Flaring Rates and the Evolution of Sunspot Group McIntosh Classifications

Abstract

Sunspot groups are the main source of solar flares, with the energy to power them being supplied by magnetic-field evolution (e.g. flux emergence or twisting/shearing). To date, few studies have investigated the statistical relation between sunspot-group evolution and flaring, with none considering evolution in the McIntosh classification scheme. Here we present a statistical analysis of sunspot groups from Solar Cycle 22, focusing on 24-hour changes in the three McIntosh classification components. Evolution-dependent >C1.0, >M1.0, and >X1.0 flaring rates are calculated, leading to the following results: (i) flaring rates become increasingly higher for greater degrees of upward evolution through the McIntosh classes, with the opposite found for downward evolution; (ii) the highest flaring rates are found for upward evolution from larger, more complex, classes (e.g. Zurich D- and E-classes evolving upward to F-class produce >C1.0 rates of 2.66 +/- 0.28 and 2.31 +/- 0.09 flares per 24 hours, respectively); (iii) increasingly complex classes give higher rates for all flare magnitudes, even when sunspot groups do not evolve over 24 hours. These results support the hypothesis that injection of magnetic energy by flux emergence (i.e. increasing in Zurich or compactness classes) leads to a higher frequency and magnitude of flaring.

arXiv comment31 pages, 11 figures

References in corpus (5)

• Global Energetics of Thirty-Eight Large Solar Eruptive Events
• Toward Reliable Benchmarking of Solar Flare Forecasting Methods
• The Evolution of Sunspot Magnetic Fields Associated with a Solar Flare
• Performance of Major Flare Watches from the Max Millennium Program (2001-2010)
• Evidence for Partial Taylor Relaxation from Changes in Magnetic Geometry and Energy during a Solar Flare

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