Research article
Biogeosciences, 19, 5667–5687, 2022
https://doi.org/10.5194/bg-19-5667-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Interannual variability of the initiation of the phytoplankton
growing period in two French coastal ecosystems
Coline Poppeschi1 , Guillaume Charria1 , Anne Daniel2 , Romaric Verney3 , Peggy Rimmelin-Maury4 , Michaël Retho5 ,
Eric Goberville6 , Emilie Grossteffan4 , and Martin Plus2
1 Ifremer,
Univ. Brest, CNRS, IRD, Laboratory for Ocean Physics and Satellite remote sensing (LOPS),
IUEM, 29280 Brest, France
2 Ifremer, DYNECO, Pelagic Ecology Laboratory (PELAGOS), 29280 Brest, France
3 Ifremer, DYNECO, Hydrosedimentary Dynamics Laboratory (DHYSED), 29280 Brest, France
4 OSU-European University Institute of the Sea (IUEM), UMS3113, 29280 Plouzané, France
5 Ifremer, Morbihan-Pays de Loire Environment Resources Laboratory (LERMPL), 56100 Lorient, France
6 Unité Biologie des Organismes et Ecosystèmes Aquatiques (BOREA), Muséum National d’Histoire Naturelle,
CNRS, IRD, Sorbonne Université, Université de Caen Normandie, Université des Antilles, Paris, France
Correspondence: Coline Poppeschi (coline.poppeschi@ifremer.fr)
Received: 29 March 2022 – Discussion started: 14 April 2022
Revised: 14 November 2022 – Accepted: 16 November 2022 – Published: 14 December 2022
Abstract. Decadal time series of chlorophyll a concentrations sampled at high and low frequencies are explored to
study climate-induced impacts on the processes inducing interannual variations in the initiation of the phytoplankton
growing period (IPGP) in early spring. We specifically detail the IPGP in two contrasting coastal temperate ecosystems under the influence of rivers highly rich in nutrients: the
Bay of Brest and the Bay of Vilaine. In both coastal ecosystems, we observed a large interannual variation in the IPGP
influenced by sea temperature, river inputs, light availability
(modulated by solar radiation and water turbidity), and turbulent mixing generated by tidal currents, wind stress, and
river runoff. We show that the IPGP is delayed by around
30 d in 2019 in comparison with 2010. In situ observations
and a one-dimensional vertical model coupling hydrodynamics, biogeochemistry, and sediment dynamics show that the
IPGP generally does not depend on one specific environmental factor but on the interaction between several environmental factors. In these two bays, we demonstrate that the IPGP is
mainly caused by sea surface temperature and available light
conditions, mostly controlled by the turbidity of the system
before first blooms. While both bays are hydrodynamically
contrasted, the processes that modulate the IPGP are similar. In both bays, the IPGP can be delayed by cold spells and
flood events at the end of winter, provided that these extreme
events last several days.
1 Introduction
Although studied for 70 years (Sverdrup, 1953), the optimal
conditions that trigger the initiation of phytoplankton growing period (IPGP) in ocean waters in early spring are not
well understood (Sathyendranath et al., 2015). Three main
theories are proposed to date: the critical depth hypothesis
(Sverdrup, 1953), the critical turbulence hypothesis (Huisman et al., 1999), and the disturbance-recovery hypothesis
(Banse, 1994; Behrenfeld, 2010; Behrenfeld et al., 2013). For
Sverdrup (1953), phytoplankton blooms occur when the surface mixed layer shoals to a depth shallower than the critical depth, according to light conditions. While Huisman et
al. (1999) agreed with Sverdrup, they proposed that relaxation of turbulent mixing allows the bloom to develop if it
occurs below a critical turbulence rate. Behrenfeld (2010) observed blooms in the absence of spring mixed layer shoaling
and declared that the initiation of bloom is controlled by a
balance between phytoplankton growth and grazing rate and
suggested a seasonal control of this balance by physical processes. No consensus emerges among these hypotheses – es-
Published by Copernicus Publications on behalf of the European Geosciences Union.
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C. Poppeschi et al.: Interannual variability of the initiation of the phytoplankton growing period
pecially because most of these concepts have been defined at
specific temporal and spatial scales (Caracciolo et al., 2021;
Chiswell et al., 2015), and the debate is still open, in particular due to the use of more efficient models, the availability
of new observations, and the ensuing collection of large in
situ datasets (Boss and Behrenfeld, 2010; Rumyantseva et
al., 2019). Coastal waters remain highly dynamic and productive ecosystems at the interface between land and sea and
are distinguished from the waters of the open sea (Gohin et
al., 2019; Liu et al., 2019). Because coastal systems are directly influenced by anthropogenic inputs from rivers, no nutrient limitation is observed in late winter. A myriad of factors and mechanisms can then affect the IPGP in coastal areas (Townsend et al., 1994; Cloern, 1996), but the incident
light at the air–sea interface (Glé et al., 2007) and sea surface
temperature (SST) (Trombetta et al., 2019) are regarded as
the main forcings. Low water turbidity also plays an important role and allows deeper light penetration (Iriarte and Purdie, 2004). This occurs in low vertical mixing conditions in
shallow waters (Ianson et al., 2001), i.e. limited advective exchanges, weak wind (Tian et al., 2011), neap tide (Ragueneau
et al., 1996), and in the absence of flooding events (Peierls et
al., 2012). Depending on the morphology and hydrodynamics of coastal zones (estuaries, bays, lagoons), the importance
of controlling factors can be variable (Cloern, 1996). Temporal variation in the IPGP is of great importance in coastal
ecosystems because it impacts not only phytoplankton by
changing species composition or the succession of species
(Ianson et al., 2001; Edwards and Richardson, 2004; Chivers
et al., 2020) but also several other biological compartments,
such as zooplankton and fish, by species replacements (Sommer et al., 2012).
By amplifying or modifying environmental forcings, it is
now well-documented that global climate change may influence the IPGP in coastal areas (Smetacek and Cloern, 2008;
Barbosa et al., 2010; Paerl et al., 2014; IPCC, 2021). Heat
waves – as opposed to cold spells – have become more frequent in recent years and can advance or delay the IPGP
(Gomez and Souissi, 2008). Wind storms, by inducing vertical mixing and sediment resuspension, can have a significant effect on water turbidity, which in turn limits light penetration and therefore influences the IPGP. Floods, following heavier rainfall, may increase continental erosion and
ultimately nutrient inputs to coastal ecosystems. Because
coastal ecosystems are strongly influenced by human activities such as changes in land use, quantifying the contribution
related to long-term climate-induced signals is challenging
(Kromkamp and Van Engeland, 2010).
Our study is based on two geographically close, but hydrodynamically different, nearshore ecosystems: (1) the Bay of
Brest, a shallow semi-enclosed bay with well-mixed waters
(Le Pape and Menesguen, 1997), and (2) the Bay of Vilaine, a
shallow open bay with long water residence times (Chapelle
et al., 1994). These two coastal ecosystems are strongly impacted by anthropogenic pressures, such as intensive agriculBiogeosciences, 19, 5667–5687, 2022
ture (Ragueneau et al., 2018; Ratmaya et al., 2019), which
induces highly rich nutrient waters.
In this study, we aim to better understand interannual local changes in the IPGP in coastal temperate ecosystems in
the current context of global climate change over the last
20 years. As most studies dealing with the IPGP are mainly
based on discrete water sampling (Iriarte et al., 2004; Tian
et al., 2011) or modelling (Townsend et al., 1994; Philippart et al., 2010), we focus here on the use of long-term
high-frequency observations to assess interannual variability of the IPGP and to identify the triggering and controlling factors. We detect and analyse the temporal variability
of the IPGP and quantify how environmental forcings influence its dynamics. To detect and analyse the IPGP in coastal
environments, we develop a numerical framework that combines high-frequency decadal in situ observations and a onedimensional vertical (1DV) hydro-sedimentary and biogeochemical coupled numerical model. The potential impact of
hydro-meteorological extreme events, such as cold spells,
flood events, and wind bursts, on the IPGP is then investigated.
2 Data and methods
2.1
Study areas
Our study focuses on two northwestern French coastal temperate ecosystems located in the Bay of Biscay: the Bay of
Brest and the Bay of Vilaine, two ecosystems impacted by
excessive nutrient inputs from watersheds but exposed to different hydrodynamic conditions.
The Bay of Biscay is a region with a complex system
of coastal currents influenced by the combined effects of
seasonal wind regimes and important river discharges modulated by large-scale gyre circulation patterns (Isemer and
Hasse, 1985; Pingree and Le Cann, 1989; Lazure and Jégou,
1998; Lazure et al., 2006; Lazure and Dumas, 2008; Ferrer
et al., 2009; Le Boyer et al., 2013; Charria et al., 2013). In
the Iroise Sea, at spring tide close to the islands and capes,
tidal currents can reach 4 m s−1 (Muller et al., 2010). This
tidal circulation combined with meteorological forcings and
sharp thermal gradients generates a strongly variable local
circulation. In the vicinity of the Loire estuary, the freshwater
discharges in the surface layers induce important density gradients driving a poleward circulation (about 10 cm s−1 ) modulated by wind forcings (Lazure and Jégou, 1998; Lazure et
al., 2006). The river plumes can propagate towards the southwest under specific conditions.
Under these hydrodynamic conditions, the Bay of Brest
is a semi-enclosed bay (180 km2 ) with 50 % of the surface
shallower than 5 m depth. The bay is connected with the Atlantic Ocean (Iroise sea) through a narrow and shallow strait.
Tidal variation reaches 8 m during spring tides, which represents an oscillating volume of 40 % of the high tide volume.
https://doi.org/10.5194/bg-19-5667-2022
C. Poppeschi et al.: Interannual variability of the initiation of the phytoplankton growing period
Freshwater inputs come from the Aulne River (catchment
area 1875 km2 , mean river flow 26 m3 s−1 ) and two smaller
rivers: the Elorn (catchment area 385 km2 , mean river flow
6 m3 s−1 ) and the Mignonne (catchment area 111 km2 , mean
river flow 1.5 m3 s−1 ). Due to the macrotidal regime, associated with a strong vertical mixing, the high nitrate concentrations do not generate important green tides (Le Pape et
al., 1997). Strong decreases in the Si : N and Si : P ratios did
not exhibit dramatic phytoplankton community shifts from
diatoms to non-siliceous species in spring (Del Amo et al.,
1997) because of the high Si recycling (Ragueneau et al.,
2002; Beucher et al., 2004).
The Bay of Vilaine is a mesotidal open bay (69 km2 ) under the influence of the Vilaine (catchment area 10 500 km2 ,
mean river flow 70 m3 s−1 ) and the Loire (catchment area
117 000 km2 , mean river flow 850 m3 s−1 ) river discharges,
with tidal ranges varying between 4 and 6 m (Merceron,
1985). The Loire River plume tends to spread northwestward, with a dilution of 20- to 100-fold by the time it reaches
the Bay of Vilaine (Ménesguen et al., 2018). The Vilaine
River plume tends to spread throughout the bay before moving westward (Chapelle et al., 1994). The water residence
time varies seasonally between 10 and 20 d (Chapelle et al.,
1994). The water circulation is mainly driven by tides, winds
and river flows (Lazure and Jegou, 1998). This bay is well
known as one of the European Atlantic coastal ecosystems
most sensitive to eutrophication (Ménesguen et al., 2019).
The Bay of Vilaine has undergone eutrophication over recent decades mainly due to high nutrient inputs from the Vilaine and Loire rivers (Rossignol-Strick, 1985; Ratmaya et
al., 2019).
below the surface every 20 min (COAST-HF-Iroise) or every
hour (COAST-HF-Molit). The Chl a fluorescence, a proxy
for phytoplankton biomass (FFU units), is measured by a
Turner CYCLOPS-7 sensor (precision ± 5 %).
Sub-surface Chl a concentrations are provided by two
French marine monitoring networks, the SOMLIT coastal
observation network and the REPHY (French Observation
and Monitoring programme for Phytoplankton and Hydrology in coastal waters).2 Samples are collected bimonthly at
the SOMLIT-Brest (48.358◦ N, 4.552◦ W) and the REPHYLoscolo (47.496◦ N, 2.445◦ W) stations, which are close
to the COAST-HF stations. Chl a concentrations are measured with either spectrophotometric or fluorimetric methods
(Aminot and Kérouel, 2004).
Daily river flows are measured at gauging stations (French
hydrology Banque Hydro database3 ), located close to the
main river mouths (Aulne-Gouezec (48.205◦ N, 4.093◦ W),
Loire-Montjean (47.106◦ N, 1.78◦ W)). The Vilaine River
flow is controlled by a dam, and data are provided by the
Vilaine Public Territorial Basin Organization (Fig. 1).4
The tide gauge stations (Shom5 ) at Brest (48.382◦ N,
4.495◦ W) and Crouesty (47.542◦ N, 2.895◦ W) record the
sea level every minute.
Precipitation, air temperature, wind direction and intensity, and the solar flux data are retrieved every 6 min from
two meteorological stations from the Météo-France observation network:6 Guipavas (48.440◦ N, 4.410◦ W) and VannesSéné (47.362◦ N, 2.425◦ W) (Fig. 1). We use the solar flux
as a proxy for subsurface PAR (photosynthetically available
radiation).
2.3
2.2
In situ observations
COAST-HF-Iroise (Rimmelin-Maury et al., 2020) and
COAST-HF-Molit (Retho et al., 2022) are two highfrequency monitoring buoys of the French national observation network COAST-HF1 (Répécaud et al., 2019; Farcy
et al., 2019; Cocquempot et al., 2019; Poppeschi et al.,
2021) located, respectively, in the Bay of Brest (48.357◦ N,
4.582◦ W) and in the Bay of Vilaine (47.434◦ N, 2.660◦ W)
(Fig. 1). COAST-HF-Iroise has been operating in the strait
between the Bay of Brest and the Iroise sea since 2000. The
COAST-HF-Molit buoy has been sampling the plume of the
Vilaine River since 2008. Buoys are deployed during the
whole year except for COAST-HF-Molit, which is only available for part of the year prior to 2018 (from mid-February
to early September, i.e. from day 50 to 250 for the period
2008–2017). Depending on the tide, the depth at the mooring sites ranges from 11 to 17 m for both COAST-HF buoys.
Environmental parameters (SST, salinity, turbidity, dissolved
oxygen, and Chl a fluorescence) are measured at 1 to 2 m
1 http://www.coast-hf.fr, last access: 20 March 2022, data available at https://www.coriolis-cotier.org, last access: 20 March 2022.
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2.3.1
MARS3D-1DV modelling experiments
MARS3D-1DV model
A 1DV (one-dimensional vertical) model configuration is implemented to simulate changes in biogeochemical variables
due to hydrodynamics and sediment dynamics in both bays.
The hydrodynamical model is based on the code developed for MARS3D (3D hydrodynamics Model for Applications at Regional Scale; Lazure and Dumas, 2008). This
model is a primitive equation model with a free surface and
uses the Boussinesq and hydrostatic pressure assumptions.
We use the 1DV configuration of the model, with 10 vertical
sigma levels for 15 m depth and a time step of 30 s.
The sediment model (MUSTANG – Le Hir et al., 2011;
Grasso et al., 2015; Mengual et al., 2017) is designed to sim2 For the SOMLIT network, see https://somlit.fr, last access: 20
March 2022. For the REPHY programme, see https://doi.org/10.
17882/47248.
3 https://www.hydro.eaufrance.fr/, last access: 20 March 2022
4 https://www.eptb-vilaine.fr/, last access: 20 March 2022.
5 http://data.shom.fr, last access: 20 March 2022.
6 https://donneespubliques.meteofrance.fr/, last access: 20
March 2022.
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C. Poppeschi et al.: Interannual variability of the initiation of the phytoplankton growing period
Figure 1. Location of the sampling sites: COAST-HF-Iroise and COAST-HF-Molit buoys (red circles); SOMLIT-Brest and REPHY-Loscolo
sampling stations (yellow circles); Brest and Crouesty tide gauge stations (blue triangles); Guipavas and Vannes-Séné meteorological stations
(purple triangles); hydrological stations of the Aulne and Vilaine rivers (black squares), with the Loire station off the map.
ulate the transport and changes in different sediment mixtures. In the sediment, 50 layers (refined near the surface) for
a total thickness of 40 cm are implemented. Four sediment
classes are considered: muds (diameter 10 µm), fine sand (diameter 100 µm), medium sand (diameter 200 µm), and coarse
sand (diameter 400 µm). The sediment dynamics (transport
in the water column, exchanges at the water–sediment interface, erosion/deposition processes) are driven by an advection/dispersion equation for each sediment class (refer to Le
Hir et al., 2011, for a detailed description of the sediment
model).
The biogeochemical model BLOOM (BiogeochemicaL
cOastal Ocean Model) is derived from the ECO-MARS
model (Cugier et al., 2005; Ménesguen et al., 2019) adding
major processes of early diagenesis. Nitrogen, phosphorus, and silica cycles are studied considering four nutrients: nitrate, ammonium, soluble reactive phosphorus, and
silicic acid (sorption/desorption of phosphate on suspended
sediment and precipitation/dissolution of phosphate with
iron processes are also included). The model is also represented by three phytoplankton classes (microphytoplankton,
dinoflagellates, pico-nano-phytoplankton), two zooplankton
classes (micro- and meso-zooplankton), and exchanges at the
Biogeosciences, 19, 5667–5687, 2022
water–sediment interface and inside the sediment compartment.
2.3.2
MARS3D-1DV model sensitivity experiments
These three models (hydrodynamical, sediment, and biogeochemical) are coupled online during simulations and allow
the nutrient and phytoplankton dynamics in both bays to be
reproduced. The simulation for the Bay of Brest does not
include nutrient inputs from the sediment because they are
considered to be negligible around the COAST-HF-Iroise station.
Dissolved and particulate variables are defined in the water column and in the sediment. Initial values for both bays
are uniform over the initial vertical profile (Table 1) and are
based on a 3D realistic coupled simulation during the year
2015. Values for the 15 February are extracted at the position
of COAST-HF-Iroise for the Bay of Brest and at the position
of the COAST-HF-Molit station for the Bay of Vilaine (Plus
et al., 2021).
To evaluate the sensitivity of the biogeochemical dynamics to environmental conditions, sensitivity experiments are then performed using the coupled
MARS3D/BLOOM/MUSTANG 1DV model configuration.
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C. Poppeschi et al.: Interannual variability of the initiation of the phytoplankton growing period
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Table 1. Initial conditions in the water column for the MARS-1DV model for the beginning of the simulation on the 15 February.
Parameters
Bay of Brest
Bay of Vilaine
9
0.05
0.05
0.05
0.5
0.5
10
16
0.5
0
0
0.03
10
0.1
0.05
0.1
0.5
0.8
30
30
0.25
0
0
0.05
Dissolved O2 (mg L−1 )
Mesozooplankton (µmolN L−1 )
Microzooplankton (µmolN L−1 )
Dinoflagellates (µmolN L−1 )
Diatoms (µmolN L−1 )
Soluble reactive phosphorus (µmol L−1 )
Silicic acid (µmol L−1 )
Nitrate (µmol L−1 )
Ammonium (µmol L−1 )
Coarse sand (g L−1 )
Fine sand (g L−1 )
Mud (g L−1 )
All simulations are started at the end of winter (15 February)
and run until the end of the year. The range of values used
in the sensitivity experiments are derived from the minimum
and maximum observed in situ data. Each parameter is tested
with a constant value for the whole simulation.
Three parameters are individually explored in both bays:
parameters are taken into account. Experiments are detailed
in Table 5.
– The air temperature in sensitivity experiments ranges
from 4 to 14 ◦ C and is controlled by the intensity of solar radiations. Air temperature represents the main controlling parameter of SST in the 1DV model. This parameter drives the radiative fluxes in the model and then
constrains SST.
To analyse high-frequency time series of in situ Chl a fluorescence, the quenching effect (Lehmuskero et al., 2018) –
a decrease in fluorescence in the presence of light (Fig. 2) –
is removed by analysing only nighttime data, as reported in
Carberry et al. (2019). Chl a fluorescence data are studied on
a daily basis, i.e. averaged from 22:00 to 05:00. Years with
less than 75 % of valid data are not considered in our analyses: for the Bay of Brest, these are the years 2005, 2006,
2008, 2009, and 2018.
– Wind intensity effect on the IPGP is explored for values
between 0 and 10 m s−1 . In the 1DV model, wind is a
source of vertical mixing in the simulation.
– The cloud coverage (CC) sensitivity experiments ranged
in value between 0 % CC and 100 % CC. This parameter
is a driver of photosynthetic available radiation (PAR) in
the ocean. For the formulation of radiative fluxes in the
1DV MARS3D model, 100 % cloud coverage allows an
inflow of 38 % of the total solar radiation into the water
column. Each individual experiment is associated with
a constant CC applied to the seasonal solar radiation.
In the Bay of Vilaine, the sediment plays a role in light
penetration and acts as an active source of nutrients: we
therefore explored the influence of mud erosion rate (values between 2.10−5 and 2.10−7 kg m−2 s−1 ) in that bay (sand
erosion rate fixed to 0.0001 kg m−2 s−1 ). For the sensitivity
experiments, it drives a mass of sediment that is eroded and
resuspended and a bottom input of nutrients in the water column.
A second set of experiments is conducted by combining
the individual effect of environmental parameters in order to
explore possible cumulative or opposite effects on the IPGP.
The upper and lower bounds of the range of environmental
https://doi.org/10.5194/bg-19-5667-2022
2.4
2.4.1
2.4.2
Data processing
Chl a fluorescence data
Detection of the IPGP
On the basis of the literature, we first apply three methods to
determine the annual IPGP dates:
1. set an arbitrary beginning and end of the phytoplankton growing period at 20 % and 80 % of the cumulative
Chl a fluorescence measured from 1 January to 31 December (Kromkamp et al., 2010),
2. consider a threshold of 5 % above the yearly median
chlorophyll (Brody et al., 2013),
3. consider the beginning of the growing period as
the maximum daily difference in Chl a fluorescence
(Philippart et al., 2010).
Because none of these methods allowed us to obtain a
valid IPGP detection – with too late (method 1) or too early
(method 2) a detection or multiple IPGP dates (method 3) –
we elaborate on a detection method based on discontinuities
of the Chl a fluorescence signal (Fig. 3): daily FFU slopes
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C. Poppeschi et al.: Interannual variability of the initiation of the phytoplankton growing period
Figure 2. The importance of the quenching effect on Chl a fluorescence is represented by COAST-HF-Iroise data from 2000 to 2019. The
standard deviation is represented by vertical black bars. The dashed lines represent the beginning and end of the selected values for the rest
of the study from 22:00 to 05:00.
are calculated based on a linear regression over a ± 2 d window for each day, from 1 January to 31 December, and each
year. The IPGP date is identified when the slope exceeds a
threshold value – defined as the median of the daily slopes –
for the first time in the year for at least 20 d. The end of the
phytoplankton growing period is determined when the slope
stabilizes below the threshold for at least 20 d for the last time
in the year. The cumulative Chl a fluorescence corresponds
to the duration of the growing period.
2.4.3
Pattern of the phytoplankton growing period
The k-means method (Hartigan and Wong, 1979) is used to
characterize the annual patterns of the phytoplankton growing period.
We exclude the year 2013 from the analysis of the Bay
of Vilaine because of a large number of missing data. When
the interval over which consecutive data are missing is no
longer than 1 week, we perform a linear interpolation to replace the missing data. A 5 d running average is applied to
the Chl a fluorescence signal, and data are then normalized
by the maximum value. We analyse Chl a fluorescence every
year for 150 d after the IPGP.
Time series from both bays are merged before application
of the k-means and the number of clusters (or centroids) is
set to two to distinguish the dominant patterns of the phytoplankton growth period at both sites. The use of a larger number of clusters is investigated and does not produce a pattern
representing a large number of observed growing periods.
2.4.4
Detection of extreme events
The peak over threshold method (see Oliver et al., 2018,
and Poppeschi et al., 2021, for further details) is used to detect hydro-meteorological extreme events such as cold spells,
Biogeosciences, 19, 5667–5687, 2022
flood events, and wind bursts. An event is regarded as extreme if values are higher than a given statistical threshold for at least 3 consecutive days. In the present study,
the 90-percentile threshold is selected to detect floods and
wind bursts, and the 10-percentile threshold is used to detect
cold spells. Seasonal anomalies are calculated over at least
20 years, by subtracting raw data from the winter average
value (for cold spells) or from the spring average value (wind
bursts and floods).
3 Results
3.1
Characterization of the phytoplankton growing
period
The high-frequency Chl a fluorescence time series at both
sites show an intense seasonal cycle with low values from
November to February and high values from March to October (Fig. 4). Focusing on the period from 2010 to 2019
in the Bay of Brest, the minimum Chl a fluorescence is observed during the years 2012 and 2013 and does not exceed
7 FFU. In contrast, some years show Chl a fluorescence values above 15 FFU but can be up to 20 FFU (such as 2010,
2014, 2015 or 2019). In the Bay of Vilaine, a similar seasonal pattern is observed with higher values reaching 50 FFU
in 2013. Small (< 20 FFU) and high (> 35 FFU) Chl a fluorescence amplitudes are observed occasionally (in 2014 and
2017 and in 2013 and 2016, respectively). The Chl a fluorescence is higher, almost double, in the Bay of Vilaine compared to the Bay of Brest with a mean cumulative Chl a fluorescence of around 580 and 360 FFU, respectively (Table 2).
The high phytoplankton biomass of the Bay of Vilaine is corroborated by the concentrations measured by low-frequency
observation programmes (SOMLIT and REPHY).
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C. Poppeschi et al.: Interannual variability of the initiation of the phytoplankton growing period
5673
Figure 3. Example of detection of the start (red line) and end (blue line) of the phytoplankton growing period in 2001 at COAST-HF-Iroise.
The threshold value – the median of slopes – is represented by a dotted grey line.
Table 2. Global characteristics of the phytoplankton growing period in the Bay of Brest and in the Bay of Vilaine.
Bay of Brest
(2001–2019)
Bay of Vilaine
(2011–2019)
Start date
(day of year)
End date
(day of year)
Duration
(days)
Cumulative Chl a
fluorescence (FFU)
Min–median–max
Min–median–max
Min–median–max
Min–median–max
50–69–102
253–274–308
165–200–256
217–364–567
53–68–93
218–269–316
165–179–239
276–582–1406
The phytoplankton growing period ranges from approximately 10 March to 30 September in both regions (Table 2).
The average duration of the phytoplankton growing period is
179 d in the Bay of Vilaine and 200 d in the Bay of Brest (Table 2). The phytoplankton growing period is characterized by
successive blooms, whose number and intensity are variable
from year to year (Fig. 4).
The main patterns of the phytoplankton growing period
are identified by two clusters (Fig. 5). Cluster 0 includes the
phytoplankton growing period with two successive marked
blooms in early spring and in summer, the intensity of the
second bloom being highly variable. Cluster 1 is characterized by a plateau during the first 2 months of the phytoplankton growing period. Most of the patterns of the Bay of Vilaine
are in cluster 0, while those of the Bay of Brest are in cluster 1 (Table 3). The years that stand out in the Bay of Brest
(2002, 2010, 2014) correspond to years with the highest cumulative Chl a fluorescence (≥ 450 FFU). The atypical years
in the Bay of Vilaine (2011, 2017 and 2019) show the lowest
cumulative Chl a fluorescence (≤ 450 FFU).
https://doi.org/10.5194/bg-19-5667-2022
3.2
Variability of the initiation of the phytoplankton
growing period (IPGP)
Calculations performed to determine the IPGP for high- and
low-frequency data yield comparable results (Fig. 6). The
mean differences between the IPGP calculated with the highand low-frequency data are 5 and 8 d for the Bay of Brest
and the Bay of Vilaine, respectively. A difference of only 4
and 6 d between the model simulations (reference year: 2015)
and the high-frequency in situ data are observed in the Bay
of Brest and the Bay of Vilaine, respectively.
A decadal variability of the IPGP is recorded from midFebruary to mid-April in both ecosystems (day 50 to day 102
in the Bay of Brest and day 53 to day 93 in the Bay of Vilaine; Fig. 6). In the Bay of Brest, early IPGPs (day < 53) are
observed in 2010 and 2013, whereas late IPGPs (day > 93)
are observed in 2001, 2017 and 2019. In the Bay of Vilaine,
the earliest IPGP is detected in 2012 (day 53) and the latest
in 2019 (day 93).
The variability of the IPGP in the Bay of Brest shows two
linear trends (Fig. 6a), with a decrease of 52 d from 2001
to 2010 (observed in both high- and low-frequency datasets),
followed by an increase (+48 d) from 2011 to 2019, a decline
also observed in the Bay of Vilaine (Fig. 6b). Over the period
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Figure 4. Temporal changes in the in situ Chl a fluorescence measured in the Bay of Brest (a) and the Bay of Vilaine (b).
Table 3. Cluster group assigned to each annual phytoplankton growing period at both sites. The “–” dash represent years with missing data.
The cross represents the year 2013 of the Bay of Vilaine, which was not considered.
Year
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
Bay of Brest
COAST-HF-Iroise
1
0
1
1
–
–
1
–
–
0
1
1
1
0
1
1
1
–
1
Bay of Vilaine
COAST-HF-Molit
–
–
–
–
–
–
–
–
–
–
1
0
X
0
0
0
1
0
1
2011–2019, the IPGP is shifted towards a later date by +3.5 d
per year in the Bay of Vilaine and +3.7 d per year in the Bay
of Brest.
3.3
3.3.1
Analysis of environmental conditions driving the
IPGP
Impact of environmental conditions on the IPGP
We next quantify the influence of environmental drivers on
the date of the IPGP (Fig. 7). These drivers represent the
major limiting factors of the phytoplankton growth and comprise input of nutrients (river flow), PAR (incident light), SST
(air temperature, incident light), and turbidity in the water
column (river flow, wind intensity, tidal range).
The median values of the environmental drivers observed
at the date of each annual IPGP are very close in both bays
(Table 4): temperate SST (10 ◦ C), weak wind (3 m s−1 ), a
medium PAR (1360 W m−2 ), a low turbidity (7 NTU), and
a weak tidal amplitude (semi-amplitude of 1.6 m in the Bay
of Brest and 0.9 m in the Bay of Vilaine). The IPGP occurs
mainly during neap tides at 68 % in the Bay of Brest and
77 % in the Bay of Vilaine. The river flow is low during the
IPGP with a runoff of 46 m3 s−1 for the Aulne, 96 m3 s−1 for
the Vilaine, and 1196 m3 s−1 for the Loire. These values are
considered to be the favourable environmental conditions for
this study.
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To assess how environmental drivers may impact (i.e. advance or delay) the IPGP, we focus on the 15 d before the
mean day of the IPGP (day 68) and of each annual IPGP.
The considered 15 d length is related to the typical water residence time in both bays (Frère et al., 2017, and Poppeschi
et al., 2021, for the Bay of Brest; Chapelle et al., 1994, and
Ratmaya et al., 2019, for the Bay of Vilaine).
The earliest IPGP dates (IPGP < day 55) are associated
with earlier occurrence of favourable environmental conditions than the other years. The earliest IPGP in 2010 and
2013 in the Bay of Brest and in 2012 in the Bay of Vilaine occurred before day 55 (Figs. 1f, 7c–2a). Early IPGPs between
day 55 to 60, also associated with favourable environmental
conditions, are found in 2002 and 2016 in the Bay of Brest
(Fig. 1b, j).
The latest IPGP dates (IPGP > day 90) are associated with
unfavourable environmental conditions until the date of the
IPGP. The latest IPGPs occurring after day 90 are observed in
2001, 2003, 2017, and 2019 in the Bay of Brest and in 2019
in the Bay of Vilaine (Figs. S1a, c, k, l–S2g). For example,
the delay detected in 2017 in both bays is due to strong wind
and a lack of PAR until the day of the IPGP (Figs. S1k–S2e).
Late IPGPs between day 70 to 90 are recorded in 2004, 2007,
and 2012 in the Bay of Brest and in 2014, 2017, and 2018 in
the Bay of Vilaine (Figs. S1d, e, g, 7d–2e, f).
The interannual variability of the date of the IPGP is therefore not controlled by a unique environmental driver. When
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Figure 5. (a) Cluster 0 and (b) cluster 1 representative of the patterns of the phytoplankton growing period observed in both bays. The
median pattern is drawn in bold.
Table 4. Characteristics of environmental drivers at the date of the IPGP except for nutrients from January to March in the Bay of Brest and
in the Bay of Vilaine.
River flow (m3 s−1 )
SST (◦ C)
Wind intensity (m s−1 )
PAR (W m−2 )
Turbidity (NTU)
Sea level (m)
PO4 (µmol L−1 )
Dissolved inorganic nutrients (DINs; µmol L−1 )
Si(OH)4 (µmol L−1 )
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Bay of Brest (2001–2019)
Bay of Vilaine (2011–2019)
Min–median–max
Min–median–max
13–46–100
8–10–12
1–3–6
915–1373–2220
1–7–21
0.5–1.6–2.9
0.1–0.4–0.6
8–20–38
4–8–16
36–96–205
8–10–11
1–3–4
814–1341–1939
0–7–22
0.6–0.9–1.6
0.1–0.8–1.4
25–57–244
8–38–112
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Figure 6. Changes in the IPGP date in (a) the Bay of Brest and (b) the Bay of Vilaine are determined with high-frequency time series
(black circles), low-frequency time series (red circles), and with the model (blue circle). The dotted black line represents the date of the
COAST-HF-Molit buoy deployment.
the values of the environmental drivers responsible for the
IPGP (Table 4) are compared to the mean values of the environmental drivers over a period of 30 d around the IPGP
(Table S1), threshold values are observed in both bays: river
flow is lower than usual (between 10 and 30 m3 s−1 ), temperature is close to the expected value (10 ◦ C), wind is weak
(0.5 to 1.5 m s−1 ), PAR is stronger (> 300 W m−2 ), and turbidity is low (about 1.5 NTU). The IPGP starts around day
68 (± 3 d) on average (Fig. 7a, b).
3.3.2
Modelling the importance of the environmental
drivers
The relative contribution of each environmental driver on
the IPGP is determined by MARS-1DV simulations starting
on 1 February (Fig. 8). Environmental drivers tested in the
model control the following:
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– sea temperature, explored in the model through air temperature (SST proxy),
– the level of water turbulence, through wind intensity,
– the available light, controlled by cloud coverage (CC, as
a sea surface PAR proxy) and the erosion rate (turbidity
proxy) limiting light penetration in the water column.
Model results show that early IPGPs are associated with
an air temperature higher than 9 ◦ C (resulting in SST higher
than 8 ◦ C), low wind intensity, weak CC, and low erosion rate. Environmental drivers responsible for early or late
IPGPs are similar in both bays. Air temperature is the main
driver with a potential deviation from the mean IPGP of 25 d
in the Bay of Brest and 40 d in the Bay of Vilaine (Fig. 8).
Wind, CC, and erosion rate have a lower impact on the IPGP
(around 6 d in the Bay of Brest and 13 d in the Bay of Vihttps://doi.org/10.5194/bg-19-5667-2022
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Figure 7. IPGP dates and environmental drivers: flow of the Aulne, Vilaine, and Loire rivers, sea surface temperature, wind intensity, PAR,
turbidity, and sea level at high tide. Illustrations for a mean IPGP date in (a) the Bay of Brest and (b) the Bay of Vilaine in 2011; for an
early IPGP date in (c) the Bay of Brest in 2013; for a late IPGP date in (d) the Bay of Vilaine in 2014. The mean IPGP date of each bay
is represented by a dashed black line, and the IPGP date of the year is represented by a solid black line. Thresholds of each environmental
driver are represented by grey horizontal lines corresponding to the mean conditions calculated 30 d around the IPGP date. Grey areas are
time periods favourable to the IPGP.
laine). In the Bay of Vilaine, the environmental drivers can
simulate a later IPGP than in the Bay of Brest.
In the Bay of Brest (Fig. 8a), only variations in air temperature have a real impact on the IPGP. If air temperature
is low (< 8 ◦ C), the IPGP is not triggered before day 74 (Table 5, Exp 1). If air temperature is high (> 13 ◦ C), the IPGP
can start on day 49 (Table 5, Exp 2).
In the Bay of Vilaine, air temperature and the erosion rate
are the two main drivers impacting the IPGP (Fig. 8b). As
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in the Bay of Brest, if air temperature is low (< 6 ◦ C), the
IPGP is late and appears only after day 80 (Table 5, Exp 1).
If temperature is equal or above 13 ◦ C, the IPGP is early and
appears on day 45 (Table 5, Exp 2). If the erosion rate is low
(2.10−7 kg m−2 s−1 ), the IPGP takes place on day 76 (Table 5, Exp 7). If the erosion rate is high (2.10−5 kg m−2 s−1 ),
the IPGP occurs late after day 87 (Table 5, Exp 8).
Even if variations in wind and CC induce weaker shifts
in the date of the IPGP, i.e. about 1 week at most (Table 5,
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C. Poppeschi et al.: Interannual variability of the initiation of the phytoplankton growing period
Figure 8. Impact of the variation in environmental drivers on the date of the IPGP in (a) the Bay of Brest and (b) the Bay of Vilaine. Steps
of 1 ◦ C for the air temperature, 1 m s−1 for the wind intensity, 10 % for the cloud coverage, and 0.0000036 kg m−2 s−1 for the erosion rate
equivalent to a variation in suspended matter between 0.02 and 0.08 mg L−1 at the IPGP.
Exp 3, 4, 5, 6), they can however explain some variations
in the IPGP. For example, the fact that the early IPGPs, observed in 2010 in the Bay of Brest and in 2012 in the Bay
of Vilaine, are due to low wind conditions (around 2 m s−1 ,
Figs. S2a–S1f) are confirmed by both in situ measurements
and the model (Fig. 8b).
The combined effect of the environmental factors can
also be explored from the MARS-1DV model simulations
(Fig. 9). The modelling conditions (hereafter called “Exp”)
are detailed in Table 5 and compared to the mean IPGP date
(day 68).
The simulations confirm the observations: late IPGPs
correspond to the most extreme unfavourable combined
environmental values (temperature of 4 ◦ C, wind intensity of 10 m s−1 , CC of 100 %, and erosion rate of
2.10−5 kg m−2 s−1 – Exp A). Due to the most unfavourable
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conditions, the IPGP occurs 9 and 64 d later in the Bay of
Brest and in the Bay of Vilaine, respectively. A late IPGP
can also be linked to the combined effect of only two factors such as “temperature and wind” and “temperature and
CC” with a delay of 5 and around 22 d, respectively (Exp B
and C). In contrast, no delay is observed for the combination
“wind and CC” (Exp D) in both bays.
Early IPGP events are found in the model simulations and
in the in situ observations when conditions correspond to a
high temperature (14 ◦ C), no wind intensity and CC, and a
low erosion rate (2.10−7 kg m−2 s−1 ) – Exp K. All the combined scenarios allow the occurrence of an earlier IPGP (by
at least 5 additional days) compared to experiments that consider a single modified parameter.
This analysis enables environmental parameters to be classified with respect to their impact on the IPGP. In both bays,
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Table 5. Assumptions are explored in the 1DV model for environmental parameters independently (1–8) and with a combined effect (A–N)
with the modified values (grey background) and text in bold for the Bay of Brest only (+ for a later IPGP, − for an earlier IPGP, = for an
equal IPGP) with the IPGP equal to the mean observed IPGP of day 68.
the temperature appears to be the key factor driving the IPGP.
By combining the environmental drivers, the IPGP can occur
even later or earlier than with a single forcing. In both bays,
the combination of wind and CC has no impact on the IPGP,
which occurs near the median day (Exp D and N). The extreme couplings of Exp A, E, F, G, and J delay the date of
the IPGP later than detected in the observations for the Bay
of Vilaine. All simulations show a higher impact on the date
of the IPGP in the Bay of Vilaine than in the Bay of Brest
(Fig. 9, Table 5).
3.4
3.4.1
Impact of extreme hydro-meteorological events on
the IPGP
Cold spells
The impact of cold spells on the IPGP is simulated with the
MARS-1DV model based on two criteria: (i) the period of
occurrence of the event, set in the middle or end of February;
(ii) the duration and intensity of the cold spell, which can be
either short and weak (8 d, 7 ◦ C) or long and intense (20 d,
5 ◦ C) (Fig. 10).
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In both bays, when the cold spell appears in mid-February,
the IPGP is not impacted. However, it is delayed by about
15 d when occurring at the end of February. The duration of
the cold spell, when longer than 15 d, also has an impact on
the IPGP, with a delay of 13 and 12 d in the Bay of Brest and
in the Bay of Vilaine, respectively.
Eight cold spells are detected in February in both bays between 2001 and 2019. In 2011, both sites are impacted simultaneously with cold spells. Long cold spells (30 d) are
observed in 2009 and 2018, leading to an anomaly of more
than −1.9 ◦ C.
The cold spell observed in 2018 in the Bay of Vilaine may
explain the later IPGP. There is no change in the IPGP in
2011 and 2013, despite the cold spell, the period of occurrence being too early during winter 2011 and the duration
too short in 2013 (only 10 d).
In the Bay of Brest, the cold spells in 2003 and 2004 may
explain the delay in the IPGP (respectively, days 93 and 85).
The presence of long and intense cold spells in 2010 and
2011 does not shift the IPGP (days 50 and 67) because they
occur too early (before day 20).
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Figure 9. Influence of combined environmental parameters for the MARS-1DV model in both bays (Bay of Brest, a, and Bay of Vilaine, b)
with detailed experiments in Table 2.
Figure 10. Impact of cold spells on the IPGP date simulated in (a) the Bay of Brest and (b) the Bay of Vilaine. Four conditions of cold spells
are explored: early (mid-February), late (end of February), short (8 d), and long (20 d). The IPGP dates are represented by dashed lines.
3.4.2
Wind bursts
Based on our model simulations, the wind bursts that occur
during at least 3 continuous days have no impact on the IPGP
in both bays, whatever the duration, the period, and the intensity (± 1 d). In the Bay of Vilaine, only one wind event is
detected in 2018 (3 d long and 6 m s−1 ). In the Bay of Brest,
several events are detected, but no significant impact is observed on the IPGP.
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3.4.3
Flood events
River floods can delay the IPGP by resuspending sediment
in the water column and therefore limiting light penetration
in the water column. Inputs of nutrients have no impact during the late winter period because nutrient concentrations are
maximal, with no limitation on phytoplankton growth. Flood
events are analysed with observation data collected in the
month prior to the IPGP date because the 1DV modelling approach does not allow the sensitivity to hydrological events
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to be simulated (i.e. it is necessary to simulate horizontal advection processes).
In the Bay of Brest, the impact of flood events depends
on their duration and intensity: when the flood exceeds 15 d,
a delay in the IPGP is detected. Shorter and more intense
floods (> 300 m3 s−1 ) do not impact the IPGP.
In the Bay of Vilaine, only two flood events are observed
close to the IPGP date in 2014 and 2015. The 2015 flood
event, which is 10 d longer and more intense (> 100 m3 s−1 )
than the 2014 one, delays the IPGP date by 10 d.
4 Discussion
4.1
Comparison of the phytoplankton growing period
in both bays
Despite their contrasting hydrodynamics (e.g. Petton et al.,
2020; Poppeschi et al., 2021; Lazure and Jegou, 1998; Ratmaya et al., 2019; Ménesguen et al., 2019), the median dates
of the start and the end of the phytoplankton growing period
are the same in the Bay of Brest and in the Bay of Vilaine,
whether they are calculated from high- and low- frequency
datasets or model simulations. The phytoplankton growing
period occurs from March to September and lasts about 190 d
in both bays. This concordance is related to a similar seasonality of the environmental drivers.
The observed cumulative fluorescence is almost double in
the Bay of Vilaine compared with the Bay of Brest. This difference in the amount of chlorophyll produced in surface waters from both bays is also recorded by the low-frequency
observation programmes and satellite observations (Ménesguen et al., 2019). It can be explained by the difference between the hydrodynamics and the influence of different watersheds. The Bay of Brest is a semi-enclosed bay with a
macrotidal regime influenced by two local rivers (Aulne and
Elorn), whereas the Bay of Vilaine has a weaker tidal regime,
is open on the continental shelf and is widely influenced by
a large river (Loire River).
Two different patterns of the phytoplankton growing period are identified by the k-means classification in both bays.
The flattened, weak, and long bloom highlighted in the Bay
of Brest can be explained by assuming that nutrients are not
limiting the phytoplankton growth during spring. The maintenance of the diatom succession throughout spring since the
1980s (Quéguiner 1982; Del Amo et al., 1997) can be explained by the combination of increasing N and P loads, intense Si recycling and a macrotidal regime (Ragueneau et al.,
2019). The phytoplankton growing period in the Bay of Vilaine is characterized by several successive peaks including
two main ones. Nutrients drive the seasonal evolution of the
phytoplankton growing period through periods of nutrientlimited conditions. These fluctuations are governed by phosphorus and nitrate loads from Vilaine and Loire rivers (Ratmaya et al., 2019) but probably also by the stoichiometry of
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recycled elements in the water and at the water–sediment interface (Ratmaya et al., 2022). At the beginning of the phytoplankton growing period (IPGP), however, the system is not
nutrient-limited in terms of nitrate, phosphorus, and silicate
(Table 4).
4.2
Validation of the method for IPGP detection
The method that we developed to detect the IPGP in
both high-frequency and low-frequency in situ observations
shows comparable results and detects similar initiation dates
for some years, while a time lag between high- and lowfrequency observations can be observed for other years. This
difference is mainly explained by the difference in the sampling frequency. The late deployment of the buoy in the Bay
of Vilaine (i.e. not deployed until mid-February before 2018)
can also explain some differences between both sites. Highfrequency data provide a more accurate detection of the day
of the IPGP, while an uncertainty of about ± 7 d is observed
with low-frequency observations. This comparison between
high- and low-frequency-based IPGP detection highlights the
sensitivity of sampling strategy in the observation of phytoplankton growing periods (Bouman et al., 2005; Serre-Fredj
et al., 2021) related to the response of the ecosystem within a
few hours after an environmental change (Lefort and Gasol,
2014; Thyssen et al., 2008).
The modelled IPGP, based on the year 2015, is coherent
with high-frequency observations (around 5 d of difference
between modelled and observed IPGP). Considering the idealized framework for modelling computations (1DV model
instead of a realistic 3D model configuration), the agreement
between observations and simulations validates the 1DV approach to explore IPGP dynamics. With the 1DV configuration, the vertical dynamics in the water column, coupled with biogeochemistry and sediment dynamics are reproduced well. Atmospheric forcings and interactions with
the bottom layer are the main environmental drivers. The
full range of impacts related to the horizontal advection (e.g.
in the considered regions, river-advected plumes can change
the hydrodynamics and the nutrient fluxes) are not evaluated,
however. In the Bay of Brest and in the Bay of Vilaine, such
advected sources exist (Poppeschi et al., 2021; Lazure and
Jegou, 1998). But inputs from rivers are not the main drivers
of the IPGP in nutrient-rich environments. Nutrient loads advected by rivers may impact the phytoplankton community
later during the growing period rather than at the IPGP (Ratmaya et al., 2019).
4.3
Identification of the environmental conditions
supporting the IPGP
The main theories to explain the initiation of phytoplankton blooms (Sverdrup, 1953; Huisman et al., 1999; Banse,
1994) are not relevant in the context of shallow and wellmixed coastal waters under the influence of river plumes.
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In the studied region, the ecosystem does not evolve with
mixed layer dynamics, as observed in deeper environments.
Both bays are permanently vertically mixed mainly by tides,
and vertical stratification only occurs in a thin surface layer
due to river runoffs at short timescales. However, the IPGP
is mainly driven and limited by similar local environmental
conditions in both bays. The ideal temperature (> 10 ◦ C) and
PAR (1300 W m−2 ) for the IPGP are in agreement with those
from previous studies conducted in similar coastal ecosystems (e.g. Glé et al., 2007; Townsend et al., 1994; Trombetta
et al., 2019). Neap tidal conditions, weak wind (lower than
3 m s−1 ), and weak river flow can also play a positive role
in observations of earlier IPGPs according to previous studies (Ragueneau et al., 1996; Tian et al., 2011). The impact of
wind direction on the IPGP is negligible.
Local changes in temperature, incident radiation, tidal
conditions, wind conditions, and river flow induce differences in detected IPGPs. In this coastal temperate ecosystem,
we observe that the beginning of the growing period is limited by light (controlled by incident radiation, turbidity in this
season) and water temperature. The IPGP also occurs during
low vertical mixing conditions.
The comparison of the individual importance of each environmental driver shows that temperature and light penetration are the key environmental drivers in both bays. When
light penetration is reduced by a combined effect of PAR and
turbidity (sediment resuspension), the delay in the IPGP can
be amplified, especially in the Bay of Vilaine. The importance of light availability in the timing and intensity of the
spring bloom is also highlighted in the North Sea (Wiltshire
et al., 2015), in the German Bight (Tian et al., 2009), and
along the UK south coast (Iriarte and Purdie, 2004).
4.4
Interannual evolutions of the IPGP
The IPGP in these two bays shows a strong interannual
variability with initiation dates varying from late winter to
spring, depending on the environmental conditions. A mean
difference of 50 d between the earliest and latest IPGP dates
is observed. It is important to note that the phytoplankton
population during the IPGP is always dominated in both bays
by the same centric diatoms, the genera Chaetoceros and
Skeletonema, whose abundance varies from year to year depending on climatic conditions (REPHY, 2021). None of the
nutrients limits the growing of phytoplankton at the IPGP
(Table 4).
The earliest IPGPs are observed and related to favourable
environmental conditions early in the year. For example, the
IPGP can occur before day 50, associated with exceptionally weak wind and river flow in addition to a sufficient PAR
and a near-optimal temperature of around 10 ◦ C (e.g. 2010
in the Bay of Brest and 2012 in the Bay of Vilaine). But if
the environmental conditions are not favourable (e.g. 2017
and 2019 in both bays), the IPGP is delayed. This can be
due to (1) strong wind for several days (not a single wind
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burst) combined with a weak PAR and sometimes enhanced
by high-turbidity events which further limits the light penetration and (2) low SST.
The IPGP appears to be more controlled by local environmental drivers than by regional environmental drivers, the
IPGP being earlier at one site than in the other during half of
the studied years: for example, the 2012 IPGP is early in the
Bay of Vilaine (day 53) but late in the Bay of Brest (day 80),
related to strong wind activity and low PAR in the last bay.
The offshore regional dynamics will induce limited impacts
on local hydrodynamical features that will change the IPGP.
Changes in the IPGP over the last 2 decades have highlighted its evolution through two trends: it occurs earlier each
year until 2010, when the trend is reversed. Changes in environmental conditions over the last 20 years were then studied
to seek a possible concordance with one of the environmental drivers, but no significant trend was detected. Because of
global warning, earlier phytoplankton blooms are expected
(Friedland et al., 2018) but not a later IPGP as observed
in our study regions. However, the mechanisms that trigger
blooms in coastal ecosystems – especially eutrophic ones –
are not similar to the processes that influence blooms in the
open ocean. No link between trends in the IPGP and environmental drivers has been identified in the southern California Bight from 1983 to 2000 (Kim et al., 2009). By investigating long-term (1975–2005) daily data, Wiltshire et
al. (2008) also observed later phytoplankton blooms in the
German bight, but no link to global warming was detected.
Henson et al. (2018) modelled a bloom shift of 5 d per decade
from 2006 to 2025, with later blooms. A possible explanation
of these later IPGPs may involve a lower spring SST (HunterCervera et al., 2016).
4.5
Extreme events
We show that a cold spell is likely to delay the IPGP if it occurs at the end of winter (after 20 February) and/or if the cold
spell lasts long enough (> 15 d). The drop in temperature related to the cold spell prevents the IPGP in both bays. This
is in accordance with the study of Gomez and Souissi (2008)
in the English Channel where cold spells can delay the date
of the IPGP, as a result of an increase in water column mixing. Cold spells may also drive local patterns by influencing
the phytoplankton communities (Gomez and Souissi, 2008;
Schlegel et al., 2021).
Flood events have an influence on the phytoplankton
biomass when they occur in spring, due to the supply of nutrients. When they occur in late winter, nutrients are already
at their maximum. The impact of floods on the IPGP is then
due to the increase in the water turbulence and to the limitation of light by increasing the turbidity. The IPGP can be
delayed only if floods are at least 15 d long. This scheme was
also observed by Saeck et al. (2013) along a river–estuary–
bay continuum and explained by a shortened water residence
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time and limited light due to flood-induced turbidity in the
coastal zone.
No relationship is observed between wind events and the
IPGP in either bay because they are weakly stratified, contrary to open seas (i.e. Black Sea, Mikaelyan et al., 2017).
In coastal stratified regions (e.g. under the influence of river
plumes), strong wind and tidal mixing can enhance the mixing and break down stratification, which does not favour phytoplankton growth (Joordens et al., 2021). During the IPGP,
except during floods, both regions are weakly stratified and
are then less sensitive to combined wind/tidal short events.
Code availability. Code used in this paper can be found at Zenodo
https://doi.org/10.5281/zenodo.7426540 (cocopom, 2022).
5 Conclusions
Author contributions. CP, GC, AD, RV, PRM, and ErG conceptualized the study. PRM, EmG, and MR collected data. MP and GC
developed the model configuration. CP, GC, AD, and RV drafted
the first versions of the paper. CP carried out all the analyses and
wrote the final version of the paper. All authors contributed to the
discussions and revisions of the study.
This study provides a new understanding of the IPGP dynamics in coastal temperate areas by using both high- and
low-frequency in situ data, in combination with simulations
from a 1DV model. Strong similarities are found in both
bays. An important interannual variability of the IPGP is
observed, with a trend towards a later IPGP over the last
decade (2010–2020). We quantify the importance of environmental conditions on the IPGP. When we compare observed IPGPs with favourable environmental conditions and
following sensitivity experiments with the 1DV model, water temperature and turbidity (limiting light penetration in the
water column) appear as the main drivers explaining interannual IPGP variability. The IPGP is a complex mechanism,
usually triggered by more than one environmental parameter.
The analysis of the influence of extreme events reveals that
cold spells and floods have a strong impact by delaying the
IPGP when episodes are long enough and occur after winter.
No effect of wind bursts is detected.
While this study shows comparable IPGP dynamics when
based on 1DV model simulations or in situ observations, we
will next investigate the effect on phytoplankton dynamics
of a fully realistic hydrodynamics (including horizontal and
vertical advections; mixing processes; remote sources of nutrients from rivers) 3D model. We will focus on exploring the
variability of phytoplankton communities during the IPGP to
assess whether community change is occurring, as observed
in other studies and for other ecosystems (Ianson et al., 2001;
Edwards and Richardson, 2004; Chivers et al., 2020). When
interannual evolutions in the phytoplankton growth are explored, the detection and the understanding of harmful algal
bloom dynamics can also be addressed based on similar approaches. Further studies will be dedicated to the simulation
of the coastal ecosystem in the future based on numerical
simulation through climate scenarios. The investigation of
other contrasting coastal environments will allow us to better understand and anticipate the expected impact of global
change on coastal phytoplankton dynamics.
https://doi.org/10.5194/bg-19-5667-2022
Data availability. Data sets used in this paper can be found at
SEANOE https://doi.org/10.17882/46529 (Retho et al., 2022) and
https://doi.org/10.17882/74004 (Rimmelin-Maury et al., 2020).
Supplement. The supplement related to this article is available online at: https://doi.org/10.5194/bg-19-5667-2022-supplement.
Competing interests. The contact author has declared that none of
the authors has any competing interests.
Disclaimer. Publisher’s note: Copernicus Publications remains
neutral with regard to jurisdictional claims in published maps and
institutional affiliations.
Special issue statement. This article is part of the special issue “Towards an understanding and assessment of human impact on coastal
marine environments”. It is not associated with a conference.
Acknowledgements. We would like to acknowledge the COASTHF (http://www.coast-hf.fr, last access: 20 March 2022), SOMLIT
(http://somlit.epoc.u-bordeaux1.fr, last access: 20 March 2022),
and REPHY (https://doi.org/10.17882/47248) national observing
networks for making data flux readily available. COAST-HF and
SOMLIT are components of the National Research Infrastructure
ILICO. We would like to thank the Shom for tidal data and also
Météo-France for wind and solar flux products. We also thank
Claire Labry for fruitful discussions and Sally Close for her proofreading. We thank the referees for their helpful and constructive
comments.
Financial support. This study is part of the State-Region Plan Contract ROEC supported in part by the European Regional Development Funds and the COXTCLIM project funded by the LoireBrittany Water Agency, the Brittany region, and Ifremer.
Review statement. This paper was edited by Ulrike Braeckman and
reviewed by Jose Iriarte and two anonymous referees.
Biogeosciences, 19, 5667–5687, 2022
5684
C. Poppeschi et al.: Interannual variability of the initiation of the phytoplankton growing period
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