It is now well recognised that the magnetic fields on the Sun are the basic cause of this variability.
Solar dynamo models that can predict the solar cycle are now possible (Choudhuri et al. 2007). Understanding how the
magnetic fields are generated and maintained on the Sun, i.e.
understanding the solar dynamo, hence, is basic to understanding the origin and nature of solar
cycle and variability, and to predict in advance its behaviour both on short and long time scales.
Micro- and macro physics of magnetic dynamos
Ever since Parker (1955) formulated the turbulent dynamo theory, varieties of very different dynamo
models for the Sun have appeared in the literature. Global dynamo models, based on Parker's original
ideas on the contributions of cyclonic convection and differential rotation, depend a lot also on the
micro-physics associated with turbulent interaction and diffusion of magnetic fields throughout the
solar convection zone. These processes are the most vigorous in the observable photospheric layers.
Studying energy and momentum coupling between radiation, convection and magnetic fields in these
layers is crucial to understand near-surface contribution to the overall working and sustenance of
the solar dynamo.
A high spatial, spectral and temporal resolution observation over a sustained period of time is a key
requirement for such studies. NLST will fulfill such requirements to facilitate answering the following
questions pertaining to the above aspects of solar dynamo: (1) how do the convective flows and associated
turbulence stretch, twist and fold the local small-scale magnetic fields, and, how do such processes
change the magnetic flux budget, locally and globally?; (2) how do strong fields in the large scale,
e.g. sunspots, filaments, etc., interact with the small-scale fields?; and (3) how do large scale
convective (super-granular and other possible larger scale) and meridional flows advect and diffuse the
magnetic field?; and how do they change with time?.
Answers to these questions will provide key observational inputs to constrain the global interior dynamo
models and thus in improving the predictive capabilities of such models