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    Solid earth sciences

    201511201511

    Dynamic responses of the Earth's outer core to assimilation of observed geomagnetic secular variation

    Kuang W, Tangborn A

    Geodynamo, Geomagnetic field, Secular variation, Core flow, Data assimilation

    The rms (OƑ)B of the magnetic field in Case II (dashed line) and Case III (solid line). In both cases, (OƑ)B≪1 and decays monotonically after the first 3 analysis cycles, and then levels off in the last 20 years. This shows the continuing improvement in the forecast accuracies. In addition, the (OƑ) results in Case III (with the assimilation of and ) are in general more than 20 % smaller than in Case II (with only the assimilation of ), showing a clear improvement in forecast accuracies.

    Assimilation of surface geomagnetic observations and geodynamo models has advanced very quickly in recent years. However, compared to advanced data assimilation systems in meteorology, geomagnetic data assimilation (GDAS) is still in an early stage. Among many challenges ranging from data to models is the disparity between the short observation records and the long time scales of the core dynamics. To better utilize available observational information, we have made an effort in this study to directly assimilate the Gauss coefficients of both the core field and its secular variation (SV) obtained via global geomagnetic field modeling, aiming at understanding the dynamical responses of the core fluid to these additional observational constraints. Our studies show that the SV assimilation helps significantly to shorten the dynamo model spin-up process. The flow beneath the core-mantle boundary (CMB) responds significantly to the observed field and its SV. The strongest responses occur in the relatively small scale flow (of the degrees L≈30 in spherical harmonic expansions). This part of the flow includes the axisymmetric toroidal flow (of order m=0) and non-axisymmetric poloidal flow with m≥5. These responses can be used to better understand the core flow and, in particular, to improve accuracies of predicting geomagnetic variability in the future.