Preliminary assessment on the hindcast skill of the Arctic Oscillation with decadal experiment by the BCC_CSM1.1 climate model

作     者：WU Li-Quan LI Qing-Quan DING Yi-Hui WANG Li-Juan XIN Xiao-Ge WEI Min 

作者机构：Key Laboratory of Meteorological Disaster Ministry of Education Joint International Research Laboratory of Climate and Environment Change Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters Nanjing University of Information Science & Technology Nanjing 210044 China Laboratory for Climate Studies National Climate Center China Meteorological Administration Beijing 100081 China National Meteorology Information Center China Meteorological Administration Beijing 100081 China 

出 版 物：《Advances in Climate Change Research》 (气候变化研究进展（英文版）)

年 卷 期：2018年第9卷第4期

页      面：209-217页

核心收录：

学科分类：07[理学] 

基　　金：National Natural Science Foundation of China (41790471, 41175065) National Key Research and Development Program of China (2016YFA0602200, 2012CB955203, 2013CB430202) 

主　　题：BCC_CSM1.1 Climate model Decadal Arctic oscillation Hindcast 

摘      要：The prediction skill of Arctic Oscillation (AO) in the decadal experiments with the Beijing Climate Center Climate System Model version 1.1 (BCC_CSM1.1) is assessed. As compared with the observations and historical experiments, the contribution of initialization for climate model to predict the seasonal scale AO and its interannual variations is estimated. Results show that the spatial correlation coefficient of AO mode simulated by the decadal experiment is higher than that in the historical experiment. The two groups of experiments reasonably reproduce the characteristics that AO indices are the strongest in winter and the weakest in summer. Compared with historical experiments, the correlation coefficient of the monthly and winter AO indices are higher in the decadal experiments. In particular, the correlation coefficient of monthly AO index between decadal hindcast and observation reached 0.1 significant level. Furthermore, the periodicity of the monthly and spring AO indices are achieved only in the decadal experiments. Therefore, the initial state of model is initialized by using sea temperature data may help to improve the prediction skill of AO in the decadal prediction experiments to some extent.