An Assessment of Convergence of Climate Reanalyses from Two Coupled Data Assimilation Systems with Identical High-Efficiently Filtering

Abstract Coupled data assimilation (CDA) uses coupled model dynamics and physics to extract observational information from measured data in multiple Earth system domains to reconstruct historical states of the Earth system, forming a reanalysis of climate variability. Due to imperfect numerical schemes in modeling dynamics and physics, models are usually biased from the real world. Such model bias is a critical obstacle in the reconstruction of historical variability by combining model and observations, and, to some degree, causes divergence of CDA results because of individual model behavior in each system. Here, based on a multitimescale high-efficiency filtering algorithm which includes a deep ocean bias relaxing scheme, we first develop a high-efficiency online CDA system with the Community Earth System Model (CESM-MSHea-CDA). Then, together with the other previously-established CDA system (CM2-MSHea-CDA) within the Coupled Model version 2.1 model that is developed by Geophysical Fluid Dynamics Laboratory, we conduct climate reanalysis for the past four decades (1978–2018). Evaluations show that due to improved representation for multiscale background statistics and effective deep ocean model bias relaxing, both CDA systems produce convergent estimation of variability for major climate signals such as variability of basin-scale ocean heat content, ENSO, PDO, etc. Particularly, both CDA systems generate similar time-mean of global and Atlantic meridional overturning circulations that converge to the geostrophic velocity estimate from climatological temperature and salinity data. The CDA-estimated mass transport at typical measurement sections is mostly consistent with the observations.