资源环境科技发展态势分析平台

The present study compares the Indian summer monsoon rainfall (ISMR) prediction skill of monsoon mission climate forecast system version 2 (CFSv2-T382) with that of the seasonal prediction models participating in US National Multi-Model Ensemble (NMME) project. In general, the present-day models simulate cooler than observed sea surface temperature (SST) in majority of the Tropics and extratropics. The model rainfall has strong dry bias over major continental regions and wet bias over tropical oceans. Meanwhile, prediction of the boundary forcing such as SST is essential for driving the atmospheric response through teleconnections. It is noted that even though the prediction skill for SST boundary forcings like El Nino-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) is not at the best in CFSv2-T382 compared to a few of the NMME models, it shows better skill for ISMR hindcasts initialized at 3-month lead time (February IC). This may be attributed to the better teleconnection pattern of ENSO and IOD in CFSv2-T382, which has minimum biases in equatorial Indo-Pacific region. It also has a better ISMR-SST teleconnections in the Tropics with a pattern correlation of around 0.6. In many of the NMME models, the better prediction skill of the inter-annual variability of SST indices is not transformed into the improvement of ISMR skill through teleconnections. It is therefore concluded that having good prediction skill for major SST boundary forcings is not sufficient, but capturing the appropriate teleconnections of these SST boundary forcings in the model is critical for the better prediction of ISMR. The study points out that the present-day seasonal prediction systems need to be improved in their simulation of tropical SST-monsoon teleconnections, which can improve the seasonal prediction skill of Indian summer monsoon further. One area where the immediate focus is required is the Indian Ocean SST and ISMR teleconnection.