How Good Is the Bipolar Approximation of Active Regions for Surface Flux Transport?
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How Good Is the Bipolar Approximation of Active Regions for Surface Flux Transport? Anthony R. Yeates1
Received: 5 June 2020 / Accepted: 10 August 2020 © The Author(s) 2020
Abstract We investigate how representing active regions with bipolar magnetic regions (BMRs) affects the end-of-cycle polar field predicted by the surface flux transport model. Our study is based on a new database of BMRs derived from the SDO/HMI active region patch data between 2010 and 2020. An automated code is developed for fitting each active region patch with a BMR, matching both the magnetic flux and axial dipole moment of the region and removing repeat observations of the same region. By comparing the predicted evolution of each of the 1090 BMRs with the predicted evolution of their original active region patches, we show that the bipolar approximation leads to a 24% overestimate of the net axial dipole moment, given the same flow parameters. This is caused by neglecting the more complex multipolar and/or asymmetric magnetic structures of many of the real active regions, and may explain why previous flux transport models had to reduce BMR tilt angles to obtain realistic polar fields. Our BMR database and the Python code to extract it are freely available.
1. Introduction Despite its simplicity, the surface flux transport (SFT) model introduced by Leighton (1964) has proved remarkably effective at mimicking the evolution of the large-scale magnetic field on the solar surface (see the reviews by Sheeley, 2005; Jiang et al., 2014; Wang, 2017). An important recent application is solar cycle prediction. Since the polar field at Solar Minimum is the best predictor for the following solar cycle amplitude (Schatten et al., 1978; MuñozJaramillo et al., 2013; Pesnell, 2016; Petrovay, 2020), the SFT model offers the possibility of extending predictions earlier since it – in turn – predicts this polar field. Several predictions of Cycle 25 have been made with this methodology (Iijima et al., 2017; Jiang et al., 2018; Upton and Hathaway, 2018), as well as by coupling SFT to interior dynamo models (Bhowmik and Nandy, 2018; Labonville, Charbonneau, and Lemerle, 2019).
B A.R. Yeates
[email protected]
1
Department of Mathematical Sciences, Durham University, Durham, DH1 3LE, UK
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A.R. Yeates
A major component of randomness in the solar cycle is now believed to arise from active region emergence. There are significant fluctuations both in the number of emerging active regions and in their properties such as emergence latitude, magnetic flux, or tilt angle (see the review by van Driel-Gesztelyi and Green, 2015). General cycle-to-cycle trends remain unclear, with some studies suggesting active regions in stronger solar cycles to have lower tilt angles (e.g., McClintock and Norton, 2013) and others finding no significant variation (Tlatova et al., 2018). Such a trend would act to stabilise the solar cycle by reducing the polar field strength at the end of strong cycles. Another stabilising effect is the tendency for active
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