Decadal Prediction Skill of BCC-CSM1.1 with Different Initialization Strategies

Abstract

 Two sets of decadal prediction experiments were performed with Beijing Climate Center climate system model version 1.1 (BCC-CSM1.1) with different initialization strategies. One experiment is relaxing modeled ocean temperature to the Simple Ocean Data Assimilation (SODA) reanalysis data (SODAInit). In the other (EnOI_HadInit) experiment, the modeled ocean temperature were relaxed toward the assimilated ocean data, which were generated by assimilating sea surface temperature (SST) of the Hadley Centre Sea Ice and Sea Surface Temperature (HadISST) data to the ocean model of BCC-CSM1.1 using Ensemble Optimum Interpolation (EnOI) method. Comparisons between EnOI_HadInit and SODAInit hindcasts show that EnOI_HadInit is more skillful in predicting SST over the North Pacific, the southern Indian Ocean, and the North Atlantic. Improved prediction skill is also found for surface air temperature (SAT) over South Europe, North Africa, and Greenland, which is associated with the skillful prediction of the Atlantic multi-decadal oscillation in EnOI_HadInit. EnOI_HadInit and SODAInit are both skillful in predicting East Asian SAT, which is related to their skillful predictions of the tropical western Pacific SST. The result indicates that assimilated data generated by the ocean model of BCC-CSM1.1 with EnOI assimilation provide better initial conditions than SODA reanalysis data for the decadal predictions of BCC-CSM1.1.