Program
 
Special Session 5: Ocean-atmosphere interaction, multi-scale climate variability and their implication for biogeochemical processes
 
 
 
Poster
Application of an EnOI in Beijing Climate Center Climate System Model (BCC_CSM): Twin experiments for assimilating sea surface data and T/S profiles
SS5-08-S
Wei Zhou, Laboratory for Climate Studies, National Climate Center, China Meteorological Administration
Jinghui Li* , Center for Earth System Science, Tsinghua University
Fanghua Xu, Ministry of Education Key Laboratory for Earth System Modeling, and Center for Earth System Science, Tsinghua University
Presenter Email: lijh10sai@163.com
Ocean plays a key role in the predictability of the climate system due to its tremendous thermal inertia compared to atmosphere or land. Accuracy of the ocean initialization significantly impact seasonal to decadal climate predictions. A common strategy to obtain the optimal initialization is to assimilate the available ocean observations into ocean models, aiming at producing best estimates of ocean states. We apply an Ensemble Optimal Interpolation (EnOI) data assimilation method in a global ocean model (MOM4.0) to estimate 3-dimensional global ocean states via assimilating sea surface variables - i.e. sea surface temperature (SST), sea surface height (SSH), sea surface salinity (SSS), temperature and salinity in an idealized twin experiment framework. Pseudo-observations of SST, SSH, SSS and T, S are first generated in a free model run (named TRUE). Then a series of sensitivity tests started from biased initial conditions are conducted, including a free run (CTRL) and seven assimilation runs. One run assimilates SST (E01), SSH (E02), both SST and SSH (E03), SSS (E04), all SST, SSH and SSS (E05), T and S (E06), all the variables (E07) over one-year period. These tests allow us to check analysis field accuracy against the “truth”. Significant improvements are achieved in E07 in terms of non-assimilated variables and some climate indices (Niño 3.4). Thus, to some extent, the EnOI is able to capture realistic spatial and temporal variations of temperature and salinity from surface to deep water in the global ocean model. One-year forecasts initialized from three assimilation runs and CTRL are conducted and compared as well. The seasonal forecast skill is clearly improved after assimilation, particularly E07.