MERCATOR OCEAN JOURNA, 
SEPTEMBER 2021 
Temperature (°C) - 1993-2018 
_BIAS 
\ 
LOO 
#4 
F 
00 4 
300 
400 | 
85 E2R2 (2018) 
AS E3R1 [20203 
38 05 49.45 49 4? aß 9 „oh 0? a9 a? 
fa} 
E — — 
a8 a? a” 
nm 
156 
20C L 
10 
Cha 
Salinity (PSU) - 1993-2018 
RMSD a _ BIAS 
L 
B5 E2R2 (20181 
AS E3R1 (2070 
a© ar 
A A 
> D nl] 
A a ah 0 5 
’h) 
-igure 3: Vertical profiles of root mean square difference and bias for a) temperature and b) salinity, by comparing the reanalysis E3R1 anc 
:he old one E2R2 against Arco data In the period Jan 19973 - Dec 2018. 
1.3 BS-MFC Biogeochemistry 
The nominal product for BS-BIO NRT [6] provides analysis 
and 10-days forecast every day, as daily means, with 
nominal start of the forecast at 00:00UTC for the following 
list of varlables: 
Chlorophyll and phytoplankton, 
dissolved oxygen, nitrate and phosphate, 
surface pressure of carbon dioxide, 
pH, 
- surface downward mass flux of carbon dioxide, 
- net primary production, 
-sea water alkalinity and concentration of dissolved 
inorganic carbon in seawater 
The first version of the operational system - operational in 
the period 2016-2018 - was based on online coupled version 
GHER3D hydrodynamical model and the BiogeochemicAl 
Model for Hypoxic and Benthic Influenced areas (BAMHBI) 
[7,8,9] on a spatial domain of 5 km resolution and over 
40 O-levels. During the Phase 2, it has evolved toward 
a new system, based on NEMO v3.6 online coupled to 
BAMHBI, aligned with BS-PHY NRT system (e.g., same 
grid, atmospheric forcing). Since Jul 2019, the BS-BIO 
NRT system solves and delivers variables describing the 
carbonate system (ie., pCO,, pH, DIC, CO, flux, alkalinity). 
Since Jun 2020, it is assimilating Chlorophyll satellite L3 
data from CMEMS 0OC TAC (CHL L3 NRT product based 
on multi-satellite composites) via the Ocean Assimilation 
Kit OAK [10], improving the surface Chlorophyll product 
Juality. Figure 4 compares the simulated and observec 
surface Chlorophyll a in typical Black Sea regions located 
ın the shelf and deep sea and displayed in Figure 5 
Table 1 gives the model bias for different regions. The 
zomparison of observed and simulated Chlorophyll 
highlights that, in regions under the direct influence of 
river discharges (ie., regions 4 and 5 under the influence 
af the Danube, Dnestr and Dnieper rivers), differences 
between model and observations persist even after data 
3assimilation. The bias is positive, and up to 0.37 and 0.26 
mg.m-3 respectively in regions 4 and 5. In these coastal 
regions, the dynamic of blooms, biogeochemical cycling and 
foodweb Is expected to be under the dominant influence 
of rivers’ discharges. The lower quality of the Chlorophyl 
product compared to more offshore areas is explained by 
the use of monthly eclimatological data of inorganic and