Solar‐Cycle Changes in Gongp‐Mode Widths and Amplitudes 1995–1998

Abstract

We search for a solar cycle variation in mode widths and amplitudes derived from 3-month GONG time series. The variation of mode width and amplitude observed in GONG data are the combined effects of fill factor, temporal variation, and measurement uncertainties.The largest variation is caused by the fill factor resulting in modes with increased width and reduced amplitude when fill is lower. We assume that the solar cycle variation is the only other systematic variation beside the temporal window function effect. We correct all currently available data sets for the fill factor and simultaneously derive the solar cycle variation.We find an increase of about 3% on average in mode width from the previous minimum to Oct. 1998 and a decrease of about 7% and 6% in mode amplitude and mode area (width x amplitude). We find no l dependence of the solar-cycle changes.As a function of frequency, these changes show a maximum between 2.7 and 3.3 mHz with about 47% higher than average values for mode width and about 29% and 36% higher ones for mode amplitude and area. We estimate the significance of these rather small changes by a pre-whitening method and find that the results are significant at or above the 99.9% level with mode area showing the highest level of significance and mode width the lowest. The variation in background amplitude is most likely not significant and is consistent with a zero change.

Localizing Width and Energy of Solar Globalp‐Modes

“…The dotted curve indicates the corresponding inverse ratio of surface magnetic activity scaled to the mean of GONG and MDI data and symmetrized at the equator. The mode energy decreases by 13%, on average, with increasing magnetic activity, as reported in the previous global analysis (Komm et al 2000b). The latitudinal variation of mode energy is clearly related to the surface activity.…”

“…These criteria are fulfilled by about 320 multiplets with on average 90:0% AE 2:5% of all 36,924 possible modes present per epoch. This is about one-fourth of the number of multiplets used previously in Komm et al (2000aKomm et al ( , 2000b. …”

“…Previously, we averaged over all m-values of a given (l, n) multiplet and then over many multiplets in order to derive significant results (Komm, Howe, & Hill 2000a, 2000b. The global solar-cycle variation of mode energy and mode energy supply rate left us with the puzzling result that while the energy contained in the modes decreases with increasing magnetic activity, the energy supplied to the modes remains nearly the same (Komm et al 2000b;. However, this result did not rule out that the energy supply rate might change locally near locations of strong magnetic activity and that averaging over all m-values of each multiplet might obscure this effect.…”

“…Hence, these gaps lead to modes with increased width and reduced amplitude. In Komm et al (2000a), we assumed that the solar-cycle variation is the only other systematic variation and showed that a multilinear regression is sufficient to describe the temporal window variation and the solar cycle variation:…”

“…We determined the c f fit parameters for each ðl; nÞ multiplet and corrected all data sets accordingly. We include only ðl; nÞ multiplets where at least one-third of all ð2l þ 1Þ values are present at a given epoch, and the multiplet has to be present in at least 90% of all analyzed epochs (for details, see Komm, Howe, & Hill 2000b). The largest number of '' good '' multiplets occurs between 1.9 and 3.6 mHz, which is the range where modes have a sufficiently large signal-to-noise ratio and can be adequately resolved from their neighboring leaks.…”