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QUALITY INFORMATION DOCUMENT
For Global Biogeochemical Analysis and Forecast
Product
GLOBAL_ANALYSIS_FORECAST_BIO_001_014
Issue: 3.0
Contributors: Perruche C., Hameau A., Paul J., Régnier C., Drévillon M.
Approval Date by Quality Assurance Review Group : October 2016
QUID for Global Biochemical Analysis and Forecast
Product
GLOBAL_ANALYSIS_FORECAST_BIO_001_014
Ref: CMEMS-GLO-QUID-001-014
Date
: 15 September 2016
Issue
: 3.0
CHANGE RECORD
Issue
Date
§
Description of Change
Author
Validated By
1.0
13/05/2014
All
Creation of the document
Perruche, C.
Drillet, Y.
1.1
29/09/2014
All
Modified after the QuARG review
Perruche, C.
Drillet, Y.
2.0
15/12/2014
§ IV.2
mostly
Forecast assessment
Perruche, C.
Drévillon, M.
2.1
17/12/2014
MyOF modifications
Y. Drillet
Y. Drillet
2.2
13/02/2015
Modified after the QuARG review
C. Perruche
C. Perruche
2.3
May 1 2015
all
Change format to fit CMEMS graphical
rules
2.4
February
2016
All
Remove old MyOcean references
3.0
September
§VI
mostly
New
release
forced
by J. Paul and C. Y. Drillet
global_analysis_forecast_phys_001 Perruche
_024
2016
© EU Copernicus Marine Service – Public
L. Crosnier
M Drevillon
Page 2/ 38
Y Drillet
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Ref: CMEMS-GLO-QUID-001-014
Date
: 15 September 2016
Issue
: 3.0
TABLE OF CONTENTS
I
Executive summary ....................................................................................................................................... 4
I.1 Products covered by this document ........................................................................................................... 4
I.2 Summary of the results ............................................................................................................................... 4
I.3 Estimated Accuracy Numbers .................................................................................................................... 4
II
Production Subsystem description ................................................................................................................ 6
II.1 Biogeochemical model PISCES ................................................................................................................ 6
II.2 Physical model NEMO .............................................................................................................................. 7
II.2.1
Until October 19th 2016 ..................................................................................................................... 7
II.2.2
From October 19th 2016 on ............................................................................................................... 7
II.3 Coupling and configuration ...................................................................................................................... 8
III
Validation framework ............................................................................................................................... 9
IV
Validation results ......................................................................................................................................... 11
IV.1 Hindcast assessment ............................................................................................................................... 11
IV.1.1
Chlorophyll ................................................................................................................................. 12
IV.1.2
Nitrates ........................................................................................................................................ 19
IV.1.3
Phosphates .................................................................................................................................. 21
IV.1.4
Silicate ........................................................................................................................................ 22
IV.1.5
Dissolved Oxygen ....................................................................................................................... 23
IV.1.6
Iron.............................................................................................................................................. 25
IV.1.7
Phytoplankton biomass in carbon ............................................................................................... 25
IV.1.8
Primary production ..................................................................................................................... 26
IV.2 Forecast assessment ................................................................................................................................ 27
V
Quality changes since previous version ...................................................................................................... 29
VI
NEW FORCING FIELDS SINCE OcTober 2016 ..................................................................................... 33
VII
References ............................................................................................................................................... 37
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I
Ref: CMEMS-GLO-QUID-001-014
Date
: 15 September 2016
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: 3.0
EXECUTIVE SUMMARY
I.1 Products covered by this document
The product described in this document is: GLOBAL_ANALYSIS_FORECAST_BIO_001_014. It is a near
real time and forecast product updated weekly.
It consists of weekly means of several biogeochemical variables (Nitrates, Phosphates, Silicates, Iron,
Dissolved Oxygen, Concentration of Chlorophyll, Phytoplankton Biomass, and Primary Production) on
a regular grid at ½° resolution (0.5° lat x 0.5° lon), with 50 vertical levels, on the global ocean.
I.2 Summary of the results
The quality of the global biogeochemical system has been assessed using a seven-year hindcast
(years 2007-2013). For bias computation, we adopt the following convention: model – observation.
The headline results for each of the variables assessed are as follows:
Chlorophyll: At sea surface, modelled chlorophyll fields show a good agreement with satellite data.
The large-scale structures corresponding to specific biogeographic regions (double-gyres, Antarctic
Circumpolar Current, etc.) are well reproduced. However, concentrations are still too high in the
tropical band. Concerning the temporal monitoring, our model succeeds well in reproducing the
seasonal cycle at mid- and high- latitudes (spring bloom), but the timing of the bloom is not yet in
phase with that of observations (the modelled bloom is too early, namely one or two-month lag). On
the global ocean, the model has a correlation of 0.59 in log10(chlorophyll) at the sea surface in
comparison with satellite chlorophyll observations. Real time one-week forecasts display some skill
with respect to the persistence of the hindcast. However, users must keep in mind that both the
hindcast and forecast products still bear large uncertainties with respect to climatologies and
observations.
Nutrients (NO3, PO4, Si): Concentration of nutrients are in good agreement with World Ocean Atlas
Climatology at global scale (correlation > 0.9). The concentrations of nutrient are globally too high at
sea surface in the model (positive mean bias). This is mainly due to the tropical band where the
model overestimates the nutrient concentrations.
Dissolved Oxygen: Oxygen presents very good scores at the sea surface (correlation > 0.9; in
comparison with World Ocean Atlas climatology). This is due to the intrinsic link between O2
concentration and temperature (and especially at sea surface). The modelled dissolved oxygen
therefore benefits from the assimilation of temperature data. In subsurface and deep layers, the
model is able to reproduce OMZs (oxygen minimum zone).
I.3 Estimated Accuracy Numbers
Estimated Accuracy Numbers are given in Table 1 for year 2013, which is the last year of the hindcast
simulation. They are computed from monthly fields. Model outputs are interpolated on the regular
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grid of data. The monthly model fields are masked with monthly data and inversely, so that the
annual mean is computed from the same number of samples. Estimated Accuracy Numbers are
computed by following conventions laid out in the MyOcean Cal/Val guidelines document (MyO2-PQguidelines-phase2). The observation products used for these statistics are described in Table 2.
Variable
RMS difference
(new model)
RMS
difference
(old model)
Mean difference
(new model - obs)
Mean
difference (old
model - obs)
Chlorophyll
0.4386 mg/m3
0.431 mg/m3
0.0048 mg/m3
0.338 mg/m3
Log10(chlorophyll)
0.317
0.375
0.0133
0.0874
Dissolved Oxygen
11.24 µmol/L
11.99 µmol/L
3.13 µmol/L
3.16 µmol/L
Nitrates
3.70 µmol/L
3.40 µmol/L
1.37 µmol/L
0.32 µmol/L
Phosphates
0.307 µmol/L
0.291 µmol/L
0.136 µmol/L
0.053 µmol/L
Silicates
10.47 µmol/L
5.83 µmol/L
Table 1: Root Mean Square Error
and Mean Error <(mod – obs)> calculated
on monthly fields at sea surface in 2013.
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II
Ref: CMEMS-GLO-QUID-001-014
Date
: 15 September 2016
Issue
: 3.0
PRODUCTION SUBSYSTEM DESCRIPTION
The product GLOBAL_ANALYSIS_FORECAST_BIO_001_014 is provided by the production unit
MERCATOR Océan of the production centre MFC-GLOBAL.
The product GLOBAL_ANALYSIS_FORECAST_BIO_001_014 is a global biogeochemical simulation
forced off-line by daily fields provided by a physical simulation PSY3V3R3 (described hereafter and in
Lellouche et al., 2013) until October 2016. From October 19th 2016, it is forced by the Copernicus
product GLOBAL_ANALYSIS_FORECAST_PHYS_001_024, which is run at a resolution of 1/12° and is
degraded offline to ¼° to force the biogeochemical model.
II.1 Biogeochemical model PISCES
The biogeochemical model used is PISCES (Aumont, in prep). It is a model of intermediate complexity
designed for global ocean applications (Aumont and Bopp, 2006) and is part of the NEMO modeling
platform (Madec et al. 2008). It has 24 prognostic variables and simulates biogeochemical cycles of
oxygen, carbon and the main nutrients controlling phytoplankton growth (nitrate, ammonium,
phosphate, silicic acid and iron). The model distinguishes four plankton functional types based on
size: two phytoplankton groups (small = nanophytoplankton and large = diatoms) and two
zooplankton groups (small = microzooplankton and large = mesozooplankton). Prognostic variables
of phytoplankton are total biomass in C, Fe, Si (for diatoms) and chlorophyll and hence the Fe/C, Si/C,
Chl/C ratios are variable. For zooplankton, all these ratios are constant and the total biomass in C is
the only prognostic variable. The bacterial pool is not modeled explicitly. PISCES distinguishes three
non-living pools for organic carbon: small particulate organic carbon, big particulate organic carbon
and semi-labile dissolved organic carbon. While the C/N/P composition of dissolved and particulate
matter is tied to Redfield stoichiometry, the iron, silicon and carbonate contents of the particles are
computed prognostically. Next to the three organic detrital pools, carbonate and biogenic siliceous
particles are modeled. Besides, the model simulates dissolved inorganic carbon and total alkalinity. In
PISCES, phosphate and nitrate + ammonium are linked by constant Redfield ratio (C/N/P = 122/16/1),
but cycles of phosphorus and nitrogen are decoupled by nitrogen fixation and denitrification.
The distinction of two phytoplankton size classes, along with the description of multiple nutrient colimitations allows the model to represent ocean productivity and biogeochemical cycles across major
biogeographic ocean provinces (Longhurst, 1998). PISCES has been successfully used in a variety of
biogeochemical studies (e.g. Bopp et al. 2005; Gehlen et al. 2006; 2007; Schneider et al. 2008;
Steinacher et al. 2010; Tagliabue et al. 2010, Séférian et al, 2013). The biogeochemical model is
initialized with the World Ocean Atlas 2001 for nitrate, phosphate, oxygen and silicate (Conkright et
al. 2002), with GLODAP climatology including anthropogenic CO2 for Dissolved Inorganic Carbon and
Alkalinity (Key et al. 2004) and, in the absence of corresponding data products, with model fields for
dissolved iron and dissolved organic carbon. Boundary fluxes account for nutrient supply from three
different sources: atmospheric deposition (Aumont et al., 2008), rivers for nutrients, dissolved
inorganic carbon and alkalinity (Ludwig et al., 1996) and inputs of Fe from marine sediments.
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II.2 Physical model NEMO
Until October 19th 2016
II.2.1
The physical ocean model is the Mercator-Ocean system PSY3R3V3 at ¼° resolution (Lellouche et al.,
2013). The main features of this dynamical ocean are:
-
NEMO 3.1 – ¼° - 50 levels
-
Atmospheric forcing from 3-hourly ECMWF analysis products, CORE bulk formulation.
-
Vertical diffusivity coefficient is computed by solving the TKE equation.
-
Sea-Ice model: LIM2 with the Elastic-Viscous-Plastic rheology.
-
Initial conditions: Levitus 2005 climatology for temperature and salinity. Ifremer/Cersat
data for sea ice concentration and GLORYS2V1 for sea ice thickness.
-
Data assimilation scheme: SAM2V1 (Kalman filter with SEEK formulation) + 3D-Var biases
correction in temperature and salinity for the slowly evolving large-scale, both with
Incremental Analysis Update.
-
Data assimilated: Sea Surface Temperature (Reynolds AVHRR-AMSR 1/4°); Sea Surface
Height (Jason2, Cryosat, Saral); InSitu temperature and salinity vertical profiles from
Coriolis Center with Extra Quality control; hybrid MSSH.
See for more details Lellouche et al. (2013).
From October 19th 2016 on
II.2.2
The
physical
model
of
ocean
is
the
Copernicus
product
GLOBAL_ANALYSIS_FORECAST_PHYS_001_024 coarsened at ¼° resolution. The main features of this
dynamical ocean are:
-
NEMO 3.1 –1/12°° - 50 levels.
-
Atmospheric forcing from 3-hourly ECMWF analysis products, CORE bulk formulation.
-
Vertical diffusivity coefficient is computed by solving the TKE equation.
-
Sea-Ice model: LIM2 with the Elastic-Viscous-Plastic rheology.
-
Data assimilation scheme: SAM2V1 (Kalman filter with SEEK formulation) + 3D-Var biases
correction in temperature and salinity for the slowly evolving large-scale, both with
Incremental Analysis Update.
-
Data assimilated: Sea Surface Temperature (CMEMS OSTIA) Sea Surface Height (all
available missions); InSitu temperature and salinity vertical profiles from CMEMS with
Extra Quality control; sea ice concentration (from CMEMS); hybrid MSSH.
See for more details in the QUID and PUM documents for product
GLOBAL_ANALYSIS_FORECAST_PHYS_001_024
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II.3 Coupling and configuration
The biogeochemical model PISCES (NEMO3.2) is forced offline by daily fields. A special treatment is
done on the vertical diffusivity coefficient (Kz): the daily mean is computed from Log10(Kz) values
after a filtering of enhanced convection (Kz increased artificially to 10 m2.s-1 when the water column
is unstable). The purpose of this Log10 is to average the orders of magnitudes and to give more
weight to small values of vertical diffusivity.
The horizontal grid is the standard ORCA025 tri-polar grid (1440 x 1021 grid points). The three poles
are located over Antarctic, Central Asia and North Canada. The ¼ degree resolution corresponds to
the equator. The vertical grid has 50 levels, with a resolution of 1 meter near the surface and 500
meters in the deep ocean.
The biogeochemical simulation starts in January 2007. Outputs are interpolated on a standard
collocated grid at 1/2 degree.
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III VALIDATION FRAMEWORK
We assess the system performance and the associated product quality by comparing systematically
(near real time) biogeochemical modeled fields (equatorial and Pacific sections, and surface maps)
with available data or climatologies (CLASS1 metrics). These eyeball comparisons are done on a
monthly and annual basis. In delayed time, we mainly monitor the seasonal cycle of chlorophyll and
compare statistics time series (median, 80th percentile, RMS misfit, Taylors and Hovmöller diagrams).
We finally monitor the model drift by plotting the time series of global vertically integrated primary
production and averages of the main variables. It has to be noticed that we did not assess the iron
concentration (although it is provided to the user) because there is no climatology to compare with.
The validation methodology and metrics classification are described in the MyOcean Cal/Val
guidelines document (MyO2-PQ-guidelines-phase2). Table 2 summarizes the type of metrics used to
monitor the system.
Variable
Class
Metric name
Description
Supporting
observation
CHL
1
BGC-CLASS1_CHL_MEAN
Map, equatorial section and
meridional section in the Pacific Ocean
of annual mean chlorophyll, bias and
RMSE of monthly mean chlorophyll in
2013
Globcolour (ACRI)
data at 25km
resolution
CHL
2
BGC_CLASS2_CHL
Hovmöller diagram in North Atlantic at
20°W
CHL
3
BGC-CLASS3-CHL_REGIONAL
CHL
3
BGC-CLASS3-CHL
Global annual average times series
Median and Percentile 80 of monthly
chlorophyll computed on North
Atlantic (30-60°N; -80:0°E) in 2013
Taylor diagram for year 2013
(correlation on monthly surface fields)
Globcolour (ACRI)
data at 25 km
resolution
Globcolour (ACRI)
data at 25 km
resolution
NO3
1
BGC-CLASS1-NITRATE_REGIONAL
NO3
3
BGC-CLASS3-NO3
PO4
1
BGC-CLASS1PHOSPHATE_REGIONAL
PO4
3
BGC-CLASS3-PO4
Si
1
BGC-CLASS1-SILICATE_REGIONAL
Si
3
BGC-CLASS3-SI
Map, equatorial section and
meridional section in the Pacific Ocean
of annual mean nitrate in 2013
Taylor diagram for year 2013
(correlation on monthly surface fields)
Map, equatorial section and
meridional section in the Pacific Ocean
of annual mean phosphate in 2013
Taylor diagram for year 2013
(correlation on monthly surface fields)
Map, equatorial section and
meridional section in the Pacific Ocean
of annual mean silicate in 2013
Taylor diagram for year 2013
(correlation on monthly surface fields)
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Globcolour (ACRI)
data at 25 km
resolution
WOA 2009
(NODC)
Garcia, 2010a
WOA 2009
(NODC)
Garcia, 2010a
WOA 2009
(NODC)
Garcia, 2010a
WOA 2009
(NODC)
Garcia, 2010a
WOA 2009
(NODC)
Garcia, 2010a
WOA 2009
(NODC)
Garcia, 2010a
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O2
1
BGC-CLASS1-OXYGEN_REGIONAL
O2
3
BGC-CLASS3-O2
PP
1
BGC-CLASS1PRIMARY_PRODUCTION
PP
3
BGC-CLASS3PRIMARY_PRODUCTION_REGIONAL
Ref: CMEMS-GLO-QUID-001-014
Date
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Issue
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Map, equatorial section and
meridional section in the Pacific Ocean
of annual mean oxygen in 2013
Taylor diagram for year 2013
(correlation on monthly surface fields)
Map, equatorial section and
meridional section in the Pacific Ocean
of annual mean of vertically
integrated primary production in 2013
Global annual integrated times series
Table 2: List of metrics that were computed to assess the system.
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WOA 2009
(NODC)
Garcia, 2010b
WOA 2009
(NODC)
Garcia, 2010b
Standard VGPM
algorithm
product
Behrenfeld and
Falkowski, 1997),
MODIS sensor
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IV VALIDATION RESULTS
IV.1 Hindcast assessment
Biogeochemical data to assess the quality of biogeochemical models are still scarce. This
biogeochemical system is evaluated by systematically comparing model fields to observations (when
it is possible) or climatologies at global scale. From now on, we will present either diagnostics on the
last year of the hindcast simulation, namely year 2013, or diagnostics on the whole period of the
simulation, namely 2007 – 2013.
Table 3 gives means and standard deviations over the global domain of chlorophyll, dissolved
oxygen, nitrates, phosphates and silicates for model and data (in 2013).
Variable
Observations
New Model
Old Model
Chlorophyll
Mean = 0.198 mg/m3
Mean = 0.203 mg/m3
Mean = 0.231 mg/m3
Std = 0.412 mg/m3
Std = 0.458 mg/m3
Std = 0.349 mg/m3
Mean = -0.944
Mean = -0.931
Mean = -0.856
Std = 0.397
Std = 0.400
Std = 0.418
Mean = 250.78 µmol/L
Mean = 254.33
µmol/L
Mean = 254.47
µmol/L
Std = 53.76 µmol/L
Std = 53.75 µmol/L
Mean = 5.235 µmol/L
Mean = 6.443 µmol/L
Mean = 5.382 µmol/L
Std = 8.337 µmol/L
Std = 8.245 µmol/L
Std = 7.505 µmol/L
Mean = 0.533 µmol/L
Mean = 0.661 µmol/L
Mean = 0.574 µmol/L
Std = 0.560 µmol/L
Std = 0.556 µmol/L
Std = 0.512 µmol/L
Mean = 7.66 µmol/L
Mean = 13.2 µmol/L
Mean = 11.11 µmol/L
Std = 14.03 µmol/L
Std = 15.57 µmol/L
Std = 17.63 µmol/L
Log10( Chlorophyll )
Dissolved oxygen
Std = 53.61 µmol/L
Nitrates
Phosphates
Silicates
Table 3: Mean and standard deviation values over the global domain in 2013 at sea surface
computed on monthly fields for model and observations on a regular grid. The monthly model
fields are masked with monthly data and inversely.
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Figure 1: Globally averaged surface chlorophyll (green), nitrate (blue), phytoplankton biomass in
carbon (red) and integrated primary production (magenta) as a function of time (years) in
GLOBAL_ANALYSIS_FORECAST_BIO_001_014.
Figure 1 shows the temporal evolution of surface chlorophyll, nitrate, phytoplankton biomass and
integrated primary production, during the whole simulation period. During these 7 years of
simulation, we observe the stabilisation of the model, which reaches a quasi-steady state around
2011.
IV.1.1
Chlorophyll
Figure 2 shows a comparison of annual chlorophyll at sea surface in 2013 between the model (left)
and the Globcolour product (right) in log scale. The monthly model fields are masked with monthly
data, thus the annual mean is computed from the same number of samples. The modelled mean
annual chlorophyll field shows a good agreement with satellite derived estimates at the global scale.
Large scale structures i.e. the main biogeographic provinces of Longhurst et al. (1998) (e.g. doublegyres, Antarctic Circumpolar Current, the tropical band, Eastern Boundary Upwellings) are well
reproduced.
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 The main oligotrophic gyres are well localised (Figure 2), with very low values of surface
chlorophyll (< 0.05 mg.m-3). They are associated with a chlorophyll maximum in subsurface
which is located between 100 and 250m depth (see Figure 3).
 The important production of the North Atlantic region is well represented (see Figure 2).
 Main eastern boundary upwelling systems (Benguela, California, Humbolt, and Canary
current systems) are correctly modelled too (see Figure 2).
 The model is able to reproduce the main features of the seasonal cycle: a bloom in spring
when the mixed layer, rich in nutrients, shoals and becomes shallower than the euphotic
layer; a decrease of chlorophyll concentration in summer due to a thin mixed layer very poor
in nutrients (nutrient limitation); a second bloom in autumn when the mixed layer deepens
and nutrients are entrained at its base; and in winter, a period of weak production (due to
light limitation) (see Figure 5).
 Concerning the interannual variability of the seasonal cycle, we can see on Figure 6 and
Figure 7 that the model succeeds in reproducing the interannual variability of the chlorophyll
concentration in the North Atlantic. For example, in the Canary current upwelling at 20°N,
positive anomalies in 2007, 2009 and 2012 are present in the model. The model also
correctly predicts a mid-latitude spring bloom that began more southerly than usual in 2009
and 2012.
However, there are some discrepancies with ocean colour data.

The subtropical gyres are slightly too oligotrophic and in the North Atlantic, the boundary
between oligotrophic waters and the the northern productive region is not zonal enough
(see Figure 2 and Figure 8).

Eastern Boundary Upwelling Systems are not productive enough (see Figure 4 and Figure 8).

The model overestimates the concentration of chlorophyll (see Figure 8) in the tropical band
(Pacific, Indian and Atlantic oceans), and the westward “arrowhead” shape of the chlorophyll
tongue in the Pacific spreads too much westward (see Figure 2).

In the Indian Ocean, the model overestimates the chlorophyll levels, except in the Arabian
Sea.

The model overestimates in average chlorophyll concentrations in the Northern High latitude
region (see Figure 2) and underestimates it in the Southern High latitude one (see Figure 4
and Figure 8).

In the North Atlantic, the bloom starts too early (one or two months, see Figure 5 and Figure
6), and it does not persist in summer as much as in data (see Figure 5). Moreover, it is too
productive (see Figure 5).
The reasons of the bloom onset observed in ocean colour data are still under debate among the
scientific community. The critical depth hypothesis of Sverdrup et al. (1953) attributed the bloom
onset to the mixed layer shoaling and considered constant loss rates (mortality and grazing). This
hypothesis is based on a bottom-up control of the bloom onset but has been recently criticized.
Behrenfeld et al. (2010, 2013) proposed a dilution-recoupling hypothesis based on a top-down
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control. Taylor and Ferrari (2011) assumed that the bloom is triggered by the shutdown of the
turbulent mixing.
Figure 2: Annual mean of surface chlorophyll concentration in 2013 (mg.m-3); (left) model; (right)
Globcolour data.
Figure 3: Section of modelled chlorophyll concentration (mg.m-3) at the equator (left) and in the
Pacific Ocean at 155°W (right).
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Figure 4: RMS Misfit for chlorophyll over year 2013, calculated from monthly data and model
outputs at the sea surface.
Figure 5: Median (left) and Percentile 80 (right) of chlorophyll concentration (mg.m-3) in the North
Atlantic (30-60°N; 80°W-0°W) for year 2013; (blue) old version of the model; (green) current
version of the model; (red) data.
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Figure 6: Hovmöller diagram of Log10( chlorophyll ) between 2007 and 2013 at 20°W in the North
Atlantic (20°S:70°N); (left) model; (right) Globcolour data.
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Figure 7: Hovmöller diagram of the chlorophyll anomaly (Log10(monthly mean) – Log10(mean over
the whole period)) between 2007 and 2013 at 20°W in North Atlantic (20°S:70°N); (left) model;
(right) Globcolour data.
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Figure 8: Monthly chlorophyll bias at sea surface (Log10(model) minus Log10(observations)) for
year 2013 (mg.m-3).
IV.1.2
Nitrates
Figure 9 shows a comparison at the sea surface of the nitrate concentration derived from the
climatological World Ocean Atlas 2009 and predicted by the model for year 2013. Globally, there is a
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good agreement between the model and the climatology except in the tropical band where nitrate
concentrations are too high. The equatorial upwelling seems to be too strong in the model. Due to
the trade winds dragging surface water westward, deep nutrient-rich water is upwelled in the
eastern part of the basins. Equatorial divergence of currents then spreads these nutrient rich waters
westward along the equator and north- and southward from the equator (Coriolis effect). This is the
reason of the arrowhead shape of the chlorophyll and nitrate tongue in the Pacific Ocean (Figure 2
and Figure 9 right). In the Pacific Ocean, the nitrate tongue is clearly too strong and stretched (and as
a result, the chlorophyll one as well). In the Atlantic and Indian Oceans, nitrate is entirely consumed
by phytoplankton whereas in the model, it becomes non-limiting. This discrepancy between model
and data is partly due to the assimilation of physical in situ, altimetric data and their associated Mean
Dynamic Topography, which creates anomalous vertical velocities. This too strong upwelling has
been improved since the last version (see Figure 35 ) but it remains work in progress.
Figure 10 and Figure 11 show respectively equatorial and Pacific sections of the nitrate concentration
in the model compared to the WOA 2009 climatology. Here again main structures such as the
subtropical gyres are well reproduced by model. Nonetheless, the stronger equatorial upwelling can
be seen near the surface, on the Pacific section (Figure 11) and on the equatorial section (Figure 10).
It has to be noticed that there is an important variability of the equatorial upwelling (mainly driven
by the El Nino Southern Oscillation).
Figure 9: Annual means of nitrate concentration at sea surface (mmol.m-3); (left) model in year
2013; (right) World Ocean Atlas 2009.
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Figure 10: Annual section of nitrate concentration at the equator (mmol.m-3); (left) model in year
2013; (right) World Ocean Atlas 2009
Figure 11: Annual section of nitrate concentration at 155°W in the Pacific Ocean (mmol.m-3); (left)
model in year 2013; (right) World Ocean Atlas 2009.
IV.1.3
Phosphates
Figure 12, Figure 13 and Figure 14 show respectively phosphate distribution in 2013 at the sea
surface, at the equator and at 155° in the Pacific Ocean, compared with WOA 2009. The same
conclusions as in the previous nitrate section can be drawn from these figures. The stronger and
westward equatorial upwelling can be seen in Figure 12 as well as near the surface in the Pacific
section (Figure 14), and between 130°E and 150°E in the equatorial section (Figure 13). We can
notice moreover that phosphate concentration in the oligotrophic Pacific gyre is slightly too high.
Figure 12: Annual means of phosphate concentration at sea surface (mmol.m-3); (left) model in
year 2013; (right) World Ocean Atlas 2009.
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Figure 13: Annual section of phosphate concentration at the equator (mmol.m-3); (left) model in
year 2013; (right) World Ocean Atlas 2009.
Figure 14: Annual section of phosphate concentration at 155°W in the Pacific Ocean (mmol.m-3);
(left) model in year 2013; (right) World Ocean Atlas 2009.
IV.1.4
Silicate
Idem to nitrates or phosphates (see above).
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Figure 15: Annual means of silicate concentration at sea surface (mmol.m-3); (left) model in year
2013; (right) World Ocean Atlas 2009.
Figure 16: Annual section of silicate concentration at the equator (mmol.m-3); (left) model in year
2013; (right) World Ocean Atlas 2009.
Figure 17: Annual section of silicate concentration at 155°W in the Pacific Ocean (mmol.m-3); (left)
model in year 2013; (right) World Ocean Atlas 2009.
IV.1.5
Dissolved Oxygen
For the oxygen variable, the maps at the sea surface are very close to the climatology (see Figure 18).
This is expected since sea surface temperature is assimilated in the physical model and temperature
strongly constrains the solubility of atmospheric oxygen at the sea surface. Hence, oxygen is also
controlled via the assimilation process.
Sections of oxygen concentration are presented on Figure 19 and Figure 20, respectively at the
Equator and in the Pacific Ocean. They show a good adequacy between model and climatology
(annual mean).
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Figure 18: Annual means of dissolved oxygen concentration at sea surface (mmol.m-3); (left) model
in year 2013; (right) World Ocean Atlas 2009.
Figure 19: Annual section of dissolved oxygen concentration at the equator (mmol.m-3); (left)
model in year 2013; (right) World Ocean Atlas 2009.
Figure 20: Annual section of dissolved oxygen concentration at 155°W in the Pacific Ocean
(mmol.m-3); (left) model in year 2013; (right) World Ocean Atlas 2009.
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Iron
Figure 22 shows annual mean of dissolved iron concentration at sea surface for year 2013 predicted
by model.
Figure 21: Annual mean of dissolved iron concentration at sea surface (nmol.L-1) for the model in
year 2013.
IV.1.7
Phytoplankton biomass in carbon
The annual mean phytoplankton biomass at sea surface predicted by the model is shown in Figure
22.
Figure 23 shows sections along the equator and in the Pacific Ocean, of the mean phytoplankton
biomass from model. For the time being, there is not enough data to compare with.
Figure 22 : Annual mean of phytoplankton biomass in carbon at sea surface (mmol.m-3) for the
model in year 2013.
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Figure 23 : Section of phytoplankton biomass (mmol.m-3) at the equator (left) and in the Pacific
Ocean at 155°W (right).
IV.1.8
Primary production
Figure 24 shows annual mean primary production vertically integrated for the year 2013, from the
model (left) and derived from MODIS satellite data (based on the standard VGPM algorithm, right).
The same characteristics seen for nutrients above are met here.

The subtropical gyres are too oligotrophic, so there is not enough primary production in
these areas (see Figure 24).

In the tropical band, there are too much nutrients, resulting in excessive production,
particularly in the eastern part of Equatorial Pacific.

Eastern Boundary Upwelling Systems are not productive enough.

The model underestimates the primary production in the Northern High latitude region.
These comparisons have to be taken with caution because there currently are several algorithms
(Standard VGPM (Behrenfeld and Falkowski, 1997), Eppley-VGPM, CbPM etc., see website
http://www.science.oregonstate.edu/ocean.productivity/ for more information) allowing to deduce
integrated primary production from satellite ocean colour, but they give distinctly different results,
and especially in the tropical band.
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Figure 24 : Annual means of vertically integrated primary production (gC m-2 day-1) for the year
2013; (left) model; (right) Modis satellite product based on VGPM algorithm (Behrenfeld and
Falkowski, 1997).
Figure 25 : Section of primary production (gC m-3 day-1) at the equator (left) and in the Pacific
Ocean at 155°W (right).
IV.2 Forecast assessment
To justify our choice to disseminate forecasts of our biogeochemical model, we compare forecast
with persistence. We have 16 weeks of forecasts and associated 8-days Globcolour ocean colour
observations from July 2014 to October 2014. We compute the RMS Error between weekly model
fields and 8-days observations for respectively the forecast (Figure 26, left) and the persistence fields
(Figure 26, right). We can see that the forecasts are relevant (give results closer to observations than
persistence) in the tropical band and in the subtropical gyres.
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Figure 26: RMSE of chlorophyll concentration computed between weekly model fields and 8-days
ocean colour observations (Globcolour product): (left) forecast; (right) persistence.
To qualify more quantitatively the relevance of forecast, we plot the skill score (Figure 27) of forecast
compared with persistence:
Skill Score = 1 – (<(forecast – obs)2>/<(persistence – obs)2)
Skill Score > 0 => the forecast is relevant.
Skill Score < 0 => the forecast is not relevant.
We see that the forecast is relevant in most part of global ocean except in the Gulf Stream region and
in the northern part of the Antarctic Circumpolar Current. This is probably due to the model inability
to reproduce the seasonal cycle in these regions (a few weeks of time lag diagnosed in these
regions).
Figure 27: Skill score of forecast compared with persistence.
The user has to keep in mind that the differences between hindcast and forecast fields remain very
weak compared with the differences with observations. Both the hindcast and forecast estimates still
bear large uncertainties with respect to climatologies and observations.
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QUALITY CHANGES SINCE PREVIOUS VERSION
This section deals with the main differences between the old and new versions of the
biogeochemical system operated by Mercator Océan.
Compared to World Ocean Atlas 2009 data, both models show great correlation for Silicate,
Phosphate, Nitrate, or dissolved Oxygen (> 0.9, see Figure 28). However, the standard deviations of
these nutrients in the new model are slightly closer to the data than those in the old model.
Moreover, Chlorophyll concentrations, compared to the Globcolour product, are better in the new
model (correlation of 0.49; 0.68 for Log10(Chl)) than in the old model (0.37; 0.59 for Log10(Chl)).
Globally, the new model shows better results than the old one.

The chlorophyll concentration is globally lower (except at high latitudes) in the new system,
which matches more closely satellite observations (Figure 2).

The impact of the higher resolution (new: 1/4°; old: 1°) of the new system is clearly visible:
coastal maxima are less diffusive and better match observations (e.g. in the Indonesian
archipelago).

The North Atlantic seasonal cycle is slightly better reproduced in the new version of the
model (see Figure 5): the chlorophyll concentration is lower during spring bloom which
better fits with data and the median is slightly better during summer (more chlorophyll
during summer).

The subtropical gyres are slightly more oligotrophic in the new model (see Figure 29), and
oligotrophic gyres extend deeper in the new model (see Figure 31).

The tropical band is less excessive in chlorophyll concentration in the new model. This is
partly due to a best vertical velocity in the physical model (see Figure 35) and to a tuning of
the biogeochemical parameters. However, the tropical band has too high concentrations of
nutrients compared to the old system.

Eastern Boundary Upwelling Systems do not extend enough offshore in comparison with data
and the old version of the system (see Figure 29).

The Indian Ocean of the new system is less productive than the old one (great improvement).
However, chlorophyll concentration in the new version is too low in the Arabian Sea, and
upwelling along the Somalia and Oman coasts is not reproduced (see Figure 29 and Figure 2).

At high latitudes, the new system is too much productive in the Northern Hemisphere while
the old system was not productive enough. In the southern hemisphere (Southern Ocean),
the new system misses the local maximas of chlorophyll due to iron fertilization around
island systems (inputs from sediments) for example Georgia Islands (Borrione et al, 2013) or
the Kerguelen Plateau. However, nutrients concentrations are closer to data in the Antarctic
Circumpolar Current for the new model (see Figure 32).
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Figure 28: Taylor diagram of modelled surface concentration of Silicate, Phosphate, Nitrate,
dissolved oxygen, chlorophyll, and LOG10(Chlorophyll), for year 2013, compared to WOA 2009 (Si,
PO4, NO3, O2) and GLOCOLOUR product (CHL, LOGCHL). Correlations are computed on monthly
fields.
Figure 29: Annual means of surface chlorophyll concentration (mg.m-3) for year 2013; (left) new
model; (right) old model.
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Figure 30: Section of chlorophyll concentration (mg.m-3) at the equator for year 2013; (left) new
model; (right) old model.
Figure 31: Section of chlorophyll concentration (mg.m-3) in the Pacific Ocean at 155°W, for year
2013; (left) new model; (right) old model.
Figure 32: Annual mean of nitrate concentration at the sea surface (mmol.m-3), for year 2013; (left)
new model; (right) old model.
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Figure 33: Annual section of nitrate concentration at the equator (mmol.m-3), for year 2013; (left)
new model; (right) old model.
Figure 34: Annual section of nitrate concentration at 155°W in the Pacific Ocean (mmol.m-3), for
year 2013; (left) new model; (right) old model.
Figure 35: Equatorial section of vertical velocities for year 2011: annual mean (m/day) ; (left) New
version of the physical system; (right) Old version of the physical system.
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VI NEW FORCING FIELDS SINCE OCTOBER 2016
From October 19th 2016 on, the ocean dynamical forcing fields are the physical CMEMS product
GLOBAL_ANALYSIS_FORECAST_PHYS_001_024. The biogeochemical system is still forced offline by
daily means, but the ocean dynamics is now run at 1/12° and then coarsened to ¼°. From this date,
users can download consistent physical and biogeochemical fields from the CMEMS web portal.
To check that there is no degradation of the biogeochemical system with these new forcing fields, we
ran it from January 8th 2014, to December 31st 2015. Hereafter, we present some results of this
experiment compared to the system forced by previous forcing fields from the Mercator-Ocean
system PSY3V3R3.
Figure 36 and Figure 39 show the comparison of annual mean of chlorophyll and nitrates respectively
at sea surface in 2015 between system forced by PSY3V3R3 (left) and forced by
GLOBAL_ANALYSIS_FORECAST_PHYS_001_024 (right). It shows a good agreement after a two-year
run.
At global scale, there is a clear conformity between the two simulations, which supports the choice
of an on-the-fly transition to the new biogeochemical system. We also checked the other variables
disseminated in the CMEMS framework (not shown).
However, there are a few differences between the two simulations:
 The Gulf Stream region is slightly less rich in chlorophyll in the new release. This feature is
observed all year long (not shown). Although an over production remains in this region
compared to observation, it is weaker than with previous forcing fields (see Figure 36).
 The subtropical gyres are slightly richer in chlorophyll (closer to observations). They were too
oligotrophic in the previous system (see Figure 36 and Figure 37).

In the Indonesian archipelago, there is a reduction of the chlorophyll concentration. This is
less consistent with observations (see Figure 36 and Figure 38).
 The equatorial tongue of nitrates is less developed in the new release which is clearly closer
to climatology (see Figure 9).
Figure 36 : Annual mean of surface chlorophyll concentration in 2015 (mg.m-3); (left) with previous
forcing fields; (right) with new forcing fields.
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Figure 37 : Annual section of chlorophyll concentration at 155°W in the Pacific Ocean in 2015
(mg.m-3); (left) with previous forcing fields; (right) with new forcing fields.
Figure 38 : Annual section of chlorophyll concentration at the equator in 2015 (mg.m-3); (left) with
previous forcing fields; (right) with new forcing fields.
Figure 39 : Annual mean of surface nitrate concentration in 2015 (mmol.m-3); (left) with previous
forcing fields; (right) with new forcing fields.
There are a few hypotheses to explain these changes but it is difficult to disentangle where and to
what extent each process acts:
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- the physical system is run at a finer spatial resolution. Even if the physical fields are degraded to a
coarser resolution (1/4°), the biogeochemical system all the same benefits from the finer resolution
physics (Levy et al, 2012).
- a corrected Mean Dynamic Topography whose aim is to diminish some inconsistencies between
SSH assimilation on the one hand and T&S assimilation on the other hand.
- the viscosity coefficient was increased : this can reduce the turbulence.
- the assimilation of sea-ice.
- a better control of the observation errors.
- the bathymetry was improved in Indonesian Archipelago.
For more details, please refer to QuID
GLOBAL_ANALYSIS_FORECAST_PHYS_001_024.
and
PUM
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of
product
Figure 40 shows the temporal evolution of surface chlorophyll, nitrate, and phytoplankton biomass
during the two-year period for the twin experiments. In grey part weekly means are used, in white
part monthly means are used. The dashed line shows the start date of the twin experiment.
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On-the-fly change of the forcing fields could create some bumps. Here we see that this is not the
case and there is no discontinuity between both simulations in the first weeks. Then we see that both
simulations keep the same seasonal cycle. However, with the new forcing the global mean value of
chlorophyll, nitrate and phytoplankton concentration is weaker than with the previous one. In the
case of nitrates, it must be due to the less extended tropical tongue. It allows to improve the global
mean value and to get closer to the climatological mean value (see Table 3). In the case of
chlorophyll, it is difficult to compare to the Globcolour global mean value of Table 3 because
Globcolour fields contain a lot of masked value in winter at high latitudes where the main differences
in this release are found.
Figure 40 : Globally averaged surface chlorophyll (green), nitrate (blue), and phytoplankton
biomass in carbon (red) as a function of time.
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VII REFERENCES
Aumont O. : PISCES. In prep (For Geoscientific Model Development).
Aumont, O. and Bopp, L. “Globalizing results from ocean in situ iron fertilization studies”. Global
Biogeochem. Cycles. 20 (2):10–1029 (2006).
Aumont, O., L. Bopp, and M. Schulz. "What does temporal variability in aeolian dust deposition
contribute to sea‐surface iron and chlorophyll distributions?." Geophysical Research Letters 35.7
(2008).
Behrenfeld, MJ, PG Falkowski. Photosynthetic rates derived from satellite-based chlorophyll
concentration. Limnology and Oceanography 42:1-20 (1997)
Behrenfeld, M. J. Abandoning Sverdrup's critical depth hypothesis on phytoplankton blooms.
Ecology, 91(4), 977-989. (2010).
Behrenfeld, M. J., S. C. Doney, I. Lima, E. S. Boss, and D. A. Siegel. Annual cycles of ecological
disturbance and recovery underlying the subarctic Atlantic spring plankton bloom, Global
Biogeochem. Cycles, 27, 526–540, doi:10.1002/gbc.20050. (2013)
Bopp L., Aumont, O., Cadule, P., Alvain, S. and Gehlen, M. Response of diatoms distribution to global
warming andpotential implications: A global model study. Geophys. Res. Lett.. 32, L19606,
doi:10.1029/2005GL023653 (2005).
Borrione, I. and Schlitzer, R. Distribution and recurrence of phytoplankton blooms around South
Georgia, Southern Ocean. Biogeosciences, 10(1) (2013).
Conkright, M.E., Locarnini, R.A., Garcia, H.E., O’Brien, T.D., Boyer, T.P., Stephens, C. and Antonov, J.I.
WorldOcean Atlas2001: Objective Analyses, Data Statistics, and Figures, CD-ROM Documentation.
National Oceanographic DataCenter, SilverSpring, MD, 17 pp (2002).
Garcia, H. E., R. A. Locarnini, T. P. Boyer, J. I. Antonov, M. M. Zweng, O. K. Baranova, and D. R.
Johnson. World Ocean Atlas 2009, Volume 4: Nutrients (phosphate, nitrate, silicate). S. Levitus, Ed.
NOAA Atlas NESDIS 71, U.S. Government Printing Office, Washington, D.C., 398 pp. (2010a)
Garcia, H. E., R. A. Locarnini, T. P. Boyer, J. I. Antonov, O. K. Baranova, M. M. Zweng, and D. R.
Johnson. World Ocean Atlas 2009, Volume 3: Dissolved Oxygen, Apparent Oxygen Utilization, and
Oxygen Saturation. S. Levitus, Ed. NOAA Atlas NESDIS 70, U.S. Government Printing Office,
Washington, D.C., 344 pp. (2010b)
Gehlen, M., Bopp, L., Emprin, N., Aumont, O., Heinze, C. and Ragueneau, O. Reconciling surface
ocean productivity,export fluxes and sediment composition in a global biogeochemical ocean model.
Biogeosciences. 1726-4189/bg/2006-3-521,521-537 (2006).
Gehlen, M., Gangstø, R., Schneider, B., Bopp, L., Aumont, O. and Ethé, C. The fate of pelagic CaCO3
production in a highCO2 ocean: A model study. Biogeoscience., 4: 505-519 (2007).
Key, R. M., Kozyr, A., Sabine, C.L., Lee, K., Wanninkhof, R., Bullister, J.L., Feely, R.A., Millero, F.J.,
Mordy, C., and Peng, T.-H. A global ocean carbon climatology: Results from Global Data Analysis
Project (GLODAP). Global Biogeochem. Cycles. 18.GB4031, doi:10.1029/2004GB002247 (2004).
© EU Copernicus Marine Service – Public
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Lellouche, J.-M., Le Galloudec, O., Drévillon, M., Régnier, C., Greiner, E., Garric, G., Ferry, N.,
Desportes, C., Testut, C.-E., Bricaud, C., Bourdallé-Badie, R., Tranchant, B., Benkiran, M., Drillet, Y.,
Daudin, A., and De Nicola, C.: Evaluation of global monitoring and forecasting systems at Mercator
Océan, Ocean Sci., 9, 57-81, doi:10.5194/os-9-57-2013, (2013).
Longhurst, A. Ecological geography in the sea. Academic Press (1998).
Ludwig, Wolfgang, Jean‐Luc Probst, and Stefan Kempe. "Predicting the oceanic input of organic
carbon by continental erosion." Global Biogeochemical Cycles 10.1: 23-41 (1996).
Madec, G. and the NEMO team: NEMO ocean engine. Note du Pôle de modélisation, Institut PierreSimon Laplace (IPSL), France, No. 27 ISSN, 1288-1619, 2008.
Schneider, B., Bopp, L., Gehlen, M., Segschneider, J., Frölicher, T.L., Cadule, P., Friedlingstein, P.,
Doney, S.C., Behrenfeld M.J.and Joos, F. Climate-induced interannual variability of marine primary
and export production in three global coupled climatecarbon cycle models. Biogeosciences. 5: 597614 (2008).
Séférian, Roland, et al. "Skill assessment of three earth system models with common marine
biogeochemistry." Climate Dynamics 40.9-10 (2013): 2549-2573.
Steinacher, M., Joos, F., Frölicher, T.L., Bopp, L., Cadule, P., Cocco, V., Doney, S.C., Gehlen, M.,
Lindsay, K., Moore, J.K.,Schneider, B., and Segschneider, J. Projected 21st century decrease in marine
productivity: a multi-model analysis.Biogeoscience. 7: 979-1005 (2010).
Sverdrup H. On conditions for the vernal blooming of phytoplankton. Journal du Conseil 18 (3), 287
(1953)
Tagliabue, A., Bopp, L., Dutay, J.-C., Bowie, A.R., Chever, F., Jean-Baptiste, Ph., Bucciarelli, E.,
Lannuzel, Remenyi, D.T.,Sarthou, G., Aumont, O., Gehlen, M. and Jeandel, C. On the importance of
hydrothermalism to the oceanic dissolved ironinventory. Nature Geoscience. 3: 252 – 256,
doi:10.1038/ngeo818 (2010).
Taylor, JR., and Ferrari R. "Shutdown of turbulent convection as a new criterion for the onset of
spring phytoplankton blooms." Limnol. Oceanogr 56.6: 2293-2307. (2011).
© EU Copernicus Marine Service – Public
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