Interannual variations of the boreal summer intraseasonal variability predicted by ten atmosphere–ocean coupled models

The reproducibility of boreal summer intraseasonal variability (ISV) and its interannual variation by dynamical models are assessed through diagnosing 21-year retrospective forecasts from ten state-of-the-art ocean–atmosphere coupled prediction models. To facilitate the assessment, we have defined the strength of ISV activity by the standard deviation of 20–90 days filtered precipitation during the boreal summer of each year. The observed climatological ISV activity exhibits its largest values over the western North Pacific and Indian monsoon regions. The notable interannual variation of ISV activity is found primarily over the western North Pacific in observation while most models have the largest variability over the central tropical Pacific and exhibit a wide range of variability in spatial patterns that are different from observation. Although the models have large systematic biases in spatial pattern of dominant variability, the leading EOF modes of the ISV activity in the models are closely linked to the models’ El Nino-Southern Oscillation (ENSO), which is a feature that resembles the observed ISV and ENSO relationship. The ENSO-induced easterly vertical shear anomalies in the western and central tropical Pacific, where the summer mean vertical wind shear is weak, result in ENSO-related changes of ISV activity in both observation and models. It is found that the principal components of the predicted dominant modes of ISV activity fluctuate in a very similar way with observed ones. The model biases in the dominant modes are systematic and related to the external SST forcing. Thus the statistical correction method of this study based on singular value decomposition is capable of removing a large portion of the systematic errors in the predicted spatial patterns. The 21-year-averaged pattern correlation skill increases from 0.25 to 0.65 over the entire Asian monsoon region after applying the bias correction method to the multi-model ensemble mean prediction.