Understanding the Effect Rotating Sunspots have on their Magnetic Environment

Abstract

Sunspot rotation is a recognised mechanism for the injection of excess magnetic energy into the solar atmosphere, where that energy may be used to power eruptive events. This work aims to extend the current understanding of how sunspot rotation transfers energy from within the Sun into the magnetic fields in the solar atmosphere by carrying out numerical simulations of sunspot rotation. An idealised model to represent a simple sunspot is developed and used to identify the processes present during sunspot rotation, to determine how much energy sunspot rotation injects and to understand how each characteristic property of a sunspot contributes to the injection of magnetic energy. The investigation identifies a number of important properties of sunspot rotation. Specifically, the penumbra is found to be fundamentally important for the injection, storage and transport of magnetic energy. All simulations show that the majority of the excess magnetic energy injected by sunspot rotation, is located in the penumbra. The significance of this is that, in the model used, the penumbra represents a relatively small region and thus this finding highlights regions of high energy density may exist in the natural phenomenon.
Secondly, a set of scaling laws are derived that give a numerical estimate of how the magnetic energy injected by rotating sunspots varies as the sunspots geometric, magnetic and rotational properties are varied. A general scaling law is derived based on the individual scaling relations. Finally we observe that the penumbral magnetic field expands as a consequence to rotation. This expansion is shown to play a fundamental role in the liberation of some of the injected magnetic energy introduced into the penumbra. A number of avenues for the next steps in the investigation are identified, but the findings of this thesis are immediately relevant and may be used to estimate the amount of energy injected by a rotating sunspot using observational measurements as an input to the model.