Mid-Term Quasi-Periodicities and Solar Cycle Variation of the White-Light Corona from 18.5 Years (1996.0 – 2014.5) of LASCO Observations

Abstract

We report on the analysis of the temporal evolution of the solar corona based on 18.5 years (1996.0 – 2014.5) of white-light observations with the SOHO/LASCO-C2 coronagraph. This evolution is quantified by generating spatially integrated values of the K-corona radiance, first globally, then in latitudinal sectors. The analysis considers time series of monthly values and 13-month running means of the radiance as well as several indices and proxies of solar activity. We study correlation, wavelet time-frequency spectra, and cross-coherence and phase spectra between these quantities. Our results give a detailed insight on how the corona responds to solar activity over timescales ranging from mid-term quasi-periodicities (also known as quasi-biennial oscillations or QBOs) to the long-term 11 year solar cycle. The amplitude of the variation between successive solar maxima and minima (modulation factor) very much depends upon the strength of the cycle and upon the heliographic latitude. An asymmetry is observed during the ascending phase of Solar Cycle 24, prominently in the royal and polar sectors, with north leading. Most prominent QBOs are a quasi-annual period during the maximum phase of Solar Cycle 23 and a shorter period, seven to eight months, in the ascending and maximum phases of Solar Cycle 24. They share the same properties as the solar QBOs: variable periodicity, intermittency, asymmetric development in the northern and southern solar hemispheres, and largest amplitudes during the maximum phase of solar cycles. The strongest correlation of the temporal variations of the coronal radiance – and consequently the coronal electron density – is found with the total magnetic flux. Considering that the morphology of the solar corona is also directly controlled by the topology of the magnetic field, this correlation reinforces the view that they are intimately connected, including their variability at all timescales.