Mercator Ocean Journal 53 - Special 20th anniversary issue

20 YEARS
OF OPERATIONAL OCEANOGRAPHY
SPECIAL ISSUE
1995-2015
JOURNAL
MERCATOR
OCEAN

TABLE
OF
CONTENTS
P.4
FOREWORD
P.5
EDITORIAL
P.6
GLOBAL
AND REGIONAL
MODELING
P.56
SEA ICE
ASSIMILATION AND
OBSERVATIONS
P.26
BIO-
GEOCHEMICAL
MODELING
P.80
COASTAL
MODELING AND
DOWNSCALING
P.102
GLOBAL
REANALYSIS AND
REPROCESSING
©2015 Mercator Océan

P.128
NEMO CODE
AND
CONSORTIUM
P.142
ATMOSPHERIC
FORCING
AND WAVES
P.162
DATA
ASSIMILATION
TECHNICS
P.208
IN-SITU
OBSERVATIONS
AND INFRASTRUCTURE
COORDINATION
P.184
GODAE OCEANVIEW,
MYOCEAN AND
COPERNICUS MARINE
SERVICE

FOREWORD
For its 20th
anniversary, we at Mercator Océan
would like to express our pride in being part of the
French, European and International operational
oceanographycommunity.MercatorOcéanhasbeen
involved in many projects or working groups over
the last 20 years,among which,GODAE OceanView,
MERSEA, MyOcean, GSOP, ICE-ARC, CLIVAR, Nemo
Consortium, Mercator Vert, Clipper, Drakkar and
many others. The work we have done on our own
and with our partners has strengthened the ope-
rational oceanography community.Mercator Océan
is justly proud to be part of this concerted effort.
Since its founding in 2001, the Mercator Océan
Newsletter has been a regular forum for many
scientists from different backgrounds; for instance
it is a space in which,as odd as it may seem,people
working on research as far removed as theoretical
data assimilation techniques or on elephant seals
monitoring the Southern Ocean all share the same
objective of a better understanding of the Ocean. It
means personal engagement of many scientists.
I would like to take the opportunity in this special
commemorative issue of paying tribute to our
late colleague, Nicolas Ferry, who made a great
contribution to high resolution global reanalysis
at Mercator Océan.
The newsletter format is simple and allows 3 to
4 issues a year with a specific theme per issue,
thus publishing the latest scientific results from
various scientific teams in the same place. This is
convenient in that it provides readers with several
scientific theories or results on a given theme, but
also in that it strengthens a feeling of belonging to
a community among authors who have the same
objectives of better observing, modeling and thus
monitoring the ocean. Building up the operational
oceanography community and sharing scientific
ideas are important objectives for Mercator Océan.
Mercator Océan has been entrusted by the Euro-
pean Union to implement the Copernicus Marine
Environment and Monitoring Service (CMEMS).
This new mandate comes with a set of demanding
constraints and challenges. An operational service
is one for which products have to be made available
promptly in order to meet user requirements. Eric
Dombrowsky,with whom I created Mercator Océan
before he became its first scientific director, has
always been the first to defend this idea that science
was a major asset of operational oceanography in
building a strong value for users. Far from driving
us away from science, we believe this European
mandate gives us an opportunity to ensure that
scientific progress remains a strong asset of Mer-
cator Océan and CMEMS in the operational phase.
At 20 years old Mercator Océan has now come of
age and this special issue of the Newsletter en-
courages us to pursue our endeavors to develop
tomorrow’s operational oceanography systems.
Pierre BAHUREL,
Mercator Océan CEO
4

The 20th
anniversary of the founding of Mercator
Océan (1995-2015) gives us an opportunity to
contemplate our past achievements but also to
look forward to the future. This issue has a spe-
cial meaning for all of us at Mercator Océan as it
pays tribute to men and women of the operational
oceanography community.We have thus portrayed
ten people you might not yet know, all of whom are
key actors (among many others) of today’s ope-
rational oceanography and who are each worthy
of our attention.
We thus have an opportunity to thank all the scien-
tists who have published their work in the Mercator
Océan Newsletter and the Editorial Board*.We have
selected 24 papers to share with you again, sorted
into 10 themes. Through this issue, we intend to
highlight the work done over the last 20 years,
but above all to thank the people who did it, for
they are the actors who continually strive to build
today’s operational oceanography.
Asyoucanimagine,selectingonly24papersamong
the last 53 issues was a tough choice for us!
This 20th
anniversary also gives us an opportunity
to look ahead.The first Mercator Océan Newsletter
was published in April 2001. Fourteen years and
fifty-three issues later, it has become a reference
for a wide scientific community: each issue is read
by between 200 and approximately 5000 people
per year depending on the theme. To modernize
and streamline its circulation we have thus decided
to introduce the following changes.
Each issue will evolve with a more spacious and
easier to read page layout. The “Mercator Océan
Newsletter” is also changing its name and will
henceforth be called the “Mercator Ocean Journal”,
thus reflecting with a more appropriate term the
fact that it collates scientific papers. The editorial
line will not be changed, with 3 to 4 issues per year
publishing papers with a common theme as well
as an annual joint issue with the Coriolis Center
dedicated to in situ Observation. The first issue of
the “Mercator Ocean Journal” will focus on MyO-
cean2 and MyOcean Follow-on scientific output
and will be published in January 2016.
We sincerely hope you will enjoy this issue as much
as we have, for its content and the evocation of
all the work done over the past 20 years, but also
because it honors the dynamic and enthusiastic
scientists who each day add their contribution to
operational oceanography.
*Members of the Editorial Board are:
Bernard Barnier, CNRS, Directeur de Recherche, LGGE
Grenoble, France / Sylvie Pouliquen, Ifremer,
Head of Coriolis and EURO-ARGO ERIC Program Manager,
Brest, France / Pierre-Yves Le Traon, Scientific Director at
Mercator Océan, Toulouse, France / Gilles Garric, Innovation
Service Manager/R&D Dpt at Mercator Océan, Toulouse,
France / Laurence Crosnier, Product Manager
at Mercator Océan, Toulouse, France
EDITORIAL
Laurence CROSNIER,
Editor in Chief
5

GLOBAL
AND
REGIONAL
MODELING

YANN
DRILLET
MERCATOR
OCÉAN
Yann Drillet is currently Head of the Research
and Development department and deputy
scientific director at Mercator Océan. He
has studied mathematics at Université de
Bretagne Occidentale in Brest and started
his oceanographic carrier in Laboratoire de
Physique des Océans at Ifremer in 1997. Its
first work realized in the framework of the
ocean modeling project CLIPPER concerned
development of lagrangian trajectory algo-
rithm and north Atlantic configuration which
was used at Mercator Océan to produce
the first near real time ocean forecast in
2001. He joined CERFACS in Toulouse in
1999 to contribute to the development of
the forecasting system for Mercator project
and in 2008 he joined Mercator Océan GIP.
Yann Drillet is involved in several national,
European and International projects and
collaborations concerning high resolution
ocean modeling and forecasting and also
dissemination of numerical products. He
contributed to the European operational
oceanography projects (Mersea and MyO-
cean) and now to the Copernicus Marine
Environment Monitoring Service where he
is in charge of the coordination of the Glo-
bal Monitoring and Forecasting Center. He
is also involved in the international GODAE
Ocean View initiative.
DO YOU
KNOW
HIM?
7

INITIALLY
PUBLISHED IN
#31
SIMULATION
OF MESO-SCALE EDDIES
IN THE MERCATOR
GLOBAL OCEAN HIGH
RESOLUTION MODEL
BY
O. LE GALLOUDEC1
/ R. BOURDALLÉ BADIE1
Y. DRILLET1
/ C. DERVAL1
/ C. BRICAUD1
1
Mercator Océan, 8-10 rue Hermes,
Parc technologique du canal, 31520 Ramonville st Agne
ABSTRACT
The simulation of ocean eddies in the global high resolution 1/12° model is compared
to altimetric observations. At global scale, eddy kinetic energy (EKE) of the global ocean
model is close to the one computed from the geostrophic velocity deduced from altimetric
maps. Although the model is generally overestimating the EKE, the main patterns corres-
ponding to the main meso-scale activity areas are well reproduced in term of intensity
and geographical position. We study in particular six areas relevant to the world ocean:
the Leeuwing and Mozambique Channel currents for the Indian Ocean, the Alaska and
Kuroshio currents for the Pacific Ocean as well as the Sargasso Sea and Aghulas currents
for Atlantic Ocean. In all those areas, the number of eddy simulated by the model is in
good agreement with satellite data. Moreover, correlations between the modelled and
observed temporal evolution of the number of cyclonic (and anticyclonic) eddies are highly
significant. The higher correlations (0.8 and more) are found in the Leeuwing Current for
cyclonic eddies as well as in the Kuroshio and in the Sargasso Sea for both kind of eddies.
8

INTRODUCTION
Mercator Océan is developing a new global high
resolution ocean forecasting system which will be
the global component of the European MyOcean
project. In this paper, we focus on the validation
and the representation of ocean eddies in the first
interannual simulation realized with the global
high resolution ocean model. Results are com-
pared to altimetry data which allow both a good
representation of the ocean meso-scale activity
and tracking of eddy structures. As it is the first
time a model allows to follow eddies in the world
ocean, a brief review of the main ocean eddy for-
mation areas is described by comparison between
a “virtual” ocean simulated by the model and the
“real” ocean observed by altimetric satellites. In
a first part, the model configuration is described.
In the second one, the eddy detection algorithm
is presented and in the last section, results in 6
areas are commented.
NUMERICAL MODEL:
DESCRIPTION
AND VALIDATION
The eddy resolving Mercator Océan 1/12° OGCM
(here after called ORCA12) is based on NEMO code
[Madec,etal.,1998].Theglobalgridisaquasiisotro-
pictripolarORCAgrid[MadecandImbard,1996],with
resolution from 9.3 km at equator to 1.8 km at high
latitudes.The vertical coordinates are z-levels with
partial cells parameterization [Barnier, et al.,2006].
The vertical resolution is based on 50 levels with
layer thickness ranging from 1 m at the surface to
450 m at the bottom.A free surface that filters high
frequency features is used for the surface boundary
condition [Roullet and Madec, 2000].The closure of
the turbulent equation is a turbulent kinetic energy
mixing parameterization (1.5 closure scheme).The
TVD advection scheme is combined to an enstrophy
and energy conserving scheme for the tracer fields
[Lévy,et al.,2001; Barnier,et al.,2006; Arakawa and
Lamb,1980].Thelateraldiffusiononthetracers(125
m2.s-1) is ruled by an isopycnal laplacian operator
and a horizontal bilaplacian is used for the lateral
diffusion on momentum (-1.25e10 m2.s-2). The
global bathymetry is processed from a combina-
tion of ETOPO2v2 bathymetry and GEBCO for the
Hudson Bay. Monthly climatological runoffs, from
the Dai&Trenberth database, are prescribed [Dai
and Trenberth,2003; Bourdalle-Badie and Treguier,
2006].The 99 major rivers are spread at mouth and
others runoffs are applied as coastal, particularly,
along the Antarctic [Jacobs, et al., 1992]. The mo-
del is initialised with the recent version of Levitus
climatology [Boyer, et al., 2005]. This simulation is
forced by the CLIO bulk formulae [Goosse, et al.,
2001] using ECMWF analyses from 1999 to 2006.
The last 4 simulated years (2002-2006) have been
chosen as a significant period to realise statistics
and to spin up the surface layer in the ocean. The
ORCA12simulationhasbeenperformedonMercator
Océan SGI computer.
The data base used in this study to validate the
ocean meso-scale activity simulated by ORCA12
model, is based on AVISO altimetry [Le Traon, et
al.,1998] which contains weekly maps of the global
sea level anomaly and the associated geostrophic
velocity. The horizontal resolution of these maps
is 1/3° which allows a representation of the main
meso-scale eddies, except the smaller one. This
point will be discussed in the following parts.
To compare the meso-scale activity in the model
and in the observation at the global scale, we have
computed the Eddy Kinetic Energy (EKE) with the
total velocity of the model surface layer and the EKE
with the geostrophic velocities deduced from the
GLOBAL
AND REGIONAL
MODELING
#31
9

altimetricmaps.TheglobalEKE(Figure1)showsthe
area in the ocean where the meso-scale activity is
the more intense.First,the main ocean currents are
visibleonthetwomapswithanintenseactivityinthe
Gulf Stream and the Kuroshio, in the tropical band
and for the southern hemisphere in the Antarctic
circumpolar current,all around the Australia,along
the South African coasts and in the Argentina basin.
In all these areas, the comparison between model
and altimetric data shows very similar patterns.
We can notice that generally, the model is more
energetic than the observations.This is particularly
true in the middle of the gyres for each basin.These
differences are not studied in this paper which
focuses more specifically on the number and size
of ocean eddies.Nevertheless,several reasons can
explain these differences:
• Considering the model, we used 3 days mean
output of the total velocity and the mean surface
EKE of the ocean is plotted (Figure 1). For the alti-
metricdata,weusedtheweeklygeostrophicvelocity
deduced from altimetry.
• EKE computed from the geostrophic velocity or
surface velocity are different. In the area where
the EKE is weak, like in the middle of the gyre, the
geostrophic velocity under estimate the EKE. In
eddy propagation area where the EKE is strong,the
underestimation by the geostrophic velocity is less
than 10% (in the Mozambic Channel, in the South
east Indian Ocean, along the Alaska Peninsula and
in the Aghulas current) but the difference is more
important (around 20%) in the Gulf Stream and
Kuroshio.
• The horizontal resolution of the altimetric data
(1/3°) can’t capture the smallest meso-scale eddies
but these eddies are represented in the model as it
is explain in the following chapters.
• The model can be too energetic, several pa-
rameters can be tuned to correct such biases (like
diffusion,viscosityoradvectionschemes),butatthis
time, the comparison with other data base (like the
surface drifters for example) doesn’t substantiate
this thesis.
FIGURE 1
Eddy kinetic energy (cm2
/s2
) for the
period 2003-2006. Top panel : EKE
computed with the surface model
velocity. Bottom panel: EKE computed
with the geostrophic velocity deduced
from the altimetry map of sea surface
elevation.
10
GLOBAL
AND REGIONAL
MODELING
#31

EDDIES DETECTION
In this study, the Okubo Weiss criteria [Weiss,
1991] is used for the ocean model output and for
the geostrophic velocity deduced from the SLA
altimetric data.
The Okubo Weiss parameter is computed thanks
to equation (1):
where u is the zonal component of the surface
current and ν the meridional part of the surface
current. In this equation, the third term is the re-
lative vorticity of the flow and the two first terms
are the deformation of the flow. An ocean eddy is
then characterised by this parameter with negative
values in the centre of the eddy where the rota-
tion dominates surrounded by positive values at
the boundary of the eddy where the deformation
dominates. An additional criteria on the sea level
anomaly is added to the Okubo Weiss criteria to
select only large amplitude eddies.The eddies with
amplitude smaller than 15 cm can’t be followed in
space and time. Moreover small structures which
are not eddies could be detected, especially in
the model output. A last criteria is based on the
minimum number of pixel in the detected eddies.
In the model the minimum number is fixed to 36
pixels, which represent around 3 grid points in
an eddy radius, whereas it is only 4 pixels for the
altimetric data. The same eddy detection method
has been used in previous ocean studies [Henson
and Thomas, 2008; Penven, et al., 2005].
OCEAN EDDIES : CHARACTERISTICS
AND STATISTICS
Eddies have been detected with the Okubo Weiss
criteria on each map for the period 2004-2006.
For the model, a map is a 3-day mean and for the
altimetric data, a map is a 7-day mean merging all
available altimetric satellites. Six areas have been
selected to perform the study of the meso-scale
activity. Two of them are in the Indian Ocean (West
coast of Australia, Mozambique channel), two in
the Atlantic Ocean (West of south Africa and Sar-
gasso Sea) and two in the Pacific Ocean (Alaska
and Kuroshio regions).The mean number of eddies
(Table 1) represents the mean number of eddies
per map for all the period and over the selected
domain described in each paragraphs.As the range
of eddy scales detected in the model is wider than
in the observations (resolution in the observations
is coarser than in the model), we also computed
the number of eddy with radius larger than 30 km
(smallest scale detected in the observations.)To
represent the spatial distribution of the eddy field
in the model and in the observation, the probability
of occurrence of an eddy in 1°x 1° boxes for the
4-year period boxes has been computed.
Informations about the size of eddies are also
provided (Table 1) with the percentage of eddies
with a radius between 30 to 60 km which are the
more common size of eddies in the study areas.
Last, the proportion of anticyclonic eddies of the
total number of eddies in the model and in the
observation is compared.
For each studied area, the evolution of the eddy
number (total,cyclonic and anticyclonic) have been
compared and correlation between simulated and
observed eddies using 21 days smoothed time
series (Table 2) have been computed.
GLOBAL
AND REGIONAL
MODELING
#31
11

LEEUWING CURRENT
The Leeuwing current is a warm and fresh ocean
surface current which flows southward along the
western Australian coast. The eddies are formed
all along this current from north (around 22°S) to
south (around 35°S) by barotropic and baroclinic
instabilities.These anticyclonic and cyclonic eddies
areadvectedintheIndianOceanaftertheseparation
from the Leeuwing Current.The probability of eddy
occurrenceillustratesthegeographicalrepartitionof
the ocean eddies in the altimetry and in the model.
Occurrence larger than 15% (and even larger than
20% in the model) represents the eddy formation
place. In the eddy pathway in the Indian Ocean,
the occurrence is larger than 10%. The eddies are
formed in two places around 30°S and 37°S south
westward from Australian coast. These eddies are
thereafter advected in the Indian Ocean following a
pathway between 20°S and 30°S. In this area, the
counting of eddy (Table 1) and the time correlation
between model and observed eddies (Table 2) are
realised on a box bounded from 71°E to 129°E and
from 39°S to 20°S.
Morrow, et al., [2004] have described characteris-
tics of these eddies detected by the altimetry data,
the results of our study and the comparison with
the ORCA12 simulation is in good agreement with
this previous study. The mean number of eddies
(around 40 per map) in the model is comparable
to the altimetry (Table 1) with more than half with
a radius smaller than 60 km. In the model as in the
altimetrydata,thenumberofanticycloniceddiesare
higherthanthecyclonicone(Table1).Thenumberof
large anticyclonic eddies (radius larger than 60 km),
in the model as in the altimetry, is larger than the
number of cyclonic eddies. For the smallest struc-
tures (radius smaller than 60 km), the proportion
of cyclonic and anticyclonic eddies are equivalent.
A strong seasonal cycle, with a maximum value in
spring (September to November in the southern
hemisphere) is observed for the cyclonic eddies.
For the anticyclonic one, the seasonal cycle is less
obvious and is not in phase with the time series of
the number of cyclonic eddies. Two maxima are
observed during fall (May to June) and summer
(January).
The number of anticyclonic (respectively cyclonic)
eddy correlation between model and altimetry for
the 4 years of study is 0.7 (respectively 0.8,table 2).
MOZAMBIQUE CHANNEL
The region around Madagascar Island is a region of
strong meso-scale activity (Figure 1). It can be split
in two domains: East of Madagascar and Mozam-
bique Channel.These two regions feed the Agulhas
current.BiastochandKrauss,[1999]haveestimated
the transport in the Agulhas current at 65 Sv in the
upper 1000m, 5 Sv coming from the Mozambique
Channeland20SvfromtheEastofMadagascar.The
observations shows maxima of EKE are reached in
theses areas. The model reproduces very well this
pattern. The major difference is the level of EKE
in the north of the Mozambique Channel, which
is more intense in the model. The box selected to
perform eddy statistics is 30°E-60°E; 34°S-10°S.
The number of eddies over the period is quite the
same in the altimetric data and in ORCA12 (around
35,see table 1).This region shows a quite homoge-
neous 20% probability to find eddies in the model
over the Madagascar area, whereas the proportion
is more important in the Mozambic channel (near
18%) than east of Madagascar (about 13%) in the
altimetric data.In the model,there is a lack of eddies
in the area around 40°- 50°E; 35°S (5%) compare
12
GLOBAL
AND REGIONAL
MODELING
#31

to the altimetric data (10%). It appears clearly that
preferential path are more localized in the current
trajectory in the model output than in the observa-
tions,where eddies are widely spread.The number
ofanticycloniceddiesismoreimportantinaltimetric
data (63%),on the contrary to the model (proportion
of anticyclonic is 44%).A very strong seasonal cycle,
both in observation and model, depending on the
monsoon, is present, with a maximum in January
and the correlation with the anticyclonic eddies is
significant (0.68).
AGHULAS CURRENT
The Aghulas current is one of the more energetic
current in the global ocean. It takes source in the
Indian Ocean and follows southward the south
eastern African coast. Then, this current leaves
the shelf, retroflects and flows backward in the
Indian Ocean. The retroflection is located between
20°E and 15°E (Figure 1).Here warm eddies,called
Aghulas rings are formed by loop occlusion.These
anticyclonic and cyclonic eddies are advected in
the South Atlantic Ocean over several thousand
kilometers [Treguier, et al., 2003; Biastoch and
Krauss, 1999].
The box selected to compute the statistics on the
meso-scale activity in this area is 10°W-20°E;
42°S- 20°S. The number of eddies during the
studied period is of the same order than in the
observations (25 eddies per map). In the data,
more anticyclonic eddies are observed (73%) but
in the model the proportion (52%) is quite the same
(see table 1). The penetration of the anticyclonic
(35°W) and cyclonic (20°W) eddies in the simula-
tion is in good agreement with data. This region
shows that the anticyclonic (cyclonic) eddies drift
north-westward (south-westward) in the model
as in the observation. A preferential path near
25°-30°S for anticyclonic eddies can be identified
in the model, with occurrence of eddy between
5 and 10% along this pathway. In the altimetric
data, anticyclonic eddies are observed between
25°S-35°S with a maximum at 33°S. The eddy
number seasonal cycle is not well marked in both
observations and model. The correlation is low
(0.4) for the anticyclonic eddies because of a phase
lag with a maximum in April for the model and in
February for data.
SARGASSO SEA
The Sargasso Sea is crossed by a south-westward
current that flows between the Gulf Stream and the
Bermuda. This near-surface flow drifts westward
the Cold Core Rings (CCRs), which pinched form
the Gulf Stream. We also can find in the Sargasso
Sea others eddies eastward of the Gulf Stream,
generated from baroclinic instabilities in the flow
field. Using insitu measurements during the pe-
riod 1996-2004, Luce and Rossby, [2008] found
the CCRs with a typical radius of 57km +/- 16 km,
in a band from 150 to 300 km of the Gulf Stream.
They also found coherent vortices due to baro-
clinic instabilities with radius of 64 +/- 18 km. In
this study, statistics are realized in a box bounded
from 81° W to 59° W and from 26°N to 37°N. The
number of eddies in ORCA12 is the same than
in altimetry data (17 eddies per map), but more
eddies are created in the modelled meanders of
Gulf Stream (as we will see below in the Kuroshio
region) with less eddies in the south of Sargasso
Sea in the simulation than in the data. The corre-
lation between the time series of number of eddies
detected in altimetry and model is significant for
both cyclonic and anticyclonic eddies (respectively
0.88 and 0.83, see Table 2).
GLOBAL
AND REGIONAL
MODELING
#31
13

ALASKA CURRENT
The circulation in the Gulf of Alaska is dominated
by a wind-forced gyre in the ocean basin bounded
southward by the North Pacific Current. It splits
as it approaches the North American continent
to form the equatorward California Current and
the poleward Alaska Current. The Alaska Current
turns south-westward at the head of the Gulf (56°N
145°W),and becomes a narrow,swift stream which
closely follows the shelf break. A portion of the
Alaskan Stream turns southward near the Aleutian
Islands (165°W, 53°N) and recirculates as part of
the North Pacific Current, closing the loop of the
Alaska Gyre. A large part of eddies is generated on
the path of the gyre, between the Queen Charlotte
Islands (132°W, 53°N) and the eastern bound of
the Gulf. The repartition of the ocean eddies in the
altimetry and in the model confirms this point,
with around 15% of occurrence of eddy for seve-
ral 1°x1° boxes at this position. These eddies are
thereafter advected along the Alaska Peninsula
and the Aleutian Islands. In this area, the statis-
tics are realised on a box bounded from 179°W
to 114°W and from 47°N to 61°N. By analysing
the altimetry maps, Henson and Thomas, [2008]
have observed, a high proportion of anticyclone
(about 85%) among eddies. Even if the studied
period is not the same, we obtain the same order
of anticyclonic eddies with 78% in the altimetry
data and 77% in the model. The seasonal cycle of
anticyclones formation is marked, with maximum
in summer as in Henson and Thomas, [2008]. The
correlation between model and altimetry for the
number of anticyclonic eddies during the 4 years
of study is 0.7 (table 2).
KUROSHIO
In the Kuroshio extension, south of Japan, the
meso-scale activity is important. Cyclonic and
anticyclonic eddies are formed in the meander of
the Kuroshio and interact with the current. Several
studies have been realised in this area especially
south of Japan for example in Ebuchi and Hanawa,
[2000] or more southward in the China Sea [Chow,
et al., 2008]. The studied area is bounded by 120°E
to 160°E and 20°N to 36°N. It includes the starting
point of the Kuroshio (north of the China Sea) to
the Kuroshio extension in the North Pacific. The
same number of eddies (Table 1) are detected
in this area in the ORCA12 simulation (46 eddies
per map) and in the altimetry data (43 eddies per
map), Ebuchi and Hanawa, [2000] obtain the same
result based on altimetry. But location of eddies in
this area are differents. In the ORCA12 simulation,
eddies are mainly situated in the Kuroshio with
occurrence larger than 25%. In the meander of
this current, eddies are mainly anticyclonic but
they don’t systemically detached from it. These
anticyclonic structures have a short lifetime (less
than 1 month for most of eddies.) They are formed
at the end of summer or in fall (from September
to November) and they rapidly disappear in the
mean flow of the Kuroshio. The minimum num-
ber of anticyclonic eddies is smaller in the model
compare to the altimetry (around 10 for ORCA12
compare to 15 for the altimetry during winter) but
the maximum of anticyclonic eddies is larger in the
simulation (larger than 40 in ORCA12 and around
35 in altimetry, not shown).
In ORCA12 simulation, eddies are mainly smaller
than 60 km against 90 km in the altimetry data
(see table 1).
14
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The correlation between simulation and data are
both 0.8 for the anticyclonic and cyclonic eddies,
that means that the seasonal cycle, which is the
main signal on the temporal evolution,is correct.We
can notice that the correlation for the total number
of eddies is in this case 0, there is no seasonal
cycle for the total number of eddies in the model
and in the observation. This is explained by the
seasonal cycle for the number of cyclonic and the
anticyclonic eddies which is in opposition of phase.
Area Number of eddies % of eddies between 30 to
60 km for eddies >30km.
% of anticyclonic eddies/
total number of eddies
ALTI ORCA12 ORCA12
(>min alti)
ALTI ORCA12 ALTI ORCA12
Leeuwing 40.7 55.6 44 55.7% 61.6% 63.3% 54.2%
Mozambique 34.2 44.3 35 38.2% 54.8% 63.1% 44.7%
Alaska 12.2 22.7 17.6 81.7% 87% 78.5% 76.8%
Kuroshio 43.2 60 46.5 41.5% 59.6% 55.5% 51.6%
Sargasso 16.9 22.2 16.4 53.% 71.1% 46% 54.9%
Aghulas 24.5 31 25.5 47% 65.5% 73.5% 52%
LEEUWING MOZAMBIQUE ALASKA KUROSHIO SARGASSO AGHULAS
Cyclonic
eddies
0.83 0.44 0.6 0.8 0.88 0.57
Anticyclonic
eddies
0.71 0.67 0.68 0.8 0.83 0.4
Total eddies 0.6 0.13 0.66 0.0 0.4 -0.37
Eddy statistics in each area.
The number of eddies is the
mean number of eddies in
the area per map. The co-
lumn ORCA12>min alti is the
number of eddies in ORCA12
when we omitted eddies
smaller than the smaller
eddy in the altimetry.
TABLE 1
Correlation coefficient
between the time serie of
the eddy number (cyclo-
nic, anticyclonic and total)
detected in the altimetry and
in the model. The correlation
is computed on a time serie
filtered at 21 days. Eddies in
ORCA12 simulation smaller
than the smallest eddy in
the altimetry are removed
from this statistic.
TABLE 2
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15

CONCLUSION
The number and the geographical distribution
of eddies, in all the studied areas, are in good
agreement with altimetric observations.
The main conclusion of this study is the really good
ability of ORCA12 model to simulate the meso-scale
activity and particularly the ocean eddies.
The seasonal cycle of the number of anticyclonic
and cyclonic eddies are also comparable to the
altimetry.These two points are of great importance
for the qualification of this simulation to provide
realistic informations for the assimilation scheme
used in Mercator Océan forecast systems. This
assimilation scheme based on the SEEK filter
[Testut, et al., 2003; Tranchant, et al., 2008] needs
3D mode data base.These modes will be computed
from the ORCA12 forced simulation. They have to
represent the ocean meso-scale variability at time
scale from one week to the seasonal cycle. But we
can notice one of the biases in the model. In all
the area, except in the Alaska current, the model
seems to produce an equivalent number of cyclonic
and anticyclonic eddies whereas the proportion is
generally not equivalent in the altimetric data. In
ongoing work, other diagnostics would be realized
to characterize the ocean eddies in the model,
particularly the 3D geometry of eddies and the
associated heat and salt transport in the ocean.
ACKNOWLEDGEMENTS
The authors wish to thank all the Mercator
Océan team, the NEMO developer committee
and the Drakkar project which largely
contributed to the advancement of the ocean
modeling.
16
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REFERENCES
Arakawa, A., and P. J. Lamb
(1980), A potential enstrophy
and energy conserving
scheme for the shallow
water equations, Monthly
Weather Review, 109, 18-36.
Barnier, B., et al. (2006),
Impact of partial steps and
momemtum advection
schemes in a global ocean
circulation model at eddy
permitting resolution, Ocean
Dynamics, DOI: 10.1007/
s10236-006-0082-1.
Biastoch, A., and W.
Krauss (1999), The role
of Mesoscale Eddies in
the source Regions of the
Aghulas Current, journal of
Physical Oceanography, 29,
2303-231 7.
Bourdalle-Badie, R., and
A.-M. Treguier (2006), A
climatology of runoff for
the global ocean-ice model
ORCA025, Mercator Océan.
Boyer, T. P., et al. (2005),
Objective analyses of
annual, seasonal, and
monthly temperature and
salinity for the world ocean
on a 0.25° grid, International
journal of climatology
Chow, C.-H., et al. (2008),
Mesosclae Dongsha Cyclonic
Eddy in the northern South
China Sea by drifter and
satellite observations.,
Journal of Geophysical
Research, 113.
Dai, A., and K. E. Trenberth
(2003), New estimates
of continental discharge
and oceanic freshwater
transport, Symposium
on Observing and
understanding the variability
of water in weather and
climate, 9-13 Feb. 2003,
Long Beach, CA.
Ebuchi, N., and K. Hanawa
(2000), Mesoscale eddies
observed by TOPEX-ADCP
and TOPEX/POSEIDON
Altimeter in the Kuroshio
recirculation Region South
of Japan., Journal of
Oceanography, 56, 43-57.
Goosse, H., et al. (2001),
Description of the CLIO
model version 3.0,
Institut d’Astronomie et
de Geophysique Georges
Lemaitre, Catholic University
of Louvain (Belgium).
Henson, S. A., and A. C.
Thomas (2008), A census of
oceanic anticyclonic eddies
in the Gulf of Alaska, Deep
Sea Research Part I, 55,
163-1 76.
Jacobs, S. S., et al. (1992),
Melting of ice shelves and
mass balance of Antarctica,
Journal of Glaciology, 38,
375-387.
Le Traon, P.-Y., et al. (1998),
An improved mapping
method of multisatellite
altimeter data, Journal of
Atmospheric and Oceanic
Technology, 15, 522-534.
Lévy, M., et al. (2001),
Choice of an advection
scheme for biogeochemical
models, geophysical
Research Letter, 28.
Luce, D. L., and T. Rossby
(2008), On the size and
distribution of rings and
coherent vortives in the
Sargasso Sea, Journal of
Geophysical Research, 113.
Madec, G., et al. (1998),
OPA 8.1 general circulation
model reference manual,
Notes de l’Institut Pierre-
Simon Laplace (IPSL)
- Université P. et M. Curie,
B102 T15-E5, 4 place
Jussieu, Paris cedex 5, 91p.
Madec, G., and M. Imbard
(1996), A global ocean mesh
to overcome the North Pole
singularity, Clim. Dyn., 12,
381 -388. Morrow, R., et al.
(2004), Divergent Pathways
of the cyclonic and anti-
cyclonic ocean eddies,
Geophysical Research
Letter, 31.
Penven, P., et al. (2005),
Average circulation,
seasonal cycle, and
mesoscale dynamics of the
Peru Current System: A
Modelling approach, Journal
of Geophysical Research,
110.
Roullet, G., and G. Madec
(2000), Salt conservation,
free surface and varying
volume: a new formulation
for ocean GCMs, J. Geophys.
Res. - Oceans, 105, 23,927-
923,942.
Testut, C.-E., et al. (2003),
Assimilation of sea-surface
temperature and altimetric
observations during 1992-1
993 into an eddy¬permitting
primitive equation model
of the North Atlantic Ocean,
Journal of Marine Systems,
40-41, 291-316.
Tranchant, B., et al.
(2008), Data assimilation
of simulated SSS SMOS
products in an ocean
forecasting system,
journal of Operational
Treguier, A.-M., et al.
(2003), Aghulas eddy fluxes
in a 1/6° Atlantic model,
Deep Sea Research Part
II, 50, 251 -280. Weiss, J.
(1991), The dynamic of
enstrophy transfer in two
dimensional hydrodynamics,
Physica D., 113
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1
UK Met Office, Exeter, U.K. 2
NOC, Liverpool, U.K
ABSTRACT
In the deep ocean data assimilation has proven itself for several years as a valuable
constituent in operational ocean forecast systems. However, data assimilation for the
tidally driven shelf presents significant additional challenges. The Met Office is developing
a new operational forecast system for the North West European Shelf that incorporates
data assimilation of SST.This system will replace the existing non-assimilative operational
forecast system based on POLCOMS. The physical model utilized in the new system is a
modified version of NEMO suitable for modeling the highly dynamic shelf seas. The sys-
tem incorporates data assimilation of SST using a modified version of the existing FOAM
system. Preliminary hindcast runs have shown that the new system provides good skill
compared to the existing extensively validated POLCOMS system for the same region.
Additionally, the data assimilation has not had an adverse effect on the simulated water
column structure both in well mixed and seasonally stratified waters. This system is
running pre-operationally at the Met Office and will constitute the V1 MyOcean Forecast
system for the North West European Shelf.
INITIALLY
PUBLISHED IN
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NEMO-SHELF,
TOWARDS OPERATIONAL
OCEANOGRAPHY WITH SST DATA
ASSIMILATION ON THE NORTH
WEST EUROPEAN SHELF
BY
E. J. O’DEA1
, J. WHILE1
, R. FURNER1
, P. HYDER1
, A. ARNOLD1
, D. STORKEY1
,
J. R. SIDDORN1
, M. MARTIN1
, H. LIU2
AND J. T. HOLT2
19

INTRODUCTION
The North West European Shelf is one of the most
studied shelf seas systems in the world owing to
the keen economic and environmental interests
of the surrounding nations. Such interests include
marine transport, petrochemical exploitation, fi-
shing,aquacultureandmorerecentlytherenewable
energy industry in the form of wind, wave and tidal
generation of power.Such interests have motivated
the development of a variety of forecast systems
for the region. Such systems have evolved from 2D
tide (Flather 1976) and surge models to fully 3D
(Holt and James 2001) systems at ever increasing
spatial resolution, including ever more complex
processes.
However, until recently the problem of including
data assimilation into an operational forecast sys-
tem for the highly dynamic shelf seas region has
not been tackled. Data assimilation has been used
extensively for a number of years in the deep ocean
with great success leading to systems with much
greater forecast skill (Martin et al. 2007). Motivated
by the potential additional skill of data assimilation,
a new forecast system is under development at the
Met Office that in the first instance includes data
assimilation of SST. The new forecast system is
based upon the existing FOAM system (Storkey et
al. 2010) for the deep ocean which utilizes NEMO
for the core physical ocean model, and an optimal
interpolation method of data assimilation.
Currently the Met Office provides an operational
forecast system based upon POLCOMS for the
North West European Shelf. The system has been
validated over a number of years with continual
developments to improve the system.This existing
system provides a firm reference point against
which any replacement system can be compared.
The old system consists of an outer domain, the
Atlantic Margins Model (AMM) at 12km resolution,
and a nested inner domain, the Medium-Resolu-
tion Continental Shelf domain at 7km resolution.
The new system aims to replace both domains
with a single AMM domain covering the original
AMM region, but at 7km resolution. Both systems
are nested into the FOAM 1/12th degree North
Atlantic model that provides temperature, salinity,
sea surface height and depth integrated current
information at the open boundaries.
The two systems are compared for a 2 year hind-
cast period against observations. The new NEMO
system is run twice, once with and once without
data assimilation. This is to ensure the underlying
physical model provides similar or improved skill
compared the existing POLCOMS system without
data assimilation and also provides a reference
to compare with our assimilative run. Such a
comparison allows us to understand how the data
assimilation of SST changes the solution and if
it produces any unrealistic modifications to the
internal dynamics away from the surface.
PHYSICAL SYSTEM
NEMO (Madec 2008) was originally developed to
model the deep ocean rather than the shelf seas.
Thus, a number of important modifications were
required to ensure that the NEMO physical model
is suitable for application in shelf seas. The first
modification is the inclusion of tidal forcing both on
the open boundary conditions via a Flather radiation
condition (Flather 1976), and the inclusion of the
equilibrium tide.Tidal Modeling also requires a non
linear free surface and this is facilitated in NEMO by
using a variable volume layer approach. The short
time scales associated with tidal propagation and
the free surface require a time splitting approach,
20
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splitting modes into barotropic and baroclinic
components. Additionally, the bottom boundary
condition now includes a log layer representation,
and a k-epsilon turbulence scheme is implemented
with the generic length scale option developed at
Mercator Océan (France).
The coordinate system is a modified version of
S-coordinates (Song and Haidvogel 1994). The
coordinates are pure sigma on the shelf and stretch
off the shelf to maintain vertical resolution near
the surface and bottom. Additionally, where the
bathymetry is particularly steep the coordinates
can intersect the bottom. The loss of vertical reso-
lution at these points is more than compensated by
reducing errors related to the horizontal pressure
gradient term and steep coordinate surfaces. The
horizontal pressure gradient scheme itself is also
updated to a pressure Jacobian scheme and it
performs very well in classical tests including sea
mounts in specified uniform stratification.
Other modifications include:
• Allowing the bilaplacian and laplacian diffusion
operators to work on geopotential or isopycnal
surfaces separately.
• River inputs to be mixed to prescribed water
depths.
• A POLCOMS style light attenuation coefficient that
varies dependant on total water depth.
• The addition of an inverse barometer effect from
atmospheric pressure forcing.
With these modifications the physical modeling
system has been validated in a constant density
case for the evaluation of tides. Additionally, a fully
baroclinic but non-assimilative simulation is used
to assess the forecast system without assimilation.
ASSIMILATION SYSTEM
Data assimilation within NEMO-shelf uses the Ana-
lysis Correction method of (Martin et al. 2007) to
assimilate SST data. In essence this is an iterative
Optimal Interpolation scheme, which treats both
model and observation errors,with their associated
covariances,as constant.Operationally,assimilation
proceeds in three steps. Firstly a 1 day model fo-
recast is performed, within which observations are
compared to model output at the nearest time-step;
this is a First Guess at Appropriate Time FGAT sys-
tem. In the second stage observation minus model
differences are converted to SST increments by
solving the Best Linear Unbiased Estimator (BLUE)
equations. In solving these equations, we use pre-
calculated values for observation error (assumed
uncorrelated), model error, and model error cova-
riances.Finally,to produce the analysis the model is
reran for the same day with the increments added
onto the SST field using the Incremental Analysis
Update (IAU, see Bloom et al. 1996) method. Incre-
ments are added into the model down to the base
of the instantaneous mixed layer, where the mixed
layer depth is determined by a 0.2°C temperature
difference from the surface.
In the present set-up assimilated satellite observa-
tions are taken from the infra-red SEVIRI, AATSR,
METOPandAVHRRinstrumentsandfromtheAMSRE
microwave sensor. Because of biases in satellite
data, a bias correction scheme is used to correct
satellite measurements, with the AATSR sensor,
which is considered to be less biased than the other
satelliteinstruments,andavailablein-situdataused
as reference ‘unbiased’ data. In addition to satellite
measurements,we also assimilate available in-situ
data from drifting buoys, moorings and ships. All
data are quality controlled using a Bayesian system
(LorencandHammon,1988)beforebeingassimilated.
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21

RESULTS
Barotropic Results
A constant density simulation with only tidal forcing
at the boundaries with online harmonic analysis of
the main tidal constituents confirms that the overall
skill of the simulated tides is similar to POLCOMS.
The SSH RMS errors for M2 are 0.188m for NEMO
and 0.179m for POLCOMS and the mean error is
-0.014m and 0.055m for NEMO and POLCOMS
respectively. Figure 1 displays the SSH amplitude
and phase errors between NEMO and observations
for the dominant M2 constituent. It should be noted
thatthe underlyingbathymetryofthePOLCOMS and
NEMO systems are the same. Further refinement
of the tides should be possible with more accurate
bathymetry at 7km resolution.
Preliminary baroclinic and assimilative results
The model system both with and without data
assimilation is compared against POLCOMS for
the hindcast period 2007-2008 for a variety of
observation types. For 2008 the non-assimilative
NEMO system has an SST RMS error of 0.66°C
and for POLCOMS it is 0.69°C. However, there is a
warm bias in NEMO of 0.3°C compared to 0.2°C in
POLCOMS. With assimilation of SST the errors are
much reduced, with an RMS SST error of 0.38°C
and mean of 0.1°C respectively. Both systems are
also compared through the water column using
data at profile points and the ICES data set for
the North Sea. Among the mean surface minus
bed temperature difference for 2008 for the non
assimilative NEMO system,POLCOMS and the ICES
data set, the NEMO results do appear to be closer
to ICES than POLCOMS.
The surface-bed temperatures for 2007 from the
assimilative and non assimilative NEMO systems
the ICES data set. Through most of the North Sea
the effect of data assimilation is small on the stra-
tification with the exception being in the Norwegian
trench where stratification is intensified.
FIGURE 1
SSH amplitude in metres (top left panel) and
phase errors in degrees (top right panel) between
NEMO and observations for the dominant M2
constituent for NEMO constant density run and
absolute SSH amplitude in metres (bottom left
panel) and phase in degrees (bottom right panel)
for model plotted against observations also for
the M2 constituent.
22
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CONCLUSIONS
The SST data assimilation has improved the fore-
cast skill of the model without marked disruption
of the 3D structure of the water column. The new
system is undergoing extensive validation tests
and is running pre-operationally at the Met Office
to ensure the system’s robustness before full
operational implementation.
In addition to the physical system described here,
the ecosystem component ERSEM is also being
coupled to the system with a view to replacing
the existing POLCOMS-ERSEM operational system
at the Met Office. Future upgrades to the system
include bathymetry, improved light attenuation,
profile data assimilation, river inputs from E-HYPE
and Baltic inflow from a Baltic model in place of
climatology.
The SST data assimilation
has improved the forecast skill of the model
without marked disruption of the 3D
structure of the water column.
A new shelf seas operational forecasting system for the North West European Shelf has been developed
based on NEMO with OI data assimilation of SST.
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REFERENCES
Bloom, S. C., Takacs, L. L.,
Da Silver A. M. and Ledvina,
D., 1996: Data assimilation
using incremental analysis
updates. Monthly Weather
Review, 124, 1256-1271
Flather, R. A., 1976:
A tidal model of the North
West European continental
shelf, Mem. Soc. R. Sci.
Liege, 10, 141-164
Holt, J.T., James, I.D.,
2001: An s-coordinate
density evolving model of
the northwest European
continental shelf Part 1
model description and
density structure. Journal of
Geophysical Research 106,
14015–14034.
Lorenc, A. C. and Hammon,
1988: Objective quality
control of observations
using Bayesian methods.
Theory, and a practical
implementation. Q. J. Roy
Met Soc, 114, 515-543
Madec G. 2008.
NEMO ocean engine. Note
du Pole de modélisation,
Institut Pierre-Simon
Laplace (IPSL), France,
No 27 ISSN No 1288–1619
Martin, M.J., Hines,
A. and Bell, M.J., 2007: Data
assimilation in the FOAM
operational short-range
ocean forecasting system:
a description of the scheme
and its impact. Q. J. Roy Met
Soc, 133, 981-995
Song, Y., and D. Haidvogel,
1994: A semi-implicit ocean
circulation model using a
generalized topography-
following coordinates
system, J. Comput. Phys.,
115, 228-244
Storkey, D., E.W. Blockley,
R. Furner, C. Giuavarc’h,
D. Lea, M.J. Martin, R.M.
Barciela, A. Hines, P.
Hyder, J.R. Siddorn, 2010.
Forecasting the ocean state
using NEMO: The new FOAM
system. J. Operational
24
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BIO-
GEOCHEMICAL
MODELING

DO YOU
KNOW
HER?
MARION
GEHLEN
CEA
Marion Gehlen (CEA) is a senior scientist at
LSCE (Laboratoire des Sciences du Climat et
de l’Environnement), Gif-sur-Yvette, France.
She holds a PhD in Earth Sciences (1994).
Her research focuses on understanding and
predictingchangesinmarinebiogeochemical
cycles and ecosystems in response to cli-
mate change and ocean acidification.Marion
Gehlen was a lead scientist in a number of
EU funded large-scale projects targeting the
marine carbon cycle and ocean acidification
(e.g.CarboOcean,CarboChange,EPOCA).She
coordinates the nationally funded research
project Green Mercator aiming at extending
Mercator Océan’s operational systems to
biogeochemistry. She is the co-chair of the
GODAE OceanView task team on ‘Marine
Ecosystem Analysis and Prediction’.
27

28
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COUPLED PHYSICAL-
BIOGEOCHEMICAL OCEAN
MODELING USING NEMO
COMPONENTS
BY
M. GEHLEN 1
, A. YOOL 2
, M. VICHI 3
, R. BARCIELA 4
,
C. PERRUCHE 5
, A. EL MOUSSAOUI 5
AND C. ETHÉ 1
1
IPSL/LSCE, Gif-sur-Yvette, France
2
NOC, Southampton, United Kingdom
3
CMCC, INGV, Bologna, Italy
4
UK Met Office Hadley Centre,
Exeter, United Kingdom
5
Mercator Océan, Toulouse, France
ABSTRACT
The growing awareness for the potential of large scale changes in marine ecosystems
in response to climate change, ocean acidification and deoxygenation has triggered the
rapid development of marine biogeochemical models. These models allow quantifying
the contribution of marine biogeochemistry to regulating Earth’s climate, assessing
anthropogenic impacts on marine ecosystems and projecting the future evolution of the
‘green’ ocean in the Anthropocene. Physical components of the NEMO system have been
used with success in biogeochemical research coupled to four biogeochemical models
of varying complexity: PISCES (provided with the passive tracer module TOP), MEDUSA,
BFM/PELAGOS and HadOCC.The range of possible applications is large spanning from the
assessment of ocean biogeochemical state and its natural variability to climate studies
(past and future). It is illustrated here by selected examples of applications for biogeo-
chemical and climate research, as well as operational oceanography. Biogeochemical
models are briefly described and output is exemplified for global nutrient distributions
(e.g. dissolved inorganic nitrogen), as well as for chlorophyll and integrated primary
production. The qualitative comparison between model output and climatological data
demonstrates the skill of models to reproduce large scale features of biogeochemical
distributions and highlights the importance of the underlying physical model.

INTRODUCTION
along with ocean acidification, eutrophication,
deoxygenation,aswellastheongoingexploitationof
living marine resources are driving major changes
in marine biogeochemical cycles and put marine
ecosystems at risk (e.g.Caldeira and Wickett,2003;
Bopp et al.,2005; Keeling et al.,2010; Lehodey et al.,
2010; Steinacher et al., 2010; Gehlen et al., 2011;
Stock et al.,2011).Biogeochemical modeling,along
with observational programs and experimental stu-
dies is a central tool for (1) understanding marine
biogeochemistry as a component of the Earth’s
climate system; (2) quantifying anthropogenic im-
pacts on marine systems; and (3) projecting trends
in ocean biogeochemistry against the backdrop of
a changing global environment.
Here we present an overview of current global bio-
geochemical applications using NEMO components
along with identifying scientific teams in charge.
The NEMO system is presently structured around
five principal components: the physical model OPA
(Madec 2008); the sea-ice model LIM (Fichefet and
Morales Maqueda,1997),the passive tracer module
In other words, the discipline deals with pathways
of cycling matter between the organic and inor-
ganic compartments of the ocean in the case of
marine biogeochemistry. Relative to large-scale
physical modeling, marine biogeochemical mode-
ling is a relatively young discipline within ocean
research.It has witnessed a rapid development over
the past 25 years leading from box models (e.g;
Broecker and Peng, 1986; Shaffer and Sarmiento,
1995), over relatively simple mixed layer models
(Fasham et al.1990) to increasingly complex 3D
representations of lower trophic levels of marine
ecosystems coupled to ocean general circulation
models (e.g. Aumont et al., 2006; Maier-Reimer
et al., 1996; Moore et al., 2004, Vichi et al., 2007a,
b; Yool et al., 2011). The vitality of the discipline
originates - at least in part - from the awareness
of the contribution of oceanic biogeochemical
processes to the mean state and variability of the
wider climate system.
The combination of global warming and concomi-
tant changes to the ocean physical environment,
Biogeochemistry refers to the study of
exchange fluxes or pathways of chemical
elements between Earth system reservoirs,
as well as processes within these reservoirs
mediated by biota.
BIO-
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MODELING
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29

TOP; the adaptive mesh refinement software AGRIF;
and the data assimilation component NEMO_TAM
(http://www.nemo-ocean.eu/).Inturn,TOPconsists
of three independent components that account for
transport (TRP, advection and diffusion routines),
sources and sinks (SMS, biogeochemistry) and
off-line configurations. In its standard set-up, TOP
includes the biogeochemical code PISCES (Aumont
and Bopp, 2006), along with modules for chlo-
rofluorocarbons and bomb C14
. Biogeochemical
studies are, however, not restricted to simulations
with PISCES, but include examples of applications
based on MEDUSA (Yool etal.,2011),BFM/PELAGOS
(Vichi et al., 2007 a, b) and HadOCC (Palmer and
Totterdell, 2001). Similarly, not all groups use the
sea-ice model LIM (Séférian et al., 2012) or data
assimilation schemes.Biogeochemical models are
either coupled on-line (i.e. run in parallel with) or
off-line (i.e. run subsequently to) with the physi-
cal-components of the NEMO system. The off-line
mode decreases computational burden.The variety
of biogeochemical models used within NEMO cor-
responds to the diversity of research questions to
address. The following sections illustrate appli-
cations from operational oceanography and from
climate research. It provides a brief description
of biogeochemical models, and overview of the
configurations used in published studies, along
with some standardized examples of model output.
COUPLED PHYSICAL-
BIOGEOCHEMICAL MODEL
CONFIGURATIONS
Biogeochemical models suitable for large scale
applications are by necessity simplifications of
the complex network of biological interactions
driving the cycling of matter between reservoirs
of the Earth system. They are distinguished from
ecological models by their focus on the processes
that are most relevant to biogeochemical cycles
rather than the organisms concerned. Examples
of these processes include: primary production,
export production, respiration, production and
dissolution of biogenic silica and carbonates, deni-
trification,nitrogen fixation and many more.Rather
than aiming at a detailed representation of the
marine ecosystem,these models group organisms
together according to their specific ‘function’ in
the corresponding biogeochemical cycle. These
so-called ‘Plankton Functional Types’ (PFTs) are
central to modern state-of-the-art biogeochemical
models (Le Quéré et al., 2005; Hood et al., 2006).
In addition, these models frequently use organism
size to differentiate between different PFTs. Size
influences both bottom-up (e.g.nutrient acquisition)
and top-down (e.g. control by predators) regulation
in plankton ecosystems, with different sizes of
organisms favored under different conditions. For
instance,fast-growing small cells tend to dominate
under oligotrophic conditions (low-nutrient; such
as those in ocean gyres) because they can uptake
nutrients more efficiently. Slower-growing large
cells, in contrast, are favored under eutrophic
conditions (nutrient-replete, such as those that
prevail at the end of winter mixing or close to rivers);
these groups can achieve large biomass values
because they are controlled less tightly by slow-
growing predators. An important simplification
frequently used in biogeochemical models stems
from the observation of near-constant elemental
ratios (C:N:P=106:16:1) of fluxes within the marine
foodweb (Redfield,1963) when averaged over space
and time (e.g. seasonal cycle). This simplification
allows models to use a single basic currency (e.g.
carbon, nitrogen or phosphorus) and to derive the
fluxes of the remaining elements from fixed stoi-
chiometric relationship,without the need to include
additional, computationally costly state variables.
Models based on this principle are often called
“Redfieldian”. Other models are instead based on
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PISCES MEDUSA-1.0 BFM/PELAGOS HADOCC
Biogeochemical cycles N (NO3
, NH4
), Si, Fe, P,
C(1)
, O2
N, Si, Fe N (NO3
, NH4
), Si, Fe, P,
C(1)
, O2
N (NO3
, NH4
), C(1)
Autotrophic PFTs Nanophytoplankton,
diatoms
Small
(picophytoplankton)
large (diatoms)
Picophytoplankton,
nanophytoplankton,
diatoms
Phytoplankton
Heterotrophic PFTs Micro-,
mesozooplankton
Micro-
and
mesozooplankton
Nano-, micro-,
mesozooplankton and
bacterioplankton
Zooplankton
BGC functions repre-
sented without explicit
PFT
CaCO3
production/
dissolution
N-
fixation/
denitrification
CaCO3
production Biogenic Si
dissolution
Biogenic Fe
dissolution
CaCO3
production
External inputs River carbon & nutrients
aeolian Fe, Si, N
sedimentary Fe source
Aeolian Fe
sedimentary Fe
(optional)
Aeolian Fe
References Aumont and Bopp
(2006)
Gehlen et al. (2007)
Tagliabue et al. (2011)
Séférian et al. (2012)
Popova et al. (2010)
Yool et al. (2011)
Popova et al. (2012)
Vichi et al. (2007ab)
Vichi and Masina (2009)
Vichi et al. (2011)
Patara et al. (2012b)
Palmer and
Totterdell (2001)
Hemmings et al. (2008)
(1)
Dissolved and particulate, prognostic alkalinity, CaCO3
production and dissolution, CO2
chemistry fully resolved.
TABLE 1
Overview of biogeochemical
models
variable stoichiometry, which allows the ratios
between the major elements to vary depending
on environmental conditions and physiological
requirements. While biogeochemical models are
largely based on empirical parameterizations,
rather than on first order principles comparable to
thoseinvolvedinphysicalmodels,theynevertheless
mostly share a common conceptual framework.
This holds for the models described below,but they
do exhibit what might be seen as a progression in
complexity from HadOCC and MEDUSA, to PISCES
to BFM/PELAGOS.Besides the discrepancies in the
number of PFTs, the major difference is that Ha-
dOCC and MEDUSA are more strictly “Redfieldian”,
while PISCES allows prognostic elemental ratios
and the BFM/PELAGOS is fully stoichiometric. The
following sections provide an overview of each of
themodelsconsideredhere,whileTable1compares
important common aspects.
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PISCES
The PISCES (Pelagic Interaction Scheme for Carbon
and Ecosystem Studies) model simulates bio-
geochemical cycles of oxygen, carbon and major
nutrients controlling phytoplankton growth (nitrate,
ammonium,phosphate,iron,silicic acid).The model
has 24 state variables. The model distinguishes
betweentwosizeclassesofphytoplankton(diatoms
and nanophytoplankton) and zooplankton (micro-
and mesozooplankton). Phytoplankton growth
depends on light, temperature and the external
availability of nutrients. Prognostic variables of
phytoplankton are total biomass in C, Fe, Si (for
diatoms) and chlorophyll and hence the internal
Fe/C, Chl/C, and Si/C ratios. For zooplankton, all
these ratios are supposed constant and thus, the
total biomass in carbon is the only prognostic va-
riable (e.g.the model is “Redfieldian”).The bacterial
pool is not modeled explicitly.The PISCES standard
version distinguishes three non-living organic car-
bon compartments: semi-labile dissolved organic
carbon (DOC) with timescales of several weeks to
several years, two size classes of particulate orga-
nic carbon (small and big particles).While the C/N/P
composition of dissolved and particulate matter is
tied to Redfield stoichiometry, the iron, silicon and
carbonate contents of the particles are computed
prognostically. Next to the three organic detrital
pools, carbonate and biogenic siliceous particles
are modeled. In the standard model version, the
parameterization of particle flux distinguishes
two particle size classes: “small” with a constant
prescribed sinking speed of 3m/d and “large” with
a sinking speed increasing with depth. Ballasting
of fluxes by biogenic Si and/or carbonate is not
taken into account. PISCES simulates dissolved
inorganic carbon and total alkalinity (carbonate
alkalinity + borate + water). The CO2
chemistry is
computed following the OCMIP protocols. Cycles
of phosphorus and the nitrogen are decoupled by
nitrogenfixationanddenitrification.Boundaryfluxes
account for nutrient supply from three different
sources: atmospheric dust deposition of Fe, Si and
N (Aumont et al., 2008), rivers for macronutrients,
dissolved carbon,and alkalinity (Ludwig et al.,1996)
and inputs of Fe from marine sediments (Johnson
et al. 1999; de Baar and de Jong 2001). The model
is fully described in Aumont and Bopp (2006).
The PISCES model was developed as a flexible
tool for global biogeochemical and carbon cycle
studies (including ocean acidification) covering a
range of time scales from glacial-interglacial cycles
to future projections. Despite its relatively simple
representation of first trophic levels of the marine
ecosystem, it is successful in reproducing ocean
productivity and biogeochemical cycles across
major ocean provinces (Schneider et al., 2008;
Steinacher et al., 2010). The model has been used
for a variety of studies coupled both on-line and
off-line to OPA from resolution ranging from 1/4°
to 2°. It is part of the IPSL Earth system model
and simulations contributed to the previous (e.g.
Friedlingstein et al., 2006; Schneider et al., 2008;
Roy et al., 2011; Steinacher et al., 2010), as well as
to the current Intergovernmental Panel on Climate
Change (IPCC; http://www.ipcc.ch/) assessment
report (Séférian et al.,2012).Moreover,PISCES has
been integrated to the Mercator Océan operational
system (El Moussaoui et al., 2011). The model
standard version is freely available through the
NEMO website.
MEDUSA
MEDUSA-1.0 (Model of Ecosystem Dynamics, nu-
trient Utilisation, Sequestration and Acidification)
is a size-based, intermediate complexity model
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that divides the global plankton community into
“ small ” and “ large ” portions, and resolves 11
state variables distributed between the nitrogen
(6), silicon (2) and iron (1) cycles. Nutrients in ME-
DUSA are represented by three state variables:
total dissolved inorganic nitrogen (the sum of all
nitrogen nutrient species), dissolved silicic acid
and total iron (all of the latter is bioavailable but
a fraction is also available to be scavenged).The
“small” portion of the ecosystem is intended to
represent the microbial loop of picophytoplankton
and microzooplankton, while the “large” portion
covers microphytoplankton (specifically diatoms)
and mesozooplankton. The intention of MEDUSA
is to separately represent small, fast-growing
phytoplankton that are kept in check by similarly
fast-growing protistan zooplankton, and large,
slower-growing phytoplankton that are able to
temporarilyescapethecontrolofslow-growingme-
tazoan zooplankton. Both modelled phytoplankton
groups are limited by the availability of light and
nitrogen and iron nutrients, with the diatoms ad-
ditionally constrained by the occurrence of silicic
acid. Diatoms and non-diatoms have separate,
prognostic chlorophyll variables to permit shifts in
the C:chl ratio, and the diatoms are described with
an additional silicon variable to allow a dynamic
Si:N ratio.MEDUSA is otherwise “Redfieldian” with
other elements such as iron and carbon (implicit
only) coupled to the nitrogen cycle via fixed stoi-
chiometries. The non-living particulate detritus
pool is similarly split between small, slow-sinking
particles that are simulated explicitly, and large,
fast-sinking particles that are represented only
implicitly. Remineralization of the latter particles
uses a ballast scheme that includes “protection”
of sinking organic material by associated bioge-
nic minerals, opal and calcite (Armstrong et al.,
2002). Calcification in MEDUSA is represented via
a function of latitude (higher at the equator, lower
at the poles) and large detrital particle production.
Since iron is scavenged from the water column,
an important regulatory process in MEDUSA is
the geographically-variable resupply of iron by
aeolian dust, which acts to limit production in
so-called high-nutrient-low-chlorophyll (HNLC)
regions. See Yool et al. (2011) for a full description
of MEDUSA-1.0.
Thus far, MEDUSA has only been simulated within
on-line instances of the NEMO physical model at
resolutions of 2°, 1° (Yool et al, 2011) and 1/4°
(Popova et al., 2010; Popova et al., 2012). A re-
vised version of the model, MEDUSA-2.0, is in
development for ocean acidification studies and
additionally includes the biogeochemical cycles of
carbon, alkalinity and oxygen, and processes such
as air-sea gas exchange and dynamic calcification.
In the results shown here, MEDUSA-1.0 is used in
NEMO v3.2 at 1° degree resolution (tripolar grid
with equatorial focusing of resolution) with 64
vertical levels.
BFM
The Biogeochemical Flux Model (BFM; Vichi et al.,
2007 a, b, http://bfm.cmcc.it) is a numerical tool
designed to study stoichiometric relationships in
the biogeochemistry of marine ecosystems. The
major chemical and biological components that de-
termine the dynamics of the planktonic ecosystem
are described in terms of theoretical concepts of
chemical functional families and living functional
groups (Vichi et al., 2007 a, b). The model extends
and advances the original philosophy of ERSEM
(European Regional Seas Ecosystem Model; Baretta
et al., 1995) in modern coding standards, taking
into account both pelagic and benthic dynamics and
the coupling between biogeochemical and physical
processes in the marine environment. The model
is freely available to the scientific community and
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it is maintained by a consortium of developers
similar to the NEMO consortium. The global ocean
configuration of the BFM is called PELAGOS (PE-
LAgic biogeochemistry for the Global Ocean,Vichi
et al., 2007b; Vichi and Masina, 2009) and uses a
subset of the BFM state variables listed in Table
1. The living functional groups simulated by BFM
are three unicellular planktonic autotrophs, three
zooplankton groups and a separate group for ae-
robic and anaerobic bacterioplankton. The other
biogeochemical tracers are nitrate, ammonium,
phosphate, silicic acid, dissolved bio-available
iron, oxygen, carbon dioxide, and dissolved and
particulate (non-living) organic matter.Diatoms are
the largest phytoplankton group and are characte-
rized by high growth rates in cool and nutrient-rich
conditions, whereas the nanophytoplankton group
is adapted to more nutrient-depleted conditions.
Nutrient uptake is parameterized following a modi-
fied Droop kinetics which allows for multi-nutrient
limitation and variable internally-regulated nutrient
ratios. Chlorophyll synthesis is also down-regu-
lated when the rate of light absorption exceeds
the utilization of photons for carbon fixation. Net
primary production is parameterized as a function
of light, temperature, chlorophyll, iron cell-content,
and dissolved silicate concentration (Vichi et al.,
2007 a). The cell availability of N and P does not
directly control photosynthesis,but the subsequent
transformation of carbohydrates into proteins and
cell material.A portion of photosynthesized carbon
is thereby released as Dissolved Organic Carbon
(DOC) according to the internal nutrient quota
(Baretta-Bekker et al., 1997; Vichi et al., 2007a).
Nutrient remineralization by bacteria is controlled
by the quality of dissolved and particulate organic
matter (i.e. the stoichiometric content of nutrient
respect to carbon), which in turn also regulates
the competition of bacteria with phytoplankton
for dissolved inorganic nutrients. This implies
that the fluxes of carbon and limiting nutrients
through the food web are not characterized by
fixed values and ratios. Finally, the ocean carbo-
nate chemistry is solved with a simplified solution
proposed by Follows et al. (2006), and sea-air CO2
fluxes are calculated using the Wanninkhof (1992)
parameterization.
PELAGOS was originally coupled with the previous
ocean engine of NEMO and it has now been ported
to the current version by CMCC in the framework
of the NEMO consortium. The next public release
will allow all NEMO users to run the coupling
with the BFM in a seamless way downloading the
BFM code from its website. PELAGOS has shown
skill at reproducing observed climatologies and
interannual variability of biogeochemistry and
plankton properties (Vichi and Masina,2009; Patara
et al. 2011) as well as responses to anthropogenic
emission scenarios (Vichi et al., 2011; Patara et al.
2012b). Its flexibility in terms of material flows and
elemental stoichiometry within the lower trophic
levels of the ecosystem make is a flexible tool for
the detection of ecological responses to climate
changes.
HADOCC
The Hadley Centre Ocean Carbon Cycle Model (Ha-
dOCC) is a simple NPZD (nutrient, phytoplankton,
zooplankton and detritus), which also includes
dissolved inorganic carbon (DIC) and alkalinity in
order to complete the carbon cycle (Palmer and
Totterdell, 2001). The main nutrient component in
HadOCC is nitrate (ammonium is also calculated),
and the NPZD variables are modeled in terms of
their nitrogen content. Conversion between carbon
and nitrogen is performed using fixed Redfield
ratios and the model uses a variable carbon to
chlorophyll ratio based on Geider et al. (1997).
HadOCC also has the option to allow phytoplankton
growth rates to increase with temperature through
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use of a Q10 parameter (Eppley, 1972; Palmer and
Totterdell, 2001), and can use the multi-spectral
light penetration model of Anderson (1993).The full
HadOCC model equations are given in Hemmings
et al. (2008).
HadOCC has been widely used for carbon cycle
studies at the Met Office Hadley Centre, and was
the ocean biogeochemical component of the first
coupled climate-carbon model (Cox et al., 2000),
which examined future climate-carbon feedbacks.
A development of the model, Diat-HadOCC (Collins
et al.,2011),which has a more complex ecosystem,
is currently being used in CMIP5 simulations that
will contribute to the Intergovernmental Panel
on Climate Change (IPCC) 5th
Assessment Report.
HadOCC has also been coupled to the operational,
global configuration of the Met Office’s Forecasting
Ocean Assimilation Model (FOAM; Storkey et al.,
2010), which is based on version 3.2 of the Nucleus
for European Modeling of the Ocean (NEMO) hy-
drodynamic model (Madec, 2008), and the second
version of the Louvain-le-Neuve sea ice model
(LIM2; Timmermann et al., 2005). At the surface
the model is forced by six-hourly mean fluxes from
the Met Office global NumericalWeather Prediction
model. A key feature of FOAM is the ability to assi-
milate remotely sensed and in situ observations of
temperature, salinity, sea-level anomaly (SLA), sea
ice concentration.The data assimilation scheme is
of optimal interpolation (OI)-type, and is described
in detail in Martin et al. (2007) and Storkey et al.
(2010). This capability has been extended to also
assimilate biogeochemical observations, such as
derived chlorophyll from satellite ocean colour
(Hemmings et al,2008; Ford et al.,2012) and in situ
pCO2
(While et al., submitted). The coupled model
assimilates chlorophyll derived from the level three
merged ocean color data provided by GlobColour.
The chlorophyll observations used are global, daily
averaged fields (with associated error estimates
and confidence flags) and they are assimilated
using the nitrogen balancing scheme described in
Hemmings et al. (2008), which directly updates all
(observed and unobserved) biogeochemical model
state variables.The observation operator performs
a comparison between observations and model
values at the observation time by using the FGAT
(First-Guess at the Appropriate Time) technique,
and this information is very useful for verifying the
biological model, in addition to being used in the
assimilation.For the merged level three GlobColour
products, where no time information is supplied,
the chlorophyll observations are taken to be valid
at 12:00 UTC. HadOCC is coupled on-line to NEMO,
and is called at every model time step (30 minutes).
The coupling is effectively one-way, which means
that the physical fields drive the biogeochemical
variables, but there is no feedback from the bio-
geochemical to the physical variables. The state
variables are all treated as oceanic tracers,and are
advectedusingtheMonotonicUpstreamSchemefor
Conservation Laws (MUSCL) scheme, which forms
part of the NEMO code (Lévy et al., 2001). Though
chlorophyll is assimilated in this study it is not a
state variable within the model, but is derived from
phytoplanktonbiomassusingnitrogentocarbonand
carbon to chlorophyll ratios. DIC and alkalinity are
controlled by the physical and NPZD variables, but
have no influence on them. Their inclusion allows
the calculation of sea surface pCO2
and air-sea CO2
flux,which are in turn affected by atmospheric pCO2
.
The FOAM system is run operationally at the Met
Office on a daily basis, producing analyses and
six-day forecasts. It is run globally at 1/4° reso-
lution, and in three 1/12° regional configurations,
covering the North Atlantic Ocean,Indian Ocean and
Mediterranean Sea. However due to the additional
computational cost of the HadOCC biogeochemical
model, for the purposes of this study a non-ope-
rational version of the FOAM system is being run
globally, using a 1° tripolar grid with 42 vertical
levels.
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PHYSICAL-BIOGEOCHEMICAL
MODEL APPLICATIONS
Marine biogeochemical cycles contribute to sha-
ping the mean state and the variability of the cli-
mate system. The world ocean exchanges major
greenhouse gases, such as CO2
, CH4
, N2
O with the
atmosphere. Integrating up to 2008, the ocean has
absorbed approximately 1/3 of total anthropogenic
emissions of CO2
(due to activities such as fossil fuel
combustion, land-use change and cement produc-
tion) since the start of industrialization (Khatiwala
et al., 2009), making the ocean an important sink
for anthropogenic CO2
. This continuous uptake,
while decreasing the climatic impact of CO2
, is
also the origin of important changes in seawa-
ter chemistry referred to as ocean acidification
(Gattuso and Hansson, 2011). Chemical changes,
along with modifications in ocean circulation in
response to global climate change are anticipated
to drive major changes in ocean biogeochemistry.
It is expected that the reorganization of the ocean
carbon cycle in response to warming and ocean
acidification will lead to a decline in the efficiency
of processes that lead to ocean storage of CO2
, and
will result in a positive feedback to atmospheric
CO2
and hence further increased radiative forcing
(e.g. Friedlingstein et al., 2006; Gehlen et al., 2011).
However, anthropogenic forcing is not the only dri-
ver of changes in the marine carbon cycle, as the
latter shows variability on interannual to decadal
time scales in response to natural climate variability
as well. The forecasting of ocean biogeochemistry
and carbon cycle over the coming centuries, along
with the detection of impacts of global change
against the background of natural variability, are
key issues in current biogeochemical research.
To capture the interannual and climatic variations,
the models described above are used in typically
one of two ways.Firstly,in traditional,forced-ocean
mode, whereby the model ocean is forced at its
surface by fixed external fields of atmospheric
properties (temperature, humidity, winds, heat
and freshwater fluxes) derived from reanalysis
of observations or from atmospheric models. Se-
condly,as part of a fully-coupled ocean-atmosphere
system, or Earth system model (ESM), in which
both geophysical fluids are explicitly simulated and
are fully interactive. All of the models described
above are used in both modes, with the exception
of MEDUSA which is currently only used in forced-
ocean mode. In the case of forced-ocean simula-
tions, an additional method involves running the
biogeochemical model in so-called “off-line mode”,
whereby ocean physics is provided by output from
a pre-existing simulation rather from one running
concurrently with the biogeochemistry. This mode
is useful since it has lower computational costs.
The following section presents selected examples
of model applications for climate and carbon cy-
cle research. This is followed by a discussion of
applications of biogeochemical models for ope-
rational oceanography. This is an emerging field
of research made possible by the increase in
computing power and the development of glo-
bal close-to-real-time observing systems (e.g.
ocean color,Eulerian time-series stations,bioargo).
These latter developments aim to provide ocean
biogeochemical state estimates (reanalysis) and
short-term forecasts mainly for marine resource
management.
1. Biogeochemical cycles
Onekeyuseofbiogeochemicalmodelsistosimulate
themagnitudeanddistributionofmajorprocessesin
order to better understand the factors that regulate
them.Toillustratemodelperformanceinthiscontext,
wehavecomparedastandardsuiteofoutputfrom5
model configurations of the four models discussed
here. We compare: (1) two ESM configurations
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(BFM/PELAGOS in CMCC-ESM and PISCES in IPSL-
CM5-LR); (2) two coupled physical-biogeochemical
configurations (MEDUSA and BFM/PELAGOS); and
(3)acoupledphysical-biogeochemicalconfiguration
constrained by the assimilation of chlorophyll data
(HadOCC). Model output for three key variables
simulated by all of the biogeochemical modes are
considered: surface dissolved inorganic nitrogen
(DIN); surface chlorophyll; and vertically-integrated
net primary production (NPP). The output conside-
red in each case is the 5-year mean for the period
2001-2005 (inclusive). Modeled DIN is compared to
the World Ocean Atlas 2009 climatology (Garcia et
al., 2010), while chlorophyll and NPP are compared
to satellite-derived observations and estimates for
the same period of time.
DIN is the reservoir of nitrogen nutrient species
(nitrate,nitrite,and ammonium) available to oceanic
“plants”, phytoplankton, and plays a critical role
in regulating the distribution of productivity in the
ocean. DIN is consumed in the sunlit surface ocean
byphytoplankton–whichultimatelyprovideasource
of food for biological resources such as fish and
shellfish – and replenished by ocean circulation
via the upwelling of deeper waters in which sinking
organic matter has been regenerated back to DIN.
Low levels of DIN prevail in permanently stratified
oligotrophic gyres, high levels are associated with
upwelling areas and areas with strong seasonal
mixed layer dynamics. Maximum levels of DIN are
typically found in the North Pacific and the Southern
Ocean – so-called High Nutrient Low Chlorophyll
(HNLC) regions – where primary production is li-
mited not by nitrate but by the micronutrient iron.
As such,large-scale patterns of DIN availability are
influenced by both the biogeochemical and physical
components of models.There are discrepancies in
some regions, such as the Equatorial Pacific and
the Southern Ocean, the model fields generally
compare well with observations.
Surface chlorophyll and vertically-integrated
primary production are related biogeochemical
quantities that respectively represent the standing
stock of phytoplankton (more accurately their
pigments) and the quantity of carbon dioxide fixed
by them into organic molecules such as sugars.
The former can be observed at the large-scale by
satellite-mounted ocean colour sensors, while the
latter is estimated from surface chlorophyll and
other ocean properties using empirical algorithms.
Global estimates of primary production range from
to 40 to 60 Gt C y-1 (Carr et al., 2006), and here a
simple average of three such algorithms – VGPM
(Behrenfeld & Falkowski,1997),Eppley-VGPM (Carr
et al.,2006) and CbPM (Westberry et al.,2008) – has
been used.While the models have broad similarity
with observed chlorophyll, there are significant
differences at the regional scale. BFM (CMCC-
CESM and PELAGOS) shows significant excess
chlorophyll in the Southern Ocean, while MEDUSA
is systematically higher at the equator and lower
in the subtropics. As expected, the agreement is
greatest with HadOCC,which utilizes observational
chlorophyll as part of its data assimilation. Pe-
rhaps unsurprisingly, model agreement on ocean
productivity is similarly mixed. The models mimic
the large-scale patterns estimated by the empirical
algorithms,but detailed correspondence is weaker.
Of particular note is that HadOCC significantly
over-estimates productivity – most notably in the
Southern Ocean – in spite of assimilating observed
chlorophyll. Table 2 provides a summary of global
averages and totals for the properties.
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OBSER-
VATIONS
BFM/PELAGOS
(CMCC-CESM)
HADOCC PISCES
(IPSL-CM5A)
MEDUSA BFM/
PELAGOS
Mean
surface
DIN
5.23
mmol
N m-3
7.52 7.11 4.66 6.15 4.90
Mean
surface
chloro-
phyll
0.220
mg chl
m-3
0.234 0.234 0.178 0.258 0.310
Total
primary
production
46.0
Gt C y-1
35.5 63.8 33.0 43.4 32.4
2. Climate research
The PISCES and BFM/PELAGOS models (coupled
to OPA) are components of Earth system mo-
dels (ESMs) that have contributed to the Climate
Model Intercomparison Project Phase 5 (CMIP5;
http://pcmdi-cmip.llnl.gov/cmip5) to the ongoing
IPCC Assessment Report (AR5). The BFM model
in its global PELAGOS configuration is part of the
CMCC-INGV ESM (Vichi et al., 2011), and its CMIP5
model identifier is CMCC-CESM. PISCES is the
marine biogeochemical component of two French
ESMs, specifically IPSL-CM5 and CNRM (Séférian
et al. 2012). These Earth system models are used
to investigate projected trends of ocean biogeo-
chemistry and associated feedbacks to the Earth’s
radiative budget under different representative
concentration pathways (RCP; Moss et al., 2008).
While IPCC AR5 is still ongoing, previous modeling
studies including OPA/PISCES, respectively OPA/
PELAGOS focused on projected trends in primary
and export production (Schneider et al., 2008;
Steinacher et al., 2010; Vichi et al., 2011; Patara et
al., 2012a, b), as well CO2
exchange fluxes with the
atmosphere (Roy et al., 2011) and ocean acidifica-
tion (Orr et al., 2005; Gehlen et al., 2007).
TABLE 2
Overview of model-
observation comparison.
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Figure 1 compares yearly mean sea-to-air fluxes
of CO2
averaged over 1996 to 2005 (positive values
for outgassing) for IPSL-CM5-LR and CMCC-ESM to
a data compilation by Takahashi et al. (2009). The
large scale pattern of source and sink regions for
CO2
reflects the combination of ocean circulation,
thermodynamics of the CO2
system, biological
production and the increasing concentration of
atmospheric CO2
(Sarmiento and Gruber, 2006).
Both models reproduce major sink regions asso-
ciated with areas of intermediate and deep water
formation (e.g.Antarctic convergence and the North
Atlantic), as well as source regions associated
with upwelling (e.g. Equatorial upwelling, Antarctic
divergence). However, there remain significant
biases in model results. For example, both data
and IPSL-CM5-LR agree on a source region linked
to the Somali / Arabian Sea upwelling, while this
region is a sink in CMCC-ESM. This feature reflects
the lack of upwelling in CMCC-ESM which probably
explained by the low resolution of the atmospheric
model and highlights to role of ocean physics as an
underlying control of biogeochemical distributions.
3. Physical-biogeochemical model applica-
tions for operational oceanography
The vast increase in computing power in recent
years has made the on-line coupling of physi-
cal-biological models possible on a routinely basis
and, together with advances in the modeling of
biological processes, has led to the development
of numerous operational and pre-operational ap-
plications, although most of these are currently
being used in coastal waters. These applications
range from aiding the design of observational
networks (Barciela and Brasseur,2012) to fisheries
management.The latter is an application which has
evolved towards an ecosystem-based approach,
where initial attempts are being made to exploit
routinely available environmental data in annual
fisheries stock assessments (Berx et al., 2011),
monitoring fish migration (Hobday and Hartman,
2006) and providing advice on pelagic fisheries
like tuna (Lehodey et al., 2010). The dissemina-
tion of freely available biological products, for all
users, is also becoming a reality through initiatives
such as the FP7 MyOcean project (www.myocean.
eu.org), whose champion users include the Euro-
Comparison of computed
distributions of sea to air
fluxes of CO2
for (a) the
data base by Takahashi et
al. (2009), (b) 1996-2005
IPSL-CM5A mean fluxes, (c)
1996-2005 CMCC-ESM mean
fluxes.
FIGURE 1
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GEOCHEMICAL
MODELING
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39

pean Environment Agency and the UK’s Centre for
Environment, Fisheries and Aquaculture Science
(Cefas).Frameworks like FOAM and Mercator Océan
provide a significant number of these products for
the global ocean and the shelf seas.
The FOAM framework consists of a suite of models
run operationally at the Met Office on a daily basis,
producing analyses and six-day forecasts. For the
deep ocean, it is run globally at 1/4° resolution
(the ORCA025 configuration), and in three 1/12°
regional configurations,covering the North Atlantic
Ocean,Indian Ocean and Mediterranean Sea.There
is also a FOAM configuration run operationally for
the coastal waters, the 7km Atlantic Margin Model
(AMM7), coupled on-line to the ERSEM model (Ed-
wards et al., submitted). FOAM has a long tradition
of applications in operational oceanography,initially
developed to meet the requirements of the UK’s
Royal Navy, but its product portfolio has broaden in
recent years to also provide operational biological
products to various users,such as the Environment
Agency and to a wider international network via
the Marine Core Service of the MyOcean project
(http://www.myocean.eu/).
Since the beginning of 2012, the French center
for operational oceanography Mercator Océan
has added near real time assessment of ocean
biogeochemistry called BIOMER to its suite of
products (delivered by the MyOcean website).
BIOMER is based on the ecosystem model PISCES
forced off-line with ocean physical fields provided
by the global operational system PSY3V3 at ¼°
spatial resolution (NEMO 3.1, 50 vertical levels,
atmospheric forcings from ECMWF operational
analysis at 3h, CORE bulk formulation), with assi-
milation of temperature, salinity and sea level data
via the SEEK method (Brasseur and Verron, 2006)
and an Incremental Analysis Update (Bloom et al.,
1996). To decrease the computational burden, the
spatial resolution of physical fields is degraded
to 1° and temporal output is averaged over one
week (El Moussaoui et al., 2011). At present, the
system does not assimilate biogeochemical data.
Model output is made available in near real time
with a lag of two weeks for the following tracers:
NO3
, PO4
, O2
, net PP, Chl. An output for week 36
of 2012 shows that distributions are not directly
comparable to those considered previously,as they
represent weekly snapshots compared to five year
averages (Fig. 1). Nevertheless, the large scale
patterns are in agreement: low chlorophyll levels
associated to well-stratified oligotrophic gyres
and increased chlorophyll levels linked to Eastern
boundary upwelling regions and areas with strong
seasonal mixed layer dynamics.At latitudes around
60° S, the on-set of austral spring goes along with
suitable conditions for phytoplankton development,
as testified by increased chlorophyll concentration.
This stands in contrast to conditions prevailing at
the end of summer in the North Atlantic where the
mixed layer is low in nutrients after spring and
summer blooms.
40
BIO-
GEOCHEMICAL
MODELING
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CONCLUSION
open question. The computational cost increases
rapidly with model complexity and in particular for
high resolution operational applications it is im-
portant to identify the minimal needed complexity.
Model data have been generated with different
biogeochemical components coupled to different
versions of NEMO-OPA. The variability in under-
lying physics precludes a detailed attribution of
causes and effects of between model differences
to either biogeochemistry or physics.Having NEMO
as a common, unifying framework to which the
biogeochemical components are embedded, it
would be straightforward to set up coupled on-
line physical-biogeochemical simulations using an
identical physical model and atmospheric forcing.
This approach would allow assessing model skill in
terms of complexity of the biogeochemical model.
ACKNOWLEDGEMENTS
MG, AE, CP and CE acknowledge financial
support provided by the French Green Mercator
project (INSU/LEFE/GMMC). MV wishes to thank
the GreenSeas project no. 265294 funded by the
EU FP7-ENV-2010. AY gratefully acknowledges
the financial support of the Natural
Environmental Research Council (NERC).
Biogeochemical ocean modeling is a rapidly ex-
panding field of marine research contributing to
a diversity of scientific questions. Coupled physi-
cal-biogeochemical models are run routinely within
NEMO and have proven skill for applications within
biogeochemical and climate research (e.g.Séférian
et al., 2012), as well as operational oceanography
(Brasseur et al.,2009).The development of the later
is made possible by a significant increase in com-
puting power, along with the growing availability of
real time and near-real time data. It is foreseeable
that operational systems including biogeochemical
variables will gain of importance in the context of
marine environmental management. The qualita-
tive comparison between biogeochemical model
output obtained for various model configurations
(coupled Earth System models, forced on-line and
off-line configurations, with and without data as-
similation) and climatological data demonstrates
the capability of models to reproduce large scale
features and highlights the importance of the
underlying physical model. Obviously, model-data
comparison goes beyond the climatological mean
state and should include temporal variability from
seasonal to inter-annual timescales. The biogeo-
chemical models presented in this paper differ
in the level of complexity of the representation of
first levels of the marine ecosystem. The level of
complexity needed for capturing the main features
of marine biogeochemistry and ecosystems is an
BIO-
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MODELING
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41

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