QUOVADIS_NUM_18_JAS_2014

May 2015
contact: qualif@mercator-ocean.fr
QuO Va Dis?
Quarterly Ocean Validation Display #18
Validation bulletin for July-August-September (JAS) 2014
Edition:
Bruno Levier, Marie Drévillon, Charly Régnier, Charles Desportes, Coralie Perruche,
(MERCATOR OCEAN/Production Dep./Products Quality)
Contributions :
Eric Greiner (CLS)
Jean-Michel Lellouche, Olivier Le Galloudec (MERCATOR OCEAN/ Production Dep./R&D)
Credits for validation methodology and tools:
Eric Greiner, Mounir Benkiran, Nathalie Verbrugge, Hélène Etienne (CLS)
Fabrice Hernandez, Laurence Crosnier (MERCATOR OCEAN)
Nicolas Ferry, Gilles Garric, Jean-Michel Lellouche (MERCATOR OCEAN)
Stéphane Law Chune (Météo-France), Julien Paul (Links), Lionel Zawadzki (AS+)
Jean-Marc Molines (LGGE), Sébastien Theeten (Ifremer), Mélanie Juza (IMEDEA), the DRAKKAR and
NEMO groups, the BCG group (Météo-France, CERFACS)
Bruno Blanke, Nicolas Grima, Rob Scott (LPO)
Information on input data:
Christine Boone, Gaël Nicolas (CLS/ARMOR team)
Abstract
This bulletin gives an estimate of the accuracy of MERCATOR OCEAN’s analyses and forecast
for the season of July-August-September 2014. It also provides a summary of useful
information on the context of the production for this period. Diagnostics will be displayed for
the global 1/12° (PSY4), global ¼° (PSY3), the Atlantic and Mediterranean zoom at 1/12°
(PSY2), and the Iberia-Biscay-Ireland (IBI) monitoring and forecasting systems currently
producing daily 3D temperature, salinity and current products. Surface Chlorophyll
concentrations from the BIOMER1 biogeochemical monitoring and forecasting system are
also displayed and compared with simultaneous observations. Finally the new release of
BIOMER at ¼° horizontal resolution is introduced. The BIOMER4 products, available since
September 2014, are shortly evaluated against the BIOMER1 products.

Table of contents
I Executive summary ............................................................................................................4
II Status and evolutions of the systems ................................................................................5
II.1. Short description and current status of the systems..................................................5
II.2. Incidents in the course of JAS 2014...........................................................................11
III Summary of the availability and quality control of the input data..................................11
III.1. Observations available for data assimilation.........................................................11
III.1.1. In situ observations of T/S profiles.....................................................................11
III.1.2. Sea Surface Temperature...................................................................................11
III.2. Observations available for validation ....................................................................12
IV Information on the large scale climatic conditions..........................................................12
V Accuracy of the products .................................................................................................15
V.1. Data assimilation performance .................................................................................15
V.1.1. Sea surface height..............................................................................................15
V.1.2. Sea surface temperature....................................................................................19
V.1.3. Temperature and salinity profiles......................................................................23
V.2. Accuracy of the daily average products with respect to observations.....................39
V.2.1. T/S profiles observations....................................................................................39
V.2.2. SST Comparisons ................................................................................................50
V.2.3. Drifting buoys velocity measurements ..............................................................51
V.2.4. Sea ice concentration.........................................................................................56
V.2.5. Closer to the coast with the IBI36V2 system: multiple comparisons ................58
V.2.5.1. Comparisons with SST from CMS ...................................................................58
V.2.6. Biogeochemistry validation: ocean colour maps...............................................67
VI Forecast error statistics....................................................................................................69
VI.1. General considerations..........................................................................................69
VI.2. Forecast accuracy: comparisons with T and S observations when and where
available ...............................................................................................................................69
VI.2.1. North Atlantic region......................................................................................69
VI.2.2. Mediterranean Sea.........................................................................................71
VI.2.3. Tropical Oceans and global.............................................................................72
VI.3. Forecast accuracy: skill scores for T and S.............................................................75
VI.4. Forecast verification: comparison with analysis everywhere ...............................77
VII Monitoring of ocean and sea ice physics.........................................................................77
VII.1. Global mean SST and SSS.......................................................................................77
VII.2. Surface EKE.............................................................................................................78
VII.3. Mediterranean outflow .........................................................................................80
VII.4. Sea Ice extent and area..........................................................................................81
VIII Comparison between BIOMER1 and BIOMER4 ............................................................81
VIII.1.1. Description of BIOMER4.................................................................................81
VIII.1.2. Validation summary........................................................................................82
VIII.1.3. Comparison over the JAS 2014 quarter..........................................................83
VIII.1.4. Conclusion ......................................................................................................86
I Annex A ............................................................................................................................87

I.1. Table of figures..........................................................................................................87
II Annex B.............................................................................................................................91
II.1. Maps of regions for data assimilation statistics........................................................91
II.1.1. Tropical and North Atlantic................................................................................91
II.1.2. Mediterranean Sea.............................................................................................92
II.1.3. Global ocean.......................................................................................................93
III.2. Algorithm of the Lagrangian verification of the Mercator Océan surface currents
forecast.95

I Executive summary
T & S
The Mercator Ocean monitoring and forecasting systems are evaluated for the period July-
August-September 2014. The 1/12° global system providing products for MyOcean V3 global
MFC, and the other Mercator “off shore” systems (global ¼° and Atlantic+Mediterranean
1/12°) display similar performance in terms of water masses accuracy. The system’s analysis
of the ocean water masses is very accurate on global average and almost everywhere
between the bottom and 200m. Between 0 and 500m departures from in situ observations
rarely exceed 1 °C and 0.2 psu (mostly in high variability regions like the Gulf Stream or the
Eastern Tropical Pacific). PSY4 is warmer (and less accurate) than PSY3 in the 0-800 m layer
this quarter which could be due to spin up effects. The evolution of this difference between
the global systems will be monitored in the next issues.
The temperature and salinity forecast have significant skill in many regions of the ocean in
the 0-500m layer, but the signal is noisy.
Surface fields: SST, SSS, SSH, currents
A warm SST bias of 0.1 °C on global average is diagnosed this quarter, mainly due to the
tropics and the ACC. The SST bias is cold in the mid latitudes and in the Arctic.
A strong fresh bias is diagnosed in the tropics, especially in the high resolution products in
link with the overestimated convective precipitations in the ECMWF atmospheric fields.
The monitoring systems are generally very close to altimetric observations (global average of
6 cm residual RMS error). Biases persist locally that correspond to known uncertainties in
the mean dynamic topography (for instance in the Indonesian region).
The surface currents are underestimated in the mid latitudes and overestimated at the
equator with respect to in situ measurements of drifting buoys (drifter velocities are
corrected of windage and slippage with a method developed by Mercator Océan). The
underestimation ranges from 20% in strong currents up to 60% in weak currents. On the
contrary the orientation of the current vectors is well represented. Lagrangian metrics show
that after 1 day, 80% of the virtual drifters trajectories performed with Mercator Ocean
forecast velocities stay within a 25 km distance of the actual drifters observed trajectories.
Regional North East Atlantic
The high resolution North East Atlantic at 1/36° (IBI36V1) with no data assimilation is
accurate on average. Tidal and residual sea surface elevations are well represented. Zones
of intense tidal mixing are less accurate. The mixed layer is too shallow in the Bay of Biscay
(the thermocline is too diffusive). The upwelling along the Iberian coasts is underestimated.
Sea Ice
Sea ice observations are not yet assimilated, nevertheless sea ice concentrations are
realistic. In JAS 2014 the sea ice melts too much in the Arctic, but the sea ice extent is still
well diagnosed. In the Antarctic the sea ice cover is overestimated (too much ice during
austral winter).

Biogeochemistry
The large scale structures corresponding to specific biogeographic regions (double-gyres,
ACC, etc…) are well reproduced by the global biogeochemical model at 1° BIOMER1.
However there are serious discrepancies especially in the Tropical band due to
overestimated vertical velocities. The latter are the source of anomalous levels of nitrates in
the equatorial surface layer. O2, however, is close to climatological estimations. The
seasonal cycle is realistic in most parts of the ocean. However the timing of the blooms is
not yet in phase with observations. This quarter, the North Atlantic bloom is still present in
the observations while it has already disappeared in BIOMER1 (the bloom is between one
and two months ahead of the observations and is shorter than in the observations).
II Status and evolutions of the systems
II.1.Short description and current status of the systems
A bit of history
PSY3V3 (global ¼°) and PSY2V4 (Altantic and Mediterranean 1/12°) systems have been
operated at MERCATOR OCEAN since 2010 December, 15th
. These systems provide the
version 1 (PSY3V3R1/PSY2V4R1, see QuOVaDis? #2) and version 2 (PSY3V3R1/PSY2V4R2, see
QuOVaDis? #5) products of the MyOcean global monitoring and forecasting centre. As
reminded in table 1 (and illustrated for PSY2V2 in Figure 1) the atmospheric forcing is
updated daily with the latest ECMWF analysis and forecast, and a new oceanic forecast is
run every day for both PSY3V3R1 and PSY2V4R2.
The PSY3V3R1 system is started in October 2006 from a 3D climatology of temperature and
salinity (World Ocean Atlas Levitus 2005) while the PSY2V4R2 is started in October 2009.
After a short 3-month spin up of the model and data assimilation, the performance of
PSY3V3R1 has been evaluated on the 2007-2009 period (MyOcean internal calibration
report, which results are synthesised in QuOVaDis? #2).
The PSY4 system (global 1/12°) is delivering operational products since the beginning of
2010, and was developed in 2009. Its first version PSY4V1 did not benefit from the scientific
improvements of PSY3V3R1 and PSY2V4R2, developed in 2010 and 2011. This system
delivers 7-day forecast (and not 14-day like PSY3V3R1 and PSY2V4R2).
An upgrade of the data assimilation systems was performed in March 2012 in order to
assimilate MyOcean V2 altimetric observations and in situ observations (instead of
respectively AVISO and CORIOLIS observations, corresponding to MyOcean V0 observations).
In consequence, more in situ observations are assimilated by all systems in the European
seas since March 2012.
Current global systems
The whole Mercator Ocean global analysis and forecasting system (including PSY4, PSY2 and
PSY3) has been updated in April 2013 (MyOcean products version 3). The current versions
names are PSY3V3R3, PSY2V4R4 and PSY4V2R2. Results from these current systems only

will be described in this issue. For more simplicity they will be called PSY3, PSY2 and PSY4
in this document. A description of most updates, as well as the evaluation process, can be
found in Lellouche et al (2013)1
. With respect to this article, several additional modifications
were made in order to stabilize the performance of the system (see table 1). A specific
paragraph is dedicated to the evaluation of these new systems: in Quo Va Dis?#12.
North east Atlantic
The IBI36 system is described in QuOVaDis? #5 and #6 (see also table 1 and Figure 1). The
nominal MyOcean production unit for IBI36 is Puertos Del Estado (Spain) while Mercator
Océan produces the back up products. The Mercator Océan IBI36V1 system was officially
operational in June 2011. The version IBI36V2 of the system was operated from December
2011 to April 2014 and was very similar to IBI36V1 except it used realistic river runoffs from
SHMI and Prévimer instead of climatological runoffs. The version IBI36V4 of the system is
operated since April 2014: the only difference with IBI36V2 is the switch of the NEMO code
version, from 2.3 to 3.4. There are however minor code upgrades. The leapfrog time
stepping scheme of 3d variables has been modified according to the work of Leclair and
Madec (2009)2
; this should have a small impact on the results. The computation of coriolis
terms in the barotropic equations has been corrected in order to be fully compliant with the
3d calculation. The locally enhanced bottom friction in the Gulf of Cadiz implemented in IBI-
V3 has been suppressed, since this leads to a relatively minor improvement of
Mediterranean overflow.
1
J.-M. Lellouche, O. Le Galloudec, M. Drévillon, C. Régnier, E. Greiner, G. Garric, N. Ferry, C.
Desportes, C.-E. Testut, C. Bricaud, R. Bourdallé-Badie, B. Tranchant, M. Benkiran, Y. Drillet,
A. Daudin, and C. De Nicola, Evaluation of global monitoring and forecasting systems at
Mercator Océan, Ocean Sci., 9, 57-81, 2013, www.ocean-sci.net/9/57/2013/,
doi:10.5194/os-9-57-2013
2
M. Leclerc, G. Madec, A conservative Leap-Frog time stepping method, Ocean Modelling
30(2-3), 88-94, 2009, http://dx.doi.org/10.1016/j.ocemod.2009.06.006

Figure 1: schematic of the operational forecast scenario for IBI36Q (green) and PSY2Q (blue). Solid lines are
the PSY2 weekly hindcast and nowcast experiments, and the IBI36 spin up. Dotted lines are the weekly 14-
day forecast, dashed lines are daily updates of the ocean forecast forced with the latest ECMWF atmospheric
analysis and forecast. The operational scenario of PSY3, PSY4 and PSY3Q, PSY4Q is similar to PSY2’s scenario.
In the case of PSY4, only weekly hindcast, nowcast and 7-day forecast are performed.
Biogeochemistry
The BIOMER1 system is described in QuOVaDis? #6 (see also table 1 and Figure 2). It is a
global hindcast biogeochemical model forced by physical ocean fields. The biogeochemical
model used is PISCES. The coupling between ocean physics and biogeochemistry is
performed offline. The physical fields from PSY3 are “degraded” to 1° horizontal resolution
and 7-day time resolution.
Figure 2: schematic of the operational forecast scenario for BIOMER..

Table 1: Main characteristics and latest updates of the Mercator Ocean global analysis and forecasting systems. The systems studied in Lellouche et al (2013) include the main characteristics (in
black) plus the updates in blue. The 2013 systems (in red) include the main characteristics (in black) plus the updates in blue and red. In the legend below one can find a description of the updates
referred to as “mix”, “colour”, etc…
System name domain resolution Physical Model Assimilation Assimilated observations Inter dependencies Status of production
PSY4V1R3
(operational in JFM
2013)
PSY4V2R2
(operational in AMJ
2013)
Global 1/12° on the
horizontal,
50 levels on
the vertical
ORCA12 LIM2 NEMO 1.09
Bulk CLIO
24-h atmospheric forcing
LIM2 EVP NEMO 3.1
Bulk CORE
mix, colour, iceberg, EMP
init WOA09: oct 2012
SAM2V1 (SEEK) + IAU
3D-Var bias correction
coast error, shelf error
new MDT, radii
Increase of Envisat error
new QC, SST bulk corr
RTG-SST, MyOcean SLA
along track,
MyOcean T/S vertical
profiles
AVHRR-AMSR SST,
new MDT
Sea Mammals T/S
profiles in CUTRR
Black Sea SLA files
Weekly 7-day
forecast
Weekly 7-day
forecast
Daily update of
atmospheric forcing
for daily 7-day
forecast
PSY3V3R1
(operational in JFM
2013)
PSY3V3R2 (described
in Lellouche et al,
2013)
PSY3V3R3
(operational in AMJ
2013)
Global 1/4° on the
horizontal, 50
levels on the
vertical
ORCA025 LIM2 EVP NEMO
3.1
Bulk CORE
mix, colour, iceberg, EMP
flux corr
no flux corr
current in wind
init WOA09:oct 2006
SAM2V1 (SEEK) + IAU 3D-
Var bias correction
MDT error adjusted
first update of radii
new QC
radii, SST bulk corr
RTG-SST, MyOcean SLA
along track,
MyOcean T/S vertical
profiles
AVHRR-AMSR SST,
new MDT
Sea Mammals T/S
profiles in CUTRR
Black Sea SLA files
Weekly 14-day
forecast
Daily update of
atmospheric forcing
for daily 7-day
forecast
PSY2V4R2
(operational in JFM
2013)
PSY2V4R3 (described
in Lellouche et al.,
2013)
PSY2V4R4
(operational in AMJ
2013)
Tropical
North Atlantic
Mediterranean
1/12° on the
horizontal,
50 levels on
the vertical
NATL12 LIM2 EVP NEMO 3.1
Bulk CORE
mix, colour
flux corr
no flux corr
current in wind
init WOA09:oct 2006
SAM2V1 (SEEK) + IAU 3D-
3D-Var bias correction
first update of radii
QC on T/S vertical profiles
radii, SST bulk corr
Larger weight of Bogus OBC
on TSUV
AVHRR-AMSR SST,
MyOcean SLA along
track , MyOcean T/S
vertical profiles
new MDT
Sea Mammals T/S
profiles in CUTRR
OBC from
PSY3V3R1
OBC and SMEMP
from PSY3V3R2
OBC and SMEMP
from PSY3V3R3
Weekly 14-day
forecast
Daily update of
atmospheric forcing
for daily 7-day
forecast
BIOMER1
upgrade in AMJ 2013
Global 1° on the
horizontal, 50
levels on the
vertical
PISCES, NEMO 2.3, offline none none Two weeks
hindcast with
PSY3V3R1 1° phy
PSY3V3R3 1° phy
1-week average two
weeks back in time.
IBI36V4
upgrade in AMJ 2014
North East
Atlantic and
West
Mediterranean
Sea (Iberian,
Biscay and
Ireland) region
1/36° on the
horizontal, 50
levels on the
vertical
NEATL36 NEMO 3.4 3-hourly
atmospheric forcing from
ECMWF, bulk CORE, tides,
time-splitting, GLS vertical
mixing, corrected
bathymetry, river runoffs
from SMHI & Prévimer
none none Two weeks spin up
initialized with
PSY2V4R4 and OBC
from PSY2V4R4
Weekly spin up two
weeks back in time.
Daily update of
atmospheric
forcings for daily 5-
day forecast
IBI36QV1

Mix = New parameterization of vertical mixing
Colour = Taking into account ocean colour monthly climatology for depth of light
extinction
Current in wind = taking 50 % of surface current for the computation of wind
stress with bulk CORE
EMP = Adding seasonal cycle for surface mass budget
SMEMP = spatial mean EMP correction
Iceberg = Adding runoff for iceberg melting
Flux corr = Large scale correction to the downward radiative and precipitation
fluxes
Coast error = Observation error s higher near the coast (SST and SLA)
Shelf error = Observation error s higher on continental shelves (SLA)
New MDT = MDT CNES/CLS09 adjusted with model solutions (bias corrected)
Radii = New correlation radii (minimum =130km)
New QC = additional QC on T/S vertical profiles computed from the innovations
SST bulk corr = Procedure to avoid the damping of SST increments via the bulk
forcing function
OBC = Open Boundary Conditions
1° phy= physical forcings are “degraded” from ¼° horizontal resolution to 1°
horizontal resolution, and weekly averaged.
CUTRR= Catch Up to Real Time Run, or calibration hindcast run (before 2013)

11
II.2. Incidents in the course of JAS 2014
Nothing to report.
III Summary of the availability and quality control of the input data
III.1. Observations available for data assimilation
III.1.1. In situ observations of T/S profiles
System PSY3V3R3 PSY4V2R2 PSY2V4R4
Min/max number of T
profiles per DA cycle
2600/3200 2600/3200 500/900
Min/max number of S
profiles per DA cycle
2200/2600 2200/2600 500/600
Table 2: minimum and maximum number of observations (orders of magnitude of vertical profiles) of subsurface temperature
and salinity assimilated weekly in JAS 2014 by the Mercator Ocean monitoring and forecasting systems.
The maximum number of in situ observations is displayed in Table 2. It is unchanged with respect to the
previous quarter.
III.1.2. Sea Surface Temperature
System PSY3V3R3 PSY4V2R2 PSY2V4R42
Min/max number (in 103
)
of SST observations
140/148 143/148 27/27
Table 3: minimum and maximum number (orders of magnitude in thousands) of SST observations (from Reynolds AVHRR ¼°)
assimilated weekly in JAS 2014 by the Mercator Ocean monitoring and forecasting systems.
III.1.3. Sea level anomalies along track
As shown in Table 4 the data assimilated this JAS 2014 season come from Jason 2, Cryosat 2, SARAL/AltiKa
and HY-2A.
system PSY3V3R3 PSY4V2R2 PSY2V4R4
) of
Jason 2 SLA observations
159/164 160/165 29/31
) of
HY-2A SLA observations
37/154 37/156 7/29
) of
AltiKa SLA observations
156/159 157/161 30/31
) of
Cryosat 2 SLA observations
22/148 22/149 4/27

12
Table 4: minimum and maximum number (orders of magnitude in thousands) of SLA observations from Jason 2, Cryosat 2,
AltiKa and HY-2A assimilated weekly in JAS 2014 by the Mercator Ocean monitoring and forecasting systems.
III.2. Observations available for validation
Both observational data and statistical combinations of observations are used for the real time validation
of the products. All were available in real time during the JAS 2014 season:
• T/S profiles from CORIOLIS
• OSTIA SST from UKMO
• Arctic sea ice concentration and drift from CERSAT
• SURCOUF surface currents from CLS
• ARMOR-3D 3D temperature and salinity fields from CLS
• Drifters velocities from Météo-France reprocessed by CLS
• Tide gauges
Grodsky et al (GRL, May 2011) show that drifters’ velocities overestimate current velocities in regions and
periods of strong winds due to undetected undrogued drifters. This information will be taken into account
for comparisons with Mercator Ocean currents.
IV Information on the large scale climatic conditions
Mercator Ocean participates in the monthly seasonal forecast expertise at Météo France. This chapter
summarizes the state of the ocean and atmosphere during the JAS 2014 season, as discussed in the
“Bulletin Climatique Global” of Météo France.
The equatorial pacific is still slightly warmer than normal. However, the ENSO conditions were neutral in
JAS 2014. No significant kelvin wave propagation is diagnosed in the seasonal mean JAS 2014 as can be
seen in Figure 4.
a)
b)

13
Figure 3: Seasonal JAS 2014 temperature anomalies with respect to GLORYS2V3 climatology (1993-2011). (a): SST anomaly
(°C) at the global scale from the 1/4° ocean monitoring and forecasting system PSY3V3R3. (b): heat content anomaly (ρ0Cp∆T,
with constant ρ0=1020 kg/m3 ) from the surface to 300m.
In the North Atlantic, a warm anomaly dominates the Irminger and North Sea. Between the subpolar gyre
and the subtropical gyre, a cold anomaly persists throughout the season crossing the Atlantic from
Newfoundland to the Bay of Biscay, which is well marked in the upper ocean heat content. The subtropical
gyre is warmer than normal while the tropical North Atlantic is colder than normal. The atmospheric
circulation in the North Atlantic was dominated alternatively by the Scandinavian blocking pattern,
negative NAO and East Atlantic West Russia patterns during this JAS season. These circulation patterns are
consistent with the persistence of warm anomalies in the northern sub basins of the Atlantic (Labrador
Sea, North Sea).
The signature of the positive phase of the PDO is well marked in the North Pacific basin, with large positive
anomalies along the west coast of the US, coincident with severe heat and droughts in this area.

14
Figure 4: Seasonal JAS 2014 temperature anomaly (°C) with respect to GLORYS2V3 climatology (1993-2011), vertical section
2°S-2°N mean, Pacific Ocean, PSY3V3R3.
Figure 5 : Arctic sea ice extent from the NSIDC, available at http://nsidc.org/arcticseaicenews/2014/10/
As can be seen in Figure 5, the Arctic sea ice minimum in JAS 2014 is lower than that of 2013, but does not
reach the lowest extent measured in 2012. The seasonal ice loss of 2014 is the 9th
largest of satellite
records, according to NSIDC.

15
V Accuracy of the products
V.1.Data assimilation performance
V.1.1. Sea surface height
V.1.1.1. Basin scale statistics (GODAE metrics)
a)
b)
c)
Figure 6 : Comparison between SLA data assimilation scores (a: average misfit in cm, b: RMS misfit in cm, c: number of
observations) for all available Mercator Ocean systems in JAS 2014. The scores are averaged for all available satellite along
track data (Saral/altiKa, Jason 2, Cryosat 2, HY-2a). For each region the bars refer to PSY2V4R4 (yellow), PSY3V3R3 (red), and
PSY4V2R2 (blue).
SLA assimilation scores for PSY4, PSY3, and PSY2 in JAS 2014 are displayed in Figure 6. The different
systems reach similar levels of performance on average. The basin scale SLA bias does not exceed 2 cm
except in the Mediterranean Sea and in the Arctic where it can reach 8 cm (Figure 6 a). The Arctic SSH
undergoes large uncertainties (MSSH, measurement errors, sea ice limit). In the Mediterranean Sea this
quarter, the global systems (PSY4 and PSY3) are less biased than the regional system (PSY2). The RMS error
(Figure 6 b) is of the order of 8-9 cm in the Mediterranean Sea, in the Atlantic and the Southern Oceans
(more than 10 cm in the Arctic). It is of the order of 6-7 cm in the Indian and Pacific oceans, and it is close

16
to 4 cm in the Tropical Atlantic Ocean and 5 cm in the Tropical Pacific Ocean. The error amplitude is
proportional to the region’s internal variability.
V.1.1.2. North Atlantic Ocean and Mediterranean Sea in all systems
The SLA assimilation scores for PSY4, PSY3, and PSY2 in JAS 2014 are displayed for sub-regions of the
Tropical and North Atlantic Ocean in Figure 7. The different systems reach identical levels of performance
on average. The biases are generally small (less than 2 cm) during the summer season and do not exceed 3
cm in JAS 2014.
a)
b)
c)
Figure 7: Comparison between SLA data assimilation scores (a: average misfit in cm, b: RMS misfit in cm, c: number of
observations) for all available Mercator Ocean systems in JAS 2014 in sub-regions of the Tropical Atlantic (TAT) and North
Atlantic (NAT). The scores are averaged for all available satellite along track data (SARAL/Altika, Jason 2, Cryosat 2, HY-2a).
For each region the bars refer respectively to PSY2V4R4 (yellow), PSY3V3R3 (red), and PSY4V2R2 (blue).
The RMS errors are almost identical in all systems, and stay below 10 cm in most regions, except regions of
high mesoscale variability (Gulf Stream) and in the gulf of Mexico. This JAS 2014 season, the RMS error in
the Gulf Stream regions is larger in PSY3 than in the other systems PSY2 and PSY4. PSY2 and PSY4 biases
are small but positive in nearly all regions, meaning that PSY2 and PSY4 are slightly lower than the
observations on average over the basin, consistently with Figure 6. On the contrary, PSY3 biases are
negative in nearly all regions.

17
In the Mediterranean Sea (Figure 8), significant positive biases (the model is lower than the observations)
appear in summer. PSY2 displays biases of 4 to 10 cm, while the global systems PSY3 and PSY4 present
smaller biases of 1 to 6 cm. A bias of 10 cm is present in PSY2 in the Aegean Sea (it was nearly 12 cm in JAS
2013). The Mediterranean sub-regions are circled by coasts, and consequently few observations are
assimilated. Figure 8 c gives the number of observations taken into account by the system, but the
observation error that is applied reduces strongly the influence of observations within a 50 km distance of
the coast.
a) b)
c)
observations) for all available Mercator Ocean systems in JAS 2014 in the Mediterranean Sea MED sub-regions: PSY2V4R4
(yellow), PSY3V3R3 (red) and PSY4V2R2 (blue). The scores are averaged for all available satellite along track data
(SARAL/Altika, Jason 2, Cryosat 2, HY-2a). See annex B for geographical location of regions.
One can also note that the variability in the Aegean Sea is difficult to model because of the presence of
many islands. PSY4 may perform better in this region due to the presence of the Black sea in the global
configuration PSY4, which is not present in PSY2. Except in the Aegean Sea, the RMS of the innovation
(misfit) is generally less than 10 cm for all systems.

18
V.1.1.3. Performance at global scale in PSY3 (1/4°) and PSY4 (1/12°)
a) b)
c)
observations) for all available global Mercator Ocean systems in JAS 2014 in sub-regions of the SAT, IND, NPA, SPA, TPA, and
ACC basins: PSY3V3R3 (red) and PSY4V2R2 (blue). The scores are averaged for all available along track satellite data
(SARAL/Altika, Jason 2, Cryosat 2, HY-2a). The geographical location of regions is displayed in annex B.
As can be seen in Figure 9 the performance of intermediate resolution global PSY3 and the performance of
high resolution global PSY4 in terms of SLA assimilation are of the same order of magnitude. A small
positive bias (less than 2 cm) is present in many regions in PSY4. A strong bias of around 10 cm persists in
the “Nino 5” box centred on the Banda Sea in Indonesia, which corresponds to a MDT problem. The RMS
error reaches its highest values in the Agulhas current where the variability is high (and in the Niño 5 box
because of the strong bias). Other regions with high variability (eastern Australia, Southern Ocean)
consistently display RMS errors of more than 8 cm.

19
V.1.2. Sea surface temperature
V.1.2.1. Basin scale statistics (GODAE metrics)
a) b)
c)
Figure 10: Comparison between Reynolds ¼°AVHRR-data assimilation scores (a: average misfit in °C, b: RMS misfit in °C, c:
number of observations) for all available Mercator Ocean systems in JAS 2014: PSY4V2R2 (blue), PSY3V3R3 (red), and
PSY2V4R4 (yellow).
This JAS season, a warm bias persists in all systems in most of the regions (Figure 10), except in the Arctic
Ocean (colder (0.5 °C) than the observations) and in the North Atlantic. It is known that not enough
Atlantic (warm) waters enter the Arctic in both PSY3 and PSY4. In parallel there is too much sea ice melting
in the Arctic this summer season (see Figure 41 and Figure 42). The Mediterranean is warmer than the
observations on average over the basin (0.1°C). The RMS error is lower than 0.8 °C except in the Arctic
Ocean, where it reaches 1.1°C on average (due to the strong bias).

20
V.1.2.2. North and Tropical Atlantic Ocean and Mediterranean Sea
a) b)
c)
Figure 11: Comparison between Reynolds ¼°AVHRR SST data assimilation scores (a: average misfit in °C, b: RMS misfit in °C, c:
number of observations) for all available Mercator Ocean systems in JAS 2014 in sub-regions of the Tropical Atlantic (TAT) and
of the North Atlantic (NAT): PSY4V2R2 (blue), PSY3V3R3 (red) and PSY2V4R4 (yellow). The geographical location of regions is
displayed in annex B.
In the Atlantic the three systems display consistent regional behaviours in terms of SST bias as illustrated in
Figure 11. In most regions the bias stays below 0.3 °C. A warm bias of around 0.3 to 0.5°C is diagnosed in
the Tropical Atlantic (Dakar, Gulf of Mexico, Florida straits, Sao Tome). In the Dakar region, the upwelling is
underestimated by all systems. Note that as for SLA, prescribed SST errors are higher within 50km off the
coast. The RMS error reaches 1°C in the Gulf Stream regions, and in the Newfoundland Iceland region.

21
a) b)
c)
Figure 12: Comparison between Reynolds ¼° AVHRR SST data assimilation scores (a: average misfit in °C, b: RMS misfit in °C, c:
number of observations) for all available Mercator Ocean systems in JAS 2014 in the Mediterranean Sea: PSY4V2R2 (blue),
PSY3V3R3 (red) and PSY2V4R4 (yellow). The geographical location of regions is displayed in annex B.
This JAS 2014 season (Figure 12) is different from JAS 2013 (not shown). The maximum bias is found in the
Mersa Matruth region (between 0.3 and 0.4°C). In the Aegan Sea PSY3 and PSY4 display opposite bias (the
Black Sea is present in PSY4 and not in PSY3 which can explain this result). The RMS error is generally of the
order of 0.5-0.7°. As in SLA, the performance of the systems is lower in the Aegean Sea. Errors are also
higher in the Alboran Sea but one can note that not many observations are available in this small region.
V.1.2.3. Performance at global scale in PSY3 (1/4°) and PSY4 (1/12°)

22
a)
b)
c)
Figure 13: Comparison between Reynolds ¼° AVHRR SST data assimilation scores (a: average misfit in °C, b: RMS misfit in °C, c:
number of observations) for all available global Mercator Ocean systems in JAS 2014 in the SAT, NPA, TPA, SPA, IND and ACC
basins: PSY3V3R3 (red) and PSY4V2R2 (blue). See annex B for geographical location of regions.
The global products exhibit a warm surface bias in most regions this JAS 2014 season (Figure 13), of
magnitude 0.1°C to 0.3°C. This warm bias is stronger in (small) coastal regions: in the cold waters of the
California current or in the Angola upwelling. In JAS, a cold bias (0.4°C) is diagnosed in the North Pacific
region. PSY3 and PSY4 exhibit the same levels of performance. The warm bias is generally stronger in PSY4
than in PSY3 by 0.05°C, and so is the RMS error, meaning that the variability is at least as well represented
in PSY4 than in PSY3. The RMS error stands between 0.4°C in the tropical Pacific, and 1°C in the Agulhas
current.

23
V.1.3. Temperature and salinity profiles
V.1.3.1. Methodology
All systems innovation (observation – model first guess) profiles from the hindcast or “best analysis”
week (J-14 to J-7) are systematically inter-compared in all regions given in annex B. In the following,
intercomparison results are shown on the main regions of interest for Mercator Ocean users in JAS 2014.
Some more regions are shown when interesting differences take place, or when the regional statistics
illustrate the large scale behaviour of the systems.
V.1.3.1.1. Basin scale statistics (GODAE metrics)
a) b)

24
c) d)
e) f)
Figure 14: Profiles of JAS 2014 innovations of temperature (°C, left column) and salinity (psu, right column), mean (solid line)
and RMS (dashed line) for PSY3V3R3 (red), PSY2V4R4 (yellow) and PSY4V1R3 (blue) in the North Atlantic NAT (a and b),
Mediterranean Sea MED (c and d) and Arctic Ocean ARC (e and f, the region starts of north of 67°N). Basin masks are applied
to keep only the main basin of interest (no Mediterranean in the Atlantic NAT, no Black Sea in the Mediterranean MED, etc…).

25
On average over the North Atlantic, PSY2 displays a warm bias from 50 m to 600 m (Figure 14). In PSY4 the
warm bias is also present but with less amplitude. PSY3 displays a slightly cold bias between 50 m and 300
m. In the mean time, the salinity bias is very small in all systems. On average over the Mediterranean Sea,
all systems display the same behaviour, with a cold and salty bias at the surface and a warm and fresh bias
between 50 m and 200 m. In the Artic PSY3 displays a cold bias from 50 m to 800 m. In PSY4, the
temperature bias is warm from 50 m to 400 m, and cold from 400 m to 800 m. PSY3 and PSY4 display a
salty bias between surface and 300 m.
On average over the Tropical Atlantic basin the high resolution systems exhibit warm (0.1°C to 0.2°C) and
salty (around 0.1 psu) biases in the 0-300m layer (Figure 15). No significant temperature or salinity biases
appear underneath the surface layer. PSY3 performs slightly better in this region.
PSY3 is less biased than PSY4 as well in the South Atlantic Basin, where PSY4 is too warm in the 0-800 m
layer (and PSY3 slightly too cold), and is too fresh at the surface. In the Indian Ocean PSY3 exhibits a cold
bias from the surface to 1000 m (up to 0.2°C near 100 m depth) that is restricted to the surface layer (0-
100m) in the case of PSY4. The surface layer from 0 to 200 m is too salty in both systems (0.02 psu).
a) b)

26
c) d)
e) f)
Figure 15: As Figure 14 in the Tropical Atlantic TAT (a and b), South Atlantic SAT (c and d) and Indian IND (e and f) basins.
On average over the Pacific Ocean (Figure 16), PSY4 is warmer (less than 0.1 °C) than the observations, and
PSY3 colder (more than 0.1°C), from the surface to 700 m.

28
e) f)
Figure 16: As Figure 14 in the North Pacific NPA (a and b), Tropical Pacific TPA (c and d) and South Pacific SPA (e and f).
For both PSY3 and PSY4 the salinity bias vertical structure is similar, and its amplitude is small (less than
0.05 psu). A salty bias is diagnosed near 100 m over the whole Pacific Ocean (higher for PSY3 than for
PSY4), and a second salty bias appears in PSY4 near 200m in the Tropical and South Pacific. The
temperature and salinity RMS errors of PSY3 and PSY4 are identical.
In the Southern Ocean in Figure 17, a warm temperature bias is observed again in PSY4 (up to 0.2 °C near
200m) while PSY3 is nearly unbiased, except for a warm (0.1 °C) and salty (0.02 psu) bias maximum near
200m.

29
a) b)
Figure 17: As Figure 14 in the ACC Southern Ocean basin (from 35°S).
V.1.3.1.2. Atlantic sub-regions
a) b)

30
c) d)
Figure 18: As Figure 14 in the North East Atlantic (IBI) and the North Madeira XBT regions.
The previous general comments apply also in smaller regions of the North East Atlantic Ocean, as shown in
Figure 18. The signature of the Mediterranean waters is clear in the T and S RMS error for all systems
(Figure 18 c and d). This quarter is characterized by the warm bias in both high resolution systems PSY2 and
PSY4 in the 50-500 m layer.
In sub-regions of the Tropical Atlantic and in the Gulf Stream (Figure 19), the vertical structure of the biases
is more complex. It reflects the underestimation of the upwelling in the Dakar region, and the errors in the
vertical structure of the currents. In the Gulf of Mexico the systems are too cold. In the Gulf Stream, the
three systems are too warm in the 100-800m layer, especially PSY4 (1°C) near 500 m. The systems are also
too salty in the 100-800m layer (0.2 psu for PSY3 and PSY4, 0.1 psu for PSY2).

31
a) b)
Figure 19: As Figure 14 in the Dakar region.
a) b)

32
c) d)
e) f)
Figure 20: As Figure 14 in the Ascension tide (a and b), the Gulf of Mexico (c and d) and the Gulf Stream 1 regions (e and f)
However, one must keep in mind that the smallest sub-regions contain only a few data per assimilation
cycle and that in this case the statistics may not be representative of the whole period, see for instance in
Figure 21.

33
a) b)
Figure 21: number of observations of T (a) and S (b) assimilated in PSY4V2R2 in the Gulf Stream 1 region during the JAS 2014
quarter.

34
V.1.3.1.3. Mediterranean Sea sub-regions
a) b)
c) d)

35
e) f)
g) h)
Figure 22: As Figure 14 in the Algerian (a and b), the gulf of Lion (c and d), the Ionian (e and f) and the Mersa Matruh (g and h)
regions.
Figure 22 shows the T and S error profiles in sub-regions of the Mediterranean Sea. In the Western part of
the basin, all systems seem to display a stratification bias. A cold and fresh bias is diagnosed at the surface,
and a warm and fresh bias appears near 50 m. In the South eastern part of the basin, the bias maximum is

36
located near 100 m. The systems are consistent with one another in average. The rms difference and bias is
higher for PSY4 in the Mersa Matruh region.
V.1.3.1.4. Indian Ocean sub-regions
a) b)
c) d)

37
e) f)
Figure 23: As Figure 14 in the North West Indian (a and b), the South West Indian (c and d) and the East Indian (e and f)
regions
The analysis of the sub-regions of the Indian basin (Figure 23) shows that PSY3 displays a cold bias mainly
in the south western part of the Basin. PSY4 being warmer than PSY3, it is less biased this season with
respect to the in situ temperature observations. On average over the Indian Ocean, PSY4 performs as well
and is even less biased than PSY3 on the whole water column.
SUMMARY: Starting in April 2013, the PSY2, PSY3 and PSY4 systems converge in terms of model version
and parameterizations. The data assimilation method and tunings are the same, and so do the data sets
assimilated by the three systems. In consequence the three Mercator Ocean systems display consistent
data assimilation statistics for the quarter JAS 2014. The systems are close to the observations, and
misfits lie within the prescribed error (most of the time misfit in T is less than 0.1 °C, misfit in S less then
0.05 psu, misfit in SLA less than 7 cm). The average and RMS errors are larger in regions of high spatial
and/or temporal variability (thermocline, regions of high mesoscale activity, upwelling, fronts, etc…). PSY4
displays a warm bias in the 0-800m layer mainly due to the initialization of the system as already
explained in previous QVD. Coastal regions are weakly constrained in all systems, and consistent biases
appear in SLA and SST (warm and high, or cold and low). There are still many uncertainties in the MDT,
mainly near the coasts and in the Polar Regions. The systems do not use the SLA misfit information in
coastal regions, but problems can appear in open ocean regions where there are still errors in the MDT. A
warm bias is diagnosed in SST this quarter. There is still too much mixing in the surface layer inducing a
cold bias in the surface layer (0-50 m) and warm bias in subsurface (50-150 m). This is particularly clear in
the Atlantic and Mediterranean Sea in JAS 2014. This bias is intensifying with the summer stratification and
the winter mixing episodes reduce the bias. The bias correction is not as efficient on reducing seasonal
biases as it is on reducing long term systematic biases. No precipitation correction is applied in the
systems, and uncertainties (overestimation) of the convective precipitations induce large fresh biases in
the tropical oceans.

39
V.2.Accuracy of the daily average products with respect to observations
V.2.1. T/S profiles observations
V.2.1.1. Global statistics for JAS 2014
a) b)
c) d)
e) f)
Figure 24: Average difference MODEL-OBSERVATION of temperature (a, c, and e, in °C) and salinity (b, d, and f, in psu) in JAS
2014 between the daily average hindcast PSY4V2R2 (a and b), PSY3V3R3 (c and d) and PSY2V4R4 (e and f) hindcast products
and all available T/S observations from the Coriolis database. The Mercator hindcast products are taken at the time and
location of the observations. Averages are performed in the 0-50m layer. The size of the pixel is proportional to the number
of observations used to compute the RMS in 2°x2° boxes.

40
a) b)
c) d)
Figure 25: RMS temperature (°C) difference (model-observation) in JAS 2014 between all available T/S observations from the
Coriolis database and the daily average hindcast PSY3V3R3 products on the left and hindcast PSY4V2R2 on the right column
colocalised with the observations. Averages are performed in the 0-50m layer (a and b) and in the 0-500m layer (c and d). The
size of the pixel is proportional to the number of observations used to compute the RMS in 2°x2° boxes.
Consistently with the data assimilation statistics the three systems PSY3, PSY4 and PSY2 are slightly
warmer (~0.1°C) than the observations at the global scale in the surface layer (0-50m) as can be seen in
Figure 24. Fresh biases also appear, mainly in the tropics, linked with overestimated convective
precipitations in the ECMWF analyses, and also to the climatological runoffs that are prescribed.
As can be seen in Figure 25, in both PSY3 and PSY4 temperature errors in the 0-500m layer have similar
amplitude and spatial patterns, and stand between 0.5 and 1°C in most regions of the globe. RMS errors in
the tropical thermocline can reach 1°C. Regions of high mesoscale activity (Kuroshio, Gulf Stream, Agulhas
current) and regions of upwelling in the tropical Atlantic and Tropical Pacific display higher errors (up to
3°C).
The salinity RMS errors (Figure 26) are usually less than 0.2 psu but can reach higher values in regions of
high runoff (Amazon, Sea Ice limit) or precipitations (ITCZ, SPCZ, Gulf of Bengal), and in regions of high
mesoscale variability. RMS errors of around 0.2 psu in the Tropics in the 0-50m layer correspond to the
fresh bias linked with overestimated precipitation.

41
a) b)
c) d)
Figure 26: RMS salinity (psu) difference (model-observation) in JAS 2014 between all available T/S observations from the
Coriolis database and the daily average hindcast PSY3V3R3 products (a and c) and hindcast PSY4V2R2 (b and d), colocalised
with the observations. Averages are performed in the 0-50m layer (a and b) and in the 0-500m layer (c and d). The size of the
pixel is proportional to the number of observations used to compute the RMS in 2°x2° boxes.
As can be seen in Figure 27, the intermediate resolution model (PSY3) and the high resolution model (PSY4)
display comparable temperature and salinity RMS difference. PSY4 displays a global average fresh bias
(0.015 psu) at the surface, while PSY3 and PSY4 are saltier than the observations (0.01 psu) on global
average between 5 m and 300 m. PSY3 is too cold on the whole water column (except in the 2000-5000 m
layer). PSY4 is slightly warmer at the surface and slightly colder in the 5-800 m layer, and closer to the
observations than PSY3 on average over the whole global domain. Both systems are more accurate than
the WOA09 climatology (Levitus 2009).

42
a) b)
c) d)
Figure 27 : JAS 2014 global statistics of temperature (°C, a and c) and salinity (psu, b and d) averaged in 6 consecutive layers
from 0 to 5000m. RMS difference (a and b) and mean difference (observation-model, c and d) between all available T/S
observations from the CORIOLIS database and the daily average hindcast products PSY3V3R3 (red), PSY4V2R2 (blue) and
WOA09 climatology (grey) colocalised with the observations. NB: average on model levels is performed as an intermediate
step which reduces the artefacts of inhomogeneous density of observations on the vertical.

43
a) b)
c) d)
Figure 28: RMS difference (model-observation) of temperature (a and b, °C) and salinity (c and d, psu) in JAS 2014 between all
available T/S observations from the Coriolis database and the daily average PSY2V4R4 hindcast products colocalised with the
observations in the 0-50m layer (a and c) and 0-500m layer (b and d)
The general performance of PSY2 (departures from observations in the 0-500m layer) is less than 0.3°C and
0.05 psu in many regions of the Atlantic and Mediterranean (Figure 28). The strongest departures from
temperature and salinity observations are always observed in the Gulf Stream and the tropical Atlantic.
Near surface salinity biases appear in the Alboran Sea, the Gulf of Guinea, the Caribbean Sea, the Labrador
Sea and the Gulf of Mexico (see also Figure 24). In the tropical Atlantic biases concentrate in the 0-50m
layer (cold and fresh bias). This is consistent with the bias correction not being at work in the mixed layer.

44
V.2.1.2. Water masses diagnostics
a) b) c)
d) e) f)
g) h) i)

45
j) k) l)
Figure 29: : Water masses (Theta, S) diagrams in JAS 2014 in the Bay of Biscay (a to c), Gulf of Lion (d to f), Irminger Sea (g to i)
and Gulf of Cadiz (j to l), comparison between PSY3V3R3 (a, d, g, j), PSY4V2R2 (b, e, h, k) and PSY2V4R4 (c, f, I, and l) in JAS
2014. PSY2, PSY3 and PSY4: yellow dots; Levitus WOA09 climatology: red dots; in situ observations: blue dots.
In Figure 29 the daily products (analyses) are collocated with the T/S profiles in order to draw “Theta, S”
diagrams. The water masses characteristics of the three systems are nearly similar, PSY4 being locally
slightly better than PSY3 and PSY2 (Mediterranean waters)
In the Bay of Biscay the Eastern North Atlantic Central Water, Mediterranean and Labrador Sea Water can
be identified on the diagram.
- Between 11°C and 15°C, 35 and 36 psu, warm and relatively salty Eastern North Atlantic Central
Water gets mixed with the shelf water masses. The saltiest ENACW waters are slightly better
captured by PSY4 than by PSY2 and PSY3 during this JAS 2014 season. PSY3 and PSY4 capture also
better the minimum of salinity of ENACW.
- The Mediterranean Waters are characterized by high salinities (Salinities near 36 psu) and relatively
high temperatures (Temperatures near 10°C).
- The Labrador Sea waters appear between 4°C and 7°C, 35.0 and 35.5 psu
In the Gulf of Lion:
- The Levantine Intermediate Water (salinity higher than 38.5 psu) is too fresh in all systems this JAS
2014 season.
- The lower salinities (lower than 37.7 psu) are slighty better reproduced by PSY4.
In the Irminger Sea:
- The Irminger Sea Water (≈ 4°C and 35 psu) and the North Atlantic water (at the surface) are well
represented by the systems.
In the Gulf of Cadiz:
- The Mediterranean outflow waters (T around 10°C) are well represented. PSY4 better captures the
spread of temperature and salinity at surface (Atlantic Surface waters).
In the western tropical Atlantic the three systems represent well the water masses.
In the eastern tropical Atlantic the three systems represent well the water masses. PSY2 (and to a lesser
extent PSY4) displays fresh waters near the surface that are not present in the observations.

46
a) b) c)
d) e) f)
Figure 30 : Water masses (T, S) diagrams in the Western Tropical Atlantic (a to c) and in the Eastern Tropical Atlantic (d to f) in
JAS 2014: for PSY3V3R3 (a, d); PSY4V2R2 (b, e); and PSY2V4R4 (c, f). PSY2, PSY3 and PSY4: yellow dots; Levitus WOA09
climatology; red dots, in situ observations: blue dots.
In the Agulhas current (Figure 31) PSY3 and PSY4 give a realistic description of water masses. The deep
waters characteristics are slightly more realistic in PSY4 than in PSY3. In the Kuroshio region the North
Pacific Intermediate waters (NPIW) are better represented in PSY4 than in PSY3 (too salty). At the surface
in the highly energetic regions of the Agulhas and of the Gulf Stream, the water masses characteristics
display a wider spread in the high resolution 1/12° than in the ¼°, which is more consistent with T and S
observations. In the Gulf Stream region, PSY4 is fresher than PSY3 in the North Atlantic Central Waters
(NACW), which is more consistent with the in situ observations of the quarter JAS 2014. The saltiest waters
near the surface (North Atlantic Subtropical Waters, NASW) are only captured by PSY4.

48
e) f)
Figure 31: Water masses (T, S) diagrams in South Africa (a and b), Kuroshio (c and d), and Gulf Stream region (e and f): for
PSY3V3R3 (a, c and e); PSY4V2R2 (b, d and f) in JAS 2014. PSY3 and PSY4: yellow dots; Levitus WOA09 climatology: red dots; in
situ observations: blue dots.
V.2.1.2.1. Focus on Greenland Sea
We plot a section in the Greenland Sea with data coming from the NAOOS project. The Figure 32 below
shows the positions of the in situ profiles between the 20140723 and the 20140930.
Figure 32 : Positions of In Situ TE profiles between the 23 July and 30 the September 2014
We compute a mean profiles each 0.1° to avoid multiple profiles in the same location and we plot the
sections along latitude and longitude for the variables temperature and salinity (cf figures below)

49
a) b) c) d)
e) f) g) h)
Figure 33 : Latitude/Depth Sections for temperature (a),b),c),d)) and salinity (e),f),g),h)) for PSY4V2R2 b),f), In situ data c),g),
PSY3V3R3 a),e), and climatology WOA09 d),h). The region is situated in the Greenland Sea between 80°N and 87°N and 0 and
800m.
a) b) c) d)
e) f) g) h)
Figure 34 : Longitude/Depth Sections for temperature (a),b),c),d)) and salinity (e),f),g),h)) for PSY4V2R2 b),f), In situ data c),g),
PSY3V3R3 a),e), and climatology WOA09 d),h). The region is situated in the Greenland Sea between 80°N and 87°N and 0 and
800m.
The Latitude/Depth diagrams show that the PSY4 and PSY3 systems are close to the observations in the
South of the section even if the observations are fresher near the surface between 80 and 82°N. The
subsurface Atlantic water appears in the observations between 100 and 500m seems to be too hot in PSY4
system but more realistic than PSY3: he is too cold in subsurface. The three core structure between 82°N
and 84°N is probably an artifact of the data sampling. We can notice that the WOA09 climatology sticks the
observations but appears too hot at the surface around 84°N.
The Longitude/Depth diagrams show the core of the Atlantic Water, it appears slightly hot for PSY4 and
good for PSY3. The surface waters in the North of the section are too salty for the two systems. Even if the
systems don’t assimilate these data the operational systems stay close to the observations.

50
V.2.2. SST Comparisons
Quarterly average SST differences with OSTIA analyses show that the systems’ SST is close to OSTIA, with
difference values staying below the observation error of 0.5 °C on average. High RMS difference values
(Figure 35) are encountered in high spatial and temporal variability regions such as the Gulf Stream, the
Kuroshio or the Agulhas current. The error is also high (more than 2°C locally) in marginal seas of the Arctic
Ocean where the sea ice limit increases the SST errors and the SST contrasts. This JAS 2014 season, the
RMS difference is also high in the eastern tropical part of the Pacific Ocean.
a) b)
c)
Figure 35 : RMS temperature (°C) differences between OSTIA daily analyses and PSY3V3R3 daily analyses (a); between OSTIA
and PSY4V2R2 (b), between OSTIA and PSY2V4R4 (c) in JAS 2014. The Mercator Océan analyses are colocalised with the
satellite observations analyses.
The systems’ tropical oceans are warmer than OSTIA especially in the East Pacific Ocean (Figure 36), as do
the south high latitudes (especially in the ACC this austral winter). In the South Indian Ocean and east of
Australia, the systems are colder than OSTIA on average, as in the closed or semi-enclosed seas: the Sea of
Okhotsk, the Mediterranean Sea; the North Sea; the Baltic Sea; the Labrador Sea and the Hudson Bay.

51
a) b)
c)
Figure 36: Mean SST (°C) daily differences inJAS 2014 between OSTIA daily analyses and PSY3V3R3 daily analyses (a), between
OSTIA and PSY4V2R2 (b) and between OSTIA and PSY2V4R4 daily analyses (c).
V.2.3. Drifting buoys velocity measurements
Recent studies (Law Chune, 20123
, Drévillon et al, 20124
) - in the context of Search-And-Rescue and drift
applications – focus on the need for accurate surface currents in ocean forecasting systems. In situ currents
are not yet assimilated in the Mercator Ocean operational systems, as this innovation requires a better
characterization of the surface currents biases.
The comparison of Mercator analyses and forecast with AOML network drifters velocities combines two
methods based on Eulerian and Lagrangian approaches.
3
Law Chune, 2012 : Apport de l’océanographie opérationnelle à l’amélioration de la prévision de la dérive océanique
dans le cadre d’opérations de recherche et de sauvetage en mer et de lutte contre les pollutions marines
4
Drévillon et al, 2012 : A Strategy for producing refined currents in the Equatorial Atlantic in the context of the
search of the AF447 wreckage (Ocean Dynamics, Nov. 2012)

52
V.2.3.1. Eulerian Quality control
a) b)
c) d)
e)
Figure 37: Near surface (15m) zonal current (m/s) comparison between the Mercator systems analyses and observed
velocities from drifters. In the left column: velocities collocated with drifter positions in JAS 2014 for PSY3V3R3 (a), PSY4V2R2
(c) and PSY2V4R4 (e). In the right column, zonal current from drifters in JAS 2014 (b) at global scale, AOML drifter climatology
for JAS with new drogue correction from Lumpkin & al, in preparation (d) and observed zonal current in JAS 2014 (b) over
PSY2’s domain.

53
a) b)
c) d)
e) f)
Figure 38 : In JAS 2014, comparison of the mean relative velocity error between in situ AOML drifters and model data (a, c and
e) and mean zonal velocity bias between in situ AOML drifters with Mercator Océan correction (see text) and model data (b, d
and f). a and b: PSY3V3R3, c and d: PSY4V2R2, e and f: PSY2V4R4. NB: zoom at 500% in order to see the arrows.
The fact that velocities estimated by the drifters happen to be biased towards high velocities is taken into
account, applying slippage and windage corrections (cf QuO Va Dis? #5 and Annex C). Once this so called
“Mercator Océan” correction is applied to the drifter observations, the zonal velocity of the model (Figure

54
37 and Figure 38) at 15 m depth and the meridional velocity (not shown) are more consistent with the
observations for the JAS 2014 period.
On average over long periods, the usual behaviour compared to drifters’ velocities is that the systems
underestimate the surface velocity in the mid-latitudes. All systems overestimate the Equatorial currents
and the southern part of the North Brazil Current (NBC).
For all systems the largest direction errors are local (not shown) and generally correspond to ill positioned
strong current structures in high variability regions (Gulf Stream, Kurioshio, North Brazil Current, Zapiola
eddy, Agulhas current, Florida current, East African Coast current, Equatorial Pacific Countercurrent).
The differences between the systems mainly appear in the North Atlantic and North Pacific Oceans on
Figure 38, in the relative error. In these regions PSY3 underestimates on average the eastward currents,
which is a bit less pronounced in the high resolution systems PSY4 and PSY2. On the contrary the systems
overestimate the equatorial westward currents on average, and this bias is less pronounced in PSY3 than in
PSY4. 50% of the ocean current feedbacks on the wind stress in PSY2 and PSY3, which slows the surface
currents with respect to PSY4.
V.2.3.2. Lagrangian Quality control
The aim of the Lagrangian approach is to compare the observed buoy trajectory with virtual trajectories
obtained with hindcast velocities, starting from the same observed initial location. The virtual trajectories
are computed with the ARIANE software5
(see annex III.2). The metric shown here (Figure 39) is the
distance between the trajectories after 1, 3 and 5 days, displayed at each trajectory initial point.
a) b)
5
http://stockage.univ-brest.fr/~grima/Ariane/

55
c) d)
e) f)
Figure 39: In JAS 2014, comparison of the mean distance error in 1°x1° boxes after a 1-day drift (a and b), after a 3-days drift (c
and d), and after a 5-days drift (e and f).between AOML drifters’ trajectories and PSY3V3R3 trajectories (a, c, and e) and
between AOML drifters’ trajectories and PSY4V2R2 (b,d, and f).
Few differences appear between the systems and most of the high velocity biases that are diagnosed in
Figure 38 imply a large distance error (120 to 180km) after a few days drift in Figure 39. In the subtropical
gyres and in the North Pacific, which are less turbulent regions, the errors rarely exceed 30 km after 5 days.

56
Figure 40: Cumulative Distribution Functions of the distance error (km, on the left) and the direction error (degrees, on the
right panel) after 1 day (blue), 3 days (green) and 5 days (red), between PSY4V1R3 forecast trajectories and actual drifters
trajectories.
Over the whole domain on a long period, cumulative distribution functions (Figure 40) show that in 80% of
cases, PSY3 and PSY4 modelled drifters move away from the real drifters less than : 30km after 1 day,
70km after 3 days, and 100km after 5 days. As explained before, the remaining 20% generally correspond
to ill positioned strong current structures in high variability regions. PSY4 currents seem to induce slightly
more errors than PSY3 currents. This may not be systematic at the regional scale (under investigation).
V.2.4. Sea ice concentration
a) b) c)

57
d) e) f)
Figure 41: Arctic (a to c) and Antarctic (d to f) J 2014 Sea ice cover fraction in PSY3V3R3 (a and d), in CERSAT observations (b
and e) and difference PSY3 – CERSAT (c and f).
On average over the JAS 2014 period, including the sea ice minimum of September, strong discrepancies
with observed sea ice concentration appear between the centre of the Arctic and the Atlantic Ocean in
PSY3 and PSY4 (Figure 41 and Figure 42) as sea ice observations are not yet assimilated by the systems.
Both PSY3 and PSY4 are melting too much ice in summer, although differences are smaller in PSY4. The
other small discrepancies that can be distinguished inside the sea ice pack will not be considered as
significant as the sea ice concentration observations over 95% are not reliable. Differences with the
observations remain significant in the marginal seas, for instance the sea ice melts too much in the
Denmark Strait.
Model studies show that the overestimation in the Canadian Archipelago is first due to badly resolved sea
ice circulation. The overestimation in the eastern part of the Labrador Sea is due to a weak extent of the
West Greenland Current; similar behaviour in the East Greenland Current.
In the Antarctic the winter sea ice concentration is overestimated on average by both PSY3 and PSY4, for
instance in the Weddell Sea. On the contrary it is underestimated along the sea ice edge.
a) b) c)

58
d) e) f)
Figure 42: Arctic (a to c) and Antarctic (d to f) AMJ 2014 Sea ice cover fraction in PSY4V2R2 (a and d), in CERSAT observations
(b and e) and difference PSY4 – CERSAT (c and f).
Figure 43 illustrates the fact that sea ice cover in JAS 2014 is less than the past years climatology, especially
in the Nansen Basin (see also section IV).
a) b)
Figure 43: JAS 2014 Arctic sea ice extent in PSY4V2R2 with overimposed climatological AMJ 1992-2010 sea ice fraction
(magenta line, > 15% ice concentration) (a) and NSIDC map of the sea ice extent in the Arctic for September 2014 in
comparison with a 1981-2010 median extend (b).
V.2.5. Closer to the coast with the IBI36V2 system: multiple comparisons
V.2.5.1. Comparisons with SST from CMS

59
The bias, RMS error and correlation calculated from comparisons with SST measured by satellite in JAS
2014 (Météo-France CMS high resolution SST at 0.02°) are displayed in Figure 44. The bias patterns are
similar to those observed in JAS 2013. The cold bias already noticed in the North Sea in AMJ 2014 is still
present and extends to the Channel (except around the western part of Britanny) and the Irish Sea. A warm
bias is present along the south Moroccan coast and Canary Islands, and along the Algerian coast. A warm
bias is also present along the shelf slope between 46 and 50°N (where internal waves can be generated),
and in the north of the British islands. In the English Channel and Celtic Sea, the biases are linked to tidal
mixing. In the Mediterranean Sea, biases are associated to the Alboran gyre and the Algerian current. Away
from the shelf, the bias is near zero and the RMS error is small (less than 0.5°C). The lower correlation is
found western and eastern of the Gibraltar strait and in the northern part of the domain. The biases are
lower along the west Iberian coast this summer JAS 2014 season with respect to the previous spring
season. The correlation for PSY2 and PSY4 is almost the same than for IBI36. The bias along the shelf slope
between 46 and 50°N is smaller in PSY2 and PSY4.
Note that the number of observations is very good for JAS 2014 except in the western part of the domain
between 40°N and 50°N.
a) b) c)
j)d) e) f)

60
g) h) i)
Figure 44 : Mean bias (model-observation) (a), RMS error (b), correlation (c) between IBI36V4 and analysed L3 SST from
Météo-France CMS for the JAS 2014 quarter. Same diagnostics for PSY2V4R4 (d,e,f) and PSY4V2R2 (g,h,i). Number of Météo-
France CMS observations for the JAS 2014 quarter (j).
V.2.5.2. Comparisons with in situ data from EN4/ENSEMBLE for JAS 2014
Averaged temperature profiles (Figure 49) show that the strongest mean bias and RMS error (more than
1.5°C) are observed between surface and 100 m depth, in the thermocline, and also around 500 m depth
(more than 1°C, already present in AMJ 2014). The bias and RMS error in the thermocline are higher than
in JAS 2013. Below 800 m depth, the mean bias is almost zero, and the strongest RMS is found at the
Mediterranean Sea Water level. In the Bay of Biscay, the RMS error is similar to those of the domain
average, except for the maximum around 500 m depth. As shown by the mean temperature profiles, IBI36
and PSY2 are very close.
a) Temperature, 0-200 m b) Temperature, 0-2000 m

61
c) Temperature, 0-200 m d) Temperature, 0-2000 m
Figure 45 : Over the whole IBI36V4 domain (a and b) and over the Bay of Biscay (c and d): mean “IBI36V4 - observation”
temperature (°C) bias (red) and RMS error (blue) in JAS 2014 (a and c), and mean profile from IBI36V4 (black), PSY2V4R4
(green) and from the observations (red) in JAS 2014 (b and d). In the lower right corner of each plot: position of the individual
profiles.
The maximum salinity bias and RMS error (Figure 46) occur near the surface. The model is too fresh
between the surface and 160 m depth, with PSY2 closer to the observations than IBI36. The RMS error is
strong at the Mediterranean Sea Water level (as for temperature). In the Bay of Biscay the Mediterranean
waters are slightly too fresh.
Note: averaged profiles are discontinuous because the number of observations varies with depth.
a) Salinity, 0-200 m b) Salinity, 0-2000 m

62
c) Salinity, 0-200 m d) Salinity, 0-2000 m
Figure 46: Over the whole IBI36V4 domain (a and b) and over the Bay of Biscay (c and d): mean “IBI36V4 - observation”
salinity (psu) bias (red) and RMS error (blue) in JAS 2014 (a and c), and mean profile from IBI36V4 (black), PSY2V4R4 (green)
and from the observations (red) in JAS 2014 (b and d). In the lower right corner of each plot: position of the individual
profiles.
V.2.5.3. MLD Comparisons with in situ data
Figure 47 shows that the distribution of modeled mixed layer depths among the available profiles is
comparable to the observed distribution for both PSY2 and IBI over the whole IBI domain. Values of the
mixed layer depth between 5 m and 25 m occur too often in the systems compared with the observations
(especially for PSY2), suggesting the mixed layer is too shallow in the systems. On the contrary, values
between surface and 5 m are under-estimated by the systems. In the Bay of Biscay IBI36 is slightly closer to
the observations than PSY2.
a) b)

63
c) d)
Figure 47 : Mixed Layer Depth distribution in JAS 2014 in IBI36V4 (a and b) and PSY2V4R4 (c and d), calculated from profiles
with the temperature criteria (difference of 0.2°C with the surface); the models are in grey, the observations in red. a and c:
classification for the whole IBI domain, b and d: classification for the Bay of Biscay.
V.2.5.4. Comparisons with moorings and tide gauges
a) b)
Figure 48 : For IBI36V4: RMS error (a, in cm) and correlation (b) for the non-tidal Sea Surface Elevation at tide gauges in JAS
2014, for different regions and frequencies.
The RMS error of the residual (non tidal) elevation of sea surface (Figure 51) computed with harmonic
decomposition (Foreman 1977) and Loess low-pass filtering is between 4 and 16 cm. It is close to 5 cm in

64
the Canary Islands, Mediterranean Sea and Iberian Coast. The RMS decreases for some frequency bands,
and the smallest values occur in the 1-10-day band. The correlation is significant at all frequencies, and
reaches high values for periods lower than 30 days (at high frequencies).

65
a) b) c)
d) e) f)
g) h) i)
Figure 49 : For IBI36V4: Bias (observation-model) (a, d and g), RMS error (°C) (b, e and h) and correlation (c, f and i) of the Sea
Surface Temperature between IBI36 and moorings measurements in July (a to c), August (d to f) and September 2014 (g to i).

66
In Figure 52 we can see that the SST correlations between the coastal moorings and the IBI model are
generally good for the three months. Maximum values of correlation are reached in September. In the Irish
Sea the bias is negative with low correlation, consistently with the L3 SST biases of Figure 44. At Villano
Sisargas (north-west corner of Spain) the correlation is also weak.
Estaca de bares
Biscay Les pierres noires
Sandettie lightship
Gulf of Lion
Figure 50: Surface temperature (°C) time series at a selection of moorings which locations are shown on the upper left panel
(no observation for Gulf of Lion).

67
V.2.6. Biogeochemistry validation: ocean colour maps
a) b)
c) d)
e) f)
Figure 51 : Chlorophyll-a concentration (mg/m
3
) in July (a and b), August (c and d) and September 2014 (e and f) for the
Mercator system BIOMER1 (a, c, and e) and Chlorophyll-a concentration from Globcolour (b, d and f).
As can be seen in Figure 51 the surface chlorophyll-a concentration is overestimated by BIOMER1 on
average over the globe. The production is especially overestimated in the Pacific and Atlantic tropical band.
Blooms are observed in the northern hemisphere subpolar gyres. In the Atlantic it is slightly decaying from

68
July to September. In BIOMER1, the blooms have already disappeared before the beginning of the JAS
quarter. Some coastal regions or semi enclosed seas like the North and Baltic seas or the China Seas do not
display local enrichments that can be detected in the observations. On the contrary, BIOMER1 displays
unobserved enrichments in the upwelling of Peru and Benguela, as well as in regions of high variability
sugh as the Zapiola eddy or the Somali Gyre. Figure 52 shows the PDF of the Chl-a bias in North Atlantic.
The positive values between 1 and 3 mg/m3 correspond mainly to the high values of the bloom in
Globcolour that are not reproduced by BIOMER1. The highest positive biases are observed in September.
a) b) c)
Figure 52 : Probability Density Function (PDF) of Chl-a bias in log scale (log10(obs)-log10(model)) in the North Atlantic (30-
70N; 80W:20E) for July (a), August (b) and September (c) 2014.
The discrepancies at the global scale appear in the RMS differences for the mean JAS 2014 season (Figure
53).
Figure 53 : RMS difference between BIOMER1 and Globcolour Chl-a concentrations (mg/m
3
) in JAS 2014.

69
VI Forecast error statistics
VI.1. General considerations
The daily forecasts (with updated atmospheric forcings) are validated in collaboration with SHOM/CFUD.
This collaboration has been leading us to observe the degradation of the forecast quality depending on the
forecast range. When the forecast range increases the quality of the ocean forecast decreases as the
initialization errors propagate and the quality of the atmospheric forcing decreases. Additionally the
atmospheric forcing frequency also changes (see Figure 54). The 5-day forecast quality is optimal; starting
from the 6th
day a drop in quality can be observed which is linked with the use of 6-hourly atmospheric
fields instead of 3-hourly; and starting from the 10th
day the quality is strongly degraded due to the use of
persisting atmospheric forcings (but not constant from the 10th
to the 14th
day as they are relaxed towards
a 10-day running mean).
Figure 54: Schematic of the change in atmospheric forcings applied along the 14-day ocean forecast.
VI.2. Forecast accuracy: comparisons with T and S observations when and where
available
VI.2.1. North Atlantic region
As can be seen in Figure 55 the PSY2 temperature products have a better accuracy than the climatology in
the North Atlantic region in JAS 2014 (note that in the 2000-5000m layer, the statistics are performed on a
very small sample of observations, and thus are not really representative of the region or layer), except for
the surface salinity.
In general the analysis is more accurate than the 3-day and 6-day forecast for both temperature and
salinity. The RMS error thus increases with the forecast range (shown for NAT region Figure 55 and MED
region Figure 56). The biases in temperature are generally small compared to the climatology’s biases
while the salinity biases are significant (0.12 psu at the surface while the climatology bias is only 0.08 psu).
The surface salinity is biased (~0.1 psu) towards saltier waters than observed.

70
a) b)
c) d)
Figure 55: Accuracy intercomparison in the North Atlantic region for PSY2V4R4 in temperature (a and c) and salinity (b and dl)
between hindcast (green), nowcast (yellow), 3-day (orange) and 6-day (red) forecast and WO09 climatology (grey). Accuracy
is measured by a mean difference (a and b) and by a rms difference (c and d) of temperature and salinity with respect to all
available observations from the CORIOLIS database averaged in 6 consecutive layers from 0 to 5000m. All statistics are
performed for the JAS 2014 period. NB: average on model levels is performed as an intermediate step which reduces the
artefacts of inhomogeneous density of observations on the vertical.

71
VI.2.2. Mediterranean Sea
In the Mediterranean Sea in JAS 2014 (Figure 56) the PSY2 products are more accurate than the
climatology on average. PSY2 is less biased in temperature than in the North Atlantic (not at surface) and
PSY2’s salinity RMS error is smaller than the climatology’s. Between surface and 800m the system is
generally too fresh.
a) b)
c) d)
Figure 56: Accuracy intercomparison in the Mediterranean Sea region for PSY2V4R4 in temperature (°C, a and c) and salinity
(psu, b and d) between hindcast, nowcast, 3-day and 6-day forecast and WO09 climatology. Accuracy is measured by a rms
difference (c and d) and by a mean difference (a and b) with respect to all available observations from the CORIOLIS database
averaged in 6 consecutive layers from 0 to 5000m. All statistics are performed for the JAS 2014 period. NB: average on model
levels is performed as an intermediate step which reduces the artefacts of inhomogeneous density of observations on the
vertical.

72
VI.2.3. Tropical Oceans and global
Results already described in previous sections are confirmed by the profiles shown in Figure 58. PSY4 is
usually warmer (less cold) than PSY3 in common regions. PSY2 and PSY4 suffer from a fresh bias in the
Tropical oceans, especially in the Tropical Atlantic. In the other tropical basins PSY3 is less biased than PSY4
in terms of salinity in surface layer.
a) b)
c) d)

73
e) f)
Figure 57: (Obs-model) mean differences in temperature (°C, a, c, and e) and salinity (psu, b, d, and f) for PSY3V3R3 (red),
PSY2V4R4 (yellow) and PSY4V2R2 (blue) systems in the Tropical Atlantic (a and b), the Tropical Pacific (c and d) and the Indian
Ocean (e and f) in JAS 2014.
PSY3, PSY4 and PSY2 display similar accuracy levels in terms of temperature RMS error in the Tropical
Oceans. PSY3’s salinity slightly outperforms PSY4 on average over the globe. RMS error increases with
forecast range (shown here for PSY3), as could be expected, and that the 6-day forecast always beats the
climatology (except for global mean salinity).
a) b)

75
g) h)
i) j)
Figure 58: same as Figure 55 but for RMS statistics and for temperature (°C), PSY3V3R3 and PSY4V2R2 systems and the
Tropical Atlantic (a and b), the Tropical Pacific (c and d) and the Indian Ocean (e and f). The global statistics are also shown for
temperature (°C, g and h) and salinity (psu, I and j). The right column compares the analysis of the global ¼° PSY3V3R3 (red)
with the analysis of the global 1/12° PSY4V2R2 (blue).
VI.3. Forecast accuracy: skill scores for T and S

76
a) b)
c) d)
Figure 59: Temperature (left) and salinity (right) skill scores in 4°x4° bins and in the 0-500m layer in JAS 2014 in PSY3V3R3 (a
and b) and PSY2V4R4 (c and d). The skill illustrates the ability of the 3-days forecast to be closer to in situ observations than
the persistence of the analysis, see Equation 1. Yellow to red values indicate that the forecast is more accurate than the
persistence.
The Murphy Skill Score (see Equation 1) is described by Wilks, Statistical Methods in the Atmospheric
Sciences, Academic Press, 2006. This score is close to 0 if the forecast is equivalent to the reference. It is
positive and aims towards 1 if the forecast is more accurate than the reference.
( )
( )∑ ∑
∑ ∑
= =
= =






−






−
−= n
k
M
m
mm
n
k
M
m
mm
ObsRef
M
ObsForecast
M
SS
1 1
2
1 1
2
1
1
1
Equation 1

77
a) b)
c) d)
Figure 60 : As Figure 59 but the reference is the WOA 2009 climatology. Temperature (a and c) and salinity (b and d) skill
scores are displayed for JAS 2014, for PSY3V3R3 (a and b) and for PSY2V4R4 (c and d). The skill illustrates the ability of the 3-
days forecast to be closer to in situ observations than the climatology, see Equation 1. Yellow to red values indicate that the
forecast is more accurate than the climatology.
The Skill Scores displayed on Figure 59 and Figure 60 show the added value of the PSY3 forecast with
respect to the persistence of the analysis (Figure 59) and to climatology (Figure 60). All Mercator Ocean
systems have a very good level of performance with respect to the climatology (see previous section).
When the reference is the persistence of the last analysis, the result is noisier and the systems 3-day
forecast seems to have skill in some regions in particular: North East Atlantic, eastern pacific, western
tropical Atlantic, western Indian basin. In some regions of high variability (for instance in the Antarctic, Gulf
Stream, Agulhas Current, Zapiola) the persistence of the previous analysis is locally more accurate than the
forecast.
VI.4. Forecast verification: comparison with analysis everywhere
Not available this quarter.
VII Monitoring of ocean and sea ice physics
VII.1. Global mean SST and SSS

78
a)
b)
c)
Figure 61: daily SST (°C) spatial mean for a one year period ending in JAS 2014 for PSY2V4R4 (a), PSY3V3R3 (b), and PSY4V2R2
(c). Mercator Ocean systems are in black and Reynolds AVHRR ¼° analysis in red.
The spatial mean of SST is computed for each day of the year, for PSY2, PSY3 and PSY4. The mean SST is
compared to the mean of Reynolds AVHRR SST on the same domain (Figure 61).
The main feature is the good agreement of the systems with the observations all year long and on global
average. A warm bias can be diagnosed in all the systems (stronger in PSY4 than in PSY3) with respect to
the observations. The bias disappears in winter for PSY2.
VII.2. Surface EKE
Regions of high mesoscale activity are diagnosed in Figure 62: Kuroshio, Gulf Stream, Niño 3 region in the
eastern Equatorial pacific, Zapiola eddy, Agulhas current. PSY3 at ¼° and PSY4 at 1/12° are in very good
agreement. EKE is generally higher in the high resolution PSY4 system, for instance this quarter at the
Equator near 120°W. Compared to JAS 2013 (not shown), the EKE is higher in PSY4 along the equator in the
Atlantic and Pacific oceans.

79
a)
b)
Figure 62: surface eddy kinetic energy EKE (m²/s²) for PSY3V3R3 (a) and PSY4V2R2 (b) for JAS 2014.

80
VII.3. Mediterranean outflow
The Mediterranean outflow displayed in Figure 63 is more diffuse in all systems than in the climatology of
the Gulf of Cadiz. All systems now display similar vertical structure in the region, with PSY2 closer to the
climatology than PSY3 and PSY4. Compared to the climatology, the systems are too salty between 300 and
500m depth, and too fresh deeper than 1100m depth.
a) b) c)
d) e) f)
Figure 63: Comparisons between JAS 2014 mean temperature (°C, a to c) and salinity (psu, d to f) profiles in Mercator systems
(black): PSY2V4R4 (a and d), PSY3V3R3 (b and e) and PSY4V2R2 (c and f), and in the Levitus WOA05 (green) and ARIVO (red)
monthly climatologies.

81
VII.4. Sea Ice extent and area
The time series of monthly means of sea ice area and sea ice extent (area of ocean with at least 15% sea
ice) are displayed in Figure 64 and compared to SSM/I microwave observations. Both ice extent and area
include the area near the pole not imaged by the sensor. NSIDC web site specifies that it is assumed to be
entirely ice covered with at least 15% concentration. This area is 0.31 million square kilometres for SSM/I.
a) b)
Figure 64: Sea ice area (a, 10
6
km2) and extent (b, 10
6
km2) in PSY3V3R3 (blue line), PSY4V2R2 (black line) and SSM/I
observations (red line) for a one year period ending in JAS 2014, in the Arctic (upper panel) and Antarctic (lower panel).
These time series indicate that sea ice products from PSY4 and PSY3 are very similar on average over the
polar domains. In the Arctic both systems perform very well in terms of average quantities. In the Antarctic
the seasonal cycle of the sea ice area is overestimated, with an overestimated sea ice area this austral
winter in both systems (while the extent is better captured), and too much melting in summer for both
systems.
VIII Comparison between BIOMER1 and BIOMER4
VIII.1.1. Description of BIOMER4
The BIOMER4 system has been operated since July 2014 and is disseminated to users since September
2014. Starting from the next issue of the “QuO Va Dis?” BIOMER4 products will be evaluated, and will
replace BIOMER1 products.
The biogeochemical model used in BIOMER1 and BIOMER4 is PISCES (Aumont, in prep). It is a model of
intermediate complexity designed for global ocean applications (Aumont and Bopp, 2006) and is part of
NEMO modeling platform. In BIOMER4 the version of NEMO is upgraded to NEMO3.2, while NEMO2.3
was used in BIOMER1. PISCES has 24 prognostic variables and simulates biogeochemical cycles of oxygen,
carbon and the main nutrients controlling phytoplankton growth (nitrate, ammonium, phosphate, silicic
acid and iron). The model distinguishes four plankton functional types based on size: two phytoplankton
groups (small = nanophytoplankton and large = diatoms) and two zooplankton groups (small =
microzooplankton and large = mesozooplankton). Prognostic variables of phytoplankton are total biomass

82
in C, Fe, Si (for diatoms) and chlorophyll and hence the Fe/C, Si/C, Chl/C ratios are variable. For
zooplankton, all these ratios are constant and total biomass in C is the only prognostic variable. The
bacterial pool is not modeled explicitly. PISCES distinguishes three non-living pools for organic carbon:
small particulate organic carbon, big particulate organic carbon and semi-labile dissolved organic carbon.
While the C/N/P composition of dissolved and particulate matter is tied to Redfield stoichiometry, the iron,
silicon and carbonate contents of the particles are computed prognostically. Next to the three organic
detrital pools, carbonate and biogenic siliceous particles are modeled. Besides, the model simulates
dissolved inorganic carbon and total alkalinity. In PISCES, phosphate and nitrate + ammonium are linked by
constant Redfield ratio (C/N/P = 122/16/1), but cycles of phosphorus and nitrogen are decoupled by
nitrogen fixation and denitrification.
The distinction of two phytoplankton size classes, along with the description of multiple nutrient co-
limitations allows the model to represent ocean productivity and biogeochemical cycles across major
biogeographic ocean provinces (Longhurst, 1998). PISCES has been successfully used in a variety of
biogeochemical studies (e.g. Bopp et al. 2005; Gehlen et al. 2006; 2007; Schneider et al. 2008; Steinacher
et al. 2010; Tagliabue et al. 2010, Séférian et al, 2013). The biogeochemical model is initialized with World
Ocean Atlas 2001 for nitrate, phosphate, oxygen and silicate (Conkright et al. 2002), with GLODAP
climatology including anthropogenic CO2 for Dissolved Inorganic Carbon and Alkalinity (Key et al. 2004)
and, in the absence of corresponding data products, with model fields for dissolved iron and dissolved
organic carbon. Boundary fluxes account for nutrient supply from three different sources: atmospheric
deposition (Aumont et al., 2008), rivers for nutrients, dissolved inorganic carbon and alkalinity (Ludwig et
al., 1996) and inputs of Fe from marine sediments.
VIII.1.2. Validation summary
The 3D physical forcing of PISCES comes from PSY3R3V3 at ¼° resolution (Lellouche et al., 2013). PISCES is
forced offline by daily fields in BIOMER4, while BIOMER1 was forced by weekly averages. A special
treatment is done on vertical diffusivity coefficient (Kz): the daily mean is done on Log10(Kz) after a
filtering of enhanced convection (Kz increased artificially to 10 m2
.s-1
when the water column is unstable).
The purpose of this Log10 is to average the orders of magnitudes and to give more weight to small values
of vertical diffusivity.
In BIOMER4, the grid of PISCES is the same as for PSY3V3R3, it is the standard ORCA025 tri-polar grid
(1440 x 1021 grid points on the horizontal). The three poles are located over Antarctic, Central Asia and
North Canada. The vertical grid has 50 levels, with a resolution of 1 meter near the surface and 500 meters
in the deep ocean. BIOMER1’s grid was ORCA1 with a 1° horizontal resolution.
The biogeochemical simulation starts in January 2007. Outputs are interpolated on a standard collocated
grid at 1/2 degree.
The quality of the global biogeochemical system has been assessed using a seven-year hindcast (years
2007-2013). The headline results for each of the variables assessed are as follows:
Chlorophyll: At sea surface, modelled chlorophyll fields are in good agreement with satellite data. The
large-scale structures corresponding to specific biogeographic regions (double-gyres, Antarctic Circumpolar
Current, etc.) are well reproduced (not shown). However, concentrations are still too high in the tropical
band. Concerning the temporal monitoring, our model succeeds well in reproducing the seasonal cycle at
mid- and high- latitudes (spring bloom), but the timing of the bloom is not yet in phase with observations
(one or two-month lag). On the global ocean, the model has a correlation of 0.59 in log10(chlorophyll) at
sea surface in comparison with satellite chlorophyll observations (see Figure 65).

83
Figure 65: Hovmöller diagram of Log10( chlorophyll ) between 2007 and 2013 at 20°W in North Atlantic (20°S:70°N); (left)
model; (right) Globcolour data.
Nutrients (NO3, PO4, Si): They display a good agreement with World Ocean Atlas Climatology at global
scale (correlation > 0.9, not shown). The concentrations of nutrients are globally too high at sea surface
(negative mean bias). This is mainly due to the tropical band where the model overestimates the nutrient
concentrations.
Dissolved Oxygen: Oxygen presents very good scores at sea surface (correlation > 0.9; in comparison with
World Ocean Atlas climatology, not shown). This is due to the intrinsic link between O2 concentration and
temperature (and especially at sea surface). Dissolved oxygen benefits of the data assimilation via the
temperature. In subsurface and deep layers, the model is able to reproduce OMZs (oxygen minimum zone).
Illustrations of the validation results and further information (Quality Information Document, or QUID) are
available online on the MyOcean website, at http://www.myocean.eu/documents/QUID/MYOF-GLO-QUID-
001-014-V2.2.pdf.
VIII.1.3. Comparison over the JAS 2014 quarter
In the following, we compare the outputs of both systems BIOMER1 and BIOMER4 on the JAS 2014
quarter, in order to document the main improvements, and describe the main strengths and weaknesses
of the new release of BIOMER.

84
a) b)
c) d)
e) f)
3
Mercator systems BIOMER1 (a, c, and e) and BIOMER4 (b, d and f).
Using the “QuO Va Dis?” diagnostics based on comparison with ocean colour observations of Chlorophyll-a
concentration, the new BIOMER4 system performs better than BIOMER1. The global overestimation of
BIOMER1 is corrected in BIOMER4, and BIOMER4 is closer to the observations than the old system (Figure
66, Figure 67 and Figure 68). Discrepancies remain anyway, like along the Argentina’s coast, or in the
subpolar gyres.

85
a) b) c)
d) e) f)
70N; 80W:20E) for BIOMER1 for July (a), August (b) and September (c) 2014. The same for BIOMER4 for July (d), August (e)
and September (f) 2014.
As can be seen in the distribution of Chlorophyll-a concentration errors in Figure 67, the shape of
BIOMER4’s distribution is much more Gaussian than that of BIOMER1. BIOMER4 displays less very high
departures from observations than BIOMER1, but a slightly larger mean bias. The highest overestimation
errors have thus been reduced.
Some of these strong biases were located in the Equatorial band, in the upwelling regions, or in regions of
high mesoscale variability (for instance around Australia, or in the Somali Gyre). A spurious enrichment
which happened in the Gulf of Bengal in BIOMER1 disappears in BIOMER4.
The RMS error with respect to ocean colour observations is strongly reduced in BIOMER4 with respect to
BIOMER1 (Figure 68). In the northern hemisphere in regions of seasonal blooms, the timing of the bloom is
still not in phase with the observations, giving rise to high RMS errors in BIOMER4 as in BIOMER1.

86
a) b)
3
) in JAS 2014 (a). The same for
BIOMER4 (b).
VIII.1.4. Conclusion
As a conclusion, even if there is still room for improvement of the biogeochemical system, the update of
the BIOMER system to BIOMER4 allows a significantly better representation of the surface Chlorophyll-a
concentration. Some limitations of the system are still present, such as the wrong timing of the North
Pacific and North Atlantic blooms.

87
I Annex A
I.1. Table of figures
Figure 1: schematic of the operational forecast scenario for IBI36Q (green) and PSY2Q (blue). Solid lines are the PSY2
weekly hindcast and nowcast experiments, and the IBI36 spin up. Dotted lines are the weekly 14-day forecast,
dashed lines are daily updates of the ocean forecast forced with the latest ECMWF atmospheric analysis and
forecast. The operational scenario of PSY3, PSY4 and PSY3Q, PSY4Q is similar to PSY2’s scenario. In the case of PSY4,
only weekly hindcast, nowcast and 7-day forecast are performed. ............................................................ 7
Figure 2: schematic of the operational forecast scenario for BIOMER.................................................................. 7
Figure 3: Seasonal JAS 2014 temperature anomalies with respect to GLORYS2V3 climatology (1993-2011). (a): SST
anomaly (°C) at the global scale from the 1/4° ocean monitoring and forecasting system PSY3V3R3. (b): heat
content anomaly (ρ0Cp∆T, with constant ρ0=1020 kg/m3 ) from the surface to 300m.............................. 13
Figure 4: Seasonal JAS 2014 temperature anomaly (°C) with respect to GLORYS2V3 climatology (1993-2011), vertical
section 2°S-2°N mean, Pacific Ocean, PSY3V3R3. ...................................................................................... 14
Figure 5 : Arctic sea ice extent from the NSIDC, available at http://nsidc.org/arcticseaicenews/2014/10/....... 14
Figure 6 : Comparison between SLA data assimilation scores (a: average misfit in cm, b: RMS misfit in cm, c: number of
observations) for all available Mercator Ocean systems in JAS 2014. The scores are averaged for all available
satellite along track data (Saral/altiKa, Jason 2, Cryosat 2, HY-2a). For each region the bars refer to PSY2V4R4
(yellow), PSY3V3R3 (red), and PSY4V2R2 (blue). ....................................................................................... 15
observations) for all available Mercator Ocean systems in JAS 2014 in sub-regions of the Tropical Atlantic (TAT) and
North Atlantic (NAT). The scores are averaged for all available satellite along track data (SARAL/Altika, Jason 2,
Cryosat 2, HY-2a). For each region the bars refer respectively to PSY2V4R4 (yellow), PSY3V3R3 (red), and PSY4V2R2
(blue). ......................................................................................................................................................... 16
observations) for all available Mercator Ocean systems in JAS 2014 in the Mediterranean Sea MED sub-regions:
PSY2V4R4 (yellow), PSY3V3R3 (red) and PSY4V2R2 (blue). The scores are averaged for all available satellite along
track data (SARAL/Altika, Jason 2, Cryosat 2, HY-2a). See annex B for geographical location of regions.. 17
observations) for all available global Mercator Ocean systems in JAS 2014 in sub-regions of the SAT, IND, NPA, SPA,
TPA, and ACC basins: PSY3V3R3 (red) and PSY4V2R2 (blue). The scores are averaged for all available along track
satellite data (SARAL/Altika, Jason 2, Cryosat 2, HY-2a). The geographical location of regions is displayed in annex B.
.................................................................................................................................................................... 18
Figure 10: Comparison between Reynolds ¼°AVHRR-data assimilation scores (a: average misfit in °C, b: RMS misfit in °C, c:
number of observations) for all available Mercator Ocean systems in JAS 2014: PSY4V2R2 (blue), PSY3V3R3 (red),
and PSY2V4R4 (yellow)............................................................................................................................... 19
Figure 11: Comparison between Reynolds ¼°AVHRR SST data assimilation scores (a: average misfit in °C, b: RMS misfit in
°C, c: number of observations) for all available Mercator Ocean systems in JAS 2014 in sub-regions of the Tropical
Atlantic (TAT) and of the North Atlantic (NAT): PSY4V2R2 (blue), PSY3V3R3 (red) and PSY2V4R4 (yellow). The
geographical location of regions is displayed in annex B........................................................................... 20
Figure 12: Comparison between Reynolds ¼° AVHRR SST data assimilation scores (a: average misfit in °C, b: RMS misfit in
°C, c: number of observations) for all available Mercator Ocean systems in JAS 2014 in the Mediterranean Sea:
PSY4V2R2 (blue), PSY3V3R3 (red) and PSY2V4R4 (yellow). The geographical location of regions is displayed in annex
B.................................................................................................................................................................. 21
Figure 13: Comparison between Reynolds ¼° AVHRR SST data assimilation scores (a: average misfit in °C, b: RMS misfit in
°C, c: number of observations) for all available global Mercator Ocean systems in JAS 2014 in the SAT, NPA, TPA,
SPA, IND and ACC basins: PSY3V3R3 (red) and PSY4V2R2 (blue). See annex B for geographical location of regions.
.................................................................................................................................................................... 22
Figure 14: Profiles of JAS 2014 innovations of temperature (°C, left column) and salinity (psu, right column), mean (solid
line) and RMS (dashed line) for PSY3V3R3 (red), PSY2V4R4 (yellow) and PSY4V1R3 (blue) in the North Atlantic NAT
(a and b), Mediterranean Sea MED (c and d) and Arctic Ocean ARC (e and f, the region starts of north of 67°N). Basin
masks are applied to keep only the main basin of interest (no Mediterranean in the Atlantic NAT, no Black Sea in the
Mediterranean MED, etc…)........................................................................................................................ 24
Figure 15: As Figure 14 in the Tropical Atlantic TAT (a and b), South Atlantic SAT (c and d) and Indian IND (e and f) basins.
.................................................................................................................................................................... 26

88
Figure 16: As Figure 14 in the North Pacific NPA (a and b), Tropical Pacific TPA (c and d) and South Pacific SPA (e and f).
.................................................................................................................................................................... 28
Figure 17: As Figure 14 in the ACC Southern Ocean basin (from 35°S). .............................................................. 29
Figure 18: As Figure 14 in the North East Atlantic (IBI) and the North Madeira XBT regions.............................. 30
Figure 19: As Figure 14 in the Dakar region......................................................................................................... 31
Figure 20: As Figure 14 in the Ascension tide (a and b), the Gulf of Mexico (c and d) and the Gulf Stream 1 regions (e and f)
.................................................................................................................................................................... 32
Figure 21: number of observations of T (a) and S (b) assimilated in PSY4V2R2 in the Gulf Stream 1 region during the JAS
2014 quarter............................................................................................................................................... 33
Figure 22: As Figure 14 in the Algerian (a and b), the gulf of Lion (c and d), the Ionian (e and f) and the Mersa Matruh (g
and h) regions............................................................................................................................................. 35
Figure 23: As Figure 14 in the North West Indian (a and b), the South West Indian (c and d) and the East Indian (e and f)
regions........................................................................................................................................................ 37
Figure 24: Average difference MODEL-OBSERVATION of temperature (a, c, and e, in °C) and salinity (b, d, and f, in psu) in
JAS 2014 between the daily average hindcast PSY4V2R2 (a and b), PSY3V3R3 (c and d) and PSY2V4R4 (e and f)
hindcast products and all available T/S observations from the Coriolis database. The Mercator hindcast products are
taken at the time and location of the observations. Averages are performed in the 0-50m layer. The size of the pixel
is proportional to the number of observations used to compute the RMS in 2°x2° boxes........................ 39
Figure 25: RMS temperature (°C) difference (model-observation) in JAS 2014 between all available T/S observations from
the Coriolis database and the daily average hindcast PSY3V3R3 products on the left and hindcast PSY4V2R2 on the
right column colocalised with the observations. Averages are performed in the 0-50m layer (a and b) and in the 0-
500m layer (c and d). The size of the pixel is proportional to the number of observations used to compute the RMS
in 2°x2° boxes............................................................................................................................................. 40
Figure 26: RMS salinity (psu) difference (model-observation) in JAS 2014 between all available T/S observations from the
Coriolis database and the daily average hindcast PSY3V3R3 products (a and c) and hindcast PSY4V2R2 (b and d),
colocalised with the observations. Averages are performed in the 0-50m layer (a and b) and in the 0-500m layer (c
and d). The size of the pixel is proportional to the number of observations used to compute the RMS in 2°x2° boxes.
.................................................................................................................................................................... 41
Figure 27 : JAS 2014 global statistics of temperature (°C, a and c) and salinity (psu, b and d) averaged in 6 consecutive
layers from 0 to 5000m. RMS difference (a and b) and mean difference (observation-model, c and d) between all
available T/S observations from the CORIOLIS database and the daily average hindcast products PSY3V3R3 (red),
PSY4V2R2 (blue) and WOA09 climatology (grey) colocalised with the observations. NB: average on model levels is
performed as an intermediate step which reduces the artefacts of inhomogeneous density of observations on the
vertical........................................................................................................................................................ 42
Figure 28: RMS difference (model-observation) of temperature (a and b, °C) and salinity (c and d, psu) in JAS 2014
between all available T/S observations from the Coriolis database and the daily average PSY2V4R4 hindcast
products colocalised with the observations in the 0-50m layer (a and c) and 0-500m layer (b and d)..... 43
Figure 29: : Water masses (Theta, S) diagrams in JAS 2014 in the Bay of Biscay (a to c), Gulf of Lion (d to f), Irminger Sea (g
to i) and Gulf of Cadiz (j to l), comparison between PSY3V3R3 (a, d, g, j), PSY4V2R2 (b, e, h, k) and PSY2V4R4 (c, f, I,
and l) in JAS 2014. PSY2, PSY3 and PSY4: yellow dots; Levitus WOA09 climatology: red dots; in situ observations:
blue dots..................................................................................................................................................... 45
Figure 30 : Water masses (T, S) diagrams in the Western Tropical Atlantic (a to c) and in the Eastern Tropical Atlantic (d to
f) in JAS 2014: for PSY3V3R3 (a, d); PSY4V2R2 (b, e); and PSY2V4R4 (c, f). PSY2, PSY3 and PSY4: yellow dots; Levitus
WOA09 climatology; red dots, in situ observations: blue dots.................................................................. 46
Figure 31: Water masses (T, S) diagrams in South Africa (a and b), Kuroshio (c and d), and Gulf Stream region (e and f): for
PSY3V3R3 (a, c and e); PSY4V2R2 (b, d and f) in JAS 2014. PSY3 and PSY4: yellow dots; Levitus WOA09 climatology:
red dots; in situ observations: blue dots.................................................................................................... 48
Figure 32 : Positions of In Situ TE profiles between the 23 July and 30 the September 2014............................. 48
Figure 33 : Latitude/Depth Sections for temperature (a),b),c),d)) and salinity (e),f),g),h)) for PSY4V2R2 b),f), In situ data
c),g), PSY3V3R3 a),e), and climatology WOA09 d),h). The region is situated in the Greenland Sea between 80°N and
87°N and 0 and 800m................................................................................................................................. 49
Figure 34 : Longitude/Depth Sections for temperature (a),b),c),d)) and salinity (e),f),g),h)) for PSY4V2R2 b),f), In situ data
c),g), PSY3V3R3 a),e), and climatology WOA09 d),h). The region is situated in the Greenland Sea between 80°N and
87°N and 0 and 800m................................................................................................................................. 49
Figure 35 : RMS temperature (°C) differences between OSTIA daily analyses and PSY3V3R3 daily analyses (a); between
OSTIA and PSY4V2R2 (b), between OSTIA and PSY2V4R4 (c) in JAS 2014. The Mercator Océan analyses are
colocalised with the satellite observations analyses.................................................................................. 50
Figure 36: Mean SST (°C) daily differences inJAS 2014 between OSTIA daily analyses and PSY3V3R3 daily analyses (a),
between OSTIA and PSY4V2R2 (b) and between OSTIA and PSY2V4R4 daily analyses (c)......................... 51

89
Figure 37: Near surface (15m) zonal current (m/s) comparison between the Mercator systems analyses and observed
velocities from drifters. In the left column: velocities collocated with drifter positions in JAS 2014 for PSY3V3R3 (a),
PSY4V2R2 (c) and PSY2V4R4 (e). In the right column, zonal current from drifters in JAS 2014 (b) at global scale,
AOML drifter climatology for JAS with new drogue correction from Lumpkin & al, in preparation (d) and observed
zonal current in JAS 2014 (b) over PSY2’s domain. .................................................................................... 52
Figure 38 : In JAS 2014, comparison of the mean relative velocity error between in situ AOML drifters and model data (a, c
and e) and mean zonal velocity bias between in situ AOML drifters with Mercator Océan correction (see text) and
model data (b, d and f). a and b: PSY3V3R3, c and d: PSY4V2R2, e and f: PSY2V4R4. NB: zoom at 500% in order to
see the arrows............................................................................................................................................ 53
Figure 39: In JAS 2014, comparison of the mean distance error in 1°x1° boxes after a 1-day drift (a and b), after a 3-days
drift (c and d), and after a 5-days drift (e and f).between AOML drifters’ trajectories and PSY3V3R3 trajectories (a, c,
and e) and between AOML drifters’ trajectories and PSY4V2R2 (b,d, and f)............................................. 55
Figure 40: Cumulative Distribution Functions of the distance error (km, on the left) and the direction error (degrees, on
the right panel) after 1 day (blue), 3 days (green) and 5 days (red), between PSY4V1R3 forecast trajectories and
actual drifters trajectories.......................................................................................................................... 56
Figure 41: Arctic (a to c) and Antarctic (d to f) J 2014 Sea ice cover fraction in PSY3V3R3 (a and d), in CERSAT observations
(b and e) and difference PSY3 – CERSAT (c and f). ..................................................................................... 57
Figure 42: Arctic (a to c) and Antarctic (d to f) AMJ 2014 Sea ice cover fraction in PSY4V2R2 (a and d), in CERSAT
observations (b and e) and difference PSY4 – CERSAT (c and f)................................................................. 58
Figure 43: JAS 2014 Arctic sea ice extent in PSY4V2R2 with overimposed climatological AMJ 1992-2010 sea ice fraction
(magenta line, > 15% ice concentration) (a) and NSIDC map of the sea ice extent in the Arctic for September 2014 in
comparison with a 1981-2010 median extend (b). .................................................................................... 58
Figure 44 : Mean bias (model-observation) (a), RMS error (b), correlation (c) between IBI36V4 and analysed L3 SST from
Météo-France CMS for the JAS 2014 quarter. Same diagnostics for PSY2V4R4 (d,e,f) and PSY4V2R2 (g,h,i). Number
of Météo-France CMS observations for the JAS 2014 quarter (j). ............................................................. 60
Figure 45 : Over the whole IBI36V4 domain (a and b) and over the Bay of Biscay (c and d): mean “IBI36V4 - observation”
temperature (°C) bias (red) and RMS error (blue) in JAS 2014 (a and c), and mean profile from IBI36V4 (black),
PSY2V4R4 (green) and from the observations (red) in JAS 2014 (b and d). In the lower right corner of each plot:
position of the individual profiles............................................................................................................... 61
Figure 46: Over the whole IBI36V4 domain (a and b) and over the Bay of Biscay (c and d): mean “IBI36V4 - observation”
salinity (psu) bias (red) and RMS error (blue) in JAS 2014 (a and c), and mean profile from IBI36V4 (black), PSY2V4R4
(green) and from the observations (red) in JAS 2014 (b and d). In the lower right corner of each plot: position of the
individual profiles....................................................................................................................................... 62
Figure 47 : Mixed Layer Depth distribution in JAS 2014 in IBI36V4 (a and b) and PSY2V4R4 (c and d), calculated from
profiles with the temperature criteria (difference of 0.2°C with the surface); the models are in grey, the
observations in red. a and c: classification for the whole IBI domain, b and d: classification for the Bay of Biscay.
.................................................................................................................................................................... 63
Figure 48 : For IBI36V4: RMS error (a, in cm) and correlation (b) for the non-tidal Sea Surface Elevation at tide gauges in
JAS 2014, for different regions and frequencies. ....................................................................................... 63
Figure 49 : For IBI36V4: Bias (observation-model) (a, d and g), RMS error (°C) (b, e and h) and correlation (c, f and i) of the
Sea Surface Temperature between IBI36 and moorings measurements in July (a to c), August (d to f) and September
2014 (g to i). ............................................................................................................................................... 65
Figure 50: Surface temperature (°C) time series at a selection of moorings which locations are shown on the upper left
panel (no observation for Gulf of Lion). ..................................................................................................... 66
3
Mercator system BIOMER (a, c, and e) and Chlorophyll-a concentration from Globcolour (b, d and f).... 67
70N; 80W:20E) for July (a), August (b) and September (c) 2014................................................................ 68
Figure 53 : RMS difference between BIOMER and Globcolour Chl-a concentrations (mg/m
3
) in JAS 2014........ 68
Figure 54: Schematic of the change in atmospheric forcings applied along the 14-day ocean forecast............. 69
Figure 55: Accuracy intercomparison in the North Atlantic region for PSY2V4R4 in temperature (a and c) and salinity (b
and dl) between hindcast (green), nowcast (yellow), 3-day (orange) and 6-day (red) forecast and WO09 climatology
(grey). Accuracy is measured by a mean difference (a and b) and by a rms difference (c and d) of temperature and
salinity with respect to all available observations from the CORIOLIS database averaged in 6 consecutive layers from
0 to 5000m. All statistics are performed for the JAS 2014 period. NB: average on model levels is performed as an
intermediate step which reduces the artefacts of inhomogeneous density of observations on the vertical. 70
Figure 56: Accuracy intercomparison in the Mediterranean Sea region for PSY2V4R4 in temperature (°C, a and c) and
salinity (psu, b and d) between hindcast, nowcast, 3-day and 6-day forecast and WO09 climatology. Accuracy is
measured by a rms difference (c and d) and by a mean difference (a and b) with respect to all available observations

90
from the CORIOLIS database averaged in 6 consecutive layers from 0 to 5000m. All statistics are performed for the
JAS 2014 period. NB: average on model levels is performed as an intermediate step which reduces the artefacts of
inhomogeneous density of observations on the vertical........................................................................... 71
Figure 57: (Obs-model) mean differences in temperature (°C, a, c, and e) and salinity (psu, b, d, and f) for PSY3V3R3 (red),
PSY2V4R4 (yellow) and PSY4V2R2 (blue) systems in the Tropical Atlantic (a and b), the Tropical Pacific (c and d) and
the Indian Ocean (e and f) in JAS 2014....................................................................................................... 73
Figure 58: same as Figure 55 but for RMS statistics and for temperature (°C), PSY3V3R3 and PSY4V2R2 systems and the
Tropical Atlantic (a and b), the Tropical Pacific (c and d) and the Indian Ocean (e and f). The global statistics are also
shown for temperature (°C, g and h) and salinity (psu, I and j). The right column compares the analysis of the global
¼° PSY3V3R3 (red) with the analysis of the global 1/12° PSY4V2R2 (blue)................................................ 75
Figure 59: Temperature (left) and salinity (right) skill scores in 4°x4° bins and in the 0-500m layer in JAS 2014 in PSY3V3R3
(a and b) and PSY2V4R4 (c and d). The skill illustrates the ability of the 3-days forecast to be closer to in situ
observations than the persistence of the analysis, see Equation 1. Yellow to red values indicate that the forecast is
more accurate than the persistence. ......................................................................................................... 76
Figure 60 : As Figure 59 but the reference is the WOA 2009 climatology. Temperature (a and c) and salinity (b and d) skill
scores are displayed for JAS 2014, for PSY3V3R3 (a and b) and for PSY2V4R4 (c and d). The skill illustrates the ability
of the 3-days forecast to be closer to in situ observations than the climatology, see Equation 1. Yellow to red values
indicate that the forecast is more accurate than the climatology. ............................................................ 77
Figure 61: daily SST (°C) spatial mean for a one year period ending in JAS 2014 for PSY2V4R4 (a), PSY3V3R3 (b), and
PSY4V2R2 (c). Mercator Ocean systems are in black and Reynolds AVHRR ¼° analysis in red................. 78
Figure 62: surface eddy kinetic energy EKE (m²/s²) for PSY3V3R3 (a) and PSY4V2R2 (b) for JAS 2014............... 79
Figure 63: Comparisons between JAS 2014 mean temperature (°C, a to c) and salinity (psu, d to f) profiles in Mercator
systems (black): PSY2V4R4 (a and d), PSY3V3R3 (b and e) and PSY4V2R2 (c and f), and in the Levitus WOA05 (green)
and ARIVO (red) monthly climatologies. .................................................................................................... 80
Figure 64: Sea ice area (a, 10
6
km2) and extent (b, 10
6
km2) in PSY3V3R3 (blue line), PSY4V2R2 (black line) and SSM/I
observations (red line) for a one year period ending in JAS 2014, in the Arctic (upper panel) and Antarctic (lower
panel).......................................................................................................................................................... 81
Figure 65: Hovmöller diagram of Log10( chlorophyll ) between 2007 and 2013 at 20°W in North Atlantic (20°S:70°N); (left)
model; (right) Globcolour data................................................................................................................... 83
3
Mercator systems BIOMER1 (a, c, and e) and BIOMER4 (b, d and f). ........................................................ 84
70N; 80W:20E) for BIOMER1 for July (a), August (b) and September (c) 2014. The same for BIOMER4 for July (d),
August (e) and September (f) 2014. ........................................................................................................... 85
3
) in JAS 2014 (a). The same for
BIOMER4 (b)............................................................................................................................................... 86
Figure 69 : illustration of QC: Quality test example chosen for windage (eg. 1%) we reject or correct a drift that differs
little from the windage (less than 70% of the drift angle <40 °) ................................................................ 94
Figure 70: Example of the surface currents Lagrangian quality control algorithm on a global map (bottom panel) and
zooms (upper panels)................................................................................................................................. 95
Figure 71: Illustration of the surface currents Lagrangian quality control algorithm.......................................... 96

91
II Annex B
II.1.Maps of regions for data assimilation statistics
II.1.1. Tropical and North Atlantic
1 Irminger Sea 12 Puerto Rico XBT
2 Iceland Basin 13 Dakar
3 Newfoundland-Iceland 14 Cape Verde XBT
4 Yoyo Pomme 15 Rio-La Coruna Woce
5 Gulf Stream2 16 Belem XBT
6 Gulf Stream1 XBT 17 Cayenne tide
7 North Medeira XBT 18 Sao Tome tide
8 Charleston tide 19 XBT - central SEC
9 Bermuda tide 20 Pirata
10 Gulf of Mexico 21 Rio-La Coruna
11 Florida Straits XBT 22 Ascension tide

92
II.1.2. Mediterranean Sea
1 Alboran 8 Ionian
2 Algerian 9 Egee
3 Lyon 10 Ierepetra
4 Thyrrhenian 11 Rhodes
5 Adriatic 12 MersaMatruh
6 Otranto 13 Asia Minor
7 Sicily

93
II.1.3. Global ocean
1 Antarctic Circumpolar Current 13 Nino3
2 South Atlantic 14 Nino4
3 Falkland current 15 Nino6
4 South Atl. gyre 16 Nino5
5 Angola 17 South tropical Pacific
6 Benguela current 18 South Pacific Gyre
7 Agulhas region 19 Peru coast
8 Pacific Region 20 Chile coast
9 North Pacific gyre 21 Eastern Australia
10 California current 22 Indian Ocean
11 North Tropical Pacific 23 Tropical indian ocean
12 Nino1+2 24 South indian ocean

94
III Annex C
III.1. Quality control algorithm for the Mercator Océan drifter data correction (Eric
Greiner)
Before estimating the bias, it is essential to conduct a quality control. We must consider an individual
monitoring of buoys, and a comparison with the geostrophy and windage. In real time, this is not possible,
and I propose below a simple test developed by position (date by date) which involves only the mean wind
(2 days) and the buoy drift. Basically, we found drifters where drift is close to argue between 0.2 and 3% of
the wind (almost the same direction with a drag corresponding to a loss of drogue). For these buoys, if the
contamination is real, then the error due to the wind is important with respect to current real at 15m
depth. We test different values of windage (wind effect for a fraction of a given wind between 0.2% and
3%). If a questionable observation is found for a given windage, we estimate a correction. We apply at the
end an average correction QC (windage among all acceptable). We although increase the error of
observation. Note that in delayed time, we could correct all the data from the buoy, at least in a 10-day
window. Note however that a buoy that has lost its drogue can give a good measure if the wind is low
• No anomaly : slippage correction of 0.07% of the 10m wind speed
• Windage > 0.2% or < 3% correction of 1% of windage
Figure 69 : illustration of QC: Quality test example chosen for windage (eg. 1%) we reject or correct a drift that differs little
from the windage (less than 70% of the drift angle <40 °)
Note that a correction of more than 3% is not normally possible (construction of the buoy). This may
correspond to breaking waves and swell. Between 2% and 3%, there is ambiguity between Stokes and
windage. In other words, it is likely that beyond 2%, we eliminate all or part of the effect of waves and
swell. If waves and swell are not aligned with the mean wind (swell remote for example), then the
correction will be approximate. Ideally, you should use the Stokes drift from a wave model like
Wavewatch3.
When calculating the equivalent models with AOML positions, which were filtered to remove 36h gravity
waves and reduce positioning errors, we must :
• add 0.07% wind averaged over 48h 10m : slippage correction
• windage correction and modify the error

95
III.2. Algorithm of the Lagrangian verification of the Mercator Océan
surface currents forecast.
Algorithm of the Lagrangian verification of the Mercator Océan surface currents forecast. The Mercator
Océan surface currents quality control now combines two methods based on Eulerian and Lagrangian
approaches using the AOML drifters network.
The Lagrangian approach is slightly different from the Eulerian approach. In the Eulerian approach, the
consecutive positions of a drifter are considered as independent buoys recording velocity observations.
Then, these observations are compared to the modeled velocity.
We aim here at studying the trajectory of the buoy along with the trajectory of a modeled buoy which
would drift from the same starting point. The computation of the trajectory of the modeled drifter is made
possible by ARIANE (see ref 1 above)
The algorithm aims at producing the maps on Figure 70. It shows the mean 1-to-5-days distance error –that
is the distance between the modeled trajectory and the observed trajectory- in 1°x1° boxes. The mean D-
days distance error is computed by averaging the D-days distance errors computed for all the drifters that
crossed the box.
The individual points of a trajectory are not independent, merely because the location of a drifter is to a
large extent determined by its former location.
Figure 70: Example of the surface currents Lagrangian quality control algorithm on a global map (bottom panel) and zooms
(upper panels).
Let us consider the example above (Figure 71). The thin grey line represents the trajectory of the drifter on
a daily frequency. The thick ones represent the system drifting, starting from an observed point. Their
colors show the distance to the corresponding point in the observed trajectory after D-days. Considering
only this drifter, if we compute the D-days distance error starting from the t=0 observed point (01/02/2013
in the example), we may compute it again only from the observed point t=D days. This way we may
reasonably assume the two distance errors are uncorrelated and use most of the data (see ref 2 above).

96
Figure 71: Illustration of the surface currents Lagrangian quality control algorithm.
References:
1- Ariane : utility developed at LPO (http://wwz.ifremer.fr/lpo_eng/Produits/Logiciels/ARIANE)
2- Scott, R. B., N. Ferry, M. Drevillon, C. N. Barron, N. C. Jourdain, J.-M. Lellouche, E. J. Metzger, M.-H.
Rio, and O. M. Smedstad, Estimates of surface drifter trajectories in the equatorial Atlantic: A multi-
model ensemble approach, Ocean Dynamics, 62, 1091-1109, 2012, doi:10.1007/s10236-012-0548-
2.

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