MERCATOR OCEAN JOURNA: 
SEPTEMBER 2021 
Scenario (A) is the final objective where the reanalysis or 
-eprocessing has a “best estimate” time extension production 
mode (making use of best upstream data and producing 
-egular time extensions) while the “interim production” making 
use of near real-time upstream data fills the gap between the 
and of the best estimate timeseries (more than 3 months 
aefore present) and the present. Most TACs can implement the 
scenario (A), except TACs relying on altimetry (Sea Level and 
Nave) for which interim production is still under development. 
Scenario (B) is a first step, providing an “interim mode time 
axtension” of the MYP, initialized at the end of the best 
astimate MYP at its entry into service, and will be 
:‚mMplemented by most MFCs. 
3. QUALITY ASSURANCE /nAMEWORK: 
DEVELOPING ROBUST INFORMATION 
>bservations. Quality information documents (QUIDs) are also 
available for OMls, including iIntercomparison whenever 
possible (see Sotillo, M.G et al, this issue). In collaboration with 
the Copernicus Climate Change Service, and following 
international standards (Global Climate Observing System), 
the appropriate quality of the products for climate studies Is 
regularly assessed, including for ocean model reanalyses. As 
reviewed by Storto (2019a), ocean reanalyses have the 
capacity to capture ocean variability and trends, and are usec 
as oceanlic Initial conditions by seasonal forecasting systems. 
However, biases and errors appear in areas where 
observations are sparse. In order to provide information on 
areas where the signals derived from ocean reanalyses 
are robust and reliable (Von Schuckmann et al., 2018), the 
Global Reanalysis Ensemble Product GREP was developec 
from four global ocean reanalyses using NEMO but differing 
on their model parameterizations and data assimilation 
systems (Storto et al., 2019b). Error bars were also directly 
derived from the standard deviation between the four 
members as shown in Figure 4. 
Ine of the major objectives of the Copernicus Marine Service 
Ss to deliver useful scientific quality information for each 
oroduct. Includina model reanalyses and reprocessed 
Volume transport 
Jnit= Sv 
30N 
S50N 
20N 
a1 
T 04 
} 1.224 0 
—20417; 445 
1 0.408- 1.4C 
| 0.724 0.66 
1017: 08 
T 0.20#/- 2.69 
h 0.774/- 0.92 
2017: 0.24 
t 
04 
17. 
Ad 
NBe‘ 
ot? 
— Lumpkin 
-.GREP 
3095 
5085 
905 
14.304- 7 
20.03+/- 3.17 
2017: 18.70 
1, 
atatype: Multi-produc, 
"lg: EU. CAgemicus Marine Service Imtanm- sie 
29+ 
7 44 
Day“ 
W017 
“* 
FA 14 
15R,65+/ 16.23 
Aa RE 
180 150W 120W 90W B60W 20W 0 20E 60E 90E 120EFE 150E 180 
-igure k&: Volume transport (units SV) from the multi-product approach averaged over the period 1993-2014 and the 2017 (both red) 
Estimates of Lumpkin and Speer (2007) have been added for comparison (blue). Uncertainty ranges are derived from the ensemble standard 
deviation. Arrows indicate the direction of the mean flow through the sections. See https://marine.copernicus.eu/access-data/ocean- 
monitoring-Indicators/mean-volume-transport-across-section.