资源环境科技发展态势分析平台

Two different flavors of El Nio-Southern Oscillation (ENSO, canonical east Pacific type and Modoki/central Pacific type) are reported in the recent decades and are found to influence the global climate in different ways. The success of a seasonal prediction system is dependent on its ability to capture these two ENSO flavors accurately, together with associated teleconnections. The present study analyses the ability of Climate Forecast System version 2 (CFSv2) in simulating the two El Nio flavors and their teleconnections. The study uses two versions of CFSv2 in which the atmospheric model horizontal resolutions are different (T126 at 100 km and T382 at 38 km) and are initialized from a calendar month, ranging from February to June. The canonical ENSO pattern is captured as prominent mode of tropical Pacific sea surface temperature (SST) by both resolutions of CFSv2. Even though the tri-polar structure of ENSO Modoki is simulated as second mode, it has some disagreement with observations. The canonical El Nio induced SST, rainfall and atmospheric circulation in the tropical Pacific in summer and fall seasons are comparable with observations in both models. Meanwhile, the teleconnections in the tropical Indian Ocean and Indian monsoon regions are close to observations in T382 only. Teleconnections associated with El Nio Modoki are proper in T382 hindcasts, in which SST bias in Indian Ocean is slightly warm and the cold bias in central Pacific is marginal (mainly for Feb IC hindcasts). The present study indicates that the distinction of ENSO flavors in summer is the major reason for the higher skill of Indian summer monsoon rainfall (ISMR) in CFSv2 T382 Feb IC hindcasts. However, teleconnections associated with two flavors of ENSO are not distinguishable in fall and winter seasons, even in higher resolution model due to the presence of strong cold SST bias in central Pacific and warm SST bias in extreme east Pacific. Thus present study confirms that, higher resolution CFSv2 is required to differentiate the flavors of ENSO and their teleconnections properly. It has better prediction skill for ISMR at a lead time of 4 months, which is significant for seasonal prediction of Indian summer monsoon.