In this study we validate the raw ensemble mean forecasts of the CCCma's GCM2 model against surface temperature and precipitation data obtained from 160 Chinese stations.It is found that despite the lagre biases,t...In this study we validate the raw ensemble mean forecasts of the CCCma's GCM2 model against surface temperature and precipitation data obtained from 160 Chinese stations.It is found that despite the lagre biases,the model was able to produce seasonal anomalies that have properties that are reasonably close to those that are observed.This anomaly is the quantity of interest when forecasting seasonal climatic conditions.The root mean squared difference(RMSD) between the forecast and observed anomaly leads us to be modestly optimistic about the prospects for using dynamical models to forecast the interannual variability of some meteorological elements. The correlation analysis of the forecast and observation also supports the result given by the RMSD analysis and provides a tool for identify the forecast confidence level in various regions,展开更多
文摘In this study we validate the raw ensemble mean forecasts of the CCCma's GCM2 model against surface temperature and precipitation data obtained from 160 Chinese stations.It is found that despite the lagre biases,the model was able to produce seasonal anomalies that have properties that are reasonably close to those that are observed.This anomaly is the quantity of interest when forecasting seasonal climatic conditions.The root mean squared difference(RMSD) between the forecast and observed anomaly leads us to be modestly optimistic about the prospects for using dynamical models to forecast the interannual variability of some meteorological elements. The correlation analysis of the forecast and observation also supports the result given by the RMSD analysis and provides a tool for identify the forecast confidence level in various regions,
