Abstract
Many researchers use outputs from large-scale global circulation models of the atmosphere to assess hydrological and other impacts associated with climate change. However, these models cannot capture all climate variations since the physical processes are imperfectly understood and are poorly represented at smaller regional scales. This paper statistically compares model outputs from the global circulation model of the Geophysical Fluid Dynamics Laboratory to historical data for the United States' Laurentian Great Lakes and for the Emba and Ural River basins in the Commonwealth of Independent States (C.I.S.). We use maximum entropy spectral analysis to compare model and data time series, allowing us to both assess statistical predictabilities and to describe the time series in both time and frequency domains. This comparison initiates assessments of the model's representation of the real world and suggests areas of model improvement.
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