This study evaluates the performance of the Grid-point Atmospheric Model of IAP LASG,version 3(GAMIL3),in simulating the Madden–Julian Oscillation(MJO),based on the CMIP6(phase 6 of the Coupled Model Intercomparison ...This study evaluates the performance of the Grid-point Atmospheric Model of IAP LASG,version 3(GAMIL3),in simulating the Madden–Julian Oscillation(MJO),based on the CMIP6(phase 6 of the Coupled Model Intercomparison Project)AMIP(Atmospheric Model Intercomparison Project)simulation.Results show that GAMIL3 reasonably captures the main features of the MJO,such as the eastward-propagating signal in the MJO frequency band,the symmetric and asymmetric structures of the MJO,several convectively coupled equatorial waves,and the MJO life cycle.However,GAMIL3 underestimates the MJO amplitude,especially for outgoing longwave radiation,as do most CMIP5 models,and simulates slow eastward propagation.展开更多
A 30-year hindcast was performed using version 4.1 of the IAP AGCM（IAP AGCM4.1）, and its potential predictability of the MJO was then evaluated. The results showed that the potential predictability of the MJO is 13 ...A 30-year hindcast was performed using version 4.1 of the IAP AGCM（IAP AGCM4.1）, and its potential predictability of the MJO was then evaluated. The results showed that the potential predictability of the MJO is 13 and 24 days, evaluated using the signal-to-error ratio method based on a single member and the ensemble mean, respectively. However, the MJO prediction skill is only9 and 10 days using the two methods mentioned above. It was further found that the potential predictability and prediction skill depend on the MJO amplitude in the initial conditions. Prediction initiated from conditions with a strong MJO amplitude tends to be more skillful. Together with the results of other measures, the current MJO prediction ability of IAP AGCM4.1 is around 10 days, which is much lower than other climate prediction systems. Furthermore, the smaller difference between the MJO predictability and prediction skill evaluated by a single member and the ensemble mean methods could be ascribed to the relatively smaller size of the ensemble member of the model.Therefore, considerable effort should be made to improve MJO prediction in IAP AGCM4.1 through application of a reasonable model initialization and ensemble forecast strategy.展开更多
基金jointly supported by the National Key Research and Development Program of China grant number 2017YFA0603903the National Natural Science Foundation of China grant numbers 41622503 and 41775101。
文摘This study evaluates the performance of the Grid-point Atmospheric Model of IAP LASG,version 3(GAMIL3),in simulating the Madden–Julian Oscillation(MJO),based on the CMIP6(phase 6 of the Coupled Model Intercomparison Project)AMIP(Atmospheric Model Intercomparison Project)simulation.Results show that GAMIL3 reasonably captures the main features of the MJO,such as the eastward-propagating signal in the MJO frequency band,the symmetric and asymmetric structures of the MJO,several convectively coupled equatorial waves,and the MJO life cycle.However,GAMIL3 underestimates the MJO amplitude,especially for outgoing longwave radiation,as do most CMIP5 models,and simulates slow eastward propagation.
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences[grant number XDA05110200]the Special Scientific Research Fund of the Meteorological Public Welfare Profession of China[grant number GYHY201406021]the National Natural Science Foundation of China[grant numbers 41575095,41175073,41575062,41520104008]
文摘A 30-year hindcast was performed using version 4.1 of the IAP AGCM（IAP AGCM4.1）, and its potential predictability of the MJO was then evaluated. The results showed that the potential predictability of the MJO is 13 and 24 days, evaluated using the signal-to-error ratio method based on a single member and the ensemble mean, respectively. However, the MJO prediction skill is only9 and 10 days using the two methods mentioned above. It was further found that the potential predictability and prediction skill depend on the MJO amplitude in the initial conditions. Prediction initiated from conditions with a strong MJO amplitude tends to be more skillful. Together with the results of other measures, the current MJO prediction ability of IAP AGCM4.1 is around 10 days, which is much lower than other climate prediction systems. Furthermore, the smaller difference between the MJO predictability and prediction skill evaluated by a single member and the ensemble mean methods could be ascribed to the relatively smaller size of the ensemble member of the model.Therefore, considerable effort should be made to improve MJO prediction in IAP AGCM4.1 through application of a reasonable model initialization and ensemble forecast strategy.
