QUOVADIS_NUM12_JFM_2013

Quo Va Dis ? Quarterly Ocean Validation Display #12, JFM 2013
August 2013
contact: qualif@mercator-ocean.fr
QuO Va Dis?
Quarterly Ocean Validation Display #12
Validation bulletin for January-February-March (JFM) 2013
Edition:
Charles Desportes, Charly Régnier, Bruno Levier, Coralie Perruche, Lionel Zawadzki, Marie Drévillon,
(MERCATOR OCEAN/Production Dep./Products Quality)
Contributions :
Eric Greiner (CLS)
Jean-Michel Lellouche, Olivier Le Galloudec (MERCATOR OCEAN)
Credits for validation methodology and tools:
Eric Greiner, Mounir Benkiran, Nathalie Verbrugge, Hélène Etienne (CLS)
Fabrice Hernandez, Laurence Crosnier (MERCATOR OCEAN)
Jean-Michel Lellouche, Olivier Le Galloudec, Nicolas Ferry, Gilles Garric (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 January-February-March 2013. 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 BIOMER biogeochemical monitoring and forecasting system are also
displayed and compared with simultaneous observations. New Lagrangian diagnostics are
displayed which measure the quality of the surface velocity forecasts. The latest updates of
the PSY2, PSY3 and PSY4 systems are introduced in section VIII , and illustrated with results
for JFM 2013.

Table of contents
I Executive summary ............................................................................................................4
II Status and evolutions of the systems ................................................................................6
II.1. Short description and current status of the systems .................................................. 6
II.2. Incidents in the course of JFM 2013..........................................................................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...................................................................................12
III.1.3. Sea level anomalies along track .........................................................................12
III.2. Observations available for validation ....................................................................13
IV Information on the large scale climatic conditions..........................................................13
V Accuracy of the products .................................................................................................16
V.1. Data assimilation performance .................................................................................16
V.1.1. Sea surface height..............................................................................................16
V.1.2. Sea surface temperature....................................................................................18
V.1.3. Temperature and salinity profiles......................................................................21
V.2. Accuracy of the daily average products with respect to observations.....................30
V.2.1. T/S profiles observations....................................................................................30
V.2.2. SST Comparisons ................................................................................................39
V.2.3. Drifting buoys velocity measurements (Eulerian comparison)..........................40
V.2.4. Sea ice concentration.........................................................................................43
V.2.5. Closer to the coast with the IBI36V2 system: multiple comparisons ................45
V.2.6. Biogeochemistry validation: ocean colour maps...............................................51
VI Forecast error statistics....................................................................................................53
VI.1. General considerations..........................................................................................53
VI.2. Forecast accuracy: comparisons with T and S observations when and where
available ...............................................................................................................................54
VI.2.1. North Atlantic region......................................................................................54
VI.2.2. Mediterranean Sea.........................................................................................55
VI.2.3. Tropical Oceans, Indian, Global: what system do we choose in JFM 2013?..57
VI.3. Forecast accuracy: skill scores for T and S.............................................................60
VI.4. Forecast accuracy: Lagrangian trajectories forecast errors (NEW!!!) ...................62
VI.5. Forecast verification: comparison with analysis everywhere ...............................63
VII Monitoring of ocean and sea ice physics.........................................................................65
VII.1. Global mean SST and SSS.......................................................................................65
VII.2. Surface EKE.............................................................................................................65
VII.3. Mediterranean outflow .........................................................................................66
VII.4. Sea Ice extent and area..........................................................................................68
VIII Evaluation of the new systems PSY4V2R2, PSY3V3R3 and PSY2V4R4: synthesis
illustrated with JFM 2013 results.............................................................................................69
VIII.1. Introduction ...........................................................................................................69
VIII.2. Water masses.........................................................................................................70
VIII.3. Surface fields..........................................................................................................71

VIII.4. Sea ice ....................................................................................................................73
VIII.5. Conclusion..............................................................................................................74
VIII.6. References .............................................................................................................74
I Annex A ............................................................................................................................75
I.1. Table of figures..........................................................................................................75
II Annex B.............................................................................................................................80
II.1. Maps of regions for data assimilation statistics........................................................80
II.1.1. Tropical and North Atlantic................................................................................80
II.1.2. Mediterranean Sea.............................................................................................81
II.1.3. Global ocean.......................................................................................................82
III Annex C.............................................................................................................................83
III.1. Quality control algorithm for the Mercator Océan drifter data correction (Eric
Greiner) 83
III.2. Algorithm of the Lagrangian verification of the Mercator Océan surface currents
forecast.84

I Executive summary
The Mercator Ocean global 1/12° products, including daily updated forecast and several
scientific updates, are delivered via MyOcean (V3 products) since April 2013. The accuracy of
temperature and salinity analyses and forecast, as well as the quality of currents (surface
and subsurface) and sea ice are significantly improved with respect to the products delivered
before April 2013 (MyOcean V2 products), and thus with respect to the products delivered in
January-February-March 2013 which are evaluated in this document. Several diagnostics and
a whole section of this document (section VIII) introduce the quality improvement of V3
versus V2 with JFM 2013 diagnostics. Further issues of the QuO Va Dis? will only display V3
products.
T & S
The Mercator Ocean global monitoring and forecasting system (MyOcean V2 products) is
evaluated for the period January-February-March 2013. 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). The temperature and salinity forecast have significant skill with respect to
the climatology in most regions of the ocean in the 0-500m layer. Work is in progress to
extract the spatial and temporal scales at which the forecast displays significant skill with
respect to the persistence of the analysis.
Surface fields: SST, SSH, currents
A cold SST (and 3DT) bias of 0.1 °C on average is observed all year long in the high resolution
global at 1/12° (MyOcean V2). The new version of the global 1/12° (MyOcean V3 products)
benefits from several scientific updates which significantly reduce most of the biases
observed in V2.
The monitoring systems are generally very close to altimetric observations (global average of
6 cm residual RMS error). The subsurface currents at the Equator are unrealistic in both
global systems, especially in the warm pools in the western equatorial Pacific and Atlantic.
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. The 1/12° global
currents are slightly closer to drifters’ observations than ¼° global currents, especially in
equatorial countercurrents. Lagrangian metrics are performed with virtual drifters evolving
within Mercator Ocean forecast velocities, seeded at true drifters’ positions. These metrics
show that after a 1-day travel, 80% of the virtual drifters stay within a 25-km distance of the
position of their true drifters’ counterparts.
The Mean Dynamic Topography is updated in the V3 versions of the systems (section VIII).
This reduces the local biases that are currently observed for instance in the Banda Sea,

improves the surface currents, and prevents the degradation of the subsurface currents at
the Equator.
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
The sea ice concentrations are overestimated in the Arctic all year round in the global 1/12°
(unrealistic rheology). The global ¼° sea ice concentrations are realistic but there is still too
much accumulation of ice in the Arctic, especially in the Beaufort Sea. The sea ice
concentration is underestimated in the Barents Sea.
Antarctic sea ice concentration is underestimated in austral winter due to atmospheric
forcing problems. The global 1/12° sea ice concentration is overestimated all year round in
the Antarctic because of rheology problems.
The sea ice is significantly improved in the new V3 systems (section VIII) except for an
overestimation of the seasonal cycle of sea ice.
biogeochemistry
The large scale structures corresponding to specific biogeographic regions (double-gyres,
ACC, etc…) are well reproduced by the global biogeochemical model at 1°. 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, local blooms in the ACC are not captured by the system.

II Status and evolutions of the systems
II.1.Short description and current status of the systems
Table 1 summarizes the main modelling and data assimilation choices made for each of the
systems described below.
PSY3V3 and PSY2V4 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 PSY4V1R3 system is delivering operational products since the beginning of 2010, and
was developed in 2009. It does 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 was performed in March 2012 in all systems mentioned above, 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 in the European seas since March
2012.
The whole Mercator Ocean global analysis and forecasting system (including PSY4, PSY2 and
PSY3) has been updated in April 2013 (MyOcean products version 3). 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: see section VIII.
The IBI36 system is described in QuO Va Dis? #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
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

operational in June 2011. The version IBI36V2 of the system is operated since December
2011 and is very similar to IBI36V1 except it uses realistic river runoffs from SHMI and
Prévimer instead of climatological runoffs.
Figure 1: schematic of the operational forecast scenario for IBI36QV1 (green) and PSY2QV4R2 (blue). Solid
lines are the PSY2V4R2 weekly hindcast and nowcast experiments, and the IBI36V1 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 PSY3V3R1 and PSY3QV3R1 is similar
to PSY2’s scenario. In the case of PSY4V1R3, only weekly hindcast, nowcast and 7-day forecast are
performed.
The BIOMER system is described in QuO Va Dis? #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 PSY3V3R1 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
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
Black Sea SLA files
Weekly 7-day
forecast
Weekly 14-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
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
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
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
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
BIOMER
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.
IBI36V2
upgrade in AMJ 2013
North East
Atlantic and
West
Mediterranean
Sea (Iberian,
Biscay and
Ireland) region
1/36° on the
horizontal, 50
levels on the
vertical
NEATL36 NEMO 2.3 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
PSY2V4R2
PSY2V4R4
and OBC from
PSY2V4R2
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.

11
II.2.Incidents in the course of JFM 2013
Jason-1 has been unavailable from 28/02/2013 to 18/03/2013, then Jason-2 from
25/03/2013 to 05/04/2013 (see Figure 3). The SLA coverage has thus been significantly
reduced during a few weeks. Fortunately Cryosat-2 observations have always been available
during this period, and Jason 1G and Jason 2 have not been in Safe Hold Mode at the same
time.
Figure 3: Weekly SLA coverage combining Jason-2 (black) and Cryosat-2 (grey) altimeters.
Consequences of these absences where quite slight and have been observed only in a few
places and only for SLA, where the level of errors remained acceptable. Current diagnostics
did not exhibit any clear degradation, any loss of quality that could be for certain attributed
to the lack of the 2 altimeters (successively).
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 PSY3V3R1 PSY4V1R3 PSY2V4R2
Min/max number of T
profiles per DA cycle
2800/3700 2800/3700 250/900
Min/max number of S
profiles per DA cycle
2400/2800 2300/2900 250/600
Table 2: minimum and maximum number of observations (orders of magnitude of vertical profiles) of
subsurface temperature and salinity assimilated weekly in JFM 2013 by the Mercator Ocean monitoring and
forecasting systems.
As shown in Table 2 the maximum number of in situ observations is nearly similar to the
previous quarter OND 2012 and statistics are quite stable in time, as shown in Figure 4.

12
Figure 4 : Depth-time diagram of the RMS error with respect to observations of temperature (left column)
and salinity (right column) assimilated each week in PSY3V3R1 during the JFM 2013 quarter.
III.1.2.Sea Surface Temperature
System PSY3V3R1 PSY4V1R3 PSY2V4R2
Min/max number (in 103
)
of SST observations
184/193 182/192 25/26
Table 3: minimum and maximum number (orders of magnitude in thousands) of SST observations (from RTG-
SST) assimilated weekly in JFM 2013 by the Mercator Ocean monitoring and forecasting systems.
RTG-SST is assimilated in PSY3V3R1 and PSY4V1R3, while the Reynolds ¼° “AVHRR only”
product is assimilated in PSY2V4R2 in JFM 2013.
III.1.3.Sea level anomalies along track
As shown in Table 4 the data assimilated this JFM season come from Jason 1 G, Jason 2 and
Cryosat 2.
system PSY3V3R1 PSY4V1R3 PSY2V4R2
) of
Jason 2 SLA observations
15/98 15/98 2/15
) of
Jason 1 G SLA observations
22/105 22/105 3/17
) of
Cryosat 2 SLA observations
81/84 81/84 14/15
Table 4: minimum and maximum number (orders of magnitude in thousands) of SLA observations from Jason
1,2 and Cryosat 2 assimilated weekly in JFM 2013 by the Mercator Ocean monitoring and forecasting
systems.

13
The minimum number of Jason 1G and Jason 2 observations is due to their successive
unavailability periods: see section II.2.
Users may witness side effects of the change of satellite cover, which is now less repetitive
from one week to the other, due to the specific orbits of Jason 1G and Cryosat 2. Some
discontinuities may appear locally, especially if one uses a time series of nowcast analyses.
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 JFM 2013 season:
• T/S profiles from CORIOLIS
• OSTIA SST from UKMO (delays in December)
• 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.
Based on PSY3V3R2 analyses, this chapter summarizes the state of the ocean and
atmosphere during the JFM 2013 season, as discussed in the “Bulletin Climatique Global” of
Météo France.
This JFM season, the eastern Pacific Ocean is cooler than the climatology (Figure 5, upper
panel). At the beginning of the quarter, the equatorial wave guide is cooling on the Eastern
part (in relationship with wave propagation under the surface). Over the Western Tropics
the warm reservoir is refilling. A colder than normal pattern exists in the Southern mid-
latitudes extending up to the central Tropics.

14
Figure 5: Seasonal JFM 2013 temperature anomalies with respect to GLORYS2V1 climatology (1993-2009).
Upper panel: SST anomaly (°C) at the global scale from the 1/4° ocean monitoring and forecasting system
PSY3V3R3. Lower panel heat content anomaly (ρ0Cp∆∆∆∆T, with constant ρ0=1020 kg/m3 ) from the surface to
300m.
In the Atlantic Ocean, the positive anomaly is strengthening over the Gulf of Guinea. The
Indian Ocean is still warmer than normal.
In subsurface (Figure 5, lower panel): heat content anomalies are mostly negative at East
and positive at West in the Pacific Ocean, and positive in the Northern Indian Ocean,
consistently with the T (Figure 6) and SST anomalies, the thermocline (depth of the 20°C
isotherm) anomalies (not shown).

15
Figure 6: Seasonal JFM 2013 temperature anomaly (°C) with respect to GLORYS2V1 climatology (1993-2009),
vertical section 2°S-2°N mean, Pacific Ocean, PSY3V3R3.
As can be seen in Figure 7, during winter the sea ice extent in the Arctic Ocean was near the
historical minimum.
Figure 7: Arctic sea ice extent from the NSIDC

16
V Accuracy of the products
V.1.Data assimilation performance
V.1.1. Sea surface height
V.1.1.1. North Atlantic Ocean and Mediterranean Sea in all systems
The Tropical and North Atlantic Ocean SLA assimilation scores for PSY4V1R3, PSY3V3R1, and
PSY2V4R2 in JFM 2013 are displayed in Figure 8. The different systems reach similar levels of
performance on average. The biases are generally small (less than 2 cm) during this winter
season. Note that prescribed errors are different in PSY2V4R2 and PSY4V1R3 which can
explain different behaviours in spite of identical resolution. PSY2V4R2 assimilates fewer
observations near the coasts, like in the Florida Strait region. Part of the biases can be
attributed to local errors in the current mean dynamical topography (MDT). The RMS errors
are almost identical in all systems, and stay below 10 cm in most regions, except regions of
high mesoscale variability. This JFM 2013 season, the RMS error in the Gulf Stream regions is
larger in PSY3V3R1 than in the high horizontal resolution systems PSY2V4R2 and PSY4V1R3.
Figure 8: Comparison of SLA data assimilation scores (left: average misfit in cm, right: RMS misfit in cm) in
JFM 2013 and between all available Mercator Ocean systems in the Tropical and North Atlantic. The scores
are averaged for all available satellite along track data (Jason 1 G, Jason 2, Cryosat 2). For each region the
bars refer respectively to PSY2V4R2 (cyan), PSY3V3R1 (green), PSY4V1R3 (orange). The geographical location
of regions is displayed in annex A.
In the Mediterranean Sea biases of more than 6 cm are present in PSY2V4R2 in the Adriatic
and Aegean Seas, while it is less than 4 cm in other regions, as can be seen in Figure 9. This

17
bias is generally higher in summer and autumn seasons (from 6 to 8 cm). These regions are
circled by coasts, and consequently few observations are assimilated. The RMS of the
innovation (misfit) of PSY2V4R2 is generally less than 10 cm. The western Mediterranean
exhibits slightly better performance than the eastern Mediterranean. However in the
eastern part of the basin, most of the RMS error is linked with the bias, and thus the
variability is well represented.
The system still shows overall good performance as the RMS of the innovation is generally
lower than the intrinsic variability of the observations in the North Atlantic and
Mediterranean (not shown).
JFM 2013 for PSY2V4R2. The scores are averaged for all available satellite along track data (Jason 1 G, Jason
2, Cryosat 2). See annex B for geographical location of regions.
V.1.1.2. Performance at global scale in PSY3 (1/4°) and PSY4 (1/12°)
As can be seen in Figure 10 the performance of intermediate resolution global PSY3V3R1 and
the performance of high resolution global PSY4V1R3 in terms of SLA assimilation are of the
same order of magnitude. The bias is small except in the “Nino 5” box centred on the Banda
Sea in Indonesia which corresponds to a MDT problem. These problems decrease when
using the MDT updated with GOCE and bias correction (see section VIII). The RMS error
reaches its highest values in the Agulhas and Falkland Currents where the variability is high.

18
JFM 2013 and between all available global Mercator Ocean systems in all basins but the Atlantic and
Mediterranean: PSY3V3R1 (green) and PSY4V1R3 (orange). The scores are averaged for all available along
track satellite data (Jason 1 G, Jason 2, Cryosat 2). The geographical location of regions is displayed in annex
B.
V.1.2. Sea surface temperature
V.1.2.1. North and Tropical Atlantic Ocean and Mediterranean Sea in all
systems
In the Atlantic the three systems display different regional behaviours in terms of SST bias as
illustrated in Figure 11. A cold bias of around 0.1 to 0.3°C is usually diagnosed in most
regions. The bias is generally larger in PSY4V1R3 than in PSY2V4R2 and PSY4V1R3. A warm
bias appears in the Gulf Stream region that could be due to a bad positioning of this warm
current (a bit too shifted northerly), visible on SST maps (see Figure 33). For the rms error,
the accuracy of the mesoscale activity and positions of the meanders around 60°W may be
the main explanations. It is noteworthy that PSY3V3R1 assimilates RTG SST products, known
to be of lower quality in the northern most regions than the Reynolds AVHRR product which
is assimilated in PSY2V4R2. In the Dakar region, the upwelling is underestimated. Note that
as for SLA, prescribed SST errors are higher in PSY2V4R2 within 50km off the coast.

19
Figure 11: Comparison of RTG-SST data assimilation scores (left: average misfit in °C, right: RMS misfit in °C)
in JFM 2013 and between all available Mercator Ocean systems in the Tropical and North Atlantic: PSY4V1R3
(orange), PSY3V3R1 (green). In cyan: Reynolds ¼°AVHRR-AMSR-E data assimilation scores for PSY2V4R2. The
geographical location of regions is displayed in annex B.
Figure 12: Comparison of SST data assimilation scores (left: average misfit in °C, right: RMS misfit in °C) in
JFM 2013 for each region for PSY2V4R2 (comparison with Reynolds ¼° AVHRR-AMSR). The geographical
location of regions is displayed in annex B.

20
The Mediterranean regions are weakly biased this season, apart from central basin that
displays a warm bias of 0.1°C on average (Figure 12). The RMS error is generally lower than
0.5°C. As in SLA, the performance of PSY2V4R2 is lower in the Adriatic Sea.
V.1.2.2. Performance at global scale in PSY3 (1/4°) and PSY4 (1/12°)
PSY4V1R3 exhibits a cold bias at the global scale this JFM season of about 0.1°C to 0.3°C. In
general PSY3V3R1 performs better than PSY4V1R3 (Figure 13). Nevertheless PSY4V1R3
performs better than PSY3V3R1 in the southern Hemisphere and Indian basin, due to a
seasonal cold bias of PSY3V3R1 in the summer hemisphere. The RMS error is of the same
order of magnitude for both systems.
Figure 13: Comparison of RTG-SST data assimilation scores (left: average misfit in °C, right: RMS misfit in °C)
in JFM 2013 and between all available global Mercator Ocean systems in all basins but the Atlantic and
Mediterranean: PSY3V3R1 (green) and PSY4V1R3 (orange). See annex B for geographical location of regions.

21
V.1.3. Temperature and salinity profiles
V.1.3.1. Methodology
All systems innovation (observation – model first guess) profiles 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 JFM 2013. 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. North Pacific gyre (global systems)
Figure 14: Profiles of JFM 2013 innovations of temperature (°C, left panel) and salinity (psu, right panel),
mean (solid line) and RMS (dotted line) for PSY3V3R1 in red and PSY4V1R3 in blue in North Pacific gyre
region. The geographical location of regions is displayed in annex B.
As can be seen in Figure 14, the ¼° global PSY3V3R1 benefits from bias correction but it is
too warm near 100 m (up to 0.25°C), due to mixing problems. PSY4V1R3 is too cold between
200 and 500 m and near 900 m. It is too salty between 0 m and 600 m (0.05 psu) while it is
fresher than observations between 600 m and 1200 m (see QuO Va Dis? #8 for a special
focus on this bias).

22
V.1.3.1.2. South Atlantic Gyre (global systems)
mean (solid line) and RMS (dotted line) for PSY3V3R1 in red and PSY4V1R3 in blue in South Atlantic gyre
In this region a large cold bias (up to 0.7 °C) is present in PSY4V1R3 between 0 and 800 m,
while PSY3V3R1 experiments a small cold bias (around 0.15 °C) at the surface and a warm
bias of similar amplitude near 150 m. PSY4V1R3 experiments a fresh bias on average which
reaches a maximum of 0.15 psu near 300 m. This region illustrates well that PSY3V3R1 is
closer to subsurface in situ observations than PSY4V1R3 thanks to bias correction.
V.1.3.1.3. Indian Ocean (global systems)
In the Indian Ocean under 800 m, PSY3V3R1 is clearly closer to the observations than
PSY4V1R3 in Figure 16. This is again due to the application of a bias correction in PSY3V3R1.
From 0 to 800 m PSY3V3R1 is less biased on average than PSY4V1R3, but it is nevertheless
saltier (0.1 psu) and colder (0.3°C) than the observations at the surface. The most significant
biases appear in PSY4V1R3 between 50 m and 150 m (cold and salty bias), and near 700 m
where PSY4V1R3 is too warm and salty (0.2°C and 0.05 psu).

23
mean (solid line) and RMS (dotted line) for PSY3V3R1 (in red) and PSY4V1R3 (in blue) in the Indian Ocean
V.1.3.2. Tropical and North Atlantic Ocean (all systems)
The regional high resolution system (PSY2V4R2) and the global 1/4° PSY3V3R1 generally
exhibit a better average performance than the global 1/12° PSY4V1R3 in the North Atlantic
in JFM 2013, again due to uncorrected biases in the PSY4V1R3 system. It is the case for the
temperature and salinity in the North Madeira region as illustrated in Figure 17. PSY2V4R2 is
still too warm in the 0-600m layer (up to 0.1°C at 100m) but the bias is far reduced with
respect to previous season (0.4°C). Biases are present in PSY4V1R3 between 1000m and
1500m, at the location of the Mediterranean outflow. The bias correction improves the
results of PSY3V3R1 and PSY2V4R2 between 800 m and 2000 m with respect to PSY4V1R3.
Mediterranean waters are too warm and salty near 800 m in PSY2V4R2, and under. We note
that PSY2V4R2 is warmer than PSY3V3R1 on most of the water column. PSY3V3R1 appears
to be slightly less biased than PSY2V4R2, while the variability is better represented in
PSY2V4R2 than in PSY3V3R1 (more bias but slightly less RMS error in PSY2 than in PSY3).

24
mean (solid line) and RMS (dotted line) for PSY4V1R3 in blue, PSY3V3R1 in red, and PSY2V4R2 in yellow in
North Madeira region. The geographical location of regions is displayed in annex B.
Dakar region. The geographical location of regions is displayed in annex B.

25
The upwelling is not well represented by any of the systems In the Dakar region (Figure 18):
it is generally too weak resulting in a warm (1°C) and salty (01 psu) bias between 50 m and
150 m this JFM season.
In the Gulf Stream region (Figure 19) all systems display similar levels of error and this winter
season the scores are significantly better than the past season. PSY2V4R2 is less biased than
the other systems and it shows the best scores in temperature in the 300-800m layer.
PSY3V3R1 is better above 100m layer, both in salinity and temperature, while PSY3V3R1 and
PSY4V1R3 are too cold and fresh (0.1 to 0.5°C, 0.1 psu). Under 100m, PSY3V3R1 and
PSY4V1R3 are too cold (0.3 to 0.5°C).
Gulf Stream 2 region. The geographical location of regions is displayed in annex B.
The Cape Verde region is characteristic of the subtropical gyre in the North Atlantic where all
systems stay close to the temperature and salinity profiles on average, as can be seen in
Figure 20. The highest errors are located near the thermocline and halocline. As in many
regions, the global high resolution system with no bias correction PSY4V1R3 is too cold from
the surface to 700m. PSY4V1R3 is not stratified enough, as it is too salty in the 0-150 m layer
and then it is too fresh in the 150-700 m layer.

26
Cape Verde region. The geographical location of regions is displayed in annex B.
Sao Tome region. The geographical location of regions is displayed in annex B.

27
Usually few profiles are sampled in the small area of the Sao Tome tide region (typically, not
more than 4 profiles are assimilated per week). As can be seen in Figure 21 the systems have
difficulties in reproducing the undercurrents in this region as a small number of profiles are
available to constrain the water masses. The bias correction partly solves this problem in
PSY2V4R2 and PSY3V3R1.
V.1.3.1. Mediterranean Sea (high resolution regional systems at 1/12°)
In the Mediterranean Sea the high resolution is mandatory to obtain good level of
performance. Only PSY2V4R2 with bias correction is displayed as it has the best level of
performance on this zone. We note in Figure 22 that the system displays a cold bias near the
surface and then a warm bias (0.1°C) with a peak at around 100 m in the Algerian region.
This bias was much stronger (0.5°C) during the previous seasons: it is present in most
Mediterranean regions in summer and autumn. In most regions a fresh bias can be detected
between 0 and 200 m. It reaches 0.15 psu in the Algerian region, where there is also a strong
salty bias at the surface this season. The fresh bias is consistent with errors in the positioning
of the separation between the Atlantic Inflow and the Levantine intermediate waters. Biases
with similar feature but with smaller amplitudes can be observed in the Gulf of Lion (Figure
23).
.
Figure 22: Profiles of JFM 2013 mean (cyan) and RMS (yellow) innovations of temperature (°C, left panel) and
salinity (psu, right panel) in the Algerian region. The geographical location of regions is displayed in annex B.

28
salinity (psu, right panel) in the Gulf of Lion region. The geographical location of regions is displayed in annex
B.
salinity (psu, right panel) in the Rhodes region. The geographical location of regions is displayed in annex B.

29
In the Rhodes region (Eastern Mediterranean basin) the strong biases of the previous
season, linked to underestimated stratification, hardly appear during this winter season. A
fresh and cold bias remains at surface (0.1°C, 0.1 psu).
Summary: While most of the deep biases disappear in the systems including bias correction,
seasonal biases remain. One of the hypotheses is that the SST assimilation is not as efficient
as it used to be. The Incremental Analysis Update together with the bulk formulation rejects
part of the increment. There is too much mixing in the surface layer inducing a cold (and
salty) bias in surface and warm (and fresh) bias in subsurface. The 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. A correction of air-sea fluxes depending on the SST increment is
considered for future versions of the system (see section VIII). The use of Reynolds ¼° L4 SST
product (AVHRR AMSR-E) for data assimilation reduces part of the surface bias in the North
Atlantic and changes the signal in the Mediterranean. The use of Reynolds ¼° AVHRR
analyses is extended to the other Mercator Ocean systems starting in AMJ 2013 (see section
VIII).
The PSY2V4R2 system is different from the other systems:
• Update of the MDT with GOCE and bias correction
• Assimilation of Reynolds ¼° AVHRR-AMSRE SST observations instead of ½° RTG-SST
• Increase of observation error for the assimilation of SLA near the coast and on the
shelves, and for the assimilation of SST near the coast
• Modification of the correlation/influence radii for the analysis specifically near the
European coast.
• Restart from October 2009 from WOA05 climatology
In PSY2V4R2:
• The products are less constrained by altimetry near the coast and on the shelves but are
generally closer to in situ observations and climatologies in these regions
• The quality is slightly degraded in the Eastern Mediterranean and in the Caribbean region
In PSY4V1R3:
A strong salinity bias (PSY4V1R3 is too salty near 100 m) is present in the North Pacific
(Alaska Gyre) and alters the global statistics.

30
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 JFM 2013
Figure 25: RMS temperature (°C) difference (model-observation) in JFM 2013 between all available T/S
observations from the Coriolis database and the daily average hindcast PSY3V3R1 products on the left and
hindcast PSY4V1R3 on the right column colocalised with the observations. Averages are performed in the 0-
50m layer (upper panel) and in the 0-500m layer (lower panel). 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 25, in both PSY3V3R1 and PSY4V1R3 temperature errors in the 0-
500m layer stand between 0.5 and 1°C in most regions of the globe. 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). PSY4V1R3 has higher
variability and no bias correction and thus departures from the observations (up to more
than 0.5°C) are higher than in PSY3V3R1 (up to 0.3 °C) on average in these regions.
PSY3V3R1 seems to perform better than PSY4V1R3 in the tropical Pacific but both systems
have cold temperature biases in the Eastern part of the Pacific basin at the surface (in the 0-
50m layer) and in the western part of the Pacific basin in the 0-500m layer (warm pool). The

31
cold bias often reaches 1°C in PSY4V1R3, while it reaches locally 0.5°C in PSY3V3R1 (not
shown).
Figure 26: RMS salinity (psu) difference (model-observation) in JFM 2013 between all available T/S
hindcast PSY4V1R3 on the right column, colocalised with the observations. Averages are performed in the 0-
50m layer (upper panel) and in the 0-500m layer (lower panel). The size of the pixel is proportional to the
number of observations used to compute the RMS in 2°x2° boxes.
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. The salinity error is generally less in PSY3V3R1
than in PSY4V1R3 for instance here in the North Pacific gyre (where a salty bias develops as
already mentioned), the Indian Ocean, the South Atlantic Ocean (Zapiola eddy) or the
Western Pacific Ocean. Precipitations are overestimated in the tropical band, leading to a
fresh bias in this region.

32
Figure 27 : JFM 2013 global statistics of temperature (°C, left column) and salinity (psu, right column)
averaged in 6 consecutive layers from 0 to 5000m. RMS difference (upper panel) and mean difference
(observation-model, lower panel) between all available T/S observations from the Coriolis database and the
daily average hindcast products PSY3V3R1 (red), PSY3V3R3 (new, pink), PSY4V1R3 (blue), PSY4V2R2 (new,
cyan) 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.
For the global region in Figure 27, statistics for the new systems (PSY3V3R3 and PSY4V2R2)
are included. For further details about their performances, one can refer to section VIII.
The intermediate resolution model (PSY3V3R1) is generally more accurate than the high
resolution model (PSY4V1R3) in terms of RMS and mean difference for both temperature
and salinity mainly thanks to the bias correction which is applied in PSY3V3R1 and not yet in
PSY4V1R3. The effects of this correction are on the whole water column for temperature
and salinity. With the new systems that both benefit from the bias correction, the difference
in performance tends to be reduced. PSY3V3R3 is slightly better in term of global RMS error
thanks to the lower resolution.

33
Both global “current” systems are too cold on the whole water column, PSY3V3R1 being
significantly closer to the observations than PSY4V1R3. PSY3V3R1 and PSY4V1R3 are globally
too salty in the 0-100 m layer and 5-800 m layer respectively. At the surface PSY3V3R1
exhibits a salty bias while all other systems are too fresh on average. Mean errors are larger
for new systems but local departures are stronger in “current” systems (not shown); RMS
errors are lower in new systems. In PSY4V1R3 the fresh bias mostly comes from the tropical
belt (not shown). All systems are more accurate than the WOA09 climatology (Levitus 2009).
Figure 28: RMS difference (model-observation) of temperature (upper panel, °C) and salinity (lower panel,
psu) in JFM 2013 between all available T/S observations from the Coriolis database and the daily average
PSY2V4R2 hindcast products colocalised with the observations in the 0-50m layer (left column) and 0-500m
layer (right column).
The general performance of PSY2V4R2 (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 Algerian
Sea, the Gulf of Guinea, the Caribbean Sea, the Labrador Sea and the Gulf of Mexico. In the
eastern tropical Atlantic biases concentrate in the 0-50m layer (cold and fresh bias), while in
the Western tropical Atlantic the whole 0-500m layer is biased (not shown). This is
consistent with the bias correction not working in the mixed layer.

34
V.2.1.2. Water masses diagnostics

35
Figure 29: Water masses (Theta, S) diagrams in the Bay of Biscay (upper panel), Gulf of Lion (second panel),
Irminger Sea (third panel) and Baltic Sea (upper panel), comparison between PSY3V3R1 (left column),
PSY4V1R3 (middle column) and PSY2V4R2 (right column) in JFM 2013. 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.
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. Warmer (less dense)
waters are slightly better represented by PSY2V4R2 but the three systems generally
miss them during this JFM season.
- The “bias corrected” systems PSY3V3R1 and PSY2V4R2 better represent the
Mediterranean Water characterized by high salinities (Salinities near 36psu) and
relatively high temperatures (Temperatures near 10°C).
- Between 4°C and 7°C, 35.0 and 35.5 psu the freshest waters of the Labrador Sea are
slightly better represented in PSY4V1R3 than in PSY2V4R2 and PSY3V3R1.
In the Gulf of Lion:
- The Levantine Intermediate Water (salinity maximum near 38.6 psu and 13.6°C) is
too fresh in all systems this JFM season. PSY4V1R3 intermediate waters are the
freshest of all systems, and PSY2V4R2 is the best performing system.
In the Irminger Sea:
- The North Atlantic Water (T > 7°C and S > 35.1 psu) is well represented by PSY3V3R1
and PSY2V4R2 but missed by PSY4V1R3.
- The Irminger Sea Water (≈ 4°C and 35 psu) is too salty and warm in the three systems
but PSY2V4R2 and PSY3V3R1 seem to be better than the global 1/12° PSY4V1R3.
- Waters colder than 4°C and ≈ 34.9 psu (Iceland Scotland Overflow waters) are too
fresh in all systems.
In the Gulf of Cadiz:
- The Mediterranean waters (T around 10°C) are quite well represented, but the three
systems miss the saltiest water, especially PSY4V1R3. PSY2V4R2 is the best system in
reproducing the spread.

36
In the western tropical Atlantic and in the Gulf of Guinea the water masses are well
represented by all systems (see Figure 30). Only PSY3V3R1 represents well the subsurface
salinity maximum between the isopycn 24 and 26 (South Atlantic Subtropical waters).
In the eastern tropical Atlantic both global systems capture the subsurface salinity
maximum, while PSY2V4R2 waters are too fresh.
Figure 30 : Water masses (T, S) diagrams in the Western Tropical Atlantic (upper panel) and in the Eastern
Tropical Atlantic (lower panel): for PSY3V3R1 (left); PSY4V1R3 (middle); and PSY2V4R2 (right) in JFM 2013.
PSY2, PSY3 and PSY4: yellow dots; Levitus WOA09 climatology; red dots, in situ observations: blue dots.
In the Agulhas current and Kuroshio Current (Figure 31) PSY3V3R1 and PSY4V1R3 give a
realistic description of water masses. In general, 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. This is especially true at the surface in the highly energetic regions of the
Agulhas and of the Gulf Stream.
In the Gulf Stream region, models are too salty from the ‘27’ to the ‘28’ isopycn, where they
miss the cold and fresh waters of the Labrador Current.

37

38
Figure 31: Water masses (T, S) diagrams in South Africa, Kuroshio, and Gulf Stream region (respectively from
top to bottom): for PSY3V3R1 (left); PSY4V1R3 (right) in JFM 2013. PSY3 and PSY4: yellow dots; Levitus
WOA09 climatology: red dots; in situ observations: blue dots.

39
V.2.2. SST Comparisons
Figure 32 : RMS temperature (°C) differences between OSTIA daily analyses and PSY3V3R1 daily analyses
(upper left); between OSTIA and PSY4V1R3 (upper right), between OSTIA and PSY2V4R2 (lower left), and
between OSTIA and RTG daily analyses (lower right). The Mercator Océan analyses are colocalised with the
satellite observations analyses.
Quarterly average SST differences with OSTIA analyses show that in the subtropical gyres the
SST is very close to OSTIA, with difference values staying below the observation error of 0.5
°C on average. High RMS difference values are encountered in high spatial and temporal
variability regions such as the Gulf Stream or the Kuroshio. The stronger is the intrinsic
variability of the model (the higher the resolution), the stronger is the RMS difference with
OSTIA. The strong regional biases that are diagnosed in summer in the PSY3V3R1 global
system in the North Pacific (see QuO Va Dis?#6) disappear in winter (Figure 33). In the
southern (summer) hemisphere, biases appear mostly in the Indian Ocean, in the South
Pacific off New Zealand and in the South Atlantic Ocean. Strong differences can be detected
near the sea ice limit in the Arctic in all the systems particularly in the Labrador Sea and in
the Barents Sea for the global systems. Part of this disagreement with the OSTIA analysis can
be attributed to the assimilation of RTG SST in PSY3V3R1 and PSY4V1R3, while Reynolds ¼°
AVHRR only is assimilated in PSY2V4R2. These products display better performance than RTG
SST2
especially in the high latitudes3
(see also section VIII). Focusing on the discrepancies in
2
http://www.star.nesdis.noaa.gov/sod/sst/squam/index.html
3
Guinehut, S.: Validation of different SST products using Argo dataset, CLS, Toulouse, Report CLS-
DOS-NT-10-264, 42 pp., 2010.

40
the Labrador sea, in the Greenland and Barents seas, we precise that no ice mask was
applied for these comparisons, which will be done in the next QuO Va Dis? issues.
Figure 33: Mean SST (°C) daily differences between OSTIA daily analyses and PSY3V3R1 daily analyses (upper
left), between OSTIA and RTG daily analyses (upper right) and between OSTIA and Reynolds ¼° AVHRR daily
analyses (lower left).
V.2.3. Drifting buoys velocity measurements (Eulerian comparison)
Recent studies (Law Chune, 20124
, Drévillon et al, 20125
) - 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 SVP drifters
velocities combines two methods based on Eulerian and Lagrangian approaches. The
Eulerian meethod used in this section compares Mercator Ocean analyses with velocities
deduced from the SVP floats trajectories. The Lagrangian approach compares trajectory
4
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
5
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)

41
forecasts with true SVP trajectories, and results are displayed in the forecast verification
section (VI.4).
Figure 34: Comparison between modelled zonal current (left panel) and zonal current from drifters (right
panel) in m/s. In the left column: velocities collocated with drifter positions in JFM 2013 for PSY3V3R1 (upper
panel), PSY4V1R3 (middle panel) and PSY2V4R2 (bottom panel). In the right column, zonal current from
drifters in JFM 2013 (upper panel) at global scale, AOML drifter climatology for JFM with new drogue

42
correction from Lumpkin & al, in preparation (middle) and zonal current in JFM 2013 from drifters (lower
panel) at regional scale.
Figure 35 : In JFM 2013, comparison of the mean relative velocity error between in situ AOML drifters and
model data on the left side and mean zonal velocity bias between in situ AOML drifters with Mercator Océan
correction (see text) and model data on the right side. Upper panel: PSY3V3R1, middle panel: PSY4V1R3,
bottom panel: PSY2V4R2. NB: zoom at 500% 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 35) at 15 m depth and the meridional
velocity (not shown) is more consistent with the observations for the JFM 2013 period.

43
The main differences between the systems appear in the North Atlantic and North Pacific
Oceans. In these regions PSY3V3R1 underestimates on average the eastward currents, which
is a bit less pronounced in the high resolution systems PSY4V1R3 and PSY2V4R2. On the
contrary the systems overestimate the equatorial westward currents on average, and this
bias is less pronounced in PSY4V1R3 than in PSY3V3R1 this JFM season.
On average over longer periods, the usual behaviour compared to drifters’ velocities is that
PSY4V1R3 and PSY3V3R1 underestimate the surface velocity in the mid latitudes. All systems
overestimate the Equatorial currents and 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).
V.2.4. Sea ice concentration
Figure 36: Comparison of the sea ice cover fraction mean for JFM 2013 for PSY3V3R1 in the Arctic (upper
panel) and in the Antarctic (lower panel), for each panel the model is on the left, the mean of Cersat dataset
in the middle and the difference on the right.
In JFM 2013 the PSY3V3R1 Arctic sea ice fraction is in agreement with the observations on
average. The relatively small discrepancies inside the sea ice pack will not be considered as

44
significant as the sea ice concentration observations over 95% are not reliable. Strong
discrepancies with observed concentration remain in the marginal seas mainly in the North
Atlantic Ocean side of the Arctic, especially in the Fram strait and the Barents Sea this JFM
2013 season (Figure 36).
Model studies show that the overestimation in the Canadian Archipelago is first due to badly
resolved sea ice circulation (should be improved with higher horizontal resolution). 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.
The calibration on years 2007 to 2009 has shown that the PSY3V3R1 system tends to melt
too much ice during the summer, while the winter sea ice covers are much more realistic in
PSY3V3R1 than in previous versions of PSY3. See Figure 60 for monthly averages time series
over the last 12 months. On the contrary PSY4V1R3 sea ice cover is unrealistic
(overestimation throughout the year) due to the use of a previous version of LIM2 and daily
atmospheric forcings.
panel) and in the Antarctic (lower panel), for each panel the model is on the left, the mean of Cersat dataset
in the middle and the difference on the right.
As expected in the Antarctic during the austral winter the sea ice concentration is
underestimated everywhere in PSY3V3R1 and overestimated in PSY4V1R3, especially near
the coasts for instance in the south of the Ross Sea , in the Weddel Sea, Bellinghausen and
Admundsen Seas and along the Eastern coast.

45
Figure 38: JFM 2013 Arctic sea ice extent in PSY3V3R1 with overimposed climatological JFM 1992-2010 sea
ice fraction (magenta line, > 15% ice concentration) (left) and NSIDC map of the sea ice extent in the Arctic
for March 2013 in comparison with a 1979-2000 median extend (right).
Figure 38 illustrates the fact that sea ice cover in JFM 2013 is less than the past years
climatology, especially in the Barents Sea, even with a slight underestimation in PSY3V3R1 in
this region in JFM 2013. In the Antarctic the model bias prevents us from commenting the
climate signal (not shown).
V.2.5. Closer to the coast with the IBI36V2 system: multiple comparisons
V.2.5.1. Comparisons with SST from CMS
Figure 39 displays bias, RMS error and correlation calculated from comparisons with SST
measured by satellite (Météo-France CMS high resolution SST at 0.02°). The biases are
reduced in winter, as expected, with respect to the summer and autumn season. One can
notice spots of maximum bias (and RMS error) along the plateau des Landes and in the
Norwegian current. The situation is similar to winter 2012, with weak correlation in the
abyssal plain west of the domain. This season is poor in observations, even more than in
winter 2012 (the average number of observations is 26 days per cell, 76 days for the
maximum, while in was respectively 33 and 87 in 2012).

46
Figure 39 : Comparisons (observation-model) between IBI36V2 and analyzed SST from MF_CMS for the JFM
2013 period. From the left to the right: mean bias, RMS error, correlation, number of observations
V.2.5.2. Comparisons with in situ data from EN3/ENSEMBLE for JFM 2013
Averaged temperature profiles (Figure 40) show that the model is close to the observations
and to the models PSY2V4R2 and PSY2V4R4 in the whole water column. In the Bay of Biscay,
the strongest mean and RMS error are observed between 800 and 1600 m depth: the
Mediterranean waters are significantly too warm. Deeper than 1200 m, PSY2V4R4 is closer
to the observations than PSY2V4R2 (and IBI36V2). Between 10 and 50 m depth, IBI36V2 is
slightly closer to the observations than the PSY2 models.
Temperature, 0-200 m Temperature, 0-2000 m

47
Temperature, 0-200 m Temperature, 0-2000 m
Figure 40 : For IBI36V2: On the left: mean “model - observation” temperature (°C) bias (red curve) and RMS
error (blue curve) in JFM 2013, On the right: mean profile of the model (black curve) and of the observations
(red curve) in JFM 2013. In the lower right corner: position of the profiles. Top panel: the whole domain;
bottom panel: the Bay of Biscay region.
The maximum salinity bias and RMS error (Figure 41) occur near the surface. The model is
too fresh near the surface. Below 100 m depth, the bias is almost zero. The RMS error is
strong at the surface and Mediterranean Sea Water level (as for temperature). In the Bay of
Biscay the surface waters and Mediterranean waters are too salty. PSY2V4R4 performs
better than the other models.
Note: averaged profiles are discontinuous because the number of observations varies with
depth.
Salinity, 0-200 m Salinity, 0-2000 m

48
Salinity, 0-200 m Salinity, 0-2000 m
Figure 41: For IBI36V2: On the left: mean “model - observation” salinity (psu) bias (red curve) and RMS error
(blue curve) in JFM 2013, On the right: mean profile of the model (black curve) and of the observations (red
curve) in JFM 2013. In the lower right corner: position of the profiles. Top panel: the whole domain; bottom
panel: the Bay of Biscay region.
V.2.5.3. MLD Comparisons with in situ data
Figure 42 shows that the distribution of modeled mixed layer depths among the available
profiles is close to the observed distribution. Only few observations are available in the Bay
of Biscay this quarter, so we display only the whole domain. IBI36V2 is slightly closer to the
observations in the 0-100 m depth range; PSY2V4R4 performs slightly better than PSY2V4R2.

49
Figure 42 : For IBI36V2 (upper panel): Mixed Layer Depth distribution in JFM 2013 calculated from profiles
with the temperature criteria (difference of 0.2°C with the surface); the model is in grey, the observations in
red. Lower panel: PSY2V4R2 (left), PSY2V4R4 (right).
V.2.5.4. Comparisons with moorings and tide gauges
Figure 43 : For IBI36V2: RMS error (cm) and correlation for the non-tidal Sea Surface Elevation at tide gauges
in JFM 2013, for different regions and frequencies.
The RMS error of the residual elevation of sea surface (Figure 43) computed with a harmonic
decomposition method (Foreman 1977) and a Loess low-pass filtering, is comprised between
3 and 13 cm. The smallest errors occur in the Canary Islands, west Iberian coast and
Mediterranean Sea regions. The largest errors occur in the Channel, Bay of Biscay and Irish
Sea regions. The RMS decreases for some frequency bands, and the smallest values occur in

50
the 1-10-day or 30-∞-day band. In comparison to the OND 2012 period, the RMS is almost
the same in all regions, except in the Bay of Biscay where it is smaller. The correlation is
significant at all frequencies, and reach high values for periods lower than 30 days (at high
frequencies).
In Figure 44 we can see that the SST correlations between the coastal moorings and the IBI
model are generally good for the first two months in nearly the whole domain. The biases
and RMS errors are generally small in this season (smaller then 0.5°C).

51
Figure 44 : For IBI36V2: Bias (observation-model), RMS error (°C) and correlation of the Sea Surface
Temperature between IBI model and moorings measurements in October (upper panel), November (middle
panel) and December 2012 (lower panel).
V.2.6. Biogeochemistry validation: ocean colour maps

52
Figure 45 : Chlorophyll-a concentration (mg/m
3
) for the Mercator system BIOMER (left panels) and
Chlorophyll-a concentration from Globcolour (right panels). The upper panel is for January, the medium
panel is for February and the bottom panel is for March 2013.
As can be seen on Figure 45 the surface chlorophyll-a (Chl-a) concentration is overestimated
by BIOMER on average over the globe. The production is especially overestimated in the
Pacific and Atlantic tropical band. On the contrary near the coast BIOMER displays
significantly lower chlorophyll concentrations than Globcolour ocean colour maps and
especially at Eastern Boundary Upwelling Systems. Figure 46 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 observed near the coast in Globcolour.
In the Antarctic, near the Scotia Sea south of the Argentine basin, a bloom appears in
January and February that is not captured by the BIOMER system.
Figure 46 : Probability Density Function (PDF) of Chl-a bias in log scale (log10(obs)-log10(model)) in North
Atlantic (30-70N; 80W:20E)
The discrepancies at global scale appear in the RMS differences for the mean JFM season
(Figure 47).

53
Figure 47 : RMS difference between BIOMER and Globcolour Chl-a concentrations (mg/m
3
) in JFM 2013.
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 48). 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 48: Schematic of the change in atmospheric forcings applied along the 14-day ocean forecast.

54
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 49 the PSY2V4R2 products have a better accuracy than the
climatology in the North Atlantic region in JFM 2013 (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).
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 49 and MED region Figure 50). The biases in temperature and salinity are
generally small (of the order of 0.1 °C and 0.02 psu) compared to the climatology’s biases (of
the order of 0.4 °C and 0.05 psu).

55
Figure 49: Accuracy intercomparison in the North Atlantic region for PSY2V4R2 in temperature (left panel)
and salinity (right panel) between hindcast, nowcast, 3-day and 6-day forecast and WO09 climatology.
Accuracy is measured by a mean difference (upper panel) and by a rms difference (lower panel) 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 JFM 2013 period. NB: average on
model levels is performed as an intermediate step which reduces the artefacts of inhomogeneous density of
observations on the vertical.
VI.2.2.Mediterranean Sea
In the Mediterranean Sea in JFM 2013 (Figure 50) the PSY2V4R2 products are more accurate
than the climatology on average. PSY2V4R2 is biased at the surface (fresh and cold bias).
Between 5 and 100m the system is generally too warm and fresh.

56
Figure 50: Accuracy intercomparison in the Mediterranean Sea region for PSY2V4R2 in temperature (°C, left
column) and salinity (psu, right column) between hindcast, nowcast, 3-day and 6-day forecast and WO09
climatology. Accuracy is measured by a rms difference (lower panel) and by a mean difference (upper panel)
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 JFM 2013 period. NB: average on model levels is performed
as an intermediate step which reduces the artefacts of inhomogeneous density of observations on the
vertical.

57
VI.2.3.Tropical Oceans, Indian, Global: what system do we choose in JFM
2013?
In this section, the scores of the new systems PSY4V2R2, PSY3V3R3 and PSY2V4R4 are
displayed in addition to the scores of the current systems PSY4V1R3, PSY3V3R1 and
PSY2V4R2. For the current systems, available in JFM 2013, PSY3V3R1 and PSY2V4R2 display
similar accuracy levels, PSY3V3R1 being slightly more accurate than PSY2V4R2 over 300 m.
PSY4V1R3 has no bias correction and thus displays poorer scores than PSY2V4R2 and
PSY3V3R1. We also note that at all depth in all regions the PSY3V3R1 RMS error increases
with forecast range, as could be expected, and that the 6-day forecast still beats the
climatology.
Now looking at the scores of the new systems (all bias corrected), one can note that all the
systems display similar accuracy levels. PSY3 is still the more accurate system between 5 and
300 m in the Tropical oceans, probably in link with representativity considerations (the
current T/S observation network is too coarse to constrain the 1/12° system).

58

59
Figure 51: same as Figure 49 but for RMS statistics and for temperature (°C), PSY3V3R1 and PSY4V1R3
systems and the Tropical Atlantic (TAT), the Tropical Pacific (TPA) and the Indian Ocean (IND). The global
statistics (GLO) are also shown for temperature and salinity (psu). The right column compares the analysis of
the global ¼° PSY3V3R1 and PSY3V3R3 (new, pink) with the analysis of the global 1/12° PSY4V1R3 and
PSY4V2R2 (new, cyan).

60
VI.3. Forecast accuracy: skill scores for T and S
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.
Figure 52 : Temperature (left) and salinity (right) skill scores in 4°x4° bins and in the 0-500m layer in JFM
2013, illustrating the ability of the 3-days forecast to be closer to in situ observations than a reference state
(climatology or persistence of the analysis, see Equation 1). Yellow to red values indicate that the forecast is
more accurate than the reference. Here the reference value is the WOA05 climatology. Upper panel:
PSY3V3R1; lower panel: PSY4V1R3.
( )
( )∑ ∑
∑ ∑
= =
= =






−






−
−= 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

61
Figure 53: As Figure 53 but the reference is the persistence of the analysis. Temperature (left column) and
salinity (right column) skill scores are displayed, for PSY3V3R1 (upper panel), PSY4V1R3 (middle panel), and
PSY2V4R2 (lower panel).
The Skill Score displayed Figure 52 show the added value of PSY3V3R1 forecast with respect
to the climatology. 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 (Figure 53), the result is noisier and the systems 3-day forecast seems to
have skill in some regions in particular: North East Atlantic, central pacific, Indian basin and
Tropical Atlantic. 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. As expected PSY4V1R3 displays less forecast skill than the other
systems with respect to the climatology, at least in terms of water masses (forecast skills
with respect to other types of observations have to be computed in the future). This is

62
especially the case in the Antarctic near the sea ice limit, in the Bering Sea, in the Zapiola
anticyclone and in the Caribbean Sea.
VI.4. Forecast accuracy: Lagrangian trajectories forecast errors
(NEW!!!)
Figure 54: In JFM 2013, comparison of the mean distance error in 1°x1° boxes between AOML drifters
trajectories and PSY3V3R1 trajectories on the left side and AOML drifters trajectories and PSY4V1R3 on the
right side. Upper panel: Mean distance error after a 1-day drift, middle panel: Mean distance error after a 3-
days drift, bottom panel: Mean distance error after a 5-days drift.
The aim of the Lagrangian approach is to compare the observed buoy trajectory with virtual
trajectories obtained with forecast velocities, starting from the same observed initial
location. The SVP floats suspected of having lost their drogue are filtered out with the
method described in annex III. The virtual trajectories are computed with modeled currents

63
at 15 m and with the ARIANE software6
(see annex III.2). The metric shown here (Figure 54)
is the distance between the trajectories after 1, 3 and 5 days, displayed at each trajectory
initial point.
Few differences appear between the systems and most of the high velocity biases that are
diagnosed in Figure 35 imply a large distance error (120 to 180km) after a few days drift in
Figure 54. For instance in the Pacific ocean, both PSY3V3R1 and PSY4V2R2 produce an error
larger than 100km after only a 1-day drift near the North Coast of Papua New Guinea.
In the subtropical gyres and in the North Pacific, which are less turbulent regions, the errors
rarely exceed 30 km after 5 days.
Figure 55: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 the JFM 2013 period, cumulative distribution functions (Figure
55, only PSY4V1R3 is shown as PSY3V3R1 displays very similar results) show that in 80% of
cases, PSY3V3R1 and PSY4V1R3 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.
VI.5. Forecast verification: comparison with analysis everywhere
The PSY3V3R1 “forecast errors” illustrated by the sea surface temperature and salinity RMS
difference between the forecast and the hindcast for all given dates of JFM 2013 are
displayed in Figure 56. The values on most of the global domain do not exceed 1°C and 0.2
PSU. In regions of high variability like the western boundary currents, the Circumpolar
current, Zapiola eddy, Agulhas current, Gulf Stream, Japan Sea and Kuroshio region the
6
http://stockage.univ-brest.fr/~grima/Ariane/

64
errors reach around 3°C or 0.5 PSU. For salinity, the error can exceed 1 PSU in regions of high
runoff (Gulf of Guinea, Bay of Bengal, Amazon, Sea Ice limit) or precipitations (ITCZ, SPCZ).
Figure 56: comparison of the sea surface temperature (°C, upper panel) and salinity (PSU, lower panel)
forecast – hindcast RMS differences for the 1 week range for the PSY3V3R1 system for the JFM 2013 period.

65
VII Monitoring of ocean and sea ice physics
VII.1. Global mean SST and SSS
Figure 57: daily SST (°C) spatial mean for a one year period ending in JFM 2013, for Mercator Ocean systems
(in black) and RTG-SST observations (in red). Upper: PSY2V4R2, middle: PSY3V3R1, lower: PSY4V1R3.
The spatial mean of SST is computed for each day of the year, for PSY2V4R2, PSY3V3R1 and
PSY4V1R3 systems. The mean SST is compared to the mean of RTG-SST on the same domain
(Figure 57), except for PSY2V4R2 where it is compared with Reynolds AVHRR SST.
The main feature is the good agreement of PSY2V4R2 and Reynolds SST, and of PSY3V3R1
and RTG-SST on global average. On the contrary the global mean of PSY4V1R3 SST is biased
of about 0.1°C all year long, consistently with data assimilation scores of section V.1.2. This
bias is mainly located in the tropics which are too cold on average. Paradoxically, local
departures from RTG-SST are much stronger in PSY3V3R1 (more than 2°C at the peak of the
seasonal bias) than in PSY4V1R3 (not shown).
VII.2. Surface EKE
Regions of high mesoscale activity are diagnosed in Figure 58: Kuroshio, Gulf Stream, Niño 3
region in the central Equatorial pacific, Zapiola eddy, Agulhas current. PSY3V3R1 at ¼° and

66
PSY4V1R3 at 1/12° are in very good agreement. EKE is generally higher in the high resolution
PSY4V1R3 system, for instance in the subtropical gyres.
Figure 58: surface eddy kinetic energy EKE (m²/s²) for PSY3V3R1 (upper panel) and PSY4V1R3 (lower panel)
for JFM 2013.
VII.3. Mediterranean outflow
In PSY3V3R1 the Mediterranean outflow is too shallow with respect to the climatology in the
Gulf of Cadiz. Anyway, consistently with Figure 31, the outflow is better reproduced by
PSY3V3R1 than by PSY4V1R3. The Mediterranean outflow of PSY2V4R2 (with high resolution
and bias correction) is the most realistic of all systems.

67
Figure 59: Comparisons between JFM 2013 mean temperature (°C, left panel) and salinity (psu, right panel)
profiles in PSY2V4R2, PSY3V3R1 and PSY4V1R3 (from top to bottom, in black), and in the Levitus WOA05
(green) and ARIVO (red) monthly climatologies.

68
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 60 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.
Figure 60: Sea ice area (left panel, 10
6
km2) and extent (right panel, 10
6
km2) in PSY3V3R1(blue line),
PSY4V1R3 (black line) and SSM/I observations (red line) for a one year period ending in JFM 2013, in the
Arctic (upper panel) and Antarctic (lower panel).
These time series indicate that sea ice products from PSY4V1R3 are generally less realistic
than PSY3V3R1 products. This is partly due to the use of two different dynamics in the two
models. PSY4V1R3 sea ice cover is overestimated throughout the year. The accumulation of
multiannual Sea Ice in the Central arctic is overestimated by the models and especially by
PSY4V1R3 all year long (see Figure 36). PSY4V1R3 overestimates the sea ice area and extent
in boreal summer, while PSY3V3R1 ice area and extent are slightly underestimated. In boreal
winter, PSY3V3R1 performs very well, with respect to observations.

69
VIII Evaluation of the new systems PSY4V2R2, PSY3V3R3 and
PSY2V4R4: synthesis illustrated with JFM 2013 results.
VIII.1. Introduction
The Mercator Ocean global analysis and forecasting system (including PSY4, PSY2 and PSY3)
has been updated in 2012 to start to deliver new products to MyOcean and Mercator Ocean
users in April 2013. A detailed description of most updates, as well as a description of the
evaluation process, can be found in Lellouche et al (2013). With respect to this article,
several additional modifications were made in order to stabilize the performance of the
system (see Table 1).
current 1/12° global new 1/12° global
Figure 61: Mean (upper panel) and RMS (lower panel) temperature (°C) difference (model-observation) in
JFM 2013 between all available T/S observations from the Coriolis database and the daily average hindcast
PSY4V1R3 products on the left and hindcast PSY4V2R2 on the right column, colocalised with the
observations. Averages are performed in the 0-500m layer. The size of the pixel is proportional to the
number of observations used to compute the RMS in 2°x2° boxes. The minimum number of data per box may
differ between the two systems because the quality control of the observations relies on the difference
observation-model.

70
This short section aims at providing a summary of the evaluation of the new system, and of
the main improvements for the following variables: temperature, salinity, surface currents
and sea ice area and extent. Of course, more detailed evaluation of the system in different
regions and seasons will follow in the QuO Va Dis? issues to come.
VIII.2. Water masses
First of all, as PSY2 and PSY3, PSY4 now includes a 3D-var bias correction of temperature
and salinity, which significantly improves the accuracy of the temperature and salinity of
the global 1/12° analyses and forecast, as illustrated in Figure 61 with the temperature
between 0 and 500m in JFM 2013. The cold bias that was diagnosed in PSY4V1R3 is no
longer present in PSY4V2R2. The RMS error is reduced in PSY4V2R2 with respect to
PSY4V1R3 in the tropical band and in the regions of high spatio-temporal variability such as
the Gulf Stream, the Kuroshio or the Antarctic Circumpolar Current.
current 1/12° North Atlantic and Mediterranean new 1/12° North Atlantic and Mediterranean
50m layer. The size of the pixel is proportional to the number of observations used to compute the RMS in
2°x2° boxes. The minimum number of data per box may differ between the two systems because the quality
control of the observations relies on the difference observation-model.
The temperature and salinity accuracy of the new versions of PSY3 and PSY2 is at least as
good as the accuracy of the current versions in JFM 2013 as illustrated in Figure 62 and
Figure 63.

71
50m layer. The size of the pixel is proportional to the number of observations used to compute the RMS in
2°x2° boxes. The minimum number of data per box may differ between the two systems because the quality
control of the observations relies on the difference observation-model.
VIII.3. Surface fields
While the performance of SLA data assimilation is quite similar (at least as good) in the new
and in the current systems (not shown), the accuracy of the PSY3 SST significantly improves
in the new version of the system, as the assimilated SST switches from RTG-SST to Reynolds
AVHRR SST. A procedure to avoid the damping of SST increments via the bulk forcing
function helps reducing significantly the seasonal bias in the summer hemisphere. That bias
could be diagnosed in JFM 2013 in the southern hemisphere as can be seen in Figure 64.

72
Figure 64: JFM 2013 mean difference (°C, upper panel) and RMS difference (°C, lower panel) between OSTIA
SST analyses and PSY4 SST analyses. Left column: current system PSY3V3R1, right column: new system
PSY3V3R3. NB: no sea ice mask is applied.
Figure 65: JFM 2013 mean difference (°C, upper panel) and RMS difference (°C, lower panel) between OSTIA
SST analyses and PSY4 SST analyses. Left column: current system PSY4V1R3, right column: new system
PSY4V2R2. NB: no sea ice mask is applied.
PSY3 and PSY4 SSTs are now of similar accuracy as can be seen in Figure 64 and Figure 65.
The cold bias of 1°C at the surface of PSY4 disappears in the new system.

73
PSY2 is not shown as few differences appear between PSY2V4R4 and PSY2V4R2, as the latter
already benefited from the assimilation of Reynolds AVHRR observations.
Figure 66: JFM 2013 mean bias in zonal velocity (m/s) of PSY3V3R1 (left column) and PSY3V3R3 (right
column) with respect to AOML drifters velocities, filtered from the direct effect of the wind.
Thanks to the use of a new MDT improving the assimilation of SLA, as well as the use of new
bulk formula including wind stress computation, the surface currents are significantly closer
to the observations in the equatorial band, as illustrated in Figure 66.
VIII.4. Sea ice
The sea ice area is well reproduced in the new PSY3 and PSY4 in the Arctic Ocean, while in
the Antarctic, one can note that the seasonal cycle of sea ice area is overestimated by both
PSY3 and PSY4. Consequently the sea ice cover is overestimated in winter and
underestimated in summer in the Antarctic.
current global systems new global systems

74
Figure 67: time evolution from April 2012 to March 2013 of the sea ice area (upper panel) and extent (lower
panel) in the Arctic and Antarctic oceans, computed with both PSY4 (black) and PSY3 (blue) systems, with
respect to SSMI observations (red). Left column: current operational systems PSY3V3R1 and PSY4V1R3, right
column: new systems PSY3V3R3 and PSY4V2R2.
VIII.5. Conclusion
The scientific qualification of the systems at the global scale is detailed in Lellouche et al
(2013). It has shown that the recent updates of the Mercator Océan systems improve the
accuracy of temperature and salinity analyses and forecast, as well as the quality of currents
(surface and subsurface) and sea ice. As can be seen in this section, the updates made since
this publication (in blue in Table 1) do not alter the quality of the analyses and forecast, but
on the contrary further improvements can be noticed: for instance the decrease of the SST
biases. These latest updates also ensure the stability in time of the performance of the
system, which was questioned in Lellouche et al (2013). Furthermore, these updates were
also applied to the high resolution global PSY4 which now delivers accurate analyses and
forecast for MyOcean on a daily basis.
VIII.6. References
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

75
I Annex A
I.1. Table of figures
Figure 1: schematic of the operational forecast scenario for IBI36QV1 (green) and PSY2QV4R2 (blue).
Solid lines are the PSY2V4R2 weekly hindcast and nowcast experiments, and the IBI36V1 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
PSY3V3R1 and PSY3QV3R1 is similar to PSY2’s scenario. In the case of PSY4V1R3, only weekly
hindcast, nowcast and 7-day forecast are performed.................................................................................. 7
Figure 2: schematic of the operational forecast scenario for BIOMER.................................................................. 8
Figure 3: Weekly SLA coverage combining Jason-2 (black) and Cryosat-2 (grey) altimeters............................... 11
Figure 4 : Depth-time diagram of the RMS error with respect to observations of temperature (left
column) and salinity (right column) assimilated each week in PSY3V3R1 during the JFM 2013
quarter........................................................................................................................................................ 12
Figure 5: Seasonal JFM 2013 temperature anomalies with respect to GLORYS2V1 climatology (1993-
2009). Upper panel: SST anomaly (°C) at the global scale from the 1/4° ocean monitoring and
forecasting system PSY3V3R3. Lower panel heat content anomaly (ρ0Cp∆T, with constant ρ0=1020
kg/m3 ) from the surface to 300m. ............................................................................................................ 14
Figure 6: Seasonal JFM 2013 temperature anomaly (°C) with respect to GLORYS2V1 climatology (1993-
2009), vertical section 2°S-2°N mean, Pacific Ocean, PSY3V3R3................................................................ 15
Figure 7: Arctic sea ice extent from the NSIDC .................................................................................................... 15
Figure 8: Comparison of SLA data assimilation scores (left: average misfit in cm, right: RMS misfit in cm)
in JFM 2013 and between all available Mercator Ocean systems in the Tropical and North Atlantic.
The scores are averaged for all available satellite along track data (Jason 1 G, Jason 2, Cryosat 2).
For each region the bars refer respectively to PSY2V4R2 (cyan), PSY3V3R1 (green), PSY4V1R3
(orange). The geographical location of regions is displayed in annex A..................................................... 16
in JFM 2013 for PSY2V4R2. The scores are averaged for all available satellite along track data
(Jason 1 G, Jason 2, Cryosat 2). See annex B for geographical location of regions. ................................... 17
in JFM 2013 and between all available global Mercator Ocean systems in all basins but the Atlantic
and Mediterranean: PSY3V3R1 (green) and PSY4V1R3 (orange). The scores are averaged for all
available along track satellite data (Jason 1 G, Jason 2, Cryosat 2). The geographical location of
regions is displayed in annex B................................................................................................................... 18
Figure 11: Comparison of RTG-SST data assimilation scores (left: average misfit in °C, right: RMS misfit in
°C) in JFM 2013 and between all available Mercator Ocean systems in the Tropical and North
Atlantic: PSY4V1R3 (orange), PSY3V3R1 (green). In cyan: Reynolds ¼°AVHRR-AMSR-E data
assimilation scores for PSY2V4R2. The geographical location of regions is displayed in annex B. ............ 19
Figure 12: Comparison of SST data assimilation scores (left: average misfit in °C, right: RMS misfit in °C) in
JFM 2013 for each region for PSY2V4R2 (comparison with Reynolds ¼° AVHRR-AMSR). The
geographical location of regions is displayed in annex B. .......................................................................... 19
Figure 13: Comparison of RTG-SST data assimilation scores (left: average misfit in °C, right: RMS misfit in
°C) in JFM 2013 and between all available global Mercator Ocean systems in all basins but the
Atlantic and Mediterranean: PSY3V3R1 (green) and PSY4V1R3 (orange). See annex B for
geographical location of regions. ............................................................................................................... 20
mean (solid line) and RMS (dotted line) for PSY3V3R1 in red and PSY4V1R3 in blue in North Pacific
gyre region. The geographical location of regions is displayed in annex B. ............................................... 21
mean (solid line) and RMS (dotted line) for PSY3V3R1 in red and PSY4V1R3 in blue in South Atlantic
gyre region. The geographical location of regions is displayed in annex B. ............................................... 22

76
mean (solid line) and RMS (dotted line) for PSY3V3R1 (in red) and PSY4V1R3 (in blue) in the Indian
Ocean region. The geographical location of regions is displayed in annex B............................................. 23
mean (solid line) and RMS (dotted line) for PSY4V1R3 in blue, PSY3V3R1 in red, and PSY2V4R2 in
yellow in North Madeira region. The geographical location of regions is displayed in annex B................ 24
yellow in Dakar region. The geographical location of regions is displayed in annex B. ............................. 24
yellow in Gulf Stream 2 region. The geographical location of regions is displayed in annex B.................. 25
yellow in Cape Verde region. The geographical location of regions is displayed in annex B. .................... 26
yellow in Sao Tome region. The geographical location of regions is displayed in annex B........................ 26
Figure 22: Profiles of JFM 2013 mean (cyan) and RMS (yellow) innovations of temperature (°C, left panel)
and salinity (psu, right panel) in the Algerian region. The geographical location of regions is
displayed in annex B................................................................................................................................... 27
and salinity (psu, right panel) in the Gulf of Lion region. The geographical location of regions is
and salinity (psu, right panel) in the Rhodes region. The geographical location of regions is
observations from the Coriolis database and the daily average hindcast PSY3V3R1 products on the
left and hindcast PSY4V1R3 on the right column colocalised with the observations. Averages are
performed in the 0-50m layer (upper panel) and in the 0-500m layer (lower panel). The size of the
pixel is proportional to the number of observations used to compute the RMS in 2°x2° boxes. .............. 30
left and hindcast PSY4V1R3 on the right column, colocalised with the observations. Averages are
performed in the 0-50m layer (upper panel) and in the 0-500m layer (lower panel). The size of the
pixel is proportional to the number of observations used to compute the RMS in 2°x2° boxes. .............. 31
Figure 27 : JFM 2013 global statistics of temperature (°C, left column) and salinity (psu, right column)
averaged in 6 consecutive layers from 0 to 5000m. RMS difference (upper panel) and mean
difference (observation-model, lower panel) between all available T/S observations from the
Coriolis database and the daily average hindcast products PSY3V3R1 (red), PSY3V3R3 (new, pink),
PSY4V1R3 (blue), PSY4V2R2 (new, cyan) 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........................................................ 32
Figure 28: RMS difference (model-observation) of temperature (upper panel, °C) and salinity (lower
panel, psu) in JFM 2013 between all available T/S observations from the Coriolis database and the
daily average PSY2V4R2 hindcast products colocalised with the observations in the 0-50m layer
(left column) and 0-500m layer (right column). ......................................................................................... 33
Figure 29: Water masses (Theta, S) diagrams in the Bay of Biscay (upper panel), Gulf of Lion (second
panel), Irminger Sea (third panel) and Baltic Sea (upper panel), comparison between PSY3V3R1
(left column), PSY4V1R3 (middle column) and PSY2V4R2 (right column) in JFM 2013. PSY2, PSY3
and PSY4: yellow dots; Levitus WOA09 climatology: red dots; in situ observations: blue dots................. 35
Figure 30 : Water masses (T, S) diagrams in the Western Tropical Atlantic (upper panel) and in the
Eastern Tropical Atlantic (lower panel): for PSY3V3R1 (left); PSY4V1R3 (middle); and PSY2V4R2

77
(right) in JFM 2013. PSY2, PSY3 and PSY4: yellow dots; Levitus WOA09 climatology; red dots, in situ
observations: blue dots. ............................................................................................................................. 36
Figure 31: Water masses (T, S) diagrams in South Africa, Kuroshio, and Gulf Stream region (respectively
from top to bottom): for PSY3V3R1 (left); PSY4V1R3 (right) in JFM 2013. PSY3 and PSY4: yellow
dots; Levitus WOA09 climatology: red dots; in situ observations: blue dots............................................. 38
Figure 32 : RMS temperature (°C) differences between OSTIA daily analyses and PSY3V3R1 daily analyses
(upper left); between OSTIA and PSY4V1R3 (upper right), between OSTIA and PSY2V4R2 (lower
left), and between OSTIA and RTG daily analyses (lower right). The Mercator Océan analyses are
colocalised with the satellite observations analyses.................................................................................. 39
Figure 33: Mean SST (°C) daily differences between OSTIA daily analyses and PSY3V3R1 daily analyses
(upper left), between OSTIA and RTG daily analyses (upper right) and between OSTIA and Reynolds
¼° AVHRR daily analyses (lower left).......................................................................................................... 40
Figure 34: Comparison between modelled zonal current (left panel) and zonal current from drifters (right
panel) in m/s. In the left column: velocities collocated with drifter positions in JFM 2013 for
PSY3V3R1 (upper panel), PSY4V1R3 (middle panel) and PSY2V4R2 (bottom panel). In the right
column, zonal current from drifters in JFM 2013 (upper panel) at global scale, AOML drifter
climatology for JFM with new drogue correction from Lumpkin & al, in preparation (middle) and
zonal current in JFM 2013 from drifters (lower panel) at regional scale. .................................................. 41
Figure 35 : In JFM 2013, comparison of the mean relative velocity error between in situ AOML drifters
and model data on the left side and mean zonal velocity bias between in situ AOML drifters with
Mercator Océan correction (see text) and model data on the right side. Upper panel: PSY3V3R1,
middle panel: PSY4V1R3, bottom panel: PSY2V4R2. NB: zoom at 500% to see the arrows. ..................... 42
Figure 36: Comparison of the sea ice cover fraction mean for JFM 2013 for PSY3V3R1 in the Arctic
(upper panel) and in the Antarctic (lower panel), for each panel the model is on the left, the mean
of Cersat dataset in the middle and the difference on the right................................................................ 43
panel) and in the Antarctic (lower panel), for each panel the model is on the left, the mean of
Cersat dataset in the middle and the difference on the right.................................................................... 44
Figure 38: JFM 2013 Arctic sea ice extent in PSY3V3R1 with overimposed climatological JFM 1992-2010
sea ice fraction (magenta line, > 15% ice concentration) (left) and NSIDC map of the sea ice extent
in the Arctic for March 2013 in comparison with a 1979-2000 median extend (right).............................. 45
Figure 39 : Comparisons (observation-model) between IBI36V2 and analyzed SST from MF_CMS for the
JFM 2013 period. From the left to the right: mean bias, RMS error, correlation, number of
observations ............................................................................................................................................... 46
Figure 40 : For IBI36V2: On the left: mean “model - observation” temperature (°C) bias (red curve) and
RMS error (blue curve) in JFM 2013, On the right: mean profile of the model (black curve) and of
the observations (red curve) in JFM 2013. In the lower right corner: position of the profiles. Top
panel: the whole domain; bottom panel: the Bay of Biscay region. .......................................................... 47
Figure 41: For IBI36V2: On the left: mean “model - observation” salinity (psu) bias (red curve) and RMS
error (blue curve) in JFM 2013, On the right: mean profile of the model (black curve) and of the
observations (red curve) in JFM 2013. In the lower right corner: position of the profiles. Top panel:
the whole domain; bottom panel: the Bay of Biscay region. ..................................................................... 48
Figure 42 : For IBI36V2 (upper panel): Mixed Layer Depth distribution in JFM 2013 calculated from
profiles with the temperature criteria (difference of 0.2°C with the surface); the model is in grey,
the observations in red. Lower panel: PSY2V4R2 (left), PSY2V4R4 (right)................................................. 49
Figure 43 : For IBI36V2: RMS error (cm) and correlation for the non-tidal Sea Surface Elevation at tide
gauges in JFM 2013, for different regions and frequencies. ...................................................................... 49
Figure 44 : For IBI36V2: Bias (observation-model), RMS error (°C) and correlation of the Sea Surface
Temperature between IBI model and moorings measurements in October (upper panel),
November (middle panel) and December 2012 (lower panel)................................................................... 51
Figure 45 : Chlorophyll-a concentration (mg/m
3
) for the Mercator system BIOMER (left panels) and
Chlorophyll-a concentration from Globcolour (right panels). The upper panel is for January, the
medium panel is for February and the bottom panel is for March 2013................................................... 52
Figure 46 : Probability Density Function (PDF) of Chl-a bias in log scale (log10(obs)-log10(model)) in
North Atlantic (30-70N; 80W:20E) ............................................................................................................. 52

78
Figure 47 : RMS difference between BIOMER and Globcolour Chl-a concentrations (mg/m
3
) in JFM 2013....... 53
Figure 48: Schematic of the change in atmospheric forcings applied along the 14-day ocean forecast............. 53
Figure 49: Accuracy intercomparison in the North Atlantic region for PSY2V4R2 in temperature (left
panel) and salinity (right panel) between hindcast, nowcast, 3-day and 6-day forecast and WO09
climatology. Accuracy is measured by a mean difference (upper panel) and by a rms difference
(lower panel) 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 JFM 2013 period. NB: average on model levels is performed as an intermediate step which
reduces the artefacts of inhomogeneous density of observations on the vertical.................................... 55
Figure 50: Accuracy intercomparison in the Mediterranean Sea region for PSY2V4R2 in temperature (°C,
left column) and salinity (psu, right column) between hindcast, nowcast, 3-day and 6-day forecast
and WO09 climatology. Accuracy is measured by a rms difference (lower panel) and by a mean
difference (upper panel) 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 JFM 2013
period. NB: average on model levels is performed as an intermediate step which reduces the
artefacts of inhomogeneous density of observations on the vertical........................................................ 56
Figure 51: same as Figure 49 but for RMS statistics and for temperature (°C), PSY3V3R1 and PSY4V1R3
systems and the Tropical Atlantic (TAT), the Tropical Pacific (TPA) and the Indian Ocean (IND). The
global statistics (GLO) are also shown for temperature and salinity (psu). The right column
compares the analysis of the global ¼° PSY3V3R1 and PSY3V3R3 (new, pink) with the analysis of
the global 1/12° PSY4V1R3 and PSY4V2R2 (new, cyan). ............................................................................ 59
Figure 52 : Temperature (left) and salinity (right) skill scores in 4°x4° bins and in the 0-500m layer in JFM
2013, illustrating the ability of the 3-days forecast to be closer to in situ observations than a
reference state (climatology or persistence of the analysis, see Equation 1). Yellow to red values
indicate that the forecast is more accurate than the reference. Here the reference value is the
WOA05 climatology. Upper panel: PSY3V3R1; lower panel: PSY4V1R3..................................................... 60
Figure 53: As Figure 53 but the reference is the persistence of the analysis. Temperature (left column)
and salinity (right column) skill scores are displayed, for PSY3V3R1 (upper panel), PSY4V1R3
(middle panel), and PSY2V4R2 (lower panel)............................................................................................. 61
Figure 54: In JFM 2013, comparison of the mean distance error in 1°x1° boxes between AOML drifters
trajectories and PSY3V3R1 trajectories on the left side and AOML drifters trajectories and
PSY4V1R3 on the right side. Upper panel: Mean distance error after a 1-day drift, middle panel:
Mean distance error after a 3-days drift, bottom panel: Mean distance error after a 5-days drift........... 62
Figure 55: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. ............................................................... 63
Figure 56: comparison of the sea surface temperature (°C, upper panel) and salinity (PSU, lower panel)
forecast – hindcast RMS differences for the 1 week range for the PSY3V3R1 system for the JFM
2013 period. ............................................................................................................................................... 64
Figure 57: daily SST (°C) spatial mean for a one year period ending in JFM 2013, for Mercator Ocean
systems (in black) and RTG-SST observations (in red). Upper: PSY2V4R2, middle: PSY3V3R1, lower:
PSY4V1R3.................................................................................................................................................... 65
Figure 58: surface eddy kinetic energy EKE (m²/s²) for PSY3V3R1 (upper panel) and PSY4V1R3 (lower
panel) for JFM 2013.................................................................................................................................... 66
Figure 59: Comparisons between JFM 2013 mean temperature (°C, left panel) and salinity (psu, right
panel) profiles in PSY2V4R2, PSY3V3R1 and PSY4V1R3 (from top to bottom, in black), and in the
Levitus WOA05 (green) and ARIVO (red) monthly climatologies. ............................................................. 67
Figure 60: Sea ice area (left panel, 10
6
km2) and extent (right panel, 10
6
km2) in PSY3V3R1(blue line),
PSY4V1R3 (black line) and SSM/I observations (red line) for a one year period ending in JFM 2013,
in the Arctic (upper panel) and Antarctic (lower panel)............................................................................. 68
Figure 61: Mean (upper panel) and RMS (lower panel) temperature (°C) difference (model-observation)
in JFM 2013 between all available T/S observations from the Coriolis database and the daily
average hindcast PSY4V1R3 products on the left and hindcast PSY4V2R2 on the right column,
colocalised with the observations. Averages are performed in the 0-500m layer. The size of the
pixel is proportional to the number of observations used to compute the RMS in 2°x2° boxes. The

79
minimum number of data per box may differ between the two systems because the quality control
of the observations relies on the difference observation-model............................................................... 69
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. The minimum number of data per box may differ
between the two systems because the quality control of the observations relies on the difference
observation-model. .................................................................................................................................... 70
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. The minimum number of data per box may differ
between the two systems because the quality control of the observations relies on the difference
observation-model. .................................................................................................................................... 71
Figure 64: JFM 2013 mean difference (°C, upper panel) and RMS difference (°C, lower panel) between
OSTIA SST analyses and PSY4 SST analyses. Left column: current system PSY3V3R1, right column:
new system PSY3V3R3. NB: no sea ice mask is applied. ............................................................................ 72
Figure 65: JFM 2013 mean difference (°C, upper panel) and RMS difference (°C, lower panel) between
OSTIA SST analyses and PSY4 SST analyses. Left column: current system PSY4V1R3, right column:
new system PSY4V2R2. NB: no sea ice mask is applied. ............................................................................ 72
Figure 66: JFM 2013 mean bias in zonal velocity (m/s) of PSY3V3R1 (left column) and PSY3V3R3 (right
column) with respect to AOML drifters velocities, filtered from the direct effect of the wind. ................ 73
Figure 67: time evolution from April 2012 to March 2013 of the sea ice area (upper panel) and extent
(lower panel) in the Arctic and Antarctic oceans, computed with both PSY4 (black) and PSY3 (blue)
systems, with respect to SSMI observations (red). Left column: current operational systems
PSY3V3R1 and PSY4V1R3, right column: new systems PSY3V3R3 and PSY4V2R2. .................................... 74
Figure 68 : 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 °)...................................... 83
Figure 70 : Illustration of the surface currents Lagrangian quality control algorithm. ........................................ 84
Figure 69: Example of the surface currents Lagrangian quality control algorithm on a global map (bottom
panel) and zooms (upper panels)............................................................................................................... 84

80
II Annex B
II.1. Maps of regions for data assimilation statistics
II.1.1. Tropical and North Atlantic
1 Irminger Sea
2 Iceland Basin
3 Newfoundland-Iceland
4 Yoyo Pomme
5 Gulf Stream2
6 Gulf Stream1 XBT
7 North Medeira XBT
8 Charleston tide
9 Bermuda tide
10 Gulf of Mexico
11 Florida Straits XBT
12 Puerto Rico XBT
13 Dakar
14 Cape Verde XBT
15 Rio-La Coruna Woce
16 Belem XBT
17 Cayenne tide
18 Sao Tome tide
19 XBT - central SEC
20 Pirata
21 Rio-La Coruna
22 Ascension tide

81
II.1.2. Mediterranean Sea
1 Alboran
2 Algerian
3 Lyon
4 Thyrrhenian
5 Adriatic
6 Otranto
7 Sicily
8 Ionian
9 Egee
10 Ierepetra
11 Rhodes
12 MersaMatruh
13 Asia Minor

82
II.1.3. Global ocean
1 Antarctic Circumpolar Current
2 South Atlantic
3 Falkland current
4 South Atl. gyre
5 Angola
6 Benguela current
7 Aghulas region
8 Pacific Region
9 North Pacific gyre
10 California current
11 North Tropical Pacific
12 Nino1+2
13 Nino3
14 Nino4
15 Nino6
16 Nino5
17 South tropical Pacific
18 South Pacific Gyre
19 Peru coast
20 Chile coast
21 Eastern Australia
22 Indian Ocean
23 Tropical indian ocean
24 South indian ocean

83
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 68 : 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

84
III.2. 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. SVP drifters that may have lost their drogue are first
filtered out thanks to annex III.1. The computation of the
trajectory of the modeled drifter is made possible by
ARIANE7
using modeled currents at 15 m.
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.
Let us consider the example above (Figure
69). 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 data8
.
7
Ariane : utility developed at LPO (http://wwz.ifremer.fr/lpo_eng/Produits/Logiciels/ARIANE)
8
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.
Figure 69 : Illustration of the surface
currents Lagrangian quality control
algorithm.
Figure 70: Example of the surface currents Lagrangian quality
control algorithm on a global map (bottom panel) and zooms
(upper panels).

QUOVADIS_NUM12_JFM_2013

More Related Content

What's hot

Viewers also liked

Similar to QUOVADIS_NUM12_JFM_2013

More from Mercator Ocean International

Recently uploaded

QUOVADIS_NUM12_JFM_2013