Systematic errors in the COLA R15 AGCM are analyzed by the SVD technique.In order to remove or reduce this kind of error source and reduce climate drift in coupled runs a way in which the wind stress anomalies simulat...Systematic errors in the COLA R15 AGCM are analyzed by the SVD technique.In order to remove or reduce this kind of error source and reduce climate drift in coupled runs a way in which the wind stress anomalies simulated by an AGCM are reconstructed is proposed by using SVD anal- ysis.Experimental results show that not only wind stress anomalies simulated by an AGCM are obviously improved by reconstructed wind stress anomalies but also this reconstruction has a func- tion of a low-pass time filter,as a result,response of the ZC ocean model to reconstructed wind stress anomalies is more realistic than that of simulated wind stress anomalies by an AGCM.In this paper,application of the hybrid couple ocean-atmosphere model is further discussed.展开更多
In recent years, there has been increasing demand for high-resolution seasonal climate forecasts at sufficient lead times to allow response planning from users in agriculture, hydrology, disaster risk management, and ...In recent years, there has been increasing demand for high-resolution seasonal climate forecasts at sufficient lead times to allow response planning from users in agriculture, hydrology, disaster risk management, and health, among others. This paper examines the forecasting skill of the North American Multi-model Ensemble (NMME) over Ethiopia during the June to September (JJAS) season. The NMME, one of the multi-model seasonal forecasting systems, regularly generates monthly seasonal rainfall forecasts over the globe with 0.5 <span style="font-family:Verdana;">-</span><span style="font-family:;" "=""><span style="font-family:Verdana;"> 11.5 months lead time. The skill and predictability of seasonal rainfall are assessed using 28 years of hindcast data from the NMME models. The forecast skill is quantified using canonical correlation analysis (CCA) and root mean square error. The results show that the NMME models capture the JJAS seasonal rainfall over central, northern, and northeastern parts of Ethiopia while exhibiting weak or limited skill across western and southwestern Ethiopia. The performance of each model in predicting the JJAS seasonal rainfall is variable, showing greater skill in predicting dry conditions. Overall, the performance of the multi-model ensemble was not consistently better than any single ensemble member. The correlation of observed and predicted </span><span style="font-family:Verdana;">seasonal rainfall for the better performing models</span></span><span style="font-family:Verdana;">—GFDL-CM2p5-FLOR-A06,</span><span style="font-family:Verdana;"> CMC2-CanCM4, GFDL-CM2p5-FLOR-B01 and NASA-GMAO-062012</span><span style="font-family:Verdana;">—</span><span style="font-family:Verdana;">is 0.68, 0.58, 0.52, and 0.5, respectively. The COLA-RSMAS-CCSM4, CMC1-</span><span style="font-family:;" "=""> </span><span style="font-family:Verdana;">CanCM3 and NCEP-CFSv2 models exhibit less skill, with correlations less than 0.4. In general, the NMME offers promising skill to predict seasonal rainfall展开更多
文摘Systematic errors in the COLA R15 AGCM are analyzed by the SVD technique.In order to remove or reduce this kind of error source and reduce climate drift in coupled runs a way in which the wind stress anomalies simulated by an AGCM are reconstructed is proposed by using SVD anal- ysis.Experimental results show that not only wind stress anomalies simulated by an AGCM are obviously improved by reconstructed wind stress anomalies but also this reconstruction has a func- tion of a low-pass time filter,as a result,response of the ZC ocean model to reconstructed wind stress anomalies is more realistic than that of simulated wind stress anomalies by an AGCM.In this paper,application of the hybrid couple ocean-atmosphere model is further discussed.
文摘In recent years, there has been increasing demand for high-resolution seasonal climate forecasts at sufficient lead times to allow response planning from users in agriculture, hydrology, disaster risk management, and health, among others. This paper examines the forecasting skill of the North American Multi-model Ensemble (NMME) over Ethiopia during the June to September (JJAS) season. The NMME, one of the multi-model seasonal forecasting systems, regularly generates monthly seasonal rainfall forecasts over the globe with 0.5 <span style="font-family:Verdana;">-</span><span style="font-family:;" "=""><span style="font-family:Verdana;"> 11.5 months lead time. The skill and predictability of seasonal rainfall are assessed using 28 years of hindcast data from the NMME models. The forecast skill is quantified using canonical correlation analysis (CCA) and root mean square error. The results show that the NMME models capture the JJAS seasonal rainfall over central, northern, and northeastern parts of Ethiopia while exhibiting weak or limited skill across western and southwestern Ethiopia. The performance of each model in predicting the JJAS seasonal rainfall is variable, showing greater skill in predicting dry conditions. Overall, the performance of the multi-model ensemble was not consistently better than any single ensemble member. The correlation of observed and predicted </span><span style="font-family:Verdana;">seasonal rainfall for the better performing models</span></span><span style="font-family:Verdana;">—GFDL-CM2p5-FLOR-A06,</span><span style="font-family:Verdana;"> CMC2-CanCM4, GFDL-CM2p5-FLOR-B01 and NASA-GMAO-062012</span><span style="font-family:Verdana;">—</span><span style="font-family:Verdana;">is 0.68, 0.58, 0.52, and 0.5, respectively. The COLA-RSMAS-CCSM4, CMC1-</span><span style="font-family:;" "=""> </span><span style="font-family:Verdana;">CanCM3 and NCEP-CFSv2 models exhibit less skill, with correlations less than 0.4. In general, the NMME offers promising skill to predict seasonal rainfall
