Preliminary Evaluation of Variable-Resolution GCM Coupled 1-D Ocean Model for Seasonal Prediction

Precipitation in Taiwan area is dominant by typhoon in the boreal summer, and significantly influenced by MJO（Madden-Julian Oscillation） in the boreal winter (Hung et al., 2014). This study is conducted by toggling this climatic character with a unique model structure. High-resolution global climate model is an important tool for typhoon activity research. HiRAM（High-Resolution Atmospheric Model）developed by GFDL（Geophysical Fluid Dynamics Laboratory）has been recognized as one of the best model for seasonal forecasts of hurricane/typhoon activity (Zhao et al., 2009; Chen & Lin, 2011; 2013). In Atlantic basin HiRAM had great success in predicting inter-annual hurricane frequency, but was not as successful in Western Pacific. Increase of horizontal resolution provides reasonable magnitude and location of maximum surface wind intensities for typhoon simulation (Knutson et al., 1998). This study further utilizes variable-resolution GCM, the stretched-grid HiRAM, for seasonal hindcast. The design that stretched-grid centered in Taiwan gives the advantage of enhanced horizontal resolution in Northwestern Pacific without increasing total model grid. For MJO forecast, the one-dimensional TKE (Turbulence Kinetic Energy ) type ocean model SIT（Snow, Ice, Thermocline） had demonstrated its skill in improving MJO eastward propagation when coupled with ECHAM5 (Tseng et al., 2015). The preliminary result of the HiRAM-SIT hindcast experiments shows similar success in MJO analysis (Fig. 1). In the hindcast experiment water temperature at 10-m deep in SIT is nudged to GODAS (Global Ocean Data Assimilation System) to avoid SST deviation induced by lack of advection in one-dimensional ocean model. By coupling the stretched-grid HiRAM with SIT, the advantage of enhanced horizontal resolution in stretched-grid HiRAM in Taiwan vicinity helps improving typhoon activity prediction, and the refined vertical resolution near ocean surface when coupling with SIT improves MJO forecasts.