The Short-Term Climate Prediction System FIO-CPS v2.0 and its Prediction Skill in ENSO

Yajuan Song,Qi Shu,Ying Bao,Zhenya Song,Xiaodan Yang

doi:10.3389/feart.2021.759339

Abstract

The climate model is an important tool for simulating and predicting the mean state and variability of the climate system. The First Institute of Oceanography-Climate Prediction System (FIO-CPS), built on a climate model with the oceanic observation initialization, has been updated from version 1.0 to 2.0, with a finer resolution and more reasonable physical processes. Previous assessments show that the mean state was well simulated in version 2.0, and its influence on the prediction was further analyzed in this study. Hindcast experiments were conducted using FIO-CPS v1.0 and v2.0, and their prediction abilities based on 27 years (1993–2019) experiment data were analyzed. The results show that the sea surface temperature (SST) biases over the eastern Pacific and the Southern Ocean are improved in the initial condition of FIO-CPS v2.0. Moreover, this new system has a higher skill for predicting El Niño-Southern Oscillation (ENSO). The prediction skill represented by the anomaly correlation coefficient (ACC) of the Niño3.4 index is greater than 0.78 at the 6-month lead time, which increases by 11.09% compared to the value of 0.70 in FIO-CPS v1.0. The root mean square error (RMSE) decreases by 0.20, which accounts for 28.59% of the FIO-CPS v1.0 result. Furthermore, the improvement of the prediction skill changes seasonally, featured by the ACC significantly increasing in the boreal winter and early spring. The improvement in the annual mean SST prediction over the Equatorial Pacific mainly contributes to the enhanced ENSO prediction skill in FIO-CPS v2.0. These results indicate that a state-of-the-art climate model with a well-simulated mean state is critical in improving the prediction skill on the seasonal time scale.

Highlights

The climate model, known as the coupled general circulation model, is an important tool for simulating the processes and interactions among the atmosphere, ocean, land, and other components (Dai et al, 2014; Zhou et al, 2020)
The annual mean results reveal that the initial values simulated using First Institute of Oceanography-Climate Prediction System (FIO-CPS) v2.0 are more consistent with the observations
The seasonal prediction system FIO-CPS v2.0 was recently developed, and its prediction ability indicated by Niño index and sea surface temperature anomaly (SSTA) in the tropical Pacific Ocean was evaluated based on the hindcast results from 1993 to 2019

Summary

Introduction

The climate model, known as the coupled general circulation model, is an important tool for simulating the processes and interactions among the atmosphere, ocean, land, and other components (Dai et al, 2014; Zhou et al, 2020). The First Institute of Oceanography-Climate Prediction System (FIO-CPS) is a seasonal prediction system, based on an atmosphere-wave-ocean-sea ice-land surface coupled climate model and an oceanic assimilation module. We had assessed the seasonal prediction of the SST based on the hindcast results of FIO-CPS v1.0 in the North Pacific.

Results

Conclusion