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ABSTRACT:A variance decomposition approach to estimate the potentially predictable component of seasonal means has been further developed to separately diagnose the boundary-forced component and the slowly varying internal dynamics component. This decomposition of the potentially predictable component has been applied to an ensemble of ten 50-year simulations of the 500 hPa geopotential height field from the Center for Ocean-Land-Atmosphere Studies global atmospheric model forced by sea-surface temperatures. The model performance is analysed for the winter season and compared with the National Centers for Environmental Prediction reanalysis. The majority of the hemispheric potentially predictable patterns, identified as the empirical orthogonal functions of the potentially predictable component of the seasonal mean fields, are captured by the model. These include all the patterns forced by the El Niño-Southern Oscillation in the Southern Hemisphere, and the dominant potentially predictable patterns in the Northern Hemisphere, i.e. the Pacific-North American pattern. However, the boreal winter Western Pacific Oscillation and Tropical Northern Hemisphere pattern, which are well simulated by some other general circulation models, are not well captured.

An assessment is made of the modes of interannual variability in the seasonal mean summer and winter Southern Hemisphere (SH) 500-hPa geopotential height in the twentieth century in models from the Coupled Model Intercomparison Project (CMIP) phase 5 (CMIP5) dataset. Modes of variability of both the slow (signal) and intraseasonal (noise) components in the CMIP5 models are evaluated against those estimated from reanalysis data. There is general improvement in the leading modes of the slow (signal) component in CMIP5 models compared with the CMIP phase 3 (CMIP3) dataset. The largest improvement is in the spatial structures of the modes related to El Niño-Southern Oscillation variability in SH summer. An overall score metric is significantly higher for CMIP5 over CMIP3 in both seasons. The leading modes in the intraseasonal noise component are generally well reproduced in CMIP5 models, and there are few differences from CMIP3. A new total overall score metric is used to rank the CMIP5 models over both seasons. Weighting the seasons by the relative spread of overall scores is shown to be suitable for generating multimodel ensembles for further analysis of interannual variability. In multimodel ensembles, it is found that an ensemble of size 5 or 6 is sufficient in SH summer to reproduce well the dominant modes. In contrast, about 13 models are typically are required in SH winter. It is shown that it is necessary that the selected models individually reproduce well the leading modes of the slow component.

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