Using Common Principal Components for Comparing GCM Simulations

Abstract

The technique of common principal components (CPC) is applied to compare the results of a number of GCM simulations. The data used are the 120 monthly mean fields from 30 Atmospheric Model Intercomparison Project (AMIP) simulations and an ensemble of five AMIP integrations from a single GCM. The spatial grid and 120 time points allows the calculation of up to 31 covariance matrices for input into the CPC analyses. The CPC methodology is applied to a variety of model comparision problems within the context of the AMIP experiment. The aspects of the simulations used for demonstration are the seasonal cycle of precipitation over the United States, the global 200-hPa velocity potential, and the difference between the 200-hPa divergence of four closely related AMIP models and the National Centers for Environmental Prediction?National Center for Atmospheric Research reanalysis and a small ensemble of simulations (five) of the European Centre for Medium-Range Weather Forecasts AMIP model. These analyses demonstrate the utility of the CPC approach in identifying models systematic errors, the reduction of data in ensembles of simulation, and in model parameterization comparisons. The common errors among the models tend to highlight the area in which a gap in knowledge or parameterization implementation exists. In addition CPC analyses provide a more complete statistical picture of an emsemble of simulations within a single model than the traditional means and variances. It is often the common aspects of the ensembles that are sought as a robust signal. The CPC analyses tend to support the observation that the models often have more in common with each other than with the observations. The CPC has the ability to answer many pertinent questions posed in the arena of model comparison when used in conjunction with other techniques.