Improved decadal climate prediction in the North Atlantic using EnOI-assimilated initial condition

Min Wei,Qingquan Li,Xiaoge Xin,Wei Zhou,Zhenyu Han,Yong Luo,Zongci Zhao

doi:10.1016/j.scib.2017.08.012

Abstract

Decadal prediction experiments of Beijing Climate Center climate system model version 1.1 (BCC-CSM1.1) participated in Coupled Model Intercomparison Project Phase 5 (CMIP5) had poor skill in extratropics of the North Atlantic, the initialization of which was done by relaxing modeled ocean temperature to the Simple Ocean Data Assimilation (SODA) reanalysis data. This study aims to improve the prediction skill of this model by using the assimilation technique in the initialization. New ocean data are firstly generated by assimilating the sea surface temperature (SST) of the Hadley Centre Sea Ice and Sea Surface Temperature (HadISST) dataset to the ocean model of BCC-CSM1.1 via Ensemble Optimum Interpolation (EnOI). Then a suite of decadal re-forecasts launched annually over the period 1961–2005 is carried out with simulated ocean temperature restored to the assimilated ocean data. Comparisons between the re-forecasts and previous CMIP5 forecasts show that the re-forecasts are more skillful in mid-to-high latitude SST of the North Atlantic. Improved prediction skill is also found for the Atlantic multi-decadal oscillation (AMO), which is consistent with the better skill of Atlantic meridional overturning circulation (AMOC) predicted by the re-forecasts. We conclude that the EnOI assimilation generates better ocean data than the SODA reanalysis for initializing decadal climate prediction of BCC-CSM1.1 model.

Highlights

Near-term climate prediction has been studied extensively using the models participated in the decadal prediction experiments of the Coupled Model Intercomparison Project Phase 5 (CMIP5) [1]
EnOI_HadInit improves forecast skill of sea surface temperature (SST) in the subpolar and mid-latitude central area of the North Atlantic compared with NoInit (Fig. 2a), while SODAInit has larger Root-mean-square error (RMSE) over most area of the North Atlantic than the NoInit (Fig. 2b)
The decadal prediction of Beijing Climate Center (BCC)-CSM1.1 for CMIP5 had poor hindcast skill in the North Atlantic, which may be caused by using the Simple Ocean Data Assimilation (SODA) reanalysis data to initialize its full-field ocean temperature

Summary

Introduction

Near-term climate prediction has been studied extensively using the models participated in the decadal prediction experiments of the Coupled Model Intercomparison Project Phase 5 (CMIP5) [1]. The prediction skills in AMO and climate over the land associated with the AMO are, model dependent [8]. Still, some models, such as the Beijing Climate Center climate system model version 1.1. Decadal climate predictability depends on both initial conditions and external forcings arising from changes in atmospheric composition [11,12]. Since the external forcing is the same for all CMIP5 models [13], different prediction skills of these models may mainly originate from their initial conditions. Modeling groups use different techniques and methodology to initialize decadal climate prediction, as summarized in Meehl et al [6]. Carrying out re-forecasts using one model and different techniques of initialization is a good approach, it is a huge task for any modeling group

Objectives

Methods

Results

Conclusion