.910:070:07 0.46 0.05 2548 0.16 0.15

    .510:030:03 0.61 0.04 0.820:050:05 0.44 0.04 2296 0.13 0.15

    .620:040:03 0.680.04 1.030:060:06 0.450.04 2594 0.11 0.14

    .870:070:06 0.84 0.05 1.510:120:11 0.61 0.05 1976 0.10 0.13

    .580:030:02 0.240.03 0.620:060:05 0.21 0.05 3727 0.14 0.14

    .740:060:05 0.60 0.05 1.010:080:08 0.45 0.05 2543 0.16 0.15

    .520:030:03 0.63 0.04 0.860:060:06 0.47 0.04 2289 0.13 0.15

    .630:040:04 0.69 0.04 1.050:070:06 0.47 0.04 2583 0.11 0.14

    .960:080:08 0.87 0.05 1.540:130:12 0.63 0.05 1965 0.10 0.13

    .580:030:03 0.25 0.03 0.650:080:07 0.28 0.06 3726 0.14 0.14

    .770:060:06 0.61 0.05 1.080:090:09 0.49 0.05 2538 0.16 0.15

    .570:040:04 0.66 0.05 0.960:080:07 0.53 0.05 2261 0.13 0.15.690:050:05 0.75 0.05 1.150:080:07 0.49 0.04 2542 0.11 0.14

    .14 0:120:11 0.92 0.07 1.820:180:16 0.70 0.06 1899 0.10 0.13

  • andF. Lhuillier et al. / Physics of the Earthcan reasonably be tted by log-normal distributions of the form gi-ven by Eq. (4). We checked that this is the case for all models, andfor any of the (a), (b) and (c) types of ltering. Values of the corre-sponding best-tting parameters are reported in Table 2 (recallSection 2.4 for the denition of these parameters). Of particularinterest is the geometric mean (and also median) mrev of these dis-tributions, which gives a simple measure of the typical duration ofthe reversals for each model. The mean durations mrev slightly in-crease with the ltering level when considering a given model. This

    Fig. 4. Analysis of chron and segment lengths for models (1), (2) and (5), using ltering (bin panels b, d and f. These empirical distributions are tted assuming a Gamma process (rcorresponding estimates are provided with their 95% dispersion bars (inserts). N is thesg=lchr (panel g) and sg=lseg (panel h) as a function of Rmwith best linear ts (colour-cod(For interpretation of the references to colour in this gure legend, the reader is referrePlanetary Interiors 220 (2013) 1936 25can be explained by the fact that the ltering of the shortest seg-ments is done by assigning such segments to neighbouring rever-sals and failed reversals, thus increasing the reversal durations.To investigate whether the mean durations mrev of reversals dis-play a similar behaviour as the mean durations lchr of chrons whenRm increases, we plotted the quantity sg=mrev as a function of Rm(Fig. 5(g)). As can be seen, contrary to sg=lchr; sg=mrev does not dis-play any monotonic trend. Rather, a maximum is observed formodel (3), which thus experiences the reversals with the shortest

    ): distribution of chron lengths in panels a, c and e; distribution of segment lengthsed curves, see Eq. (3)) with index k and mean duration l expressed in units of sg . Thetotal number of chrons (resp. segments). Also shown with their uncertainties areed inserts and lines) for the ve dynamos (15) and the three types of ltering (ac).d to the web version of this article.)

  • and26 F. Lhuillier et al. / Physics of the Earthdurations. Note that model (1), with an insulating inner-core,roughly ts the same general trend as all other models, eventhough these have a conducting inner-core.

    A similar analysis can be done for durations of failed reversals(see Fig. 5(b), (d) and (f); and Table 2). Although the agreement be-tween the empirical distribution and the best tting log-normaldistribution is no longer as good for model (1) as when we consid-ered reversal durations (an observation which can be explained bythe little number of failed reversals observed for this model), theoverall ts to log-normal distributions remain satisfactory. As inthe case of mrev, we observe that the mean duration mfrev of failed

    Fig. 5. Analysis of reversal and failed reversal durations for models (1), (2) and (5), usinfailed reversal durations in panels b, d and f. These empirical distributions are ttedgeometric mean m expressed in units of sg . The corresponding estimates are provided wreversals). Also shown with their uncertainties are sg=mrev (panel g) and sg=mfrev (panel hand c). (For interpretation of the references to colour in this gure legend, the reader isPlanetary Interiors 220 (2013) 1936reversals slightly increases with the ltering level when consider-ing a given model. We also plotted the quantity sg=mfrev as a func-tion of Rm (Fig. 5(h)), which again reveals a non monotonic trend,with a maximum for model (3). Model (3) thus also experiencesfailed reversals with the shortest mean durations. Model (1), withan insulating inner-core, no longer ts the general trend dened bythe other models. It is also worth noting that, for any given modeland chosen ltering, failed reversals last longer than reversals onaverage. This is marginally true for model (1), which leads tomfrev=mrev ratios on the order of 1.1, but very clear for all othermodels with ratios ranging from 1.3 to 1.7. This can be

    g ltering (b): distribution of reversal durations in panels a, c and e; distribution ofassuming a log-normal law (blue curves, see Eq. (4)) with scale parameter r andith their 95% dispersion bars (inserts). N is the total number of reversals (resp. failed) as a function of Rm for the ve dynamos (15) and the three types of ltering (a, b,referred to the web version of this article.)

  • andqualitatively understood by inspection of Fig. 1(a), where it can beseen that for a reversing event to end as a failed reversal, the eldhas to spend some additional time (compared to a true reversal) inthe reversed polarity before reverting to its original polarity.

    Extrapolations from the bell-shaped trends observed in Fig. 5(g)and (h) are clearly uncertain. Nevertheless, extrapolating the trendas Rm increases beyond the Rm 117 value of model (3) leads tointeresting insight. Comparing Fig. 5(g) and (h) with Fig. 4(h) re-veals that, as Rm increases, both reversal and failed reversal dura-tions increase while segment lengths decrease. For model (5)indeed, segment lengths are already short enough and reversaland failed reversal durations long enough, that all are of compara-ble size (lseg 2:22sg;mrev 0:96sg and mfrev 1:54sg in the caseof ltering (b)). It can thus be anticipated that models with Rmeven larger would end up being in a permanent state of reversal,never producing any well dened segments or chrons.

    3.3. Dipole eld behaviour during reversals and failed reversals

    We now turn to a detailed examination of the dynamics of thedipole eld during reversals and failed reversals. For each reversalidentied in each model run, we computed the central time t0 ofthe interval dening the event (ai; bi as dened in Section 2.3)and selected an interval of total width 6sg centred on that time.We next synchronised all selected intervals based on their centralvalue and stacked all corresponding ADMs and DP latitudes to pro-duce average ADM and DP latitude curves. In the case of DP lati-tudes, however, we rst did this separately for reversals startingfrom a positive polarity and for those starting from a negativepolarity. But as this revealed exactly the same behaviour, to withina polarity change, we eventually produced a single nal joint DPlatitude stack by simply changing the polarity of events startingfrom a negative polarity. Fig. 6 shows plots of such average curvesfor models (1), (2) and (5), when considering ltering (b). In eachcase, the rst twenty of all intervals used to produce the stackedcurves are also plotted to illustrate the variety of behaviours ofthe individual ADM and DP latitude curves during reversals. Simi-lar plots built to investigate failed reversals are shown in Fig. 7.Models (3) and (4), and ltering (a) and (c) were also investigated(not shown). This revealed only a mild inuence of the lteringused and analogous behaviours to that observed for models (2)and (5), except for the length of the events, consistent with ourprevious ndings (recall Table 2 and Fig. 5(g) and (h)).

    Most striking in Figs. 6 and 7 is the very different behaviour dis-played by the dipole eld in model (1) compared to all other mod-els. The ADM of model (1) clearly displays an asymmetricdynamical behaviour through both reversals and failed reversals.It rst decreases to zero and then displays a very distinct overshootbefore settling in the next chron (or segment). Also, Fig. 6 showsthat during reversals the DP latitude goes on uctuating close toits stable polarity average value for some time while the ADM al-ready started decreasing, before crossing zero in a short time andreaching a value very close to 90. It then remains close to thisvalue with little variability while the ADM is regaining strength,only to start uctuating again about the new stable polarity aver-age latitude at the time of the ADM overshoot. A very similarbehaviour is observed during failed reversals, except for the factthat the DP latitude goes back to a value very close to 90, insteadof reaching 90 (Fig. 7). Then again the DP remains close to thislatitude while the ADM is regaining strength, only to start uctuat-ing again about the original average stable polarity latitude at thetime of the ADM overshoot.

    Now, consider model (2), very close to model (1) but with a con-

    F. Lhuillier et al. / Physics of the Earthducting inner-core. As can be seen in Figs. 6 and 7, the ADM has amuch more symmetric behaviour during both reversals and failedreversals; it no longer displays any systematic overshoot. Further-more, individual reversals and failed reversals are less similar fromone event to the next, which is also reected in the DP latitudebehaviour. In particular it may sometimes happen that doubleand triple events occur while the ADM is low, i.e., events startingfrom a polarity, leading to a nearly successful reversal but immedi-ately followed by another reversal (leading in effect to a failedreversal), or even followed again by another reversal, nally qual-ifying as a successful reversal. Note also how often the ADM mayreach low values before and after a reversal or failed reversal,sometimes because of a nearby event (in which case the ADMdrops to zero, and the DP latitude crosses the 0 value), but also of-ten as a result of what we dened as SLI events (recall Section 2.3),i.e. times when the ADM is low but does not reach zero and the di-pole remains of the same polarity. This general behaviour is typicalof all models with conducting inner-core we investigated, the dif-ference among models being essentially in the proportion of multi-ple events, model (5) being the richest of all in such events (seeFigs. 6 and 7), consistent with the fact that reversals and failedreversals last longer for this model than for any of the other models(recall Fig. 5(g) and (h)).

    3.4. ADM versus chron and segment lengths

    In this section, we now explore whether ADM values during achron (or segment) could be correlated in some way with thelength of the corresponding chron (or segment). To this end, wesimply computed the average ADM for each chron (or segment),and plotted it as a function of the chron (or segment) length. Wedid this for all models and types of ltering, but only report hereon models (1), (2) and (5) in the case of ltering (b), for consistencywith the results provided so far, and because these are enough toconvey the main conclusions of our investigations.

    To ease interpretation, all ADM values were renormalised to las dened in Section 2.3, i.e., to the average absolute value of theADM during all identied segments of the model. We also plottedfor reference the l r and l r levels, where r is the standarddeviation of the ADM over the same segments, again as denedin Section 2.3. Fig. 8(a) and (b) show the corresponding plots whenconsidering chrons and segments in model (1). As can be seen, bothplots show that average ADM values in a chron (or segment) dis-play more dispersion for short chrons (or segments) than for longones. This dispersion is also nicely symmetrical about the averagevalue l and within the l r and l r levels. Such a behaviour isexactly what one would expect for an ADM uctuating in the sameway about its average value l, whether the length of the chron (orsegment) is short or long. Average ADM values for a given chron (orsegment) length are then simply estimates of l, all the more accu-rate as the length of the chron (or segment) is long. This shows thatmodel (1) does not lead average ADMs (and therefore individualADM estimates) to correlate in any way with chron or segmentlengths.

    Interpretation of our results for model (2) is a little more subtle.Fig. 8(c) and (d) show the corresponding plots, which look essen-tially the same for both chrons and segments. Compared to theprevious plots, however, ADM values appear to be biased, withthe scatter being about an average value closer to l r than tol when chrons (or segments) are short. The reason for this lies inthe fact that just before and after a reversal or failed reversal, theADM value must be close to l r. All chrons and segments thushave low ADM transitional periods at their onset and end. As a re-sult, when these transitional periods last a signicant fraction ofthe chron (or segment), the average ADM over the correspondingchron (or segment) is bound to be biased towards l r. It may

    Planetary Interiors 220 (2013) 1936 27even take lesser values, since the ADM can temporarily explore val-ues less than l r, during SLI events. This is typically the case forthe shortest chrons and segments produced by model (2), which

  • and28 F. Lhuillier et al. / Physics of the Earthcan last as little as a fraction of sg, i.e., only a few times the secular-variation timescale sSV 3:05 102sg (recall Table 1), and com-

    Fig. 6. ADM and DP latitude behaviour during reversals for models (1), (2) and (5), usingover an interval of 6sg centred on the central time t0 of the event (colour curves), togetheblack curve); see text for more details. (For interpretation of the references to colour inPlanetary Interiors 220 (2013) 1936parable to the average length of SLI events (mSLI 0:16sg, recall Ta-ble 2). The correlation this effect produces between average ADMs

    ltering (b). In each plot, twenty examples of ADM (or DP latitude) curves are shownr with a stack of the ADM (or DP latitude), computed from all available events (thickthis gure legend, the reader is referred to the web version of this article.)

  • andF. Lhuillier et al. / Physics of the Earthand chron or segment lengths is however very weak, and would bevery difcult to detect even if quite a few individual ADM esti-mates were available. It was not observed for model (1), whichshares similar secular-variation timescale (sSV 2:90 102sg)and average length of SLI events (mSLI 0:14sg), because model(1) produces much less short chrons and segments.

    Fig. 7. Same as Fig. 6 but for failed rePlanetary Interiors 220 (2013) 1936 29Model (5) further illustrates how difcult the interpretation ofADM versus chron length plots can be (Fig. 8(e) and (f)). For thismodel, only short chrons and segments occur. As a result, signi-cant dispersion is always found. As in model (2), a bias towardsl r is nevertheless observed for the shortest of all chrons andsegments (Fig. 8(f)). But chrons and segments now plot quite

    versals; see text for more details.

  • and30 F. Lhuillier et al. / Physics of the Earthdifferently, with ADMs being less intense on average during chronsthan during segments. This reects the fact that, contrary to seg-ments, chrons may include time spent in failed reversals, whenthe ADM is low. This time is not signicant for models (1) and(2), but for model (5) it does make a signicant fraction of a chron,leading the ADM to be underestimated on average.

    3.5. A phase space perspective

    We nally focus on the behaviour of the various models in thephase space described by the rst three Gauss coefcients

    g01; g11;h11 characterising the dipole eld produced by the models.Combined with the empirical distributions of the signed ADM (i.e.,

    g01), of the total dipole momentg012 g112 h112

    qand of the DP

    latitude, these phase-space plots provide very useful complemen-tary information, enlightening many of our previous ndings.

    Fig. 8. Correlations between ADM and chron/segment lengths for models (1), (2) and (5),from the algorithm described in Section 2.3 (i.e. average value over all segments). Alsoalgorithm. (For interpretation of the references to colour in this gure caption, the readPlanetary Interiors 220 (2013) 1936Fig. 9 (left column) shows such phase-space plots for all vemodels investigated so far, where values from segments of positivepolarity are plotted in red, those from segments of negative polar-ity in green, those from reversals in blue, and those from failedreversals in grey. These plots immediately reveal a major distinc-tion between model (1) and all other models. Model (1) only hastwo symmetric stable polarity states showing as red and greenballs, whereas all other models clearly display an additional dis-tinct central unstable state, showing as a (mainly) blue and greyball. Note how each of these three balls appear to be remarkablydelineated by the colour coding originally dened by the algorithmwe used in Section 2.3 to dene segments of stable polarity, rever-sals and failed reversals. This directly illustrates the fact that rever-sals and failed reversals occur in these dynamos only once theyleave a stable polarity state to reach the central unstable statewhere the dynamo spends some time (the time of a reversal or afailed reversal) before moving again to one of the two stable

    using ltering (b). ADMs are normalised with respect to their average value inferredshown in red dashed lines are the limits dened by the r, also inferred from thiser is referred to the web version of this article.)

  • andF. Lhuillier et al. / Physics of the Earthpolarity states. This behaviour is very different from that observedin model (1), which displays no central state, in which case rever-sals (and the few failed reversals) happen as a much more straight-forward transition (or failed transition) from one state of stablepolarity to the other one.

    Plotting the empirical distribution of the signed ADM (i.e., g01)for each model is equally useful. This is done in Fig. 9 (second col-umn from left), where we now distinguish contributions from SLIevents within stable segments only (in yellow), other contributionswithin these stable segments (blue) and contributions at times of

    Fig. 9. Phase-space behaviour of models 1 (rst line), 2 (second line), 3 (third line), 4 (foubehaviour for each of these dynamos in the g01; g11; h11 space, with colour code as follows:segment, blue (resp. grey) when the dynamo is experiencing a reversal (resp., a failed rempirical distribution for the signed ADM (e.g., g01) where we distinguish contributionssegments (blue), and contributions at times of reversals or failed reversals (grey); also shand l r values (dashed lines). Third column from the left (panels c, g, k, o, s) shows emcolumn (panels d, h, l, p, t) shows empirical distributions for the DP latitude, again using tCriteria for identifying positive (resp. negative) polarity segments, reversals and failed reinterpretation of the references to colour in this gure legend, the reader is referred toPlanetary Interiors 220 (2013) 1936 31reversals and failed reversals (grey). This colour coding ensuresthat the distribution of signed ADMwhen the dynamo is in a stablesegment (adding yellow and blue contributions), and the entiredistribution of signed ADM at all times (further adding grey contri-butions) can also be visualised. Comparing these signed ADMempirical distributions for the various models, again highlightsthe specic case of model (1) (Fig. 9(b)). For this model, mostlow-value ADMs occur as SLI events (i.e., when the dynamo is ina stable polarity segment). In contrast, for all other models, thefraction of low ADM values associated with a reversal or failed

    rth line), and 5 (fth line). Left column (panels a, e, i, m, q) shows plots of the dipolered (resp. green) when the dynamo is evolving in a positive (resp. negative) polarityeversal). Second column from the left (panels b, f, j, n, r) shows the correspondingfrom SLI events (yellow), other contributions within positive or negative polarityown are the locations of the l and l values (solid line), and l r;l r;l rpirical distributions for the full dipole moment, using the same colour coding. Righthe same colour coding (insets show enlargements for values between 50 and 50).versals, as well as SLI events, are those dened in Section 2.3, using ltering (b). (Forthe web version of this article.)

  • andstrong preference for the location of the DP pole while in thisspherical-shell shaped central unstable state. In model (1),although DP latitudes also appear to be quite evenly distributedbetween 50 and 50 during reversals and failed reversals (ingrey), Fig. 9(d) reveals that the DP latitude distribution has stron-ger contributions for values close to 90 and 90, both at timesreversals and failed reversals (in grey, as a careful look will reveal),and at times of stable polarity (in blue). This now reects thedynamics we already observed for the dipole pole in model (1) dur-ing and just after reversals and failed reversal (Figs. 6 and 7), withthe dipole pole crossing all latitudes before spending some amountof time very close to 90 (or 90, depending on the nal polarity),while the ADM experiences its overshoot.

    4. Discussion

    One of the most striking ndings of the present study is thestrong impact of the conducting or non-conducting nature of theinner-core on the properties of the dynamo models we, and previ-ous authors, investigated (see, e.g. Olson, 2007; Olson et al., 2009;Driscoll and Olson, 2009a,b, 2011; Olson et al., 2010, 2011). As canbe seen in Fig. 9, model (1) (with an insulating inner-core) missesthe central unstable mode found in all other models (with aconducting inner-core). This qualitative difference turns out to bethe main cause of all major quantitative differences we otherwisenoted between model (1) and all other models.

    In particular, reversals or failed reversals occur on much rareroccasions in model (1) (recall Table 2), than even in models (2)and (3), which operate in exactly the same parameter range andonly differ by a slightly different RaH in the case of model (2),and a slightly different Rm in the case of model (3) (recall Table 1).In the case of model (1), the dynamo simply cannot access themissing central mode and must directly reach the very low ADMand low DP latitudes required for such events to occur. Interest-ingly, we note that Dharmaraj and Stanley (2012) recently foundan opposite effect in their dynamos (thermally-driven, run in a dif-ferent parameter regime, and at a lower Ekman number, though formuch shorter periods of time), these being more stable with, thanreversal strongly increases as the ADM values decrease. Low-valueADMs are then clearly associated with an enhanced probability ofreversal or failed reversal, reecting the fact that such events occuronce the dynamo has entered the central unstable mode.

    Plotting the empirical distribution of the total dipole momentg012 g112 h112

    qfor each model further helps to characterise

    this central mode (Fig. 9, third column from left, where the samecolour coding is being used). Not surprisingly, no remarkable sig-nature is found in the case of model (1). In contrast, the centralmode shows up in the form of a hump for low total dipole mo-ments, the size of which increases as we move from model (2) tomodel (5), reecting the evolution of this central mode frommodelto model. Interestingly, we also note that, in contrast to what wasseen in the signed ADM distributions, this hump is not centred onthe zero value, implying that contrary to the signed ADM, the totaldipole moment rarely drops to very low values. This indicates that,in the phase space, the central mode is more spherical-shell thanball shaped, reecting the fact that during reversals and failedreversals, the total dipole moment always keeps a minimum value.

    The spherical-shell shape of this unstable mode is also re-ected in the DP latitude set of distributions (Fig. 9, right column,where the same colour coding is being used). In models (2) to (5),contributions from reversals and failed reversals (in grey) are al-most uniform over all DP latitude values, reecting a lack of any

    32 F. Lhuillier et al. / Physics of the Earthwithout a conducting inner-core. Perhaps is this because, in suchregimes, the consequences of the electrical nature of the inner-coreis the opposite, leading to a transitory central mode only when theinner-core is insulating (a conjecture we could not check our-selves). In any case, what these results clearly show is that contraryto previous claims (see, e.g., Wicht, 2002, 2005; Amit et al., 2010),the exact electrical nature of the inner core can inuence thebehaviour of dynamos and in a very radical way, by modifyingthe nature of the dynamo states that can be reached. This inu-ence, however, is not directly related to the inner-core diffusivetimescale, which hardly plays any role in quantifying the frequencyof reversals.

    The fact that the ADM and DP latitude behave so differentlythrough reversals and failed reversals in model (1) (Figs. 6 and7), can also be related to a lack of central mode for this model. Withno such mode, a common dynamical path must necessarily befound by model (1) to produce each reversal or failed reversalthrough the bottleneck seen in the corresponding phase spaceplot (Fig. 9(a)). All other models produce much less of a systematicpattern, simply because reversals and failed reversals result fromtransiting through the spherical-shell shaped central mode,within which both the ADM and the DP latitude can uctuate sub-stantially (possibly producing multiple events) before reaching oneof the two stable polarity modes. Note that the specic behaviourof the ADM and DP latitude in model (1) is in fact very similar tothe one observed in experimental dynamos and simple models(e.g., Ptrelis et al., 2009), where an analogous ADM overshoot isobserved during reversals. These systems too are governed by onlytwo stable xed points (equivalent to the two stable polarity stateswe observe here).

    Other dynamo properties also appear to be affected by the elec-trical nature of the inner-core, and the lack of central mode, thoughnot as strongly. As noted in Section 3.2 (recall Fig. 5), failed rever-sals appear to last less time in model (1) than in models (2) and (3),while the average duration of successful reversals in model (1) ap-pears to match the trend observed for all other models. But a care-ful look at Fig. 5(a) and (b) reveals much less dispersion in theduration of these events in model (1) than in all other models, tes-tifying again for the more systematic way the eld reverses inmodel (1). In contrast, models (2) and (3) can have reversals andfailed reversals displaying a wider variety of durations, simply be-cause of the wider range of time the dynamo can spend in the cen-tral mode. Also worth pointing out, is that in all cases, the basicreference time controlling reversal and failed reversal durationsis sg (and not sSV, recall Tables 1 and 2), indicating a clear controlof diffusion during reversals in all these dynamos, even when theinner-core is insulating (which further points at diffusion withinthe entire core, rather than just within the inner-core, being keyin dening this basic reference time, contrary to what has beensuggested could be the case for the Earth (Gubbins, 1999)).

    The two quantities that are least affected by the electricalnature of the inner-core are the secular-variation timescale sSVand the error-growth timescale se, both of which are dynamicalcharacteristics of the dynamo at times of stable polarity. Theseappear to be mainly governed by Rm (recall Fig. 2), in agreementwith previous results (Hulot et al., 2010a; Lhuillier et al.,2011a,b), and are insensitive to the presence or absence of a centralmode.

    Analysing Fig. 9 also brings important insight into the nature ofthe evolution of the dynamos when increasing the strength withwhich they are driven. As RaH increases (from model (2) to model(5), all other parameters being kept unchanged), a change in therelative contributions of the central unstable mode and of thetwo positive and negative stable modes can be observed. Whereasmodel (2) displays a relatively weak and diffuse central mode con-trasting with strong and well-dened stable modes, models (3) to

    Planetary Interiors 220 (2013) 1936(5) progressively display a stronger, better dened, and more com-pact central mode. In the case of model (5), this central mode hasessentially become as signicant and as often occupied as the two

  • to being dominantly multipolar with a single central unstablemode. Our results are in complete agreement with this view, andillustrate the crucial role played by the central unstable mode incontrolling most aspects of reversals (and failed reversals) in thesedynamos.

    Another important outcome of the present study is the propertyfound in all our models that, provided that the nite duration ofreversals and failed reversals is properly taken into account, chronand segment lengths (dened as described in Section 2.1) always


    andstable modes. This evolution is accompanied by a slight decrease ofthe dipolarity measure f 13dip and a slight increase in the equatorial toaxial dipole measure f eqax (recall Table 1). It is also related to thetrend observed in Fig. 4 for chrons and segments to last less (andtherefore for the reversal frequency to increase) as RaH (and there-fore Rm) increases, and to the evolution seen in Fig. 5 for the dura-tions of reversals and failed reversals. The non-linear nature of thisevolution can now be understood as a consequence of the complextransition from model (2) to (5).

    To explore the way the dynamo evolves as RaH is further in-creased, two additional (shorter) runs have been computed (mod-els (6) and (7)), the phase-space behaviours of which have beenplotted in Fig. 10, using the same analysis procedure as previously.As can be seen, the central unstable mode now becomes so prom-inent, that it merges with the two stable modes and that the sep-aration in terms of these modes (and the identication of chrons,segments, reversals, failed reversals and SLI events) becomes irrel-evant. Virtually only a central unstable mode remains to be seen inmodel (7), despite the attempt made by our algorithm to identify

    Fig. 10. Phase space behaviour of additional models 6 (rst

    F. Lhuillier et al. / Physics of the Earthstable modes. These models essentially keep on reversing (as hadbeen anticipated based on Figs. 4 and 5), and no stable valuescan be dened for the various quantities reported in Table 2. Basicparameters for these models are otherwise reported in Table 3. Forthese two models, and particularly model (7), the eld is nowmuch less dipolar, and with a much stronger equatorial to axial di-pole ratio.

    What we observe in terms of dynamo evolution thus comple-ments what Olson et al. (2011) recently observed in an analogousset of dynamo simulations, which they however ran over muchshorter periods of time (typically ten to twenty times sg D2=g,to be compared to the several thousands of sg over which our dy-namo models were run, recall Table 1). These dynamos also differfrom ours in several other aspects see for details (Olson et al.,2011). But they are in similar regimes, with the same Prandtl num-ber Pr 1, a slightly weaker magnetic Prandtl number of Pm 10,and comparable Ekman numbers. In particular, Olson et al. (2011)investigated the evolution of dynamos with E 1 103 as theRayleigh number is progressively increased. They too noted a ma-jor evolution from their case (a), which did not reverse over thecourse it was run and displayed a single polarity stable mode, totheir multipolar permanently reversing case (d), displaying a singlecentral unstable mode, with several intermediate cases (see theirFig. 2). Although these short runs were not analysed in the waywe did, they already revealed the way dynamos can evolve frombeing dominantly axial dipolar with two stable polarity modes,

    ) and 7 (second line). Same plotting convention as in Fig. 9.

    Table 3Properties of the two additional models (6) and (7). Same denitions as in Table 1.

    Model Inner-core T=sg RaH Rm f 13dip feqax sSV=sg

    (6) Conducting 250 8:5 103 165 0.44 0.43 2:19 102(7) Conducting 250 1:1 102 202 0.36 0.48 1:91 102

    Planetary Interiors 220 (2013) 1936 33closely follow a Poisson distribution (Section 3.1). This conrmssimilar ndings by, e.g. Wicht et al. (2009) (who investigated ther-mally-driven dynamos, with an even higher Ekman number ofE 1 102). It also implies that these dynamos do not have anymemory of their past behaviour beyond the duration of a reversalor failed reversal. This is consistent with the fact that the error-growth timescale se, dening the limit of predictability of thesedynamos at times of stable polarity (see Hulot et al., 2010a;Lhuillier et al., 2011b for details), is always found to be an orderof magnitude smaller than the duration of reversals and failedreversals (recall Tables 1 and 2). Note that such a small value ofse only implies that the time when the dynamo leaves a stablemode cannot be predicted much ahead of time. But it does not pre-clude some amount of longer-term deterministic behaviour of theeld during reversals, as is particularly well illustrated by model(1). In this model, as soon as the dynamics leads the system to en-ter the bottleneck seen in Fig. 9(a) , the dynamo almost unavoid-ably produces either a reversal or a failed reversal (reversals beingve times more likely than failed reversals, recall Section 3.1 andTable 2), following the near-systematic pattern shown in Figs. 6and 7. Even in the case of models (2) to (5), some amount of deter-minism is also to be found. For these models, once the system hasentered the central unstable mode, either a reversal or a failedreversal will almost inevitably occur (with reversals again beingslightly more likely, recall Section 3.1 and Table 2), though the

  • Much more constructive conclusions can be drawn from theanalysis of the g01; g11;h11 phase-space plots shown in Fig. 9. This

    andanalysis taught us that it is the occurrence and size of a centralunstable mode that controls most of the reversing properties ofthe investigated dynamos (independently of the reason why suchan unstable mode would be found). Unfortunately no analogousexact way the event will unravel is then much less reproducible.Finally, we note that the previous results are also consistent withthe fact that no clear correlation could be found between ADMsand the duration of chrons or segments (Section 3.4).

    5. Concluding remarks: implications for paleomagnetism andthe geodynamo

    Dynamos investigated in this paper belong to a class of dyna-mos that have been extensively used to understand and tentativelyreproduce important features of the Earths magnetic eld (e.g.Driscoll and Olson, 2009a,b, 2011; Olson et al., 2010, 2011,2013). Although not all concepts introduced in this study exactlycompare with equivalent paleomagnetic data-derived concepts,our results do bring important complementary insight.

    First, consider the distribution of chron lengths. As discussedabove, this distribution is always found to be very nearly Poisso-nian for this class of dynamos. This is very similar to what is beingobserved for geomagnetic polarity reversals, the history of which iswell known for the past 160 Myr, thanks to the record of oceanicmagnetic anomalies (e.g. Cande and Kent, 1992a; Channell et al.,1995). Indeed, when analysed in terms of a Gamma process (as de-ned by Eq. (3)), this data leads to the conclusion that contrary tol, which may vary through geological times, k does not signi-cantly differ from unit (e.g. Lowrie et al., 2004), even though itsdetermination remains limited by our imperfect knowledge ofchrons shorter than 30 kyr and by the still discussed issue of thephysical origin of the tiny wiggles also known as cryptochrons,corresponding to sea-oor magnetic anomalies of very short wave-length (e.g. Cande and Kent, 1992b; Bouligand et al., 2006). Theseobservations thus suggest that, just like the dynamos investigatedhere, the geodynamo produces reversals as a result of a processwith no memory beyond the duration of a reversal, a conclusionalso consistent with a predictability horizon of a few decades forthe geodynamo (Hulot et al., 2010a; Lhuillier et al., 2011b), muchshorter than the duration of a reversal.

    If the above conclusion holds, the results of Section 3.4 wouldthen also suggest that no clear-cut correlations should be expectedbetween ADMs and the duration of chrons, beyond the type of ef-fects seen in Fig. 8, with more dispersion and slightly weaker val-ues when chrons are short. Precise relative paleointensity recordsfrom pelagic Oligocene sediments (Tauxe and Hartl (1997), see alsoConstable et al. (1998)), and similar records for the past 2 Myr(Valet et al., 2005), only suggest a weak correlation, which a veryrecent study on Eocene to Oligocene sediments (Yamazaki et al.,2013) further argues could be due to some lithological contamina-tion of the paleointensity records. Lava ow and submarine basal-tic glass samples also only suggest a weak correlation, with verysubstantial dispersion that could be enhanced when the ADM islarger (e.g. Tauxe, 2006; Tauxe et al., 2007). Although clearer corre-lations might eventually emerge when considering longer timeperiods (then possibly reecting long-term changes in the param-eters driving the geodynamo, see, e.g. Hulot et al. (2010b) for adiscussion), what our results thus suggest is that recovering moreaccurate information from such correlations is clearly bound toremain a challenge.

    34 F. Lhuillier et al. / Physics of the Earthplot can directly be built from the data for the geodynamo. Butindirect paleomagnetic signatures can be looked for. Comparingthe distribution of signed VADM from Sint-2000 (Valet et al.,2005) to those of the signed ADM in Fig. 9 suggests that the geody-namo is much closer to model (1) (Fig. 9b) than to any other model.The same data also reveals a systematic pattern of the VADMbehaviour through reversals, with an overshoot akin to the onewe observe for model (1) (compare Fig. 4 of Valet et al., 2005 toFig. 6). Finally, and as recently pointed out by Valet et al. (2012),the VGP latitude might also display a systematic pattern, again asthe DP latitude in model (1), though the details of this systematicpattern are clearly different, and the comparison of VGP and DP lat-itudes might not be fully appropriate. Such a combination of char-acteristics would then suggest that the geodynamo currently hasno signicant central unstable mode.

    Other evidence, however, suggests that the situation might notbe as simple. Model (1), which produces a systematic pattern inboth the ADM and the DP during reversals, also does so duringfailed reversals. Yet no analysis of paleomagnetic data have re-vealed such patterns in the VADM and VGP so far. This could be be-cause our dynamo-based concept of failed reversal does notexactly match the more empirical data-based paleomagnetic con-cept of excursion (Laj and Channell, 2007). Indeed, both failedreversals and strong SLI events (based on our denitions in Sec-tion 2.3) can lead to events that would qualify as excursions fromthe paleomagnetic point of view. Since SLI events only are one-sig-ma-plus uctuations of the eld within the stable modes (as can bechecked in Table 2, our dynamos indeed all spend roughly 15% oftheir stable polarity time in such SLI events), they do not produceany systematic pattern. The pattern of failed reversals could thusbe blurred by the occurrence of SLI events qualifying as excursions.In this respect, selecting paleomagnetic data in a way bettermatching our denition of failed reversals, could be very useful.It might eventually reveal a more systematic pattern and shed evenmore light on the reversal process. It could also bring new insightinto the interpretation of still poorly understood short events, suchas microchrons, excursions and cryptochrons (e.g. Cande and Kent,1992b; Bouligand et al., 2006; Laj and Channell, 2007).

    Some paleomagnetic data further show that complex rever-sals (as dened by Coe and Glen, 2004) can also sometimes occur,closer to the type of reversals model (2), rather than model (1),would produce. This has been the case, for instance, for the lastknown (MatuyamaBrunhes) reversal. Reversals with similarbehaviours were investigated recently by Olson et al. (2011), usingdynamos similar to ours. Their model (i), that best accounted forthis behaviour, displays a small, but clear, central mode signature(in the ADM distribution, see their Fig. 2), similar in this respectto our model (2). The average duration of paleomagnetic chrons( 230 kyr) over the past 30 Myr (segment C of the geomagneticpolarity timescale dened by Gallet and Hulot (1997) and Hulotand Gallet (2003)), also appears to be closer to the average chronlength of 180 kyr found in model (2) than to the average chronlength of 500 kyr found in model (1), both rescaled with respectto sSV. This now suggests that the present geodynamo could have asmall central mode introducing some amount of complexity inreversals, small enough, however, to not jeopardise the tendencyfor reversals to usually display some systematic patterns (Valetet al., 2005, 2012).

    If such is the case, the geodynamo could thus belong to a dyna-mo family displaying a small central mode with a strong sensitivityto changes in driving parameters. The relative size and role of thiscentral mode would then evolve in response to such changes as ourmodels evolved in response to changes in the Rayleigh number RaH

    (Fig. 9). In particular, the frequency of reversals would display astrong sensitivity to even minor changes, with this frequency beingproportional to some distance from a critical state, as illustrated by

    Planetary Interiors 220 (2013) 1936sg=lchr being proportional to the distance Rm Rmc , withRmc 90 in our models (recall Fig. 4). Since this would then alsoimply a total lack of reversals as soon as one reaches Rm Rmc ,

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    Statistical properties of reversals and chrons in numerical dynamos and implications for the geodynamo1 Introduction2 Models and tools2.1 Numerical models2.2 Timescales of interest2.3 Defining reversals, failed reversals, stable low intensity events, chrons and segments2.4 Statistical distributions of interest

    3 Results3.1 Distribution of chron and segment lengths3.2 Duration of reversals and failed reversals3.3 Dipole field behaviour during reversals and failed reversals3.4 ADM versus chron and segment lengths3.5 A phase space perspective

    4 Discussion5 Concluding remarks: implications for paleomagnetism and the geodynamoAcknowledgementsReferences


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