Core flow modelling assumptions

Modelling of core flows at the core-mantle boundary from secular variation (SV) requires a range of both physical and mathematical assumptions in order to derive a solution. We investigate the role of certain assumptions and an L1L1 norm iterative inversion method to derive core flow models. Using three datasets of SV, we separate the effects of: (a) the assignment of observation errors through the data covariance matrix, (b) the a priori constraints placed upon the solution and (c) the type of flow regime assumed to be present in the core. Flow is calculated directly from the time derivatives of the X, Y and Z   components of ground-based observatories rather than Gauss coefficients of the SV. We find the L1L1 iterative method improves the fit of the SV generated by the flow models to the observed data, compared to the L2L2 norm (least-squares) method. Using this method, we find a new class of flow solutions explaining the SV: purely poloidal flows, which fit the input data adequately and, for one of our datasets, better than toroidal-only flows. The patterns of motions is very different from that seen in previous flow models, which are dominated by their toroidal component.