Solar activity forecast with a dynamo model
Jie Jiang, Piyali Chatterjee, and Arnab Rai Choudhuri

TL;DR
This paper develops a high-diffusivity solar dynamo model that incorporates observational poloidal field data to predict solar cycle strength and asymmetry, emphasizing the role of magnetic diffusivity and stochastic poloidal field generation.
Contribution
It introduces a systematic method to integrate observational poloidal field data into a high-diffusivity dynamo model for solar cycle prediction.
Findings
Cycle 24 predicted to be very weak
Hemispheric asymmetry matches observational data
High diffusivity explains polar field-cycle correlation
Abstract
Although systematic measurements of the solar polar magnetic field exist only from mid 1970s, other proxies can be used to infer the polar field at earlier times. The observational data indicate a strong correlation between the polar field at a sunspot minimum and the strength of the next cycle, although the strength of the cycle is not correlated well with the polar field produced at its end. This suggests that the Babcock Leighton mechanism of poloidal field generation from decaying sunspots involves randomness, whereas the other aspects of the dynamo process must be reasonably ordered and deterministic. Only if the magnetic diffusivity within the convection zone is assumed to be high, we can explain the correlation between the polar field at a minimum and the next cycle. We give several independent arguments that the diffusivity must be of this order. In a dynamo model with…
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