Climatology of Non-Gaussian Atmospheric Statistics

Abstract

Abstract A common assumption in the earth sciences is the Gaussianity of data over time. However, several independent studies in the past few decades have shown this assumption to be mostly false. To be able to study non-Gaussian climate statistics, one must first compile a systematic climatology of the higher statistical moments (skewness and kurtosis; the third and fourth central statistical moments, respectively). Sixty-two years of daily data from the NCEP–NCAR Reanalysis I project are analyzed. The skewness and kurtosis of the data are found at each spatial grid point for the entire time domain. Nine atmospheric variables were chosen for their physical and dynamical relevance in the climate system: geopotential height, relative vorticity, quasigeostrophic potential vorticity, zonal wind, meridional wind, horizontal wind speed, vertical velocity in pressure coordinates, air temperature, and specific humidity. For each variable, plots of significant global skewness and kurtosis are shown for December–February and June–August at a specified pressure level. Additionally, the statistical moments are then zonally averaged to show the vertical dependence of the non-Gaussian statistics. This is a more comprehensive look at non-Gaussian atmospheric statistics than has been taken in previous studies on this topic.