On Oceanic Initial State Errors in the Ensemble Data Assimilation for a Coupled General Circulation Model
Yihao Chen, Zheqi Shen, Youmin Tang
Abstract
Abstract In the construction of an ensemble‐based data assimilation system for a complex fully coupled general circulation model (CGCM), the model state errors at initial time of assimilation have an important influence on assimilation quality. In this study, with the Community Earth System Model (CESM) and Data Assimilation Research Testbed (DART), we found that the influence of initial states errors persists throughout a vicious cycle and cannot be automatically remedied via consequent assimilations. As such, two strategies were applied to alleviate the initial state errors, and a reliable assimilation system was developed. Data assimilation experiments using oceanic observations were conducted over the period from 2005 to 2014 to investigate the impact of these different strategies. The evaluation revealed that the assimilation of observation‐derived climatological data is an effective approach to reduce initial state errors and preserve the balance between different variables to the largest extent, which significantly improved the performance of the assimilation system in the investigated time period. It was further found that the developed assimilation system can produce high‐quality oceanic analysis results comparable to the ECDA and GODAS, two widely used reanalysis products. Perspectives toward further improvement of coupled data assimilation are also outlined.