Dr Kristian Strommen's main work is on developing well calibrated stochastic schemes for representing uncertain processes in climate models.
Stochastic schemes have already proven themselves in the context of short-term weather forecasting, but their potential for improving long-term climate projections is still a very new area of research. Dr Strommen's focus is on the impact such schemes have on model variability, mean state and equilibrium climate sensitivity (i.e. global warming), with the goal of producing a 'stochastic climate model' which better represents the climate system. Somewhat paradoxically, the deliberate introduction of uncertainties in the small-scale processes of a model can thereby reduce uncertainties in the large-scale aspects that we are most interested in, such as projections of global warming.
Dr Strommen is also working on understanding issues around seasonal predictability, particularly for European winters. In particular the questions of identifying sources of skill for predicting the winter North Atlantic Oscillation, as well as understanding the nature of this skill. He is actively working on understanding how regime behaviour in Euro-Atlantic weather influences predictability on longer time-scales.
His DPhil thesis, Galois Groups and Anabelian Reconstruction, straddled number theory, field theory and logic. This background in pure maths has prompted an attempt to use more sophisticated mathematical tools to help understand climate data. One example of this is using persistent homology to analyse topological features of the climate attractor and relating these to regime structure.
- Climate models
- The North Atlantic Jetstream
- Small-scale processes
- Stochastic physics