Sophia Wisniewska

Profile

I am PhD student in the Institute for Climate and Atmospheric Science (ICAS). I am funded by the NERC UNRISK CDT and hold a CASE partnership with the Met Office.

Before starting my PhD, I graduated in a Physics (MSci) from the University of Bristol, and worked for four years as a data science consultant in London. Through my data science role, I gained hands-on experience working with diverse datasets, using machine learning and data science techniques to gain insight from company data to drive business decisions. In my spare time, I enjoyed reading about atmospheric science, and gained a particular interest in cloud processes. This motivated me to conduct research in climate modelling to reduce the uncertainty in climate predictions – at a time when fostering global commitment to reducing the impact of anthropogenic climate change is more critical than ever.

Research interests

Cloud behaviour is governed by a wide range of complex processes occurring across multiple scales, from microphysical interactions within clouds to large-scale cloud dynamics. Cloud feedbacks and cloud-aerosol interactions are still poorly represented in climate models, and accurately simulating these phenomena requires a multi-faceted approach. Atmospheric motions are typically resolved using Large Eddy Simulations (LES), but certain interactions occur on scales smaller than the simulation grid size, and must be represented through parameterisations. However, identifying optimal parameter combinations that align with observational data is computationally intensive. My PhD research at the University of Leeds employs a novel methodology involving Perturbed Parameter Ensembles (PPEs) to train machine learning emulators. This approach enables efficient exploration of parameter space to identify ideal parameter sets, thereby improving the representation of clouds in climate models and enhancing their predictive capabilities.

Qualifications

• MSci Physics, University of Bristol (2020)