Mathematical approaches to the study of predictability in a minimal model of weather and global climate

Nesbitt, C. (2024) Mathematical approaches to the study of predictability in a minimal model of weather and global climate. PhD thesis, University of Reading. doi: 10.48683/1926.00129530

Abstract/Summary

The climate system is a forced, dissipative, complex system comprised of interacting components that pose a huge challenge to scientists trying to understand and predict its behaviour. With regards to prediction, the varying timescales of the climate system mean that one must distinguish between different kinds of prediction dependent on the timescale of interest. Simply put, the challenge of understanding the jet stream is different from the challenge of understanding ice ages. This thesis will be concerned with two essential features of the climate system that make the challenge of predictability difficult on different scales: chaos and multistability. The chaotic nature of the atmosphere means that weather prediction is notoriously difficult as even small errors in initial conditions can lead to large errors in a forecast. Multistability, on the other hand, is relevant for understanding the Earth’s climate on longer timescales. Indeed, the Earth can stably support more than one climate, something that should be kept in mind when considering the possible impact of tipping points and safe operating spaces for humanity in the context of anthropogenic climate change. We will study both of these concepts within a single minimal model, that couples a bistable energy balance model to the chaotic Lorenz 96 model. To study the chaoticity of the model, we will use Lyapunov exponents and Covariant Lyapunov Vectors. We will also study the multistability of the model by examining two boundary crises that occur as the chaotic attractors of the model collide with the basin boundary. Finally, we will examine the impact of noise on the model through the lens of large deviation theory and the quasipotential.

Item Type	Thesis (PhD)
URI	https://centaur.reading.ac.uk/id/eprint/129530
Identification Number/DOI	10.48683/1926.00129530
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Mathematics and Statistics
Date on Title Page	December 2023
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