Improved Seasonal Precipitation Forecasts for the Asian Monsoon Using 16 Atmosphere–Ocean Coupled Models. Part II: Anomaly

Abstract

Abstract This is the second part of a paper on the improved seasonal precipitation forecasts for the Asian monsoon using 16 atmosphere–ocean coupled models. This study utilizes a large suite of coupled atmosphere–ocean models; this second part largely addresses the skill of rainfall anomaly forecasts. These include both deterministic and probabilistic skill measures such as the RMS errors, anomaly correlations, equitable threat scores, and the Brier skill score. It was possible to improve the skills of rainfall climatology from the use of a downscaled multimodel superensemble to very high levels, and it is of interest to ask how far this methodology would go toward improving the skills of seasonal rainfall anomaly forecasts. It is possible to go through a sequence of multimodel post processing to improve upon these skills by using a dense rain gauge network over Asia, downscaling forecasts for each member model, and constructing a multimodel superensemble that benefits from the persistence of errors of the member models. This paper addresses the spinup issues of the downscaling and the superensemble results where the number of years of model data needed for training phase, for the downscaling, and for the construction of the superensemble, is addressed. In the context of cross validation, the training phase includes 14 seasons of monsoon data. The forecast phase is only one season; it is this season that was not included in the training phase each time. The relationship between data length and the number of models needed for enhanced skills is another issue that is addressed. Seasonal climate forecasts over the larger monsoon Asia domain and over the regional belts are evaluated. The superensemble forecasts invariably have the highest skill compared to the member models globally and regionally. This is largely due to the presence of large systematic errors in models that carry low seasonal prediction skills. Such models carry persistent signatures of systematic errors, and their errors are recognized by the multimodel superensemble. The probabilistic skills show that the superensemble-based forecasts carry a much higher reliability score compared to the member models. This implies that the superensemble-based forecasts are the most reliable among all the member models. It is possible to examine the performance of models and of the superensemble during periods of heavy monsoon rainfall versus those for deficient monsoon rainfall seasons. One of the conclusions of this study is that given the uncertainties in current modeling for seasonal rainfall forecasts, post processing of multimodel forecasts, using the superensemble methodology, seems to provide the most promising results for the rainfall anomaly forecasts. These results are confirmed by an additional skill metric where the RMS errors and the correlations of forecast skills are evaluated using a normalized precipitation anomaly for the forecasts and the observed estimates.