Hannah Christensen

Research Interests

Processes occur in the atmosphere on a wide range of spatio-temporal scales, from cloud processes on the micrometre scale, through convective aggregation on the hundreds of kilometre scale, up to global scale emergent phenomena such as the El Nino-Southern Oscillation (ENSO). Yet we require our weather and climate models to capture all of these processes to make accurate predictions – a formidable challenge! To improve predictions we need a deeper understanding of the underlying physics together with an accurate characterisation of uncertainties associated with the modelling process. Any projects under this overarching theme would be developed in discussion with the student, but could involve investigation into sources of atmospheric predictability such as the El Nino-Southern Oscillation; the use of extremely high-resolution simulations to understand small-scale atmospheric processes; or atmospheric model development, with the potential for developments to be incorporated into world leading weather and climate models. The student would use a range of techniques, from modelling simple chaotic systems to global climate simulations. A growing area of research in Hannah’s group is also the application of Machine Learning tools to the climate system. The overarching aim of any project is to understand the behaviour of the atmosphere, and improve our ability to predict this behaviour.

Professional Qualifications and Experience

Hannah has lectured on chaos and dynamical systems for both undergraduate physicists and for the DTP at the University of Oxford. She has taught Masters level classes in ‘Physics of the Atmosphere and Ocean’, and has tutored a range of courses for undergraduate Physicists and Mathematicians here at Oxford. She supervises Oxford research students, including students studying for DPhil, MPhys, and MMath degrees. She also demonstrates in the undergraduate atmospheric physics laboratories.


Personal Research Keywords

weather and climate modelling, uncertainty, predictability and chaos, machine learning, atmospheric physics, stochastic processes