Estimation of Weather Noise in Coupled Ocean-Atmosphere Systems Using Initialized Simulations
Estimation of Weather Noise in Coupled Ocean-Atmosphere Systems Using Initialized Simulations
复制标题
使用初始化模拟估计海洋-大气耦合系统中的天气噪声
DOI:
10.1175/jcli-d-15-0737.1
复制
发表时间:
2016
影响因子:
4.9
通讯作者:
Shukla Jagadish
中科院分区:
文献类型:
--
作者:
Zhu Jieshun;Shukla Jagadish
This study presents a new method to estimate atmospheric weather noise from coupled models, which is based on initialized simulations with a CGCM. In this method, the weather noise is estimated by removing the signal part, as determined from the coupled ensemble mean simulations. The weather noise estimated from coupled models is compared with that estimated from uncoupled AGCM simulations. The model used in this study is CFSv2. The initialized simulations start from each April during 1982–2009 paired with four members and extend for 6 months. To make a clear comparison between weather noise in coupled and uncoupled simulations, a set of uncoupled AGCM (the atmospheric component of CFSv2) simulations are conducted, which are forced by the daily mean SSTs from the above initialized CGCM simulations. The comparison indicates that, over the Asia–Pacific monsoon region where the local air–sea coupling is important, the noise variances are generally reduced as a result of air–sea coupling, as are the total and signal variances. This result stands in contrast to the results of previous studies that suggested that the noise variance for coupled and uncoupled models is the same. It is shown that the previous conclusion is simply an artifact of the assumption applied in the AGCM-based approach (i.e., the signal is the same between coupled and uncoupled simulations). In addition, the variance difference also exhibits a clear seasonality, with a larger difference over the monsoon region appearing toward boreal summer. Another set of AGCM experiments forced by the same SST suggests that the CGCM-based method generally remains valid in estimating weather noise within 2 months of its initial start.