| description abstract | A novel way of quantifying the variance of a time series is presented. The method first involves filtering the time series using filters with different temporal characteristics, and then using a moving window to calculate the variances in each filtered time series. The use of a moving window allows the original temporal resolution to be retained, as well as allowing one to study how the variance changes with time. Air?sea interaction time series from Ocean Weather Station (OWS) Bravo in the Labrador Sea are analyzed as an example. High-pass, bandpass, and low-pass filters are used to isolate the diurnal signal, the storm/cyclone signature, and the weather regime transition signal, respectively. The variance during the winter months is found to be strongly influenced by weather systems in the bandpass and the low-pass frequency range. The variance during the summer months, on the other hand, is dominated by the shortwave radiation in the high-pass frequency range. | |
