Climate modelling between bits and bytes

Hi there. I’m Milan.

I’m a climate scientist at the University of Oxford where I am a Schmidt AI in Science Fellow. I did my Postdoc at the Massachusetts Institute of Technology after completing a PhD in climate computing at the University of Oxford. Before I even started my PhD, I had a very comprehensive education in climate physics from different universities in Germany, France, and Norway. During that time, I crossed the Tropical Atlantic on an oceanographic research vessel and spent a winter in Svalbard studying Arctic meteorology. The combination of fieldwork with the theoretical and computational work of my research has allowed me to see the bigger picture and to better understand the climate system as a whole. My research interests and areas of expertise include

  • Climate modelling: Combining machine learning and dynamical models, atmosphere and ocean, grid-point and spectral, dynamical core development, stochastic parameterizations, turbulence closures.
  • Computing: High-performance, low-precision, parallel, CPU vs GPU, number formats, stochastic rounding, efficiency.
  • Data compression: Lossy and lossless, information theory, data formats.
  • Predictability of weather and climate: Chaos, uncertainty, ensemble prediction, error growth, weather forecasting.
  • Software engineering: Open source, multiple dispatch and code composability, automatic differentiation, and the Julia programming language.
  • Aviation and decarbonisation: Global warming impact, non-CO2 effects, emission scenarios, carbon footprints.

As a climate scientist, I feel responsible to voice urgency and hope on the climate catastrophe, both through media and in teaching. While my main research develops computationally efficient climate models, I see great potential in interdisciplinary work to tackle other urgent problems to sustain the health of our planet.