On Oceanic Initial State Errors in the Ensemble Data Assimilation for a Coupled General Circulation Model

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. Plain Language Summary Data assimilation is a widely used way to constrain the model with real world observations. Based on Bayesian Theorem, a good initial state is required as the prior distribution estimate for the assimilation. It is still an open question of how to construct the initial states. In this study, we proposed a new approach to focus on this issue. Via comparison of different experiments, the effectiveness of the approach was revealed. The results indicated that the current data assimilation system with the constructed initial state can produce high-quality initial conditions for seasonal predictions.

Automated summary

This study investigates the influence of model state errors at the initial time of assimilation on the quality of an ensemble-based data assimilation system for a complex fully coupled general circulation model. Two strategies are applied to alleviate the initial state errors and a reliable assimilation system is developed. The assimilation of observation-derived climatological data is found to be an effective approach to reduce initial state errors and improve the performance of the assimilation system.