Abstract
El Nino-Southern Oscillation (ENSO) is the strongest interannual signal that is produced by basinscale processes in the tropical Pacific, with significant effects on weather and climate worldwide. In the past, extensive and intensive international efforts have been devoted to coupled model developments for ENSO studies. A hierarchy of coupled ocean-atmosphere models has been formulated; in terms of their complexity, they can be categorized into intermediate coupled models (ICMs), hybrid coupled models (HCMs), and fully coupled general circulation models (CGCMs). ENSO modeling has made signifiscant progress over the past decades, reaching a stage where coupled models can now be used to successfully predict ENSO events 6 months to one year in advance. Meanwhile, ENSO exhibits great diversity and complexity as observed in nature, which still cannot be adequately captured by current state-of-the-art coupled models, presenting a challenge to ENSO modeling. We primarily reviewed the long-term efforts in ENSO modeling continually and steadily made at different institutions in China; some selected representative examples are presented here to review the current status of ENSO model developments and applications, which have been actively pursued with noticeable progress being made recently. As ENSO simulations are very sensitive to model formulations and process representations etc., dedicated efforts have been devoted to ENSO model developments and improvements. Now, different ocean-atmosphere coupled models have been available in China, which exhibit good model performances and have already had a variety of applications to climate modeling, including the Coupled Model Intercomparison Project Phase 6 (CMIP6). Nevertheless, large biases and uncertainties still exist in ENSO simulations and predictions, and there are clear rooms for their improvements, which are still an active area of researches and applications. Here, model performances of ENSO simulations are assessed in terms of advantages and disadvantages with these differently formulated coupled models, pinpointing to the areas where they need to be further improved for ENSO studies. These analyses provide valuable guidance for future improvements in ENSO simulations and predictions.
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