As part of the Copernicus Marine Environment Monitoring Service
(CMEMS), a physical–biogeochemical coupled model system has been developed
to monitor and forecast the ocean dynamics and marine ecosystem of the
European waters and more specifically on the Iberia–Biscay–Ireland (IBI)
area. The CMEMS IBI coupled model covers the north-east Atlantic Ocean from
the Canary Islands to Iceland, including the North Sea and the western
Mediterranean, with a NEMO-PISCES 1/36∘ model application. The
coupled system has been providing 7 d weekly ocean forecasts for CMEMS since
April 2018. Prior to its operational launch, a pre-operational qualification
simulation (2010–2016) has allowed assessing the model's capacity to
reproduce the main biogeochemical and ecosystem features of the IBI area.
The objective of this paper is then to describe the consistency and skill
assessment of the PISCES biogeochemical model using this 7-year
qualification simulation. The model results are compared with available
satellite estimates as well as in situ observations (ICES, EMODnet and BGC-Argo).
The simulation successfully reproduces the spatial distribution and seasonal
cycles of oxygen, nutrients, chlorophyll a and net primary production, and
confirms that PISCES is suitable at such a resolution and can be used for
operational analysis and forecast applications. This model system can be a
useful tool to better understand the current state and changes in the marine
biogeochemistry of European waters and can also provide key variables for
developing indicators to monitor the health of marine ecosystems. These
indicators may be of interest to scientists, policy makers, environmental
agencies and the general public.
Introduction
The north-east Atlantic waters are subject to natural climate variability as
well as intense human pressures that can have significant impacts for the
marine ecosystem. In addition to intense fishing activity, human pressures
also include aquaculture, agriculture, maritime transport, oil and gas
extraction, tourism and urbanisation. In order to regulate, sustainably
manage, protect and conserve the maritime areas of the north-east Atlantic
waters, the European Union adopted the OSPAR Convention (Convention for
the Protection of the Marine Environment of the North-East Atlantic) in 1992. The
European Union has also set up an Earth observation programme, the
Copernicus European Programme, formerly known as GMES (Global Monitoring for
Environment and Security). Copernicus aims to develop an operational and
autonomous Earth observation capacity of European Union to serve the general
interest and help public authorities and other international organisations
to improve the quality of life. Its marine monitoring component, the
Copernicus Marine Environment Monitoring Service (CMEMS; http://marine.copernicus.eu/, last access: 28 October 2019), is coordinated and led by Mercator
Ocean, a service provider of ocean information in real and delayed time
(http://www.mercator-ocean.eu, last access: 28 October 2019). Gathering satellite, in situ and model
data, CMEMS provides regular and systematic information on the state and
variability of the ocean dynamics and marine ecosystems for the global ocean
and the European regional seas over six different areas: Arctic Ocean,
Baltic Sea, north-west European shelf seas, Black Sea, Mediterranean Sea and
Iberia–Biscay–Ireland (IBI) seas.
(a) IBI extended domain on a curvilinear grid and IBI service
domain extending from -19 to 5∘ E and 26
to 56∘ N on a regular grid (red rectangle). The 12 black boxes represent
the different areas described in Sect. 2 and used for evaluation
in Sect. 4. They represent the North Atlantic (boxes 1, 2, 3), North Sea
(boxes 4 and 5), English Channel (box 6), Bay of Biscay (box 7), Iberian
upwelling (box 8), Gulf of Cádiz (box 9), Moroccan upwelling (box 10) and
western Mediterranean (boxes 11 and 12). (b) Location of in situ
biogeochemical data used for validation. ICES
(International Council for the Exploration of the Sea) data are in black, OVIDE (Observatoire de la Variabilité Interannuelle et Décennale en Atlantique Nord)
section and PELGAS (Pélagiques Gascogne) data of the north-east Atlantic EMODnet (European
Marine Observation and Data Network) dataset are,
respectively, in red and blue, the Mediterranean Sea EMODnet dataset is in
green, the APEX BGC-Argo float in the Atlantic is in orange, and the PROVOR
BGC-Argo float in the Mediterranean is in yellow. The blue colour shading
represents the bathymetry and the dashed line is the 200 m isobath delimiting
the shelf region.
The CMEMS IBI Monitoring and Forecasting Centre (IBI-MFC) is in charge of
delivering multi-decadal reanalysis and operational analysis and short-term
forecasts over the IBI European waters. It is managed by a consortium of
centres, coordinated by Puertos del Estado, and that includes Mercator Ocean,
Météo-France, the Spanish Met Office (AEMET), the Irish Marine Institute and
the Galician Supercomputing Centre (CESGA). The IBI area covers part of the north-east
Atlantic Ocean from the Canary Islands to Iceland, the North Sea and the
western Mediterranean (hereinafter referred as IBI extended domain). However,
the IBI-MFC delivers IBI products to CMEMS end users over a smaller area,
extending from -19 to 5∘ E and 26 to
56∘ N (hereinafter referred to as IBI service domain). IBI
extended and service domains are shown in Fig. 1a, and further details on
the IBI-MFC and the IBI region definition are available in Sotillo et al. (2015).
To reach the IBI-MFC needs, Mercator Ocean has developed an analysis and
forecast system adapted to the IBI area, which is a complex region to
simulate for numerical models because the highly variable bathymetry gives
rise to a wide spectrum of physical and biogeochemical ocean processes. The
IBI ocean dynamics and thermodynamics are resolved by version 3.6 of the
NEMO modelling platform (Nucleus for European Modelling of the Ocean; Madec et al., 1998; Madec, 2008) at 1/36∘
spatial resolution and constrained through data assimilation of in situ and
satellite physical data. The oceanic passive tracers of the lower trophic
levels' ecosystem dynamics are simulated by the PISCES
biogeochemical model
(Pelagic Interactions Scheme for Carbon and Ecosystem Studies; Aumont et al., 2015). NEMO and PISCES are “online” coupled on the same
1/36∘ model grid resolution, placing the model into the
submesoscale-permitting regime. This resulting system, hereinafter referred
to as IBI36, has been used to produce a pre-operational qualification
simulation that has served as initial conditions to start the operational
analysis and forecast system. This system has been providing, on a weekly basis, a
short-term (7 d) forecast of the ocean dynamics and the main
biogeochemical variables of the marine ecosystem, since April 2018. The
physical and biogeochemical products are disseminated through the CMEMS website, and described and validated in Maraldi et al. (2013), Sotillo et al. (2015, 2018), Bowyer et al. (2018) and Amo et al. (2018, 2019).
PISCES simulates the lower trophic levels of the marine food web, from
nutrients to mesozooplankton. It has already been successfully used in
various biogeochemical studies at global, regional scales and up to process
studies, at low and high spatial resolutions as well as for short-term and
long-term analyses (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; Gutknecht et al., 2016). PISCES is also used
in operational oceanography (Brasseur et al., 2009), for the CMEMS global
ocean analysis and forecast system at 1/4∘ (Perruche
et al., 2016) and the Indonesian seas operational system at 1/12∘
(the INDESO project; Gutknecht et al., 2016).
Although PISCES has been used so far to answer a wide range of scientific
questions, it has never been used at such a resolution before. First of all,
due to its high resolution, the IBI36 system represents a challenge in terms
of numerical, computational and operational constraints. Moreover, although
the biogeochemical equations of PISCES remain unchanged between the CMEMS
global ocean system at 1/4∘ and IBI36, the
distribution of biogeochemical tracers is impacted by a better resolution of
ocean dynamics, notably through finer bathymetry and a more accurate
coastline. In addition, IBI36 considers small-scale phenomena and
high-frequency processes of primary importance such as tides and atmospheric
pressure forcing. The detailed validation of marine processes at the
regional and coastal scale by Maraldi et al. (2013) showed the model's
ability to correctly reproduce tidal fronts, narrow boundary currents,
surges, etc. All these contrasting dynamic regimes create different
biogeochemical environments that impact the availability of nutrients,
oxygen and light for phytoplankton growth, thus impacting the rest of the
marine food chain.
In addition to IBI36, European waters are also covered by other
biogeochemical models, as several MFCs share a part of their model domain
with IBI. The operational system for the north-west European shelf seas
(Edwards et al., 2012; O'Dea et al., 2017) is based on NEMO and the ERSEM
biogeochemical component (Blackford et al., 2004; Butenschön et al.,
2016) at 1/15∘ latitudinal resolution and 1/9∘
longitudinal resolution (∼7 km). The Mediterranean Sea
forecasting system (Lazzari et al., 2012; Tonani et al., 2014; Teruzzi et
al., 2018; Salon et al., 2019) is built on NEMO and BFM (BiogeochemicalFlux Model) (Vichi et al., 2013) at 1/24∘ resolution. These regional
coupled systems, and of course the 1/4∘ NEMO-PISCES global ocean
operational system, are the subject of intercomparisons. Ocean dynamics
forecast models, all of them based on NEMO, are already being compared in
Lorente et al. (2019) and Mason et al. (2019). For the marine
biogeochemistry and ecosystem dynamics, intercomparison is initiated in the
framework of CMEMS but represents a significant work because the
biogeochemical models differ in complexity (Gehlen at al., 2015).
In this paper, the pre-operational qualification simulation (which extends
from January 2010 to December 2016) of the IBI36 system is evaluated using
different observational datasets (satellite, oceanographic historical
databases and BGC-Argo float network). This paper represents the first
validation of the biogeochemical component of the IBI36 system. The
objective is to assess the performance of the PISCES model in reproducing
the main biogeochemical characteristics of IBI European waters and verify
that PISCES is suitable at such a resolution and can be used for operational
analysis and forecast applications. Oxygen, nutrients, chlorophyll a
(hereinafter denoted Chl a)
and net primary production (NPP) are assessed in
terms of consistency and quality (or accuracy) using GODAE-like metrics
(Hernandez et al., 2009, 2015). Despite some of the areas presented here being
outside of the IBI service domain (where the IBI-MFC delivers IBI products
to CMEMS end users), the model is evaluated over the IBI extended domain in
order to take advantage of the in situ observation coverage.
The paper is organised as follows. Section 2 presents an overview of the IBI
European seas with emphasis on the main drivers of the ecosystem dynamics.
Section 3 describes the IBI36 system, the model initialisation and boundary
conditions, the external forcing, the regional adaptations and the data used
for evaluation. In Sect. 4, the biogeochemical tracers are compared to
satellite and in situ observations. Finally, the discussion and conclusions are
provided, respectively, in Sects. 5 and 6.
IBI European waters
Phytoplankton dynamics is controlled by the complex interaction between
ocean dynamics, nutrients and light availability. The biogeochemical and
ecosystem dynamics of IBI European waters are synthesised in this section,
with an emphasis on several areas (please see the 12 black boxes added to
Fig. 1a). These areas are adopted in the validation framework (Sect. 4.1)
and are named throughout the following description.
In the IBI European waters, phytoplankton dynamics usually follows a
seasonal cycle typical of temperate seas, governed by the alternation
between winter mixing and summer stratification of the water column (Barton
et al., 2015). A rapid increase in phytoplankton biomass starts in spring,
when seasonal re-stratification begins and when the mixed layer depth (MLD)
becomes shallower (Sverdrup, 1953; Behrenfeld, 2010; Taylor and Ferrari, 2011).
This spring bloom is followed by a summer decrease in biomass, when the
increase in stratification of the water column reduces the vertical supply
of nutrient to the euphotic layer (the layer where phytoplankton grows)
(Barton et al., 2015).
In the north-east Atlantic Ocean, primary productivity increases from south
to north (boxes 3, 2 and 1 in Fig. 1a). In the subtropical North Atlantic,
wind stress induces Ekman downwelling that deepens the nutricline and the
warm waters maintain a stratification of the water column throughout the
year (Barton et al., 2015). The annual primary production is then limited
and so is its seasonal variations. The subtropical gyre is separated from
the subpolar gyre by the meandering Subpolar Front which covers a relatively
wide region (Rossby, 1996) and represents a transition zone between the two
regimes. In the subpolar North Atlantic, the seasonal surface cooling
deepens the mixed layer in winter, winds drive Ekman upwelling and make the
nutricline shallower (Barton et al., 2015). However, light supply limits the
phytoplankton growth in winter. A strong spring bloom is triggered by water
column re-stratification in spring, while during summer the stratification
limits the nutrient supply to the surface (Williams et al., 2000). This
seasonal upward flow of deep and nutrient-rich waters triggers a higher
productivity and a strong seasonal cycle.
Moving toward the coast, Moroccan and Iberian upwelling systems (boxes 10
and 8 in Fig. 1a) are part of the Canary Current upwelling system, one of
the four main eastern boundary upwelling systems of the world and thus a
very productive ecosystem and an active fishery (Aristegui et al., 2004). The
season for upwelling along the Iberian coast begins in May–June with the
establishment of northerly winds and continues throughout the summer
(June–September; Wooster et al., 1976; Nykjær and Van Camp, 1994). Along
the Moroccan coasts, upwelling intensifies from the north, where it is
highly seasonal, to the south where it can be considered permanent and
intense, with maximum activity from April to September (Pelegriì and
Benazzouz, 2015).
The IBI European waters also cover part of the western Mediterranean Sea
(boxes 11 and 12 in Fig. 1a). From a biogeochemical perspective, the
Mediterranean Sea shows a high N : P ratio (N : P ∼20 for the
western basin; Ribera d'Alcalà et al., 2003; Lazzari et al., 2016) and
relatively high oxygen consumption rates compared to the Atlantic and
Pacific oceans (Christensen et al., 1989; Roether and Well, 2001).
Mediterranean outflow water (MOW) flows into the Gulf of Cádiz (box 9 in
Fig. 1a) and the Atlantic through a sill depth of only 290 m at the
Strait of Gibraltar. This salty and denser water flows out at the bottom of
the sill and a northward-moving MOW core spreads on the continental slope of
Portugal at 1000 m depth, enters the Bay of Biscay and follows the shelf
break further north.
The Bay of Biscay and Celtic seas are moderately productive ecosystems (UNEP
LME report, 2008). The seasonal cycle of phytoplankton in the Bay of Biscay
(box 7 in Fig. 1a) is typical of temperate seas (Fernaìndez and Bode, 1991;
Valdés et al., 1991; Lavín et al., 2006) but spatial variability is
high. The bay is characterised by a weak anticyclonic circulation in the
oceanic part, a coastal upwelling, the northerly flow of MOW (OSPAR, 2000;
Lavín et al., 2006) and river discharge (Gohin et al., 2003). In the
oceanic part of the bay, a major biomass peak can be observed in spring due
to oligotrophic conditions. However, in the coastal part of the bay,
phytoplankton remains relatively high during winter for isobaths less than
100 m in the region of freshwater influence (ROFI).
The continental shelf widens in the Bay of Biscay. It is quite narrow along
the Spanish coast but increases rapidly with latitude along the French
coasts, from 10 km in the south to more than 200 km wide in the north of the
bay. The most extensive continental shelf areas are in the Celtic seas and
the North Sea. The continental shelf along the European coasts is the site
of intense tidal amplitude and turbulent mixing that prevent stratification
(Lam et al., 2003, Lavín et al., 2006). To the
west of the Celtic seas, a significant and permanent front can be observed
in Chl a at the edge of the shelf, extending from the northern Bay of Biscay
to the Faroe–Shetland Channel and associated with the shelf edge current
(Belkin et al., 2009; Aquarone et al., 2008). The English Channel (box 6 in
Fig. 1a), connecting the North Sea to the Atlantic, is generally mixed and
strongly influenced by winds. The North Sea (boxes 4 and 5 in Fig. 1a) is
characterised by significant river discharge and permanently mixed water
column in the south, supplying the highest coastal primary production rates.
The north part is characterised by a seasonal stratification and a deep
channel in the north-east. Finally, as the eastern boundary of the IBI domain,
the Skagerrak and Kattegat connect the North Sea to the Baltic Sea.
Discharge of fresh and nutrient-rich water from rivers is a strong forcing
factor for European waters. In addition to natural inputs related to the
watershed erosion, many European coastal ecosystems are damaged by
eutrophication due to human activities such as wastewater, agriculture and
fish farming (Valdés and Lavín, 2002). Eutrophication affects coastal
areas, fjords and estuaries, mainly within the Celtic seas, the Bay of
Biscay and the Iberian coast (OSPAR, 2003). Excessive nutrient enrichment,
usually due to increased nitrogen and phosphorous concentrations in rivers,
leads to high primary production rates and reduced oxygen concentrations in
the bottom water. Oxygen deficiency was reported in the bottom waters of the
north-west European shelf (OSPAR, 2013; Ciavatta et al., 2016) and can be
used as an indicator of the health of marine ecosystems.
The IBI36 configurationThe coupled model system
Within the framework of CMEMS, the IBI-MFC team has deployed an operational
forecast service based on a coupled physical–biogeochemical model
application. The model domain covers part of the north-east Atlantic Ocean
from the Canary Islands (26∘ N) to Iceland (64∘ N) and
from 20∘ W to the North Sea (14∘ E) and the western
Mediterranean (10∘ E), using a curvilinear grid (Fig. 1a) with a
horizontal resolution of 1/36∘,
corresponding to ∼2 km for latitudes covered by the IBI domain, and 50 vertical levels.
As already discussed in the introduction, the physical model is based on the
NEMO 3.6 hydrodynamic model (Madec et al., 1998; Madec, 2008), developed by
the NEMO consortium. The NEMO modelling system is freely available
(http://www.nemo-ocean.eu, last access: 28 October 2019), and specific numerical choices
include time-splitting and non-linear free surface to correctly simulate
high-frequency processes such as tides. The ocean dynamics is constrained
through data assimilation of in situ and satellite physical data (temperature and
salinity vertical profiles, sea surface height and sea surface temperature).
The IBI36 physical component is described in Maraldi et al. (2013), Sotillo
et al. (2015, 2018) and Amo et al. (2018), and the data assimilation method
is described in Amo et al. (2018).
The biogeochemical model PISCES v2 (Aumont et al., 2015), part of the NEMO 3.6 modelling platform, is an intermediary complexity model taking into
account 24 prognostic variables. The model considers five nutrients that
limit phytoplankton growth (nitrate, ammonium, phosphate, silicate and iron)
and four living compartments: two phytoplankton size classes
(nanophytoplankton and diatoms) and two zooplankton size classes
(microzooplankton and mesozooplankton); the bacterial pool is not explicitly
modelled. PISCES distinguishes three non-living detrital pools for organic
carbon, particles of calcium carbonate and biogenic silicate. In addition,
the model simulates the carbonate system and dissolved oxygen.
Biogeochemical parameters are based on the standard parameters of PISCES v2.
Please refer to Aumont et al., 2015, for the full description of the model.
For this regional configuration, physics and biogeochemistry are running
simultaneously (“online” coupling), with the same 1/36∘ spatial
resolution. For the reason of numerical cost (optimisation of the computing
time), the numerical scheme for biogeochemical processes is forward in time
(Euler), while the physical component uses the leap-frog scheme. To respect
the mass conservation, the coupling between biogeochemical and physical
components is done every other time. The time step of the biogeochemical
model is therefore twice that of the physical component, i.e. 300 s. The
advection scheme for biogeochemistry is the same QUICKEST scheme (Leonard,
1979) used for the physical part but using the limiter of Zalezak (1979).
The IBI36 biogeochemical component is described in Bowyer et al. (2018) and
Amo et al. (2019).
Model initialisation, external forcing and boundary conditions
The pre-operational qualification simulation starts on 6 January 2010 and
runs until 31 December 2016. Ocean dynamics (temperature, salinity,
currents and free surface) is initialised and forced to the open boundaries
by the daily outputs of the NEMO global ocean analysis and forecasting
system at 1/12∘ (Lellouche et al., 2016, 2018) of CMEMS. Both
regional and global systems are forced every 3 h with atmospheric fields
from the ECMWF. The biogeochemistry is initialised with the NEMO-PISCES
global ocean analysis and forecasting system at 1/4∘
(Perruche et al., 2016) of CMEMS for the same date, and open boundary
conditions come from the same global product on a weekly basis. The global
biogeochemical system is also forced by the coarsened solution of the global
physics system mentioned just above, making the global and IBI components of
CMEMS consistent.
Other boundary fluxes account for the external supply of nutrients and
carbon from three different sources. The model includes the atmospheric dust
deposition of Fe, Si, P and N at the ocean surface (Aumont et al., 2015).
River discharge of nutrients comes from the Global NEWS 2 datasets (Mayorga
et al., 2010) and carbon comes from Ludwig et al. (1996). An iron source
corresponding to sediment reductive mobilisation on continental margins is
also considered. For more details on external nutrient supplies, please
refer to Aumont et al. (2015).
Two adaptations are necessary in order to meet regional specificities. The
first adaptation concerns the becoming of particles reaching the bottom
boundary. Within the standard version of PISCES v2, the exchange between the
ocean and the sediments assumes that a fraction of particulate material
reaching the sea floor is permanently buried in the sediments, while the
remaining organic matter is dissolved or degraded and released into the
water column. Concerning the IBI configuration, strong tidal currents
prevent organic matter from settling on the bottom and being stored in the
sediments over much of the north-west European continental shelf (De Haas et
al., 2002). Thus, no permanent burial to the sediments is considered in the
IBI36 system. The second adaptation concerns the supply of nutrients from
rivers. As mentioned above, nutrient inputs come from the annual climatology
at 1/2∘ spatial resolution of Global NEWS 2. They
represent a realistic hydrology for the reference year 2000, considered as
representative of the contemporary conditions (Mayorga et al., 2010). Inputs
are injected into the model in the form of surface runoff in the river
plumes of the Rhône and the German Bight and along the coastline for
other rivers, with the caution of conserving the nutrient flows estimated by
Global NEWS 2. However, Global NEWS 2 seems to underestimate nutrient runoff
in western Europe (Mayorga et al., 2010). The only contribution of
Global NEWS 2 is not sufficient to support the high coastal biological
production of the IBI European waters (not shown). Additional inputs of
nitrate and phosphate are then introduced into the system at source points
of the 33 main rivers of the IBI extended domain (please refer to Maraldi et al. (2013) for the location of the rivers) and are linked to the physical
flow. These additional nutrients come from rivers monitored and listed by
the European Environment Agency (https://www.eea.europa.eu/, last access: 28 October 2019) on the
basis of annual averages. This adaptation leads to higher coastal Chl a and
allows the model to reproduce the maximum Chl a observed along the European
coasts (not shown). It also allows representing the nutrients in excess
likely to cause eutrophication in downstream coastal waters and oxygen
deficiency in the bottom waters.
Satellite and in situ observational data sources used for model validation
The model results are compared with satellite and in situ observational data. Chl a
and NPP are derived from remote sensing estimations. Dissolved oxygen,
nutrients (nitrate, phosphate, silicate and ammonium) and Chl a
concentrations are gathered in regional databases such as ICES
(International Council for the Exploration of the Sea), EMODnet (European
Marine Observation and Data Network) and the Biogeochemical-Argo (BGC-Argo)
floats. Chl a concentration is expressed in mg Chl m-3. NPP is
expressed in mg C m-2 d-1. Oxygen and nutrient concentrations, for
standardisation purposes, are converted in µmol L-1. The spatial
distributions of ICES, EMODnet and BGC-Argo data are presented in Fig. 1b.
Remote sensing estimations of surface Chl a are provided by the Ocean Colour
– Climate Change Initiative project of the European Space Agency (ESA OC-CCI
product), distributed via CMEMS. The regional ESA OC-CCI product for the
North Atlantic Ocean has a resolution of 1 km. It merges SeaWiFS,
MODIS-Aqua, MERIS and VIIRS sensors. Chl a (in mg Chl m-3) is estimated
from the OC5CI regional algorithm case1/case2, a combination of OCI (Hu et
al., 2012) and OC5 (Gohin et al., 2008). A combined algorithm is required
because wide and shallow north-west European shelf seas are supplied in
sediment and organic material by many estuaries, which makes the water
turbid and disturbs the measurement of Chl a concentrations. A detailed
description of the ESA OC-CCI processing system can be found in
Sathyendranath et al. (2012).
Three NPP products using the ocean colour data of the MODIS ocean colour
sensor are distributed by Oregon State University (http://www.science.oregonstate.edu/ocean.productivity/,
last access: 28 October 2019): the Vertically
Generalized Production Model (VGPM; Behrenfeld and Falkowski, 1997; usually
recognised as the standard product), an “Eppley” version of the VGPM product
(Eppley-VGPM; Behrenfeld and Falkowski, 1997) and the Carbon-based
Production Model (CbPM; Westberry et al., 2008). These global ocean estimates
are monthly averages with a resolution of 1/6∘ and are expressed
in mg C m-2 d-1. Due to the high uncertainty in NPP products
(Henson et al., 2010; Emerson, 2014), PISCES estimates are compared with the
three products mentioned above.
The ICES oceanographic database (http://www.ices.dk/marine-data/data-portals/Pages/default.aspx,
last access: 28 October 2019) gathers quality-controlled in situ
observational data for the north-east Atlantic Ocean, the North Sea, the
Baltic Sea and the Arctic Ocean. Dissolved oxygen, nitrate, phosphate,
silicate and ammonium are all expressed in µmol L-1 and Chl a
in mg Chl m-3. Over the period of the IBI36 pre-operational qualification
simulation, ICES data are mainly located in the shallow and coastal waters
of the northern seas.
EMODnet collects, validates and provides access to relevant marine chemistry
data to assess the state of ecosystems in accordance with the Marine
Strategy Framework Directive. The chemistry component of EMODnet has adopted
and adapted SeaDataNet standards and services, and delivers regional
aggregated datasets receiving additional quality control of metadata and
data (https://www.emodnet-chemistry.eu/products). These in situ observation
collections contain oxygen, nitrate, phosphate, silicate and ammonium
profiles all in µmol L-1 and Chl a profiles in mg Chl m-3.
The north-east Atlantic Ocean dataset includes data from the OVIDE (Observatoire de la Variabilité Interannuelle et Décennale en Atlantique Nord) section
between Portugal and Greenland in spring 2010 and data from the springtime
PELGAS (Pélagiques Gascogne) cruises on the Bay of Biscay. For the Mediterranean Sea dataset, only
Chl a is presented, as it has the best spatial cover as compared to other
variables.
BGC-Argo floats are autonomous profiling floats advected by currents
(Biogeochemical-Argo Planning Group, 2016). These floats acquire vertical
profiles of temperature, salinity and key biogeochemical variables over
complete seasonal cycles. In this study, we use the vertical profiles of
dissolved oxygen, nitrate (both estimated in µmol kg-1 and
converted in µmol L-1) and Chl a concentrations (in mg Chl m-3)
collected with two BGC-Argo floats in the IBI region. The first
float is an APEX profiler (World Meteorological Organization (WMO) no. 5904479), deployed in the North Atlantic Ocean by the University of
Washington (Seattle) in February 2014 and which provided biogeochemical
measurements until December 2016. The second float is a PROVOR-II profiler
(WMO no. 6901648), deployed in the western Mediterranean Sea by the
French Villefranche Oceanographic Laboratory in July 2014 and recovered in
May 2016. The float data can be downloaded from the Argo Global Data
Assembly Centre in France (ftp://ftp.ifremer.fr/ifremer/argo, last access: 28 October 2019;
Argo Data Management Team, 2017). The conductivity–temperature–depth (CTD)
and trajectory data are quality controlled following Wong et al. (2015).
The raw BGC signals are transformed into Chl a, oxygen
and nitrate concentrations following Schmechtig et al. (2015), Thierry et al. (2016)
and Johnson et al. (2016), respectively. Finally, corrections are
applied on each variable to reduce calibration biases and sensor drifts. For
the APEX float observations, the three variables are “delayed mode” data
and are adjusted following Johnson et al. (2017). For the PROVOR float
observations, oxygen and nitrate are “real-time” data, and Chl a is
“adjusted” data; they are adjusted following Mignot et al. (2019), and the
first 5 months of nitrate measurement were masked due to spurious values.
IBI36 evaluation
The skills of the pre-operational qualification simulation are evaluated by
comparing model results for the main biogeochemical variables (Chl a, NPP,
nutrients and oxygen) to satellite-derived estimations and in situ observations
between 2010 and 2016. In function of data availability, a daily to seasonal
timescale is evaluated. The spatial distribution (two-dimensional
longitude–latitude plots), time series, vertical profiles and statistics
performance are presented using GODAE-like metrics (Hernandez et al., 2009,
2015), in order to assess the quality of the PISCES biogeochemical component
in terms of consistency and quality/accuracy. The GODAE “Class 1” metrics
are a direct comparison to observed quantities and give a general overview
of the model's ability to be consistent with the general features of the IBI
European waters. The GODAE “Class 4” metrics provide a series of
statistics and quantify the differences between model and observations at
their location and time.
Satellite-derived estimations
For comparison to the satellite-derived estimations (Chl a and NPP), the
model is interpolated onto the data grid. Satellite estimates are scarce
north of 50∘ N during the winter season, especially between
November and February due to omnipresent cloud coverage that dramatically
limits the observation of Chl a concentrations. Consequently, the model
outputs (Chl a and NPP) are masked based on data availability; thus, the
annual average is done on the same number of samples. The annual average is
calculated using the 7 years of simulation, from 2010 to 2016. Time series,
Hovmöller diagrams and time correlation are based on monthly averages.
The time series are presented for several small boxes defined and presented
in Sect. 2 and Fig. 1a. Some of them are located offshore to the open
ocean (boxes 1 to 4, 11 and 12) and the others follow the coastal areas
(boxes 5 to 10).
The model sea surface Chl a concentration is compared to the ESA OC-CCI
product. The annual average Chl a spatial distribution, the bias and the
root mean square error (RMSE) are presented in Fig. 2. The time evolution of
Chl a at 15∘ W longitude (Hovmöller diagram) is shown in Fig. 3 in
order to discuss the seasonal dynamics of the North Atlantic part. Time
series for the 12 boxes already introduced as well as the spatial
distribution of the temporal correlation at each grid point are presented in
Fig. 4. Global statistics are synthesised in Table 1.
Sea surface Chl a. (a) Annual average of ESA OC-CCI
product, (b) annual average of IBI36, (c) averaged bias of Chl a (model-obs)
and (d) RMSE (〈(model-obs)2〉), all expressed in mg Chl m-3. Statistics are computed from
monthly fields between 2010 and 2016.
Synthesis table for Chl a (mg Chl m-3) and NPP (mg C m-2 d-1) assessment against satellite-derived estimations. Mean and
standard deviation, mean error =〈(model-obs)〉, RMSE =〈(model-obs)2〉, percent bias (%) =|meanerror/meanobs| and correlation are computed for the IBI extended
domain, using model and observations averaged over the length of the
simulation (2010–2016).
The averaged Chl a over the IBI domain (0.615±0.69 mg Chl m-3)
is close to the ESA OC-CCI product (0.555±0.63 mg Chl m-3),
resulting in a low percent bias of 10.8 % and a high correlation of 0.81
(Table 1). The large-scale distribution of Chl a is correctly reproduced:
the North Atlantic subtropical gyre with low surface concentrations
(<0.1 mg Chl m-3), increasing concentrations when moving to
the north and the highest values on the continental shelf. The Chl a
signature of the shelf-slope front is well marked west of the British Isles
to the Faroe–Shetland Channel. The maximum coastal Chl a is supplied by
nutrient input from rivers, resuspension by strong tidal currents in the
northern shelf and upwelling off the Iberian and Moroccan coasts.
Major biases are located on the continental shelf (Fig. 2c). The model
simulates a higher annual average in the northern part (southern North Sea,
English Channel, Irish Sea and Faroe Islands), the French coasts of the Bay
of Biscay and the ROFI of the Ebro and Rhône rivers. The model
underestimates Chl a concentrations off the coast of Morocco (south of
Agadir) and in the region linking the North Sea to the Baltic Sea (Kattegat
and Skagerrak) (Fig. 2c). The spatial distribution of the RMSE (Fig. 2d)
between the simulation and the satellite product is comparable to the annual
average of Chl a (Fig. 2a and b). RMSE increases from south to north in the
North Atlantic part and is the highest in coastal areas (Fig. 2d).
Hovmöller diagram for sea surface Chl a at 15∘ W
between 2010 and 2016. (a) ESA OC-CCI product,
(b) IBI36 and (c) bias of Chl a (model-obs), all expressed in mg Chl m-3. Monthly
fields between 2010 and 2016 are used.
Time series of sea surface Chl a (mg Chl m-3) between 2010
and 2016. IBI36 is in black and ESA OC-CCI product in red.
Chl a is averaged over the 12 boxes defined in Fig. 1 and reported on the
map in the top right. The RMSE (〈(model-obs)2〉) and correlation (r) between the model and the data are indicated for
each time series. The top right panel represents the spatial distribution of
temporal correlation between the model and the observation. Note the
different scales on the y axis.
The seasonal dynamics of the North Atlantic spring phytoplankton bloom,
expressed as Chl a, is depicted by the Hovmöller diagram at
15∘ W (Fig. 3) and the time series in boxes 1 to 3 (Fig. 4). In
the subtropical North Atlantic, Chl a concentrations are limited throughout
the year. A moderate Chl a peak develops in March in the southern part of
the domain (peak to 0.4 mg Chl m-3 in box 3) and gradually moves
northward while intensifying (peak to 1.3 mg Chl m-3 in box 1). The
bloom onset is well reproduced in the south (r=0.91 in box 3), but it
spreads more rapidly to the north. The observed peak reaches Iceland in
summer (June–July), while the simulated peak reaches Iceland in May. The
summer decrease after the bloom is then earlier and sometimes more
pronounced in the model, explaining the alternation of positive and negative
biases in the Hovmöller diagram at 15∘ W (Fig. 3c), the
increasing RMSE from south to north (Fig. 2d) and the lower temporal
correlation to the north of the domain (Fig. 4). The south part has limited
seasonal variations, while the north part shows a strong seasonal cycle. The
ESA OC-CCI product also highlights a large interannual variability in the
north part, while the model seems to be dominated by the seasonal dynamics
(Figs. 3 and 4). But one part of the signal is however missing due to cloud
cover masking several months each winter.
Depth-integrated NPP (mg C m-2 d-1). (a) Mean of the
three NPP products (VGPM, Eppley-VGPM and CbPM), (b) IBI36, (c) standard
deviation of the three NPP products and (d) bias (IBI36-meanNPPpoducts).
Statistics are computed from monthly fields between 2010 and
2016.
In the southern half of the IBI domain, south of 50∘ N in the
Atlantic part and in the Mediterranean, the simulated seasonal cycle of
Chl a is in phase with satellite product in view of the low RMSE (Fig. 2d)
and high temporal correlation (Fig. 4). Coastal ecosystems of the Bay of
Biscay (box 7) show a peak biomass during spring bloom, while the upwelling
off Portugal and Morocco (boxes 8 and 10) presents a maximum in spring with
more interannual variability off Morocco. In the Gulf of Cádiz (box 9) and
the western Mediterranean (boxes 11 and 12), IBI36 succeeds in reproducing
the seasonal cycle of Chl a (Fig. 4), with a high correlation coefficient (r>0.71) with the satellite product.
In shallow northern seas, the model does not match satellite product (boxes 4 to 6
in Fig. 4). In the open North Sea (box 4), the first peak is usually
reproduced, but the data present a strong interannual variability. In the
southern North Sea (box 5) and the English Channel (box 6), model and data
are dominated by the seasonal dynamics. The spring bloom is in phase but
high Chl a concentrations persist in summer in the model, while remote
sensing estimates show a sharp decrease after the spring bloom. These
coastal regions present the highest biases, highest RMSE and low temporal
correlation.
Net primary production
Simulated depth-integrated NPP is compared to the three NPP products (VGPM,
Eppley-VGPM and CbPM). Figure 5 presents the annual average distribution for
the simulation and the mean of the three NPP products, the standard
deviation of the three NPP products and the bias between the simulation and
the mean of the three NPP products. For time series (Fig. 6), the three
products are presented separately because they do not all have the same
seasonal behaviour, and therefore an average would prevent any analysis.
Global statistics are synthesised in Table 1.
On annual average, the IBI36 system provides for a NPP of 230 g C m-2 yr-1
at the western boundary of the domain that gradually increases
towards the coasts (Fig. 5b). The highest NPP (1700 g C m-2 yr-1)
is found in the coastal regions of the North Sea, where rivers and mixed
water columns supply the euphotic layer with nutrients. Compared to the mean
of the three NPP products (Fig. 5a), the large-scale distribution is
reproduced. The cross-shore gradients are reproduced; the signature of the
shelf-slope front west of the British Isles to the Faroe–Shetland Channel is
captured. However, the IBI36 system underestimates the NPP by a factor of 1.5
on average over the domain. The most important differences concern the
Kattegat/Skagerrak area and the Norway current, with a factor of 3–4. On the
other hand, it should be noted that the dispersion between VGPM, Eppley-VGPM
and CbPM products is considerable (Fig. 5c). Except the open boundaries,
the bias of the model is almost equal to the variability of the NPP products.
Time series of depth-integrated NPP (mg C m-2 d-1)
between 2010 and 2016. IBI36 is in black, VGPM in red, Eppley-VGPM in green
and CbPM in blue. NPP is averaged over 12 small boxes defined in Fig. 1.
Note the different scales on the y axis. The RMSE (〈(model-obs)2〉) and correlation (r) between the model and the NPP products (using
corresponding colours) are indicated for each time series.
Taylor diagram summarising the skill of the IBI36 system to
estimate the main biogeochemical variables: oxygen (circle), nitrate
(square), phosphate (triangle pointing upwards), silicate (triangle pointing
down), ammonium (pentagon) and Chl a (star) from ICES (black), North
Atlantic EMODnet product (white), OVIDE (red), PELGAS (blue), Mediterranean
EMODnet product (green), APEX BGC-Argo in the Atlantic (orange) and PROVOR
BGC-Argo in the Mediterranean (yellow).
Density plots for (a) oxygen, (b) nitrate,
(c) phosphate, (d) silicate, (e) ammonium and
(f) log10(Chl a). ICES data are on the x axis and
IBI36 on the y axis. Oxygen and nutrients are expressed in µmol L-1. Each axis is divided in 100 bins and the colour bar represents the
density of the match-ups (number of overlapping points). Note the different
scales for the variables. N indicates the total number of match-ups. Daily
averaged IBI36 outputs and ICES data are collocated in space and time
between 2010 and 2016. All depths are presented, keeping in mind that ICES
data are mainly located in the shallow and coastal waters of the northern
seas.
The time series confirm the considerable spreading between the three NPP
products (Fig. 6). The simulated NPP is generally in line with the two
VGPM-based products (VGPM and Eppley-VGPM) with a time correlation higher
than 0.7 in the majority of boxes. The very good correlation in the south part
of the Atlantic (box 3, r=0.91 with the VGPM) decreases northward (boxes 2 and 1), as IBI36 produces a moderate and above all an earlier production
peak. The behaviour of the NPP in the North Atlantic part is consistent with
the seasonal dynamics of the sea surface Chl a described in the previous
section. The coastal waters of the northern seas and Atlantic part as well
as the Mediterranean (boxes 4 to 12; Fig. 6) also show a simulated seasonal
cycle of NPP close to the VGPM-based products. On the other hand, the
correlation is low compared to the CbPM product, but the latter delivers a
seasonal cycle that is generally very different from the VGPM-based products. The
CbPM signal is sometimes in phase opposition with IBI36 (boxes 3, 5, 9, 10
and 11), while the comparison with VGPM results in high correlation in these
same boxes.
The averages simulated NPP (441.7±203.5 g C m-2 yr-1) is
close to CbPM (518.1±660.9 g C m-2 yr-1) and twice as low as VGPM (871.5±557.2 g C m-2 yr-1) (Table 1). But spatial
distribution is better correlated with the VGPM-based products (Table 1).
The VGPM is the most productive product, with a marked cross-shore gradient
and the highest seasonal amplitude (Fig. 6). The Eppley-VGPM behaves the
same way as the VGPM but is less productive (Fig. 6). The CbPM is the less
productive, with a poorly marked cross-shore gradient, the lowest coastal
production and a less pronounced seasonal cycle, sometimes out of phase with
VGPM-based products (Fig. 6). A few extreme values near rivers increase the
averaged NPP of CbPM and give rise to a high standard deviation.
In summary, IBI36 provides an averaged NPP similar to CbPM. The spatial
distribution, cross-shore gradients and seasonal variations are generally in
good agreement with the VGPM-based products, but IBI36 is half as productive
(mean factor of 1.5). The modelled NPP is thus within the range of
variability of the satellite-derived estimates.
In situ historical data
In the following, the simulation is compared to ICES and EMODnet in situ historical databases
using daily averaged model outputs. ICES data are mainly located in the
shallow and coastal waters of the northern seas (Fig. 1b). EMODnet regional
datasets cover the north-east Atlantic Ocean and the western Mediterranean
Sea. Global statistics are summarised in a Taylor diagram (Fig. 7).
Northern seas
Shallow northern seas are assessed using oxygen, nutrients and Chl a from
the ICES database. Dispersion diagrams for the full set of match-ups are
presented in Fig. 8. Sea surface spatial distribution and seasonal cycle are
in Figs. 9 and 10.
Surface concentrations of (a) oxygen, (b) nitrate, (c) phosphate, (d) silicate, (e) ammonium and (f) Chl a from ICES database (left of each panel)
and IBI36 (right of each panel). Oxygen and nutrients are expressed in
µmol L-1 and Chl a in mg Chl m-3. Daily averaged IBI36
outputs and ICES data are collocated in space and time between 2010 and
2016. Match-ups are averaged between 0 and 10 m depth, gridded and
averaged on a horizontal grid of 1∘×1∘ resolution.
The oxygen match-ups are well aligned along the bisector with a good
correlation (r=0.77) and a normalised standard deviation of 0.91,
indicating that the model reproduces the amplitude and variability of the
observations (Figs. 7 and 8). Temporal evolution of sea surface
concentrations shows the realistic amplitude and phase (Fig. 10). Sea
surface oxygen is slightly overestimated in the North Sea and English
Channel, with an average bias of 10.7 µmol L-1, corresponding to
a percent bias of 4 % (Fig. 9). In addition, the model does not capture
the lower sea surface oxygen concentrations measured during the 2014–2015 period
(Fig. 10). This anomaly is located in the region linking the North Sea to
the Baltic Sea (Kattegat and Skagerrak), the eastern open boundary of the
domain. But no reference to this event has been found in the literature.
Time series of surface concentrations of (a) oxygen, (b) nitrate,
(c) phosphate, (d) silicate, (e) ammonium and (f) Chl a from ICES (red) and
IBI36 (black). Oxygen and nutrients are expressed in µmol L-1
and Chl a in mg Chl m-3. Daily averaged IBI36 outputs and ICES data are
collocated in space and time between 2010 and 2016. Match-ups are averaged
between 0 and 10 m depth, and averaged daily. Time series are smoothed
using a 10 d window.
(a) Minimum oxygen (µmol L-1) in ICES data (left) and
IBI36 collocated to ICES (right) between 2010 and 2016. (b) Time series of
minimum oxygen (µmol L-1) in ICES data (red) and IBI36 IBI36
collocated to ICES (black). The deficiency threshold of oxygen (6 mg L-1 or 187.5 µmol L-1) is represented by the dashed line.
The number of available data in ICES is added to the right axis (area plot in
gray). (c) Surface area (in km2) vulnerable to oxygen deficiency, i.e. where oxygen decrease is below the deficiency threshold (blue; right axis)
and associated mean oxygen concentrations (µmol L-1; red; left
axis) using the whole IBI36 simulation (not only IBI36 collocated to ICES).
The three subplots are for the continental shelf (bathymetry <=200 m).
The distribution of nitrate also follows the bisector, with a noticeable
dispersion (Fig. 8) which deteriorates the statistics (Fig. 7). The model
generally underestimates sea surface nitrate with an average bias of -1µmol L-1 (9.6 % percent bias) (Fig. 9). The time series shows
a seasonal cycle in phase, but excessive nitrate concentrations are
simulated in spring and summer when the observed concentrations are very low
(Fig. 10). Very high values of 100 to 300 µmol L-1 are simulated
throughout the year in the vicinity of river flows between the Rhine and
Elbe and impact the time series.
Phosphate and silicate are overestimated for low concentrations during
spring–summer seasons, while higher concentrations during winter conditions
are better captured (Figs. 8 and 10). The phosphate dispersion diagram shows
two high-density zones. The spring–summer overestimation is mainly along the
coasts. Winter conditions are better captured, although still a little high.
The data show a marked seasonal cycle, while simulated phosphate levels
remain too high throughout the year. The average bias of
0.22 µmol L-1 or 48.3 % percent bias is reduced to 31.5 % when the pathway
to the Baltic Sea is excluded. Silicate has
an average bias of 2.1 µmol L-1 or 46.8 % percent bias. They are slightly overestimated in the
open North Sea and underestimated along the coasts between the Rhine and the
Elbe. In addition, percent bias decreases to 30.8 % when the pathway to
the Baltic Sea is excluded. Ammonium shows a high dispersion but the magnitude
is captured. The model does not reproduce the variability observed in data
(Fig. 8e), and the seasonal cycle is out of phase (Fig. 10e). The statistics
thus give poor performance for phosphate and silicate, even outside the
Taylor diagram for ammonium (Fig. 7).
Density plots (top) and vertical profiles (bottom) for oxygen (a, e),
nitrate (b, f), phosphate (c, g) and silicate (d, h) from the OVIDE section
data (EMODnet dataset) and IBI36. All nutrients are expressed in µmol L-1.
OVIDE data are on the x axis and IBI36 on the y axis of the
density plots. Each axis is divided in 100 bins and the colour bar represents the
density of the match-ups (number of overlapping points). Note the different
scales for the variables. N indicates the total number of match-ups. Daily
averaged IBI36 outputs and OVIDE section data are collocated in space and
time.
Chl a provides a satisfying spatial distribution (Fig. 8) but mean Chl a
concentrations along the coasts are underestimated (Fig. 9). The seasonal
cycle is captured, although the model predicts a slow spring increase
instead of a strong bloom in mid-March (Fig. 10). Coastal Chl a appears to
be underestimated compared to ICES in situ data, while it is overestimated compared to
satellite estimates (see Sect. 4.1.1 and Fig. 2). The statistics are not
satisfying (Fig. 7), while the density plot, surface distribution and time
series (Figs. 8 to 10) give a quite positive evaluation.
Statistics are not really rewarding (Fig. 7) because they are strongly
degraded by extreme values at the mouth of rivers or highly targeted areas
such as Kattegat/Skagerrak. They alone do not allow understanding the
characteristics of the IBI36 system. To interpret and complete the
statistics, the mean spatial distribution and daily averaged temporal
evolution are necessary. These usually give a more positive assessment
because extreme values are filtered (see the details in the legend of Figs. 9
and 10). Oxygen is the best-performing variable in the northern seas, and
its satisfying statistics allow deepening the analysis of oxygen match-ups
between ICES and IBI36.
Surface concentrations of oxygen (a), nitrate (b), phosphate (c),
silicate (d), ammonium (e) and Chl a(f) from the PELGAS data of the EMODnet
database (left of each panel) and IBI36 (right of each panel). Oxygen and
nutrients are expressed in µmol L-1 and Chl a in mg Chl m-3. IBI36 and PELGAS data are collocated in space and time between
2010 and 2016. Match-ups are averaged between 0 and 10 m depth, gridded
and averaged on a horizontal grid of 0.2∘×0.2∘
resolution.
Oxygen content is a key element in biogeochemical cycles and can be an
indicator of the health of marine ecosystems; for this reason, the minimum
oxygen concentrations are now analysed. For that, the absolute minimum is
extracted for each pixel of ICES and collocated IBI36. The lowest
concentrations are located in the eastern part of the North Sea (Fig. 11a).
The minimum remains high in winter, while it sharply decreases or even
reaches anoxic conditions in summer (Fig. 11b). The minimum reported by ICES
remains lower than usually during 2011 and 2015 winters, but they come from a
few measurement points very close to the coast in the vicinity of river
mouth, not captured by the IBI36 system. The spatial distribution of the
simulated minimum as well as its seasonal evolution is consistent with the
data (r=0.77). However, please remember that ICES data only permit identifying
the North Sea because the data density strongly decreases outside. So,
extending this analysis to the full set of simulated oxygen over the IBI
domain (not only the match-ups with ICES), IBI36 also simulates minimum
levels in the Celtic seas, Armorican shelf, coastal areas of Scotland and
western Ireland. Ciavatta et al. (2016) and the OSPAR Convention point out
these aforementioned regions as eutrophication problem areas, and Breitburg
et al. (2018) also report low and declining oxygen levels in almost all
coastal waters of the north-west European shelf.
Sea surface Chl a from EMODnet dataset (a) and IBI36 (b) in mg Chl m-3. IBI36 and EMODnet dataset are collocated in space and time
between 2010 and 2016. Match-ups are averaged between 0 and 10 m depth,
gridded and averaged on a horizontal grid of 0.2∘×0.2∘
resolution.
Continental shelf areas vulnerable to oxygen deficiency were estimated by
Ciavatta et al. (2016), considering vulnerable area when at least one daily
value is below the 6 mg L-1 (187.5 µmol L-1) threshold
during the time of the simulation. Using the same method as Ciavatta et al. (2016), the IBI36 system predicts a maximum surface area exposed to oxygen
deficiency of 280 000 km2. The vulnerable surface area is almost
non-existent in winter because waters are well oxygenated due to strong
mixing, and it extends to an average surface area of 85 000 km2 in summer
(Fig. 11c), associated with deoxygenated waters that can reach anoxic
conditions in the North Sea and along the west coasts of France.
North-east Atlantic waters
The north-east Atlantic part is evaluated using the EMODnet regional
dataset. Global statistics are very satisfying (Fig. 7), as in situ measurements
cover the entire water column. However, performance between the vertical and
sea surface distribution differs greatly. To illustrate this contrasting
performance, comparisons to the OVIDE section and PELGAS data are detailed below
using Figs. 12 and 13.
Density plots for oxygen (a, b), nitrate (c, d) and log(Chl a)
(e, f) from BGC-Argo data and IBI36 for the Atlantic (top) and Mediterranean
(bottom). Oxygen and nitrate are expressed in µmol L-1. Argo
data are on the x axis and IBI36 on the y axis of the density plots. Each
axis is divided in 100 bins and the colour bar represents the density of the
match-ups (number of overlapping points). Note the different scales for the
variables. N indicates the total number of match-ups. IBI36 and Argo data
are collocated in space and time.
The OVIDE radial section sampled in June 2010 between Portugal and Greenland
(Fig. 12) illustrates the vertical distribution of biogeochemical tracers in
the open Atlantic. Model oxygen and nutrients show very good statistics with
OVIDE data, with coefficient correlation higher than 0.95 (Fig. 7). The
dispersion diagram for oxygen shows two pools of high density: one for low
oxygen values and the other one for high concentrations. Throughout the
OVIDE vertical section, the minimum oxygen level is around 1000 m deep.
Low oxygen content in the eastern part of the section is due to MOW on the
shelf of the Iberian Basin. Oxygen maximum around 2500 m relies on
recently ventilated Labrador Sea water (Garcia-Ibanez et al., 2015) that
reaches the western part of the section. The three nutrients present a
maximum around 1000 m, the lower values at this depth being due to MOW.
High silicate (45–50 µmol L-1) near the bottom reflects the
influence of Antarctic Bottom Water in the north-east Atlantic Ocean
(Garcia-Ibanez et al., 2015). However, vertical profiles of oxygen are
somewhat smoothed. The minimum and maximum at, respectively, 1000 and 2500 m
are not pronounced enough, resulting in a normalised standard
deviation of 0.74 (Fig. 7). Nutrient profiles are also smoothed, but this is
less visible (normalised standard deviation close to 1; Fig. 7) than on
oxygen, as the latter has much stronger vertical gradients.
Time series of oxygen (top), nitrate (middle) and Chl a (bottom)
from BGC-Argo data and IBI36 for the Atlantic (left) and Mediterranean
(right). Oxygen and nitrate are expressed in µmol L-1 and Chl a
in mg Chl m-3. IBI36 and Argo data are collocated in space and time.
The white line represents the MLD computed from density criterion of 0.03 kg m-3 difference from surface.
The PELGAS spring data (Doray et al., 2018a, b) are used to illustrate
the mean sea surface distribution in the Bay of Biscay for spring conditions
(Fig. 13). Surface statistics are significantly degraded compared to
vertical statistics (Fig. 7). Simulated sea surface oxygen concentrations
present an average bias of +16.4µmol L-1, which corresponds
to a percent bias of 6.3 % (Fig. 13). Nutrient distribution is
realistically simulated, except at the ROFI of French rivers. Surface oxygen
bias and excessive nutrient discharge were already highlighted in the
northern seas using the ICES database. The mean surface Chl a distribution
is similar to the data: the cross-shore gradient is realistic with
concentrations of 0.3 mg Chl m-3 offshore, which increase to 6 mg Chl m-3 along the French coast.
Mediterranean Sea
The Mediterranean Sea is assessed using EMODnet regional dataset that has a
very good spatial coverage for oxygen and Chl a tracers, while nutrient data
are limited to the northern part of the domain. Oxygen comparison gives the
same conclusions as for the North Sea and the Atlantic: the model succeeds
in reproducing the amplitude and variability of oxygen, but a constant bias
persists. So only the sea surface Chl a distribution is presented here (Fig. 14).
High coastal values are located along the Catalan coast, in the ROFI of
the Ebro, along the Costa Blanca and along Algeria. Two highly productive
areas are located further offshore: one in the convection zone of the Gulf
of Lion and the other in the Algerian Basin between Sardinia and Algeria.
Everywhere else, Chl a is lower. The model simulates Chl a higher than
EMODnet in the Alboran Sea and in the ROFI of the Rhône River. But in a
general way, the model reproduces the mean spatial distribution of surface
Chl a in the Mediterranean.
BGC-Argo data
The free-drifting BGC-Argo profiling floats allow continuous monitoring of
dissolved oxygen, nitrate and Chl a of the upper 1000 m of the ocean. For this analysis, two BGC-Argo
floats are used: one in the North Atlantic Ocean and the other in
the western Mediterranean Sea, in order to discuss the model quality in
reconstructing the seasonal vertical dynamics and the key coupled
physical–biogeochemical processes. Density plots between the BGC-Argo data
and simulated fields are presented in Fig. 15 and time evolution of the
vertical profiles of oxygen, nitrate and Chl a along the float trajectory
are shown in Fig. 16. The quantitative comparison is summarised by the statistics
of Fig. 7.
Overall, the model predictions are in good agreement with the BGC-Argo
observations with correlation coefficients greater than 0.8 for oxygen and
nitrate profiles (Fig. 7). The model tends to overestimate low
concentrations and underestimate high concentrations of oxygen and nitrate
as shown by the distribution of match-ups which deviate from the bisector
(Fig. 15). Time evolution of the vertical profiles in Fig. 16 shows that the
deep oxygen minimum and nitrate maximum are not pronounced enough in the
model. Oxygen remains 20 µmol L-1 too high and nitrate
2 µmol L-1 too low. The smoothing of the vertical profiles of
oxygen and nutrients was already highlighted by the comparison to OVIDE.
The IBI36 system succeeds in reproducing the winter vertically mixed water
column that enriches the first few hundred metres of the water column with
oxygen and supplies the surface with nutrients. Seasonal re-stratification
and the shoaling of the MLD trigger the onset of the spring phytoplankton
bloom (Fig. 16). In the Atlantic Ocean (Fig. 16, left side), the MLD reaches
400–500 m depth during winter. Depth of the ventilation has a clear
interannual variability, as shown by the deeper mixing during winter 2015
with respect to the following year. This ventilation also enriches the
surface in nutrients. If winter processes are well reproduced, the onset of
the simulated bloom is however too early. The intensity of the bloom is
misrepresented in the model, as surface Chl a concentrations remain
significantly lower than BGC-Argo data during the spring bloom, decrease
rapidly after the bloom and remain at a low level during summer. This
behaviour in regard to the BGC-Argo data is consistent with the comparison
to ESA OC-CCI product in box 2 of Fig. 4. The time evolution of
vertical profiles highlights that the high surface Chl a associated with the
spring bloom migrate to the subsurface during the stratified season in the
model, while they remain at the sea surface in the data. Indeed, a deep Chl a
maximum (DCM) develops in summer in the model simulation, while in the
observations, the Chl a maximum is maintained at the surface during
summer.
In the Mediterranean Sea (Fig. 16, right side), the seasonal cycles of Chl a
and oxygen are characterised by the formation of a DCM (Mignot et al., 2014;
Lavigne et al., 2015), which is typically established during the stratified
season. The DCM is associated with a deep oxygen maximum (DOM) at the layer of
the DCM due to intense phytoplankton production during spring and summer
(Estrada et al., 1985). These maxima are also associated with the limit
between nutrient-depleted and nutrient-rich layers, termed nutricline
(Estrada et al., 1993). The model
correctly reproduces the time evolution of the nutricline, as well as the
temporal evolution, vertical displacement and intensity of the DCM and DOM.
The IBI36 system compares well with the Mediterranean float to reproduce the
vertical dynamics of the phytoplankton chlorophyll and oxygen, suggesting
that the seasonal succession of physical–biogeochemical processes is
captured.
Discussion
An extended validation of the pre-operational qualification simulation has
allowed understanding the strengths and weaknesses of the biogeochemical
component of the IBI36 system, providing the trails for improvement to be
explored, which are here discussed.
Mismatches between simulated and satellite-derived estimations of Chl a and
NPP increase when approaching the continental shelf. The uncertainties of
the modelled Chl a with respect to the ESA OC-CCI product are determined by
calculating the bias and RMSE. Highest uncertainties are located in coastal
areas and can be explained by temporal discrepancies between the simulation
and ESA OC-CCI product. For NPP, the uncertainties are apprehended by the
comparison of the standard deviation of the three NPP products and the bias
between the simulation and the mean of the three NPP products. Bias of the
model is included in the standard deviation of the NPP products. The
modelled NPP is then included within the range of uncertainty of the
satellite-derived products.
Continental margins are very productive regions and play an important role
in the biogeochemical cycle of nutrients and carbon. They are the site of
complex interactions between physical, chemical and biological processes
that include exchanges between shelf and the open ocean, sediment–water
interactions, air–sea fluxes and land–ocean freshwater inputs. In addition,
coastal systems are locally strongly affected by human activities. All these
interactions make the continental shelf a challenging location to obtain realistic
models.
Continental margins are also the areas where the uncertainties of satellite
products are the greatest. Coastal areas are complicated areas for satellite
sensors to measure due to interference from Chl a content with other
optically absorbing elements such as suspended matter, coloured dissolved
organic matter and bottom reflectance, resulting in a 100 % uncertainty in
the estimate of Chl a, compared to 30 % for the open ocean (Moore et al.,
2009). A good example is given in the North Sea example, where the model
underestimates coastal Chl a with respect to the ICES in situ data (Sect. 4.2.1),
which, however, appear in contrast with the overestimation with respect to
the satellite ESA OC-CCI product (Sect. 4.1.1). The dispersion between the
three NPP products is also considerable. Campbell et al. (2002) pointed out
that the “best-performing algorithms generally fall within a factor of 2 of
the estimates derived from 14C”, and that NPP products have poor
performance for water columns with depths less than 250 m (Saba et
al., 2011). Schourup-Kristensen et al. (2014) also reported that the VGPM
product is twice as productive as biogeochemical models along the European
coasts. This high uncertainty in NPP products prevents a quantitative
assessment. Additionally, they do not have the same seasonal dynamics; CbPM
has a seasonal cycle distinct or even out of phase from the two others NPP
models in a major part of the domain (Sect. 4.1.2). An extensive dataset of
measures of primary production in the IBI European waters would be necessary
to evaluate the three NPP products and deepen the analysis.
As mentioned above, continental margins are very sensitive to the boundary
conditions of the model such as air–sea interactions, river inputs,
water–sediment interactions but also open boundary conditions. IBI36
performance decreases as these limits approach, as detailed below:
Oxygen concentrations at the sea surface are very sensitive to
ocean–atmosphere exchanges, as ocean oxygen balances with oxygen from the
atmosphere within a few weeks. The slight overestimation (around 4 %–6 %) of
the IBI system is not yet fully understood. Solubility of modelled oxygen is
similar to the one from ICES and EMODnet in situ historical databases (not shown),
suggesting that biases in sea surface temperature or salinity cannot
explain the biases in sea surface oxygen. The other key components (gas
transfer velocity and biological production) need to be further explored to
better understand this overestimation and reduce it.
The continental shelf ecosystem is strongly driven by river discharge,
especially in the northern seas. The seasonal cycle of phosphate and
silicate is not sufficiently marked, and the spring bloom is not as intense
as the one reported in ICES data. In the coupled IBI36 system,
nutrient inputs at river points are prescribed using annually averaged
values, while inputs usually follow a seasonal cycle related to
precipitation and watershed erosion. The increased discrepancies when
approaching the coasts are related to a poor representation of river
nutrient discharge due to a crucial lack of available measurements. A time
evolution or at least a seasonal variation would be necessary to apprehend
the phytoplankton dynamics in the coastal areas triggered by river plume
events.
Permanent burial in sediments is not considered in the IBI36 system
because strong tidal currents prevent organic matter from reaching the
bottom and accumulating in the sediments. This assumption may be too
restrictive for the whole model domain. The future system will adjust the
efficiency of permanent burial based on bottom friction. But the fact
remains that the treatment applied to the ocean floor remains very basic in
the standard version of PISCES. A sediment module that takes into account
biogeochemical processes in sediments and at the sediment–water interface is
most certainly required for the IBI configuration where the continental
shelf covers a large area.
Open boundary conditions are also fundamental. A perfect example is in
the Kattegat/Skagerrak area, connecting the North Sea to the Baltic Sea, at
the eastern boundary of the IBI domain. The statistics for the northern seas
are strongly affected by extreme values at the mouth of rivers (discussed
above) and by the highly targeted area that the Kattegat/Skagerrak area is. The
CMEMS Baltic Sea regional configuration instead of the global product should
be tested at this eastern open boundary of the IBI36 system.
Moving away from the continental margins, statistics are very satisfying for
oxygen and nutrients, as the entire water column is considered. The model
performs well in reproducing the vertical structure of oxygen and nutrients but
the profiles appear too smoothed. The deep minima and maxima are not
pronounced enough. This behaviour is also observed in the global model at
1/4∘ resolution (Perruche et al., 2016) used to set up the
initial and open boundary conditions and can originate from the physical or
biogeochemical models. Different approaches are currently under study: in
particular, vertical diffusion could explain the loss of peaks and minima in
vertical profiles, but biogeochemical processes (e.g. parameterisation of
remineralisation processes, rate of sinking of particulate detritus,
vertical migration of zooplankton which export organic matter at depth) will
also be investigated.
Finally, BGC-Argo floats allowed better understanding the phytoplankton and
oxygen vertical dynamics. The Mediterranean Sea dynamics is well captured by
the model, in terms of timing, vertical migration of the maximum chlorophyll
and the formation of an oxygen maximum linked to the DCM. In the Atlantic
part, winter processes are captured but the bloom onset is early. In fact,
the onset of the spring bloom is correct in the south part of the domain,
but it spreads more rapidly to the north (see the comparison to satellite
data; Sect. 4.1.1). The summer decrease after the bloom is then earlier and
sometimes more pronounced in the model, and the Chl a maximum migrates to
the subsurface during summer with the formation of a DCM, while maximum
Chl a remains at the surface in BGC-Argo estimates. Indeed, once the spring
bloom is over, PISCES cannot maintain the phytoplankton on the surface;
there is always an element that becomes limiting at the end of spring
(oligotrophic conditions). This behaviour is also present in the global model
(Perruche et al., 2016, 2018). In fact, it is not clear how biological
production can be maintained at the surface throughout the summer with low
nitrate content as observed in BGC-Argo (Fig. 16). However, the analysis is
limited to only one float simultaneously measuring oxygen, nitrate and Chl a
in the north-east Atlantic part of the IBI domain. Additional floats are
essential to understand the seasonal dynamics of phytoplankton, oxygen and
nitrate and better apprehend the involved physical–biogeochemical coupled
processes.
Conclusions
In the framework of CMEMS, the IBI-MFC team has developed an operational
system in order to monitor and forecast the ocean dynamics and marine
ecosystems of the IBI European waters. A 7-year pre-operational
qualification simulation (2010–2016) delivers the initial conditions to the
analysis and forecast system. This paper provides an extended validation of
this pre-operational qualification simulation in order to evaluate the
capacity of the IBI36 system to reproduce the surface and vertical
distributions, as well as seasonal cycles of the main biogeochemical
variables (Chl a, NPP, nutrients and oxygen) using GODAE-like metrics. The
different kinds of metrics (direct comparison and statistics) are necessary
and complementary in order to have a complete description of the model's
performance in terms of consistency and quality/accuracy. This paper
represents the first validation of the biogeochemical component of the IBI36
system: the objective is to show that PISCES can be used for operational
applications and that it is a suitable tool at such a resolution.
Chl a and NPP are compared to satellite estimates, describing here their
mean spatial distribution and seasonal cycle. Oxygen, nutrients and Chl a
concentrations are compared to in situ observations from ICES, EMODnet and the
BGC-Argo float network, using daily averages of the model outputs.
Observational data are available for the northern seas, the north-east
Atlantic waters and the western Mediterranean, and allow evaluating the
vertical distribution as well as shallow and coastal distributions. Some of
these areas are outside of the IBI service domain (that is the geographical
domain covered by the CMEMS IBI-MFC products), but in order to take
advantage of their in situ observational coverage, we evaluated the IBI extended
domain. The main results are summarised here:
The mean distribution and the seasonal cycle of sea surface Chl a is in
line with satellite estimates, particularly south of 50∘ N in the
Atlantic and the Mediterranean. The BGC-Argo floats suggest that the
seasonal succession of vertical physical–biogeochemical processes is well
captured by the model in the Mediterranean, with the development of a
seasonal DCM below the MLD. On the other hand, on the north-east Atlantic
waters, the spring phytoplankton bloom spreads more rapidly to the north and
Chl a maximum is not able to maintain at the surface during the stratified
season and migrates to the subsurface instead. The BGC-Argo floats,
although their spatial coverage is limited, open new doors to understand
and improve the seasonal dynamics of phytoplankton in the models.
NPP is a complex field to evaluate, as satellite-derived products give
widely different estimates among themselves. The model-averaged spatial
distribution is close to CbPM product, but spatial distribution, cross-shore
gradients and the seasonal variations are better correlated with the
VGPM-based products. The modelled NPP is thus within the range of
variability of the satellite-derived estimates.
Vertical distribution of oxygen and nitrate obtains very good statistics.
The amplitude and variability of the observations are captured by the model,
but the vertical profiles of oxygen and nitrate appear somewhat smoothed in
the wider ocean.
The continental shelf area shows the highest biases in nutrients and Chl a
as river nutrient discharge and sedimentary processes strongly influence
the seasonal cycle of nutrients and thus phytoplankton dynamics.
The continental shelf area of IBI domain appears vulnerable to oxygen
deficiency, especially the wide continental shelf covering the northern seas
and the Bay of Biscay. Maximum surface area can reach 280 000 km2
during the time of the simulation, but the mean seasonal extension varies
from a very restricted surface area in winter to 85 000 km2 in summer.
This extended evaluation has allowed understanding the strengths and
weaknesses of the biogeochemical component in the IBI36 system. The
pre-operational qualification simulation performs well in reproducing the main
biogeochemical characteristics of IBI European waters. PISCES is then a
suitable tool at such a resolution and can be used for operational analysis
and forecast applications. Future improvements were also explored. Finally,
the operational analysis and forecast IBI36 system can be a useful tool to
better understand and monitor the health of marine ecosystems (von
Schuckmann et al., 2016, 2018).
Code availability
The IBI36 configuration is based on the NEMO 3.6 version developed by the
NEMO consortium. NEMO modelling system is freely available at http://www.nemo-ocean.eu (last
access: 28 October 2019). The biogeochemical model PISCES v2 is part of
the NEMO modelling platform and is available via the NEMO website. Model
initialisation and boundary conditions are available via CMEMS (http://marine.copernicus.eu/, last access: 28 October 2019).
Data availability
The regional ESA Ocean Colour CCI product for the North Atlantic and Arctic
oceans with a resolution of 1 km is distributed via CMEMS (http://marine.copernicus.eu/,
last access: 28 October 2019). Primary production products are
distributed by Oregon State University (http://www.science.oregonstate.edu/ocean.productivity/,
last access: 28 October 2019). The International Council
for the Exploration of the Sea (ICES) oceanographic database is available at
https://www.ices.dk/marine-data/data-portals (last access: 28 October 2019). The European Marine
Observation and Data Network (EMODnet) is available at https://www.emodnet-chemistry.eu/products
(last access: 28 October 2019). EMODnet regional aggregated datasets
are generated by EMODnet chemistry under the support of DG MARE Call for
Tenders MARE/2008/03-lot3, MARE/2012/10-lot4 and EASME/EMFF/2016/006-lot4.
The north-east Atlantic Ocean regional dataset is aggregated, standardised
and quality controlled by IFREMER/IDM/SISMER – Scientific Information
Systems for the SEA (2018) from France and the Mediterranean Sea dataset by
Hellenic Centre for Marine Research, Hellenic National Oceanographic Data
Centre (HCMR/HNODC) (2018) from Greece. Biogeochemical-Argo (BGC-Argo) float
data can be downloaded from the Argo Global Data Assembly Centre in France
(ftp://ftp.ifremer.fr/ifremer/argo, last access: 28 October 2019).
Author contributions
EG (Mercator Ocean) contributed to the set-up of the IBI36
system, performed the evolution of model outputs by comparison to satellite
and in situ datasets, prepared the figures and is the main writer of the
paper. GR (Mercator Ocean) designed and developed
the IBI36 physical–biogeochemical coupled system and performed the
simulations. AM (Mercator Ocean) provided the corrected BGC-Argo
dataset. TD (Marine Institute) is responsible of the
evaluation and validation of the reanalysis and analysis/forecast systems in
CMEMS IBI-MFC. MGS (Puertos del Estado) has the
leadership responsibility of CMEMS IBI-MFC.
Competing interests
The authors declare that they have no conflict of interest.
Special issue statement
This article is part of the special
issue “The Copernicus Marine Environment Monitoring Service (CMEMS):
scientific advances”. It is not associated with a conference.
Acknowledgements
This work has been carried out as part of CMEMS. CMEMS is
implemented by Mercator Ocean International in the framework of a delegation
agreement with the European Union. The authors thank their colleagues from Mercator
Ocean for their contribution to the model development and evaluation (Bruno
Levier, Mounir Benkiran). We would also like to thank the PISCES community,
and especially Olivier Aumont, for all the constructive discussions.
Finally, the authors are grateful to the two anonymous reviewers for their
relevant and constructive comments.
Review statement
This paper was edited by Marilaure Grégoire and reviewed by two anonymous referees.
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