Variability of seasonal-mean fields arising from intraseasonal variability: part 1, methodology

We propose a method for studying the influence of intraseasonal variability on the interannual variability of seasonal mean fields. The method, using monthly mean data, provides estimates of the interannual variance and covariance, in the seasonal mean field, associated with intraseasonal variability. These estimates can be used to derive patterns of interannual variability associated with meteorological phenomena that vary significantly within a season, such as atmospheric blocking, or intraseasonal oscillations. By removing this intraseasonal component from the total interannual variance/covariance, one can define a slow component of interannual variability that is closely related to very slowly varying (interannual/supra-annual) external forcings and internal dynamics. Together these patterns may help in our understanding of the source of climate predictive skill, and also the influence of intraseasonal variability on interannual variability. To show the efficacy of our methodology, we have tested it on synthetic data, using Monte Carlo simulations of the 500-hPa geopotential heights for boreal winter over the North Pacific/North American region. The synthetic data has been constructed in such a way that the intraseasonal and slow components of interannual variability are known a priori. It is demonstrated that our methodology can effectively separate the spatial patterns of both components of variability. The methodology is also applied to diagnose meteorological phenomena that play major roles in the variability and predictability of DJF New Zealand temperatures.