Based on an intermediate coupled model （ICM）, a probabilistic ensemble prediction system （EPS） has been developed. The ensemble Kalman filter （EnKF） data assimilation approach is used for generating the initial ensemble conditions, and a linear, first-order Markov-Chain SST anomaly error model is embedded into the EPS to provide model-error perturbations. In this study, we perform ENSO retrospective forecasts over the 120 year period 1886-2005 using the EPS with 100 ensemble members and with initial conditions obtained by only assimilating historic SST anomaly observations. By examining the retrospective ensemble forecasts and available observations, the verification results show that the skill of the ensemble mean of the EPS is greater than that of a single deterministic forecast using the same ICM, with a distinct improvement of both the correlation and root mean square （RMS） error between the ensemble-mean hindcast and the deterministic scheme over the 12-month prediction period. The RMS error of the ensemble mean is almost 0.2℃ smaller than that of the deterministic forecast at a lead time of 12 months. The probabilistic skill of the EPS is also high with the predicted ensemble following the SST observations well, and the areas under the relative operating characteristic （ROC） curves for three different ENSO states （warm events, cold events, and neutral events） are all above 0.55 out to 12 months lead time. However, both deterministic and probabilistic prediction skills of the EPS show an interdecadal variation. For the deterministic skill, there is high skill in the late 19th century and in the middle-late 20th century （which includes some artificial skill due to the model training period）, and low skill during the period from 1906 to 1961. For probabilistic skill, for the three different ENSO states, there is still a similar interdecadal variation of ENSO probabilistic predictability during the period 1886~2005. There is high skill in the late 19th century from 1886 to 1905,
Analyses of cloud water path （CWP） data over China available from the International Satellite Cloud Climatology Project （ISCCP） are performed for the period 1984-2004. Combined with GPCP precipitation data, cloud water cycle index （CWCI） is also calculated. The climatic distributions of CWP are found to be dependent on large-scale circulation, topographical features, water vapor transport and similar distribution features which are found in CWCI except in the Sichuan Basin. Influenced by the Asia monsoon, CWP over China exhibits very large seasonal variations in different regions. The seasonal cycles of CWCI in different regions are consistent and the largest CWCI occurs in July. The long-term trends of CWP and CWCI are investigated, too. Increasing trends of CWP are found during the period with the largest increase found in winter. The decreasing trends of CWCI dominate most regions of China. The differences in long-term trends between CWP and CWCI suggest that CWP only can influence the variation of CWCI to a certain extent and that other factors need to be involved in cloud water cycle researches. This phenomenon reveals the complexity of the hydrological cycle related to cloud water.
