Articles | Volume 15, issue 12
Geosci. Model Dev., 15, 4805–4830, 2022
Geosci. Model Dev., 15, 4805–4830, 2022
Model evaluation paper
23 Jun 2022
Model evaluation paper | 23 Jun 2022

An online ensemble coupled data assimilation capability for the Community Earth System Model: system design and evaluation

Jingzhe Sun et al.

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Cited articles

Anderson, J. L.: An ensemble adjustment Kalman filter for data assimilation, Mon. Weather Rev., 129, 2884–2903, 2001. 
Anderson, J. L.: A local least squares framework for ensemble filtering, Mon. Weather Rev., 131, 634–642, 2003. 
Anderson, J. L. and Anderson, S. L.: A monte carlo implementation of the nonlinear filtering problem to produce ensemble assimilations and forecasts, Mon. Weather Rev., 127, 2741–2758, 1999. 
Anderson, J. L., Wyman, B., Zhang, S., and Hoar, T.: Assimilation of surface pressure observations using an ensemble filter in an idealized global atmospheric prediction system, J. Atmos. Sci., 62, 2925–2938, 2005. 
Anderson, J. L. and Collins N.: Scalable Implementations of Ensemble Filter Algorithms for Data Assimilation, J. Atmo. Ocean Tech., 24, 1452–1463,, 2007. 
Short summary
An online ensemble coupled data assimilation system with the Community Earth System Model is designed and evaluated. This system uses the memory-based information transfer approach which avoids frequent I/O operations. The observations of surface pressure, sea surface temperature, and in situ temperature and salinity profiles can be effectively assimilated into the coupled model. That will facilitate a long-term high-resolution climate reanalysis once the algorithm efficiency is much improved.