Boyle, J. S., 1996b: Intercomparison of low-frequency variability of the global 200 hPa circulation for AMIP simulations. PCMDI Report 32, Program for Climate Model Diagnosis and Intercomparison, Lawrence Livermore National Laboratory, 53 pp.

In the Atmospheric Model Intercomparison Project (AMIP) a number of GCMs are integrated for a 10 year period, 1979-1988, all using the same monthly mean sea surface temperature (SST). This permits a useful intercomparison of the response of the models to the imposed SST. The variables used here for the intercomparison are the 200 hPa divergence and streamfunction. The data used are in the form of monthly averages and are filtered to a spatial resolution of T10, although the actual spatial resolution of the models varies from R15 to T42. The data are manipulated in this manner to concentrate on the low frequency, large scale response. The tools of the analysis are principal components analysis (PCA) and common principal components (CPC). These analyses are carried out on the 120 months of data with the seasonal cycle removed and in the case of the streamfunction with the zonal average also removed. The 1979-1988 period encompasses two El Niño / Southern Oscillation (ENSO) events (1982/83 and 1986/87), and as could be expected the ENSO characteristic response has a prominent impact in the model simulations.

The results indicate that :

  1. The PCA of the divergence has a dominant mode which is similar for all the models and has the signature of an (ENSO) response. It has an east-west dipole of divergence anomaly centered on the equator in the western Pacific. This mode accounts for 29% to 53% of the explained variance for the models considered.
  2. The streamfunction PC analysis also exhibits an ENSO type response as the dominant mode, but this accounts for only 8% to 21% of the variance.
  3. The CPC analysis allows a direct comparison of the data from all the models on a common set of vectors. The component identified with the ENSO mode represents 27% to 52% of the variance explained for the divergence there is a strong variation in the amplitude of the corresponding modes.
  4. The variance explained by the leading mode for the CPC streamfunction is between 5% and 19%, and there is less commonality in the higher components than seen in the divergence. This appears to be related to the stronger streamfunction response in the mid-latitudes, which is presumably more affected by nonlinearity and intrinsic variability of the model integrations.
  5. Based on results using an ensemble of five decadal runs using the ECMWF GCM an estimate is made of the variation of explained variance due to intrinsic variability for a single model. It is found that in general the inter-model variation is somewhat greater than the intra-model ensemble variation using the ECMWF model.
  6. A probability density function (PDF) analysis in the space spanned by the first two CPCs for the velocity potential (which explain over 70% of the variance for all but one model) yields distinctive dynamical signatures. Some models populate a somewhat larger PDF space than others. There is a strong implication that the models differ beyond the variation due to intrinsic variability in the dynamical system. Some of the models have distinctly different responses to a common SST forcing. The disparate results indicate that consensus on the representation of the physics of the atmosphere has not been reached, and the present uncertainty in the parameterizations is greater than the intrinsic uncertainty of the model system as shown by ensemble simulations.