Seasonal predictions of sea surface height in BCC-CSM1.1m and their modulation by tropical climate dominant modes

Abstract

Sea surface height (SSH) variation has profound impacts on ecological system and socioeconomical activities, and thus skillful prediction for SSH on seasonal to interannual scale is quite desirable. This study evaluates the seasonal prediction skills of global SSH based on outputs from Beijing Climate Center Climate System Model (BCC-CSM1.1m) seasonal forecasting system and further investigates the predictability source of SSH. Compared with the damped persistence forecast, superiority of dynamic prediction of this model is mainly found over tropical Pacific and Indian Ocean. Prediction skills depend on initiated month and length of lead time and also feature the spring prediction barrier related to El Niño-Southern Oscillation (ENSO), which are clearly modulated by ENSO and Indian Ocean Dipole (IOD), respectively. During El Niño/La Niña, there are higher skills of SSH prediction over tropical Pacific and when IOD event occurred, higher skills initiated in boreal autumn emerge over the tropical Indian Ocean. What's more, SSH over tropical western Pacific is significantly correlated with ENSO/annual cycle combination mode. Further analysis indicates that ENSO/IOD is the main source of predictability for SSH over tropical Pacific/Indian Ocean. We demonstrate that the performance of SSH prediction in the model is largely dependent on the capability of representing such observed relationships and predicting ENSO/IOD. The considerable skill in predicting large-scale SSH variation on seasonal scale indicates that BCC-CSM1.1m could join as a member of ensemble of multiple global models and contribute to improved seasonal sea level prediction.