Effects of geodynamo priors and geomagnetic data on inverted core surface flows

High quality continuous satellite magnetic data gives unprecedented insights into core surface motions but the inversion process remains under-determined, thus requiring additional information. We make use of the pygeodyn inversion tool, in which the core surface dynamics is forecasted with stochastic equations anchored to prior information from a geodynamo free run. There is no clear overview of the relative importance of the different ‘ingredients’ entering in the core flow inversion process, or of the robustness of the inferred flow features. We document the impact of three factors (or ‘ingredients’): (1) choice of geodynamo prior, (2) choice of geomagnetic field model, and (3) choice of maximum spherical harmonic degree for secular variation. We compare three field models (CHAOS-7, COV-OBS.x2 and Kalmag) and three truncation degree for secular variation. The six dynamo priors allow us to analyse the influence of changing the input parameters towards Earth-like values, changing the dynamo set-up (isotropic boundary conditions or not, stratified or not), and the torques at play. We quantitatively show that changing the prior has a greater impact on the core surface flow than the choice of magnetic field model used as observations. This difference becomes less pronounced in the past due to the data quality decreasing and the field models showing more divergence. Furthermore, we find that the slowly evolving ‘background’ component of flow is more variable among models than the transient component. Transient flow motions appear very coherent for all considered dynamo priors and field models. The recovery from satellite measurements of subdecadal wave dynamics and of a growing eastward motion under the Pacific are robust and appear data driven. Finally, we quantify the efficiency of core flows for generating magnetic variations as a function of frequency. Normalized to their magnitude, rapid changes generate relatively more magnetic signal than slow variations. This is interpreted as the signature of a slow core dynamics approaching a state where induction is mitigated because of dynamical adjustment. As a consequence, a larger part of core surface motions enters the null-space of the kinematic core flow inverse problem towards long time-scales. • Evaluate prior information & magnetic data influence on core surface flow inversion . • Changing the dynamo set-up has the largest impact on recovered motions . • Wave motions & decadal trends are only weakly sensitive to the prior & field model . • Quantify the efficiency of transient motions at producing magnetic field changes . • Towards long time-scales the field & flow self-adjust as to minimize induction .