Statistical downscaling methods based on APCC multi-model ensemble for seasonal prediction over South Korea

An investigation was conducted to optimize the application of the multi-model ensemble (MME) technique for statistical downscaling using 1- to 6-month lead hindcasts obtained from six operational coupled general circulation models (GCMs) participating in the APEC Climate Center (APCC) MME prediction system. Three different statistical downscaling MME methods (SDMMEs) were compared and estimated over South Korea. The study results revealed that under the same number of ensemble members, simple changes in the statistical downscaling method, such as an applicative order or a type of MME, can help to improve the predictability. The first method, the conventional technique, performed MME using data downscaled from the single-model ensemble means of each GCM (SDMME-Sm), whereas the second and third methods, newly designed in this study, calculated the simple ensemble mean (SDMME-Ae) and the weighted ensemble mean (SDMME-We) after statistical downscaling for each member of all model ensembles. These three methods were applied to predict temperature and precipitation for the 6-month summer-fall season over 23 years (1983-2005) at 60 weather stations over South Korea. The predictors were variables from hindcasts integrated by the six coupled GCMs. According to the analysis, both SDMME-Ae and SDMME-We showed increased predictability compared with SDMME-Sm. In particular, SDMME-We showed more significant improvement in long-term prediction. In addition, in order to assess the dependence of predictability on the number of downscaled ensemble members and the type of MME, an additional experiment was performed, the results of which revealed that the model performance was closely related to the number of downscaled ensemble members. However, after approximately 30 ensemble members, the predictive skills became rapidly saturated when using the SDMME-Ae method. SDMME-We overcame the limited skills that can be achieved by merely increasing the number of downscaled ensemble members, thereby improving the performance.