Improved ENSO and PDO Prediction Skill Resulting from Finer Parameterization Schemes in a CGCM

Abstract

Coupled general circulation models (CGCMs), as tools of predicting climate variability, are constantly being improved due to their immense value in a host of theoretical and practical, real-world problems. Consequently, four new parameterization schemes are introduced in the First Institute of Oceanography Earth System Model (FIO-ESM), and a new climate prediction System (CPS) is built up based on modified and original FIO-ESM. Here, turbulence from the sea surface to deep ocean were fully described, and seasonal forecasts of El Niño-Southern Oscillation (ENSO) and year-to-year prediction of Pacific Decadal Oscillation (PDO) were made with both the modified and original FIO-ESM-CPS. The results illustrate that the anomaly correlation coefficient (ACC) of the Niño 3.4 index significantly increased, and the root mean square error (RMSE) significantly decreased, respectively, in the modified FIO-ESM-CPS as compared to the original. The RMSE is improved by over 20% at 4- and 5-month lead times. Over longer leads, and in the modified FIO-ESM-CPS, forecast ENSO amplitudes are far closer to observations than the original CGCM, which significantly overestimates amplitudes. PDO prediction skill is also improved in the modified FIO-ESM-CPS with ACC improving by 36% at the 4-year lead time and RMSE decreasing by 21% at the 3-year lead time.