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Mantle Convection and Surface Expressions


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of 250 km thickness. All of the models include time‐dependent prescribed surface plate motions, which shape the large‐scale structure of mantle flow. We adopt plate motions from a recent paleogeographic reconstruction by Matthews et al. (2016), which spans 410 Ma‐present, although some of the calculations do not include the entire plate motion history. All of the models except Case 40 (Table 1.1) impose the initial plate motions for a period of 150 Myr to spin‐up the model and initialize large‐scale structure following Zhang et al. (2010). The mechanical boundary conditions at the core‐mantle boundary are free‐slip, and the temperature boundary conditions are isothermal with a nondimensional temperature of 0 at the surface and 1 at the core–mantle boundary. We use a temperature‐ and depth‐dependent viscosity with the form η(z) = ηz(z) exp [E(0.5 − T)], where ηz(z) is a depth‐dependent viscosity prefactor and E = 9.21 is a dimensionless activation energy, which gives rise to relative viscosity variations of 104 due to temperature variations. The models are heated by a combination of basal and internal heating, with a dimensionless internal heating rate Q = 100.

      1.2.3 Inversions for Viscosity

      We carried out inversions for the mantle viscosity profile constrained by the long‐wavelength nonhydrostatic geoid. The amplitude and sign of geoid anomalies depend on the internal mantle buoyancy structure as well as the deflection of the free surface and core‐mantle boundary, which, in turn, are sensitive to the relative viscosity variations with depth (Richards and Hager, 1984; Hager et al., 1985). Because the long‐wavelength geoid is not very sensitive to lateral viscosity variations (e.g., Richards and Hager, 1989; Ghosh et al., 2010), we neglect these, solving only for the radial viscosity profile. The geoid is not sensitive to absolute variations in viscosity, so the profiles determined here show only relative variations in viscosity, and absolute viscosities could be constrained using a joint inversion that includes additional constraints such as those offered by observations related to glacial isostatic adjustment. In order to estimate the viscosity profile, we first convert buoyancy anomalies from mantle tomographic models into density anomalies and then carry out a forward model to generate model geoid coefficients. We then compare the modeled and observed geoids using the Mahalanobis distance

      (1.3)equation

      where images denotes a vector of geoid spherical harmonic coefficients calculated from the viscosity model with parameters images, images is the data‐plus‐forward‐modeling covariance matrix. The Mahalanobis distance is an L2‐norm weighted by an estimate of data+forward modeling uncertainty, and is sensitive to both the pattern and amplitude of misfit.

      We used geoid coefficients from the GRACE geoid model GGM05 (Ries et al., 2016) and the hydrostatic correction from Chambat et al. (2010). We use a transdimensional, hierarchical, Bayesian approach to the inverse problem (e.g., Sambridge et al., 2013), based on the methodology described in (Rudolph et al., 2015). We carry out forward models of the geoid using the propagator matrix code HC (Hager and O’Connell, 1981; Becker et al., 2014). Relative to our previous related work (Rudolph et al., 2015), the inversions presented here differ in their treatment of uncertainty, scaling of velocity to density variations, and parameterization of radial viscosity variations.

Case z lm Δηlm LVC? Spinup time Phase transition? Start time
Case 8 660 km 100 No 150 Myr No 400 Ma
Case 9 660 km 30 No 150 Myr No 400 Ma
Case 18 1000 km 100 No 150 Myr Yes 400 Ma
Case 32 660 km 100 No 150 Myr Yes 400 Ma
Case 40 660 km 100 Yes 0 Myr Yes 250 Ma
Graphs depict the (A) Viscosity profiles used in our geodynamic models. For comparison, we also show viscosity profiles obtained in a joint inversion constrained by glacial isostatic adjustment (GIA) and convection-related observables, a combination of geoid, GIA, geodynamic constraints and a joint inversion of GIA data including the Fennoscandian relaxation spectrum. (B) Spectral slope vs. depth computed from the dimensionless temperature field of the geodynamic models. (C) Correlation at spherical harmonic degrees 1–4 between each of the geodynamic models and SEMUCB-WM1.