Improved EOF-based bias correction method for seasonal forecasts and its application in IAP AGCM4.1

An effective improvement on the empirical orthogonal function(EOF)–based bias correctionmethod for seasonal forecasts is proposed in this paper,by introducing a stepwise regression method into the process of EOF time series correction.Using 30-year(1981–2010)hindcast results from IAP AGCM4.1(the latest version of this model),the improved method is validated for the prediction of summer(June–July–August)rainfall anomalies in Southeast China.The results in terms of the pattern correction coefficient(PCC)of rainfall anomalies shows that the 30-year-averaged prediction skill improves from 0.01 to 0.06 with the original correction method,and to 0.29 using the improved method.The applicability in real-time prediction is also investigated,using 2016 summer rainfall prediction as a test case.With a PCC of 0.59,the authors find that the new correction method significantly improves the prediction skill;the PCC using the direct prediction of the model is?0.04,and using the old bias correction method it is 0.37.

MJO potential predictability and predictive skill in IAP AGCM 4.1

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.