Dr Luke Abraham (Department of Chemistry, University of Cambridge)
Dr Fiona O’Connor (UK Meteorological Office)
Professor Manoj Joshi (School of Environmental Sciences, University of East Anglia)
State-of-the-art computer models of Earth’s climate are key to our understanding of climate change. These Earth system models (or ESMs) are based on fundamental scientific knowledge of physical, chemical and biological processes shaping the Earth’s atmosphere and oceans. A particularly important ESM component is atmospheric chemistry, which modulates global warming by influencing atmospheric composition, and which gives rise to the existence of the stratospheric ozone layer (best known for its role in protecting us from harmful solar ultraviolet radiation). However, atmospheric chemistry also doubles the time it takes to run ESM simulations on supercomputers, and therefore pose a central bottleneck in climate change simulations.
Your project will focus on a recently developed machine learning approach to resolve this bottleneck, with the goal to substantially speed-up climate change simulations. Specifically, you will develop a new hybrid model version of the UK Earth System Model (UK-ESM; https://ukesm.ac.uk/) in which atmospheric chemistry is replaced by a machine learning model learned from NASA satellite observations and existing ESM data. You will benchmark the performance of your novel approach against established methods, compare a variety of machine learning tools, and evaluate the hybrid-model concerning its ability to realistically model the physics and chemistry of past and possible future climates.
You will become an expert in the science of climate change and in advanced data science tools, including machine learning and supercomputing. You will further gain insights into the science behind the ozone hole. You will acquire these highly sought-after skills by working with leading experts at UEA, the University of Cambridge and the UK Met Office, where you will visit and work with UK-ESM developers. As a member of an interdisciplinary cohort of PhD candidates, you will develop many other transferable skills, and you will present your work at national and international conferences.
We are looking for a highly motivated individual with a keen interest in learning more about machine learning, programming and the science behind climate change. We welcome applications from candidates of all numerical disciplines with degrees in Mathematics, Physics, Computer Science, Chemistry, Engineering or Environmental Modelling.