Can advanced core-flow modelling, combining inverse and machine learning approaches, improve our understanding of the geodynamo and reduce uncertainties associated with the South Atlantic Magnetic Anomaly?

Hannah is an observationally-motivated geomagnetist whose research focuses on understanding the dynamics of Earth’s core through inverse methods and data assimilation. Her background is in geophysics, having completed an undergraduate degree at Durham University followed by a Masters by Research and PhD at the University of Edinburgh, where she developed and applied regional spectral inverse methods to study secular variation and core flow. After her PhD, she moved to Grenoble, France, for a two-year postdoctoral position within the ERC-funded GRACEFUL project, where she investigated the influence of geodynamo priors and geomagnetic field models on core flow inversions and incorporated localized geomagnetic field approximations into the pygeodyn framework. Between 2024 and 2026, she worked at the University of Leeds, comparing palaeomagnetic observations and geodynamo simulations to investigate Earth’s magnetic field morphology and variability through time. In August 2026, Hannah begins an ESA Living Planet Fellowship, where she will continue developing innovative approaches to studying Earth’s magnetic field and core dynamics. Alongside her research, she is active in science communication and outreach as Chair of the IAGA Communication and Networking Working Group, contributing to documentaries, educational videos, and short scientific films.

Research abstract and objectives

Earth’s magnetic field is a fundamental but poorly understood part of our planet, generated by the chaotic convection of liquid metal deep within Earth’s outer core. During severe space weather events, harmful electromagnetic energy and particles stream from the sun towards the Earth where the geomagnetic field acts as a protective shield, safeguarding electronic infrastructure including the vast network of man-made satellites essential to modern society. A geomagnetic storm on the scale of the 1859 Carrington Event (the largest in written history) could result in an estimated global loss of over $2.6 trillion, highlighting how essential it is to model Earth’s geomagnetic shield and the core-flow that dictates how it will change. There are two key aspects of the geomagnetic field that we currently cannot explain: why the strength of the magnetic dipole has decreased by nearly 6% per century since the 1800s, and why there is a growing anomalously weak region of the field in the South Atlantic. The low geomagnetic field intensity in the South Atlantic has been linked to higher radiation and data corruption in ESA Swarm satellites (2013-), suggesting future space weather hazard will increase as the local field weakens and expands westwards. Currently, we cannot accurately predict the future of the geomagnetic field, highlighting the uncertainty of future space hazard, due to our lack of understanding of the dynamics of Earth’s core. In particular, global modelling techniques used in state-of-the-art core-flows have limited applicability to predict localised changes in geomagnetic shielding because they suffer from leakage of noisy data near the poles to lower latitudes, and (by design) describe globally large-scale rather than local features. In this fellowship I propose to create a new paradigm of purely regional data-driven models of core flow, REgional core Flow Inversions (REFI), which will resolve local core flow based on only local geomagnetic data. In doing so, I will be able to innovatively characterise and understand the local dynamics of the core with three new inversion methodologies, leading to a new quantitative understanding of the changes in the geomagnetic field and its impact for shielding future space weather hazard. This work will incorporate multi-mission data from ESA and its partner agencies to develop novel data products and new scientific insights, maximising scientific return. It will mutually benefit on-going ESA activities such as the Swarm DISC (Data, Innovation, and Science Cluster), ESA 4D Earth: Core+ and the ESA-China Dragon-6 collaboration and contribute to the upcoming ESA NanoMagSat mission. The project will be hosted at the University of Leeds in one of the largest and most interdisciplinary deep-Earth research groups internationally. This research within REFI will achieve the following objectives (O): (O1) Develop state-of-the-art inversion methods for regional core surface flow models, including the first application of Sparse Bayesian Learning. These will be validated using known flow and field extracted from existing cutting-edge geodynamo simulations, (O2) Model regional core surface flow in areas linked to space weather hazard including incorporating uncertainties related to different inversion methods, flow modelling assumptions, and data uncertainty.

Scientific questions addressed by the project:

• Can regionally modelling the flow of liquid iron at the top of Earth’s outer core improve our ability to understand the complex dynamics generating the geodynamo, especially in relation to the anomalously weak magnetic field over South America?
• Can we improve core flow uncertainty estimates by utilising a range of methodologies from traditional inverse methods to new machine learning techniques?