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igure &: Overview of state variables and their interaction in ERGOM 
The coupling between ERGOM and NEMO is done via the 
NEMO TOP (Tracers in the Ocean Paradigm) component. 
The tracers transport, mixing and dilution are solved by the 
standard/native TRP (Passive Tracer Transport) modules, 
whereas users are free to define the bio-geo-chemical 
processes in the so-called «source-minus-sinks» module. 
This interface passes the column-wise concentration 
fields to the subroutines, which further evaluate the bio- 
Jgeo-chemical dynamics and update the concentrations. 
Additionally, this interface links ERGOM with the water 
temperature to estimate: 
- growth rate of temperature sensitive plankton species 
and oxygen solubility calculation, 
current speed for the sediment resuspension, 
short wave radiation and salinity to calculate seawater 
aptical parameters (Wan et al., 2013), 
- wind and temperature to estimate the gas-exchange 
through the surface. 
Validation with Chlorophyll and nutrient concentration 
observations showed that the new coupled NEMO-ERGOM 
system was able to reproduce observed dynamics with 
lower bias than the previous HBM-ERGOM system (Spruch 
et al., 2020). Preliminary results showed somewhat larger 
biases for oxygen and nutrients in the deeper layers. The 
Implementation of a less diffusive 4th order flux corrected 
transport advection scheme in NEMO improved these 
Inflow events with a positive effect on the ERGOM results. 
1.3 Data assimilation 
The Parallel Data Assimilation Framework (PDAF) developed 
by the Alfred Wegener Institute (AWI) in Germany has been 
zhosen for the data assimilation task (Nerger and Hiller, 
2013). During the past six years, the PDAF system has been 
ımplemented and tested in close contact with AWI, for both 
-he near real-time forecast production and the reanalysis 
products. The implementation and testing started with the 
PDAF-HBM interface using a Local Ensemble Square Root 
ıransform Kalman Filter (LESTKF) to assimilate the SST 
L3 Copernicus Marine Service dataset covering the North 
Sea - Baltic Sea. It was shown that by assimilating SST, 
the system greatly reduced not only the SST bias and Root 
Mean Square differences but also had a positive impact on 
forecasting of the sea ice concentration and thickness. The 
PDAF-HBM system never ran In operational mode within the 
ZMEMS asthe decision toward the NEMO system was taken 
Hence, since 2018, focus has been to develop a PDAF- 
NEMO-ERGOM system based on the LESTKF filter for both 
the forecast and the reanalysis products. Since late 2020, 
the coupled system PDAF-NEMO-ERGOM produces the 
near real time forecast with a univariate SST assimilatior 
scheme. Further developments have been performed 
and the next release of a new multi-year reanalysis 
product will include a multi-variate SST assimilation 
scheme, as well as a univariate scheme for assimilation of 
temperature and salinity profile observations. Assimilation 
of biogeochemical parameters such as oxygen and nutrient 
profiles is under development.