To test the skill of the Eta RCM in predicting warm season precipitation anomalies, two summertime cases (1999 and 2000) were chosen, representative of both wet and dry soil moisture anormalies in the southwest United States. Most previous studies of RCM seasonal simulation driven by analysis lateral boundary conditions and observed SST employed only "1-member" executions from one single initial condition date. In contrast, we executed 6 members whose initial conditions vary by one and a half day. The study period is from June to September and the executions were started from late May and continued to early October. In both cases, we use lateral boundary conditions from both the NCEP Global Reanalysis II and global SFM, ensemble mean of total precipitation for each month and 200 mb and 500 mb heights were examined , where the monthly mean precipitation was compared to the CPC unified daily precipitation data, and the 200 and 500 mb geopotential heights were compared to the NCEP Reanalysis data, respectively. As part of this study, we also tested the impact of initial land states and Sea Surface Temperature (SST) on seasonal precipitation predictions. To do this, we use a suite of combinations of climatological land states and SST versus their realtime fields with different sources of lateral boundary conditions.
The resulting ensemble mean shows that the Eta RCM successfully simulates and predicts the wet and dry bias in soil moisture over the southwest U.S. and has substantial member-to-member variability of seasonal precipitation. This suggests that previous RCM studies that employed only one member may be misleading by failing to represent the inherent internal variability. Comparison of results obtained from using different sources of land states and SST indicates that the Eta RCM is sensitive to the initial land surface conditions and the choice of SST and shows a great variability in the simulated monthly and total precipitation, suggesting that a careful initialization of land states as well as an accurate source of SST are important to seasonal predictions.