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 ex...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.展开更多
本文利用国家气候中心气候系统模式(Beijing climate center climate System Model,BCC_CSM1.1m)提供的1991—2014年海表温度回报数据,将逐步回归模态投影方法(stepwise Pattern Projection Method,SPPM)应用到改进BCC_CSM1.1m模式El N...本文利用国家气候中心气候系统模式(Beijing climate center climate System Model,BCC_CSM1.1m)提供的1991—2014年海表温度回报数据,将逐步回归模态投影方法(stepwise Pattern Projection Method,SPPM)应用到改进BCC_CSM1.1m模式El Nino和南方涛动(ENSO)预报研究。SPPM是一种经验性模式误差订正方法,其主要思路是在大尺度模式预报因子场中找寻出与格点观测预报变量相关性高的信号,通过投影将这种信号反演出来,然后建立回归方程得到订正后的预报结果。本文交叉检验和滚动独立样本检验的结果表明,利用SPPM可以有效地提高BCC_CSM1.1m气候系统模式的预报技巧,尤其是在热带太平洋地区以及印度洋海区,24年交叉检验Nino3.4指数提前6个月预报的相关系数技巧可以提高8%~10%,预报误差得到显著降低。不同季节SPPM订正效果略有不同,其中对秋季的预报技巧提升最为显著。与此同时,交叉检验结果还显示,SPPM对El Nino中心纬向位置的预报也有一定程度的改进。展开更多
基金National Natural Science Foundation of China (41790471, 41175065)National Key Research and Development Program of China (2016YFA0602200, 2012CB955203, 2013CB430202).
文摘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.
