Estimating core dynamics via the assimilation of magnetic field models into numerical dynamos

A significant portion of the Earths observed magnetic field is sustained by fluid motion in the planets outer core (geodynamo) and varies over time. Records of the past magnetic field come from a variety of sources including, paleo- and archaeomagnetic data. In the modern era, satellite-based observations from missions such as SWARM, have led to a new level of spatial and temporal resolution in our knowledge of the magnetic field. Such observations of the fields secular variation (SV) can provide a unique window into the deep interior of the Earth. However, understanding the origins and implications of observed SV calls for connecting data to models of Earths core dynamics. Over the last 10-15 years, there has been increasing interest in using data assimilation (DA) to connect numerical dynamo simulations with magnetic field observations. DA is a general term for methods by which one can produce a weighted combination of numerical models and observations, to estimate a systems overall state. This approach is widely used in applications such as numerical weather prediction, where DA is used to, for example, determine initial conditions for forecasts. We present recent work in the development of DA as a tool for understanding the Earths deep interior, using NASAs Geomagnetic Ensemble Modeling System (GEMS). In simple terms, we nudge an ensemble of numerical geodynamo model runs toward observed magnetic field variations according to an Ensemble Kalman Filter (EnKF) framework. This process has the potential to recover information about dynamics which cannot be directly observed, such as the fluid flow and magnetic field deep within the interior. We highlight recently improved capabilities of GEMS, investigate its ability to constrain the core state, and discuss the impact of SWARM data on this work.