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The variability inherent in solar wind composition has implications for thevariability of the physical conditions in its coronal source regions, providingconstraints on models of coronal heating and solar wind generation. We presenta generalized prescription for constructing a wavelet power significancemeasure (confidence level) for the purpose of characterizing the effects ofmissing data in high cadence solar wind ionic composition measurements. Wedescribe the data gaps present in the 12-minute ACE/SWICS observations ofO7+/O6+ during 2008. The decomposition of the in-situ observations into a `goodmeasurement' and a `no measurement' signal allows us to evaluate theperformance of a filler signal, i.e., various prescriptions for filling thedata gaps. We construct Monte Carlo simulations of synthetic O7+/O6+composition data and impose the actual data gaps that exist in the observationsin order to investigate two different filler signals: one, a linearinterpolation between neighboring good data points, and two, the constant meanvalue of the measured data. Applied to these synthetic data plus filler signalcombinations, we quantify the ability of the power spectra significance levelprocedure to reproduce the ensemble-averaged time-integrated wavelet power perscale of an ideal case, i.e. the synthetic data without imposed data gaps.Finally, we present the wavelet power spectra for the O7+/O6+ data using theconfidence levels derived from both the Mean Value and Linear Interpolationdata gap filling signals and discuss the results.

Analysis of High Cadence In-Situ Solar Wind Ionic Composition Data Using Wavelet Power Spectra Confidence Levels