Chaos, potential predictability and model validation of climate variations

Abstract

In this paper, we review recent computational techniques which make it possible to separate interannual climate variability of seasonal means into chaotic and potentially predictable components. Based on analysis of variance and frequency analysis of daily time series, the techniques are applicable to both observed data sets and ensembles of multidecadal simulations using atmospheric general circulation models forced by observed sea surface temperatures and different initial conditions. A new technique for validating the interannual variability in ensembles of model simulations is also outlined. The methodologies have been applied to a study of the interannual variability of the global 200hPa geopotential height field.