Simulations of magnetic-flux transport in solar active regions

Solar Physics ◽  
1985 ◽  
Vol 102 (1-2) ◽  
pp. 41-49 ◽  
Author(s):  
C. R. DeVore ◽  
N. R. Sheeley ◽  
J. P. Boris ◽  
T. R. Young ◽  
K. L. Harvey
2015 ◽  
Vol 815 (2) ◽  
pp. 90 ◽  
Author(s):  
Ignacio Ugarte-Urra ◽  
Lisa Upton ◽  
Harry P. Warren ◽  
David H. Hathaway

2008 ◽  
Vol 26 (2) ◽  
pp. 259-267 ◽  
Author(s):  
M. Dikpati

Abstract. Various dynamo-based techniques have been used to predict the mean solar cycle features, namely the amplitude and the timings of onset and peak. All methods use information from previous cycles, including particularly polar fields, drift-speed of the sunspot zone to the equator, and remnant magnetic flux from the decay of active regions. Polar fields predict a low cycle 24, while spot zone migration and remnant flux both lead to predictions of a high cycle 24. These methods both predict delayed onset for cycle 24. We will describe how each of these methods relates to dynamo processes. We will present the latest results from our flux-transport dynamo, including some sensitivity tests and how our model relates to polar fields and spot zone drift methods.


2017 ◽  
Vol 846 (2) ◽  
pp. 165 ◽  
Author(s):  
Ignacio Ugarte-Urra ◽  
Harry P. Warren ◽  
Lisa A. Upton ◽  
Peter R. Young

2002 ◽  
Vol 573 (2) ◽  
pp. 851-856 ◽  
Author(s):  
Debi Prasad Choudhary ◽  
P. Venkatakrishnan ◽  
Sanjay Gosain

2010 ◽  
Vol 720 (1) ◽  
pp. L20-L25 ◽  
Author(s):  
Andrés Muñoz-Jaramillo ◽  
Dibyendu Nandy ◽  
Petrus C. H. Martens ◽  
Anthony R. Yeates

2014 ◽  
Vol 796 (1) ◽  
pp. 20 ◽  
Author(s):  
Jaroslav Dudík ◽  
Elena Dzifčáková ◽  
Jonathan W. Cirtain

2004 ◽  
Vol 219 ◽  
pp. 478-482 ◽  
Author(s):  
A. Fludra ◽  
J. Ireland

We study the relationship between EUV spectral line intensities and the photospheric magnetic field in solar active regions, using magnetograms from SOHO-MDI and EUV spectra of the Fe XVI 360.8 Â line (2 × 106 K) and the O V 629.7 A line (220,000 K) from the Coronal Diagnostic Spectrometer on SOHO, recorded for several active regions. We overlay and compare spatial patterns of the O V emission and the magnetic flux concentrations, with a 4″ x 4″ spatial resolution, and search for a relationship between the local O V line intensity and the photospheric magnetic flux density in each active region. While this dependence exhibits a certain amount of scatter, it can be represented by a power law fit. The average power index from all regions is 0.7 ± 0.2. Applying static loop models, we derive the dependence of the heating rate on the magnetic flux density, Eh ∝ B0.8, and compare it to the dependence predicted by the coronal heating models.


2021 ◽  
Vol 12 (1) ◽  
Author(s):  
D. MacTaggart ◽  
C. Prior ◽  
B. Raphaldini ◽  
P. Romano ◽  
S. L. Guglielmino

AbstractThe magnetic nature of the formation of solar active regions lies at the heart of understanding solar activity and, in particular, solar eruptions. A widespread model, used in many theoretical studies, simulations and the interpretation of observations, is that the basic structure of an active region is created by the emergence of a large tube of pre-twisted magnetic field. Despite plausible reasons and the availability of various proxies suggesting the accuracy of this model, there has not yet been a methodology that can clearly and directly identify the emergence of large pre-twisted magnetic flux tubes. Here, we present a clear signature of the emergence of pre-twisted magnetic flux tubes by investigating a robust topological quantity, called magnetic winding, in solar observations. This quantity detects the emerging magnetic topology despite the significant deformation experienced by the emerging magnetic field. Magnetic winding complements existing measures, such as magnetic helicity, by providing distinct information about field line topology, thus allowing for the direct identification of emerging twisted magnetic flux tubes.


Solar Physics ◽  
1991 ◽  
Vol 131 (1) ◽  
pp. 53-68 ◽  
Author(s):  
Haimin Wang ◽  
Harold Zirin ◽  
Guoxiang Ai

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