摘要
本研究发展了一个全球海洋资料同化系统ZFL_GODAS。该系统是一个短期气候数值预测业务系统的子系统,为短期气候预测海气耦合模式提供全球海洋初始场。系统能够同化的观测资料包括卫星高度计资料、卫星海表温度(SST)资料,以及Argo、XBT、TAO等各种不同来源的现场温盐廓线资料。系统使用的海洋模式为中国科学院大气物理研究所大气科学和地球流体力学数值模拟国家重点实验室开发的气候系统海洋模式LICOM1.0,同化方案为集合最优插值(EnOI)方案。系统使用一个由海洋模式自由积分得到的静态样本来估计背景场误差协方差。这样的基于集合样本的背景场误差协方差具有多变量协变、各向异性的特征,且能反映海洋物理过程固有的空间尺度特征。针对EnOI同化程序的特点,开发了一套特色鲜明、负载均衡、高效的并行化同化程序。本文通过与不同类型观测资料的比较,对同化系统的性能进行了评估。通过比较海表温度和海面高度的年际变率,海表温度异常随时间的变化,SST、海面高度异常(SLA)以及次表层温盐预报产品的均方根误差,5年平均温度偏差廓线、平均盐度廓线、平均纬向流速廓线等发现:系统工作正常、同化效果较好;经过同化以后,各变量都更加接近观测,误差更小,与观测场的相关性更好,可以为短期气候预测系统提供较好的海洋初始场,也可以为物理海洋学的研究提供有效的再分析资料。
A global ocean data assimilation system named ZFL_GODAS is developed in this study. The system is a sub-system of an operational system for short-range climate forecasting, and provides the global ocean initial state field for coupled ocean-atmosphere models. It can assimilate observations such as satellite altimetry, sea surface temperature （SST）, in situ temperature, and salinity from Argo, XBT, TAO, and other sources. ZFL_GODAS uses LICOM1.0 （a global OGCM developed by LASG/IAP） as its ocean model. It is an ensemble optimal interpolation system that uses an ensemble of a series of model states from a free LICOM running to estimate the background error covariances （BECs）. The ensemble-based BECs are multivariate and inhomogeneous and they can reflect the length scales, anisotropy, and covariability of oceanic physical processes. To enhance the efficiency, a set of distinctive, efficient, and load-balanced parallelized Ensemble Optimal Interpolation （EnOI） programs have been developed.
The performance of ZFL_GODAS is evaluated by comparing its results with a range of satellite-derived and in situ observations. We compare the interannual variability of SST and sea surface height, the evolution of the SST anomaly at the equator, and the root-mean-square error of model results of SST, sea level anomaly, and sub-surface temperature and salinity. We also show the five-year-mean profiles for temperature bias, salinity, and zonal velocity. We find that the ocean data assimilation shows a very positive impact on the modeled fields. We can preliminarily conclude that ZFL_GODAS performs well, so it can provide a desirable global ocean initial state for the ocean model component of the climate forecasting system, and provide effective reanalysis data for improving our understanding of the oceans.
出处
《气候与环境研究》
CSCD
北大核心
2014年第3期321-331,共11页
Climatic and Environmental Research
基金
国家重点基础研究发展计划2012CB417404
2012CB955202
中国科学院战略性先导科技专项XDA10010405
关键词
海洋资料同化
集合最优插值(EnOI)
多变量同化
业务系统
Ocean data assimilation
Ensemble Optimal Interpolation （EnOI）
Multivariate assimilation
Operational oceanography
