Global ocean biogeochemistry models currently employed in climate change projections use highly simplified representations of pelagic food webs. These food webs do not necessarily include critical pathways by which ecosystems interact with ocean biogeochemistry and climate. Here we present a global biogeochemical model which incorporates ecosystem dynamics based on the representation of ten plankton functional types (PFTs): six types of phytoplankton, three types of zooplankton, and heterotrophic procaryotes. We improved the representation of zooplankton dynamics in our model through (a) the explicit inclusion of large, slow-growing macrozooplankton (e.g. krill), and (b) the introduction of trophic cascades among the three zooplankton types. We use the model to quantitatively assess the relative roles of iron vs. grazing in determining phytoplankton biomass in the Southern Ocean high-nutrient low-chlorophyll (HNLC) region during summer. When model simulations do not include macrozooplankton grazing explicitly, they systematically overestimate Southern Ocean chlorophyll biomass during the summer, even when there is no iron deposition from dust. When model simulations include a slow-growing macrozooplankton and trophic cascades among three zooplankton types, the high-chlorophyll summer bias in the Southern Ocean HNLC region largely disappears. Our model results suggest that the observed low phytoplankton biomass in the Southern Ocean during summer is primarily explained by the dynamics of the Southern Ocean zooplankton community, despite iron limitation of phytoplankton community growth rates. This result has implications for the representation of global biogeochemical cycles in models as zooplankton faecal pellets sink rapidly and partly control the carbon export to the intermediate and deep ocean.
Phytoplankton, zooplankton and heterotrophic bacteria (including both
Dynamic green ocean models have been developed and used in global
biogeochemical studies to understand and quantify the interactions between
marine ecosystems and the environment. In these models, phytoplankton and
zooplankton are grouped by taxa into plankton functional types (PFTs)
according to their specific and unique roles in marine biogeochemical cycles
(Hood et al., 2006; Le Quéré et al., 2005). Although generally only a
small number of PFTs are treated explicitly, their inclusion has been shown
to improve the realism of model simulations. For example, the explicit
inclusion of diatoms in marine ecosystem models is required to reproduce the
observed response to natural or purposeful iron fertilisation in the ocean
(Aumont and Bopp, 2006), and observed changes in export production during
glacial cycles (Bopp et al., 2002). The representation of diazotrophs (i.e.
N
Fewer studies have examined the role of different zooplankton PFTs in global ocean biogeochemistry, even though there are zooplankton physiological data sets (e.g. Hirst and Bunker, 2003; Straile, 1997). The simulation of phytoplankton biomass was improved in published studies when more mechanistic parameterisations of zooplankton dynamics constrained by observations were included in a global model (Buitenhuis et al., 2006, 2010). Similarly, the seasonal cycle of phytoplankton (Aita et al., 2003) and the open-ocean oxygen depletion (Bianchi et al., 2013) were improved when the influence of zooplankton vertical migration was included in global biogeochemical models. The choice of the grazing formulation in particular was found to influence phytoplankton diversity (Prowe et al., 2012; Vallina et al., 2014b) and the resulting food-web dynamics (Sailley et al., 2013; Vallina et al., 2014a), and to have implications for energy flow to higher trophic levels (Stock et al., 2014).
Zooplankton can influence the fate of exported materials through several processes, including grazing, repackaging of organic matter in faecal pellets, and the vertical migrations and transport of carbon and nutrients into the mesopelagic zone (e.g. Stemmann et al., 2000; Steinberg et al., 2008). Furthermore, there are important interactions among grazing, nutrient cycles, and environmental conditions, as was shown in studies based on regional models and observations in the equatorial Pacific (Landry et al., 1997; Price et al., 1994), North Pacific (Frost, 1991), the Atlantic (Daewel et al., 2014; Steinberg et al., 2012) and the Southern Ocean (Banse, 1995; Bishop and Wood, 2009). The importance of grazing was also highlighted during iron enrichment experiments (Henjes et al., 2007; Latasa et al., 2014), in part explaining why some experiments led to increased carbon export and others did not (Martin et al., 2013). Thus, a more explicit representation of different zooplankton PFTs in global models could provide important clues for the functioning of marine biogeochemistry.
Here, we present a new dynamic green ocean model with ten PFTs. The
parameterisation of vital rates associated with these PFTs is based on an
extensive synthesis of published information on growth rates and other
relevant parameters. We use the model to examine a long-standing paradox in
biological oceanography: the low phytoplankton biomass in the Southern Ocean
despite the high concentrations of macronutrients. This has been attributed
to lack of iron (Fe) because of the distance to continental dust sources
(Geider and La Roche, 1994; Martin, 1990). Increases in phytoplankton biomass
have been produced in more than a dozen open ocean iron fertilisation
experiments (Boyd et al., 2007; Smetacek et al., 2012). The influx of Fe has
been proposed as a driver for the drawdown of atmospheric CO
The PlankTOM10 dynamic green ocean model is a global ocean biogeochemistry
model that includes plankton ecosystem processes based on the representation
of 10 PFTs and their interactions with the environment. PlankTOM10
incorporates six autotrophic and four heterotrophic PFTs: picophytoplankton
(pico-eukaryotes and non N
Schematic representation of the PlankTOM10 (top) and PlankTOM6 (bottom) marine ecosystem models. The arrows show grazing fluxes by protozooplankton (purple), mesozooplankton (red), and macrozooplankton (green). Only fluxes with weighing factors above 0.1 are shown (Table 3).
The current version of the PlankTOM10 model was developed from the model of Buitenhuis et al. (2013a), using the strategy for regrouping PFTs described by Le Quéré et al. (2005). It does not include new equations for growth and loss terms compared with previous versions of the PlankTOM model, but it includes an additional trophic level in the zooplankton PFTs (i.e. macrozooplankton). Parameterisations are based on more data related to the vital rates of individual PFTs, where new information was available. Previous studies have shown that model results are highly sensitive to PFT growth rates (Buitenhuis et al., 2006, 2010), and considerable effort was made to constrain these rates using observations from LaRoche and Breitbarth (2005), Bissinger et al. (2008), Buitenhuis et al. (2008, 2010), Sarthou et al. (2005), Schoemann et al. (2005), Rivkin and Legendre (2001), Hirst and Bunker (2003), and Hirst et al. (2003).
The complete set of model equations and parameter values are provided in the Supplement. Here, we describe the elements that are most important for the analysis of the Southern Ocean and the strategy used to determine parameter values for PFT growth and loss processes.
PlankTOM10 simulates the growth of ten PFTs in response to environmental
conditions. The PFT biomasses are produced by the model for each grid box
based on the growth and loss term equations presented in the Supplement. The
model includes three detrital pools: large and small particulate organic
matter, and semi-labile dissolved organic matter. The sinking rate of large
particles is based on the mineral (ballast) content of particles following
Buitenhuis et al. (2001), while the sinking rate of small particles is
constant at 3 m d
The growth rate parameters for the ten PFTs in PlankTOM10 are based on a
compilation of growth rates as a function of temperature (Sect. 2.2).
Phytoplankton PFT growth rates are also limited by light and inorganic
nutrients (P, N, Si, and Fe) using a dynamic photosynthesis model that
represents the two-way interaction between photosynthetic performance and
Fe
We used a two-step approach to define the nutrient limitation parameters,
which are not well constrained by observations. Firstly, we assigned initial
PFT-specific half-saturation values to each phytoplankton PFT based on
literature-derived values, using the value for a similar-sized PFT when
PFT-specific information was not available. We then examined the
covariations of surface Chl concentrations with the limiting nutrient
concentrations as shown in Fig. 3, and adjusted the magnitude of the
half-saturation parameters of phytoplankton PFT to approximately fit the
observations, keeping the ratios of
Maximum growth rates for 10 plankton functional types as a function of temperature for the phytoplankton PFTs (left) and for the heterotrophic PFTs (right). The PFTs are presented from the smallest (top) to the largest (bottom) in size. The fit to the data used in the model is shown in black, using the parameter values from Table 1. See Table 1 for references.
Covariation between Chl concentration and (left) potentially
limiting nutrients and (right) biomass of zooplankton groups for the World
Ocean. Chlorophyll data from SeaWiFS satellite are the same in each panel,
and are averaged over 1998–2009. The NO
Growth rates of PFTs at 0 and 20
Initial values for the half-saturation concentrations of P (
Iron uptake was computed using a cell quota model (Buitenhuis and Geider,
2010; Geider et al., 1997), where the Fe uptake by phytoplankton PFTs is
explicitly regulated by the light conditions. The three parameters needed are
the minimum, the maximum and the optimal Fe quotas. The minimum and maximum
quotas were set at the same value of 2.5 and
20
The half-saturation parameters of zooplankton grazing rate were initially based on the relationship between metabolic rates and body volume of Hansen et al. (1997). We used the same approach as for nutrient limitation of the phytoplankton PFTs, and adjusted the half-saturation parameters for grazing based on the observed covariations between surface Chl concentrations and zooplankton biomass (Fig. 3). The selected set of parameter values that approximately fit the observed covariations in Fig. 3 is reported in Table 2.
Zooplankton food preferences were assigned based on predator–prey size ratio
(Table 3), as there were insufficient data to determine these parameters
directly across the range of zooplankton and phytoplankton considered here.
This approach assumes that protozooplankton generally have a high preference
for bacteria and a low preference for diatoms, that mesozooplankton have a
higher preference for protozooplankton and a low preference for
N
Model parameters constraining the resource limitations of growth rates. See text and model equations in the Supplement for definitions of parameters.
Relative preference of zooplankton for food. The preferences are weighted with the biomass to obtain the model parameter value as in Buitenhuis et al. (2010).
Global mean values for rates and biomass from observations (data)
and the PlankTOM10 and PlankTOM6 models averaged over 1998–2009. The
reported confidence levels refer to the observations and are from the
author's assessment of confidence with high (H): most likely within
The gross growth efficiency (the part of grazing that is incorporated into
biomass) was defined based on the mean across available observations: 0.21
for bacteria (data from Rivkin and Legendre, 2001), and 0.29, 0.25,
and 0.30 for protozooplankton, mesozooplankton and macrozooplankton,
respectively (data from Straile, 1997). Respiration and mortality
parameters were based on observations from Buitenhuis et al. (2010) for
protozooplankton, Buitenhuis et al. (2006) for mesozooplankton, and Moriarty (2013) for macrozooplankton. The temperature-dependence of respiration and
mortality was fitted to all data as for the growth rate (Sect. 2.2),
except for the mortality of macrozooplankton and mesozooplankton. There are
nine observations on macrozooplankton mortality and we tuned this term based
on the resulting biomass. The fitted relationship for the mortality of
mesozooplankton was reduced by a factor of
The most important trait that distinguishes the various PFTs is the rate at
which they grow under different conditions (Buitenhuis et al., 2006, 2010).
We compiled maximum growth rates as a function of temperature (Table 1). We
fit an exponential growth relationship to the observations by optimising the
relation
Growth rate parameters estimated with this method are well constrained
(
We used relationships between observed concentrations of Chl and both
inorganic nutrients (e.g. NO
Chlorophyll concentrations covary with NO
PlankTOM10 is coupled to the Ocean General Circulation Model (OGCM) NEMO
version 3.1 (NEMOv3.1). We used the global configuration (Madec and Imbard,
1996), which has a resolution of 2
PlankTOM10 is initialised from observations of dissolved inorganic carbon
(DIC) and alkalinity from Key et al. (2004), O
To understand the interaction pathways among ecosystems, biogeochemistry and
climate, we developed a simplified version of the model that included only
six PFTs (PlankTOM6) (Fig. 1). PlankTOM6 is identical to PlankTOM10, except
that the growth rates of N
The data show systematic patterns in growth rates that differ among PFTs.
The growth rates of all PFTs increase with increasing temperature, but not
to the same extent (Fig. 2). The growth rate of phytoplankton PFTs increases
with PFT size, from 0.15 d
PlankTOM10 reproduces the main characteristics of observed surface Chl, with
high concentrations in the high latitudes and low concentrations in the
subtropics, higher Chl concentration in the Northern compared to the Southern
Hemisphere, and in the South Atlantic compared to the South Pacific Ocean
(Fig. 4). The global biogeochemical fluxes simulated by PlankTOM10 are
generally below or at the low end of the range of observed values (Table 4),
with global primary production of 42.4 PgC yr
Surface Chl (mg m
PlankTOM10 produces distinctive geographical distributions of carbon
biomasses among PFTs (Fig. 5). About a third of the phytoplankton biomass
occurs as picophytoplankton, followed in descending abundance by diatoms and
Annual mean surface carbon biomasses for individual plankton
functional types as simulated by the PlankTOM10 model
(
The model underestimates bacterial biomass by a factor of 10 compared with observations. This possibly reflects the fact that the model only represents highly active bacteria and a substantial fraction of observed biomass is from low activity and ghost cells. The model underestimates protozooplankton by a factor of 1.5–5 (in absolute value) or 2–3 (as a fraction of total biomass value) compared to observations (Table 4). This discrepancy could be caused by the underestimation of bacterial biomass, as bacteria are an important source of food for protozooplankton. The simplified representation of the range of protozooplankton grazers in a single PFT representing both heterotrophic nanoflagellates and microzooplankton could also play a role. Simulated mesozooplankton biomass is only slightly below the observed range, while simulated macrozooplankton biomass is within the observed range, although the uncertainty here is large (0.010–0.64 PgC). Overall the balance is slightly skewed towards relatively more biomass than observed in the larger zooplankton (53 % compared to 3–47 %) compared to the smaller zooplankton groups (13 % compared to 27–31 %; Table 4).
The geographic distribution of each simulated PFT is also distinctive
(Figs. 6–7). Satellite data products indicate that small phytoplankton
(picophytoplankton and N
Dominance of picophytoplankton (top), haptophytes (middle) and
diatoms (bottom) in the ocean surface (fraction of time). Left panels show
the frequency of the dominance of each PFT detected from satellite data by
Alvain et al. (2005) for each pixel during 1998–2006. Right panels show
model results, as the surface Chl for each PFT divided by the total Chl. For
the model results, picophytoplankton include both the picophytoplankton and
N
Frequency of blooms of
The marine ecosystem as a whole appears to function realistically: mesozooplankton grazing on phytoplankton is somewhat overestimated relative to the 5.5 Pg yr estimated by Calbet (2001), so they have taken over the role of principal herbivores. Possibly the faster turnover rates of small copepods are overrepresented in the observational data on mesozooplankton, leading to a trophic position of mesozooplankton somewhat too low in the food chain. Export production, phytoplankton biomass and metazoan zooplankton biomass are realistic in the model, leading to realistic seasonal cycles, but the regenerated part of primary production is underestimated, concomitant with low protozooplankton biomass, which impacts the model on shorter timescales of days.
PlankTOM10 and PlankTOM6 generally produce similar results in surface Chl concentration, nutrient distribution, primary and export production (Fig. 8), except that PlankTOM6 fails to reproduce the observed low Chl concentration in summer in the Southern Ocean (Fig. 4; Sect. 3.4). The overall differences between the two models, quantified statistically using a Taylor distribution (Taylor, 2001), are less than 0.1 in either correlation or normalised standard deviation (Fig. 8). PlankTOM10 does slightly better than PlankTOM6 for the distribution of Chl, primary and export production, but slightly worse for the distribution of silica and nitrate, with similar performance for phosphate (Fig. 8). These differences are small in part because of the short duration of the simulations presented here (20 years), which allow equilibration of the ocean surface only. The models are generally similar also in their representations of the distribution of biomass among phytoplankton PFTs, with most of biomass being in picophytoplankton in both models (Fig. 9 and Table 4). However, PlankTOM6 allocates more biomass to protozooplankton compared to PlankTOM10, though PlankTOM6 is still at the low end of observed concentrations (Table 4).
Taylor diagram comparing the distributions of surface concentration
in annual mean Chl, NO
The failure of PlankTOM6 to reproduce the observed low Chl concentration in the Southern Ocean during summer is further highlighted in Fig. 10, which shows the seasonal cycle of mean Chl for the Northern Hemisphere and the Southern Ocean, where it is most pronounced. In PlankTOM6, the seasonal cycles in the north and south are very similar, with the slightly lower concentrations in the Southern Ocean during summer caused by a slightly deeper summertime mixed-layer depth (29 m compared to 19 m). In contrast, in PlankTOM10, the seasonal cycle of Chl in the south is smaller and concentrations are always below those in the north, as is the case for observations. As PlankTOM6 and PlankTOM10 have identical physical environments (including mixed-layer depth), the north–south differences are due to ecosystem structure. In the following sections, we focus our analysis on the model parameters that influence the low Chl concentration in the Southern Ocean the most.
The observed phytoplankton biomass, including the low Chl concentrations in high-nutrient low-chlorophyll (HNLC) regions, reflects the balance between phytoplankton growth and loss. Phytoplankton growth rates vary with temperature, light, and nutrient supply, whereas losses result mainly from grazing by zooplankton, respiration, cell death, sinking to depth, and dilution by vertical mixing. Any process that reduces the net rate of increase in phytoplankton biomass (i.e. differences between growth and loss) may lead to low residual Chl concentration. For example, Platt et al. (2003a) showed that deep mixing by wind dilutes Chl in the surface layer and reduces the average irradiance experienced by the phytoplankton. This results in low growth rate and demand for nitrate, the conditions generally observed in HNLC regions. Here we further examine the consequences of high zooplankton-mediated grazing losses.
We use the north
Zonal mean distribution of phytoplankton (left) and zooplankton
(right) PFTs for the PlankTOM10 (dark grey) and PlankTOM6 (light grey) models
(
Monthly variations of surface Chl concentration in the North (full
solid lines) and South (dashed lines; mgChl m
We tested the specific effect of macrozooplankton on Chl by running four
additional model experiments (Fig. 11): in the Z1 simulation, we added
macrozooplankton to PlankTOM6, in Z2 we parameterised the top grazer in
PlankTOM6 using the same growth and loss rate parameters as macrozooplankton,
in Z3 we removed macrozooplankton from PlankTOM10, and in Z4 we parameterised
the top grazer in PlankTOM10 using the same growth and loss rate parameters
as mesozooplankton. These sensitivity studies were identical to the
PlankTOM10 (or PlankTOM6) simulation in all other respects. Experiments Z1
and Z2 both include macrozooplankton, but in different food-web positions.
These experiments maintain a high north
We examined the impact of macrozooplankton grazing in sensitivity tests in
which the grazing rate of macrozooplankton was varied within the range of the
observed growth rates (Fig. 2; Table 1). These simulations show that
macrozooplankton grazing rate has a strong influence on the Chl
north
We tested the relative role of atmospheric iron deposition compared with
grazing for the north
North
As a means of validating the model results, we also tested the response of
PlankTOM10 to Fe-fertilisation to verify that the model reproduced the
observed Chl blooms under Fe enrichment conditions (Boyd and al., 2007). This
was done by saturating the surface layer of the ocean with Fe for 1 month
(February). In this experiment, surface Chl south of 40
Model simulations could be influenced by the model structure and parameters,
the physical transport, meteorological data, or the choice of dust deposition
fields. We assessed the combined effects of model choices by comparing our
results with outputs from seven other models: a version of the PISCES model
(Aumont and Bopp, 2006), the CCSM-BECs model (Doney et al., 2009), and the
NEMURO model (Kishi et al., 2007), IPSL-CM5A-LR (Dufresne et al., 2013),
GRDL-ESM2M (Jones et al., 2011), HadGEM2-ES (Giorgetta et al., 2013), and
CanESM2 (Arora et al., 2011). All of these other models focus on the
representation of phytoplankton groups and parameterise grazing pathways in a
simpler fashion than PlankTOM10. They produce a north
North
Mean surface concentrations of the biomass of phytoplankton (green),
macrozooplankton (black), mesozooplankton (red), and protozooplankton (blue).
Results are shown for (left) the PlankTOM10 model and (right) the PlankTOM6
model, and for (top) the north and (bottom) the south. All data are averaged
for 1998–2009, and between 30 and 55
The development of PlankTOM10 has benefited from the existence of the very extensive range of observations to develop realistic parameterisations of key processes, particularly PFT growth rates. Although the simulated global biogeochemical fluxes are generally below or at the low end of the range of observed values and several regional discrepancies exist between observed and modelled biomass and fluxes, the model reproduces both the relative importance of different PFTs and the geographic patterns in their abundance. Thus, while not perfect, the model is sufficient to explore the role of ecosystem dynamics in determining ocean biogeochemistry.
Our analyses suggest that Southern Ocean Chl during summer is primarily controlled by zooplankton grazing, particularly the presence of a slow-growing zooplankton, and the structure of the pelagic food web, rather than the low supply rate of iron. Trophic cascading appears to account for the differences between the results from PlankTOM10 and PlankTOM6 (Fig. 13; Zollner et al., 2009). For example, protozooplankton graze on phytoplankton (and bacteria), which reduces their prey's biomass. However, mesozooplankton graze on phytoplankton and protozooplankton, and macrozooplankton graze on phytoplankton and both protozooplankton and mesozooplankton. Thus the grazing pressure of larger zooplankton on smaller zooplankton can indirectly reduce the overall grazing pressure on phytoplankton. In PlankTOM10, macrozooplankton concentration is higher in winter in the Northern Hemisphere Pacific sector where the surface layer is more stratified and food is abundant, compared with the Southern Ocean Pacific sector where the surface layer is more mixed and food is scarce. Thus when the spring bloom starts in the north, the biomass and grazing pressure exerted by macrozooplankton is high enough to reduce the biomass of smaller zooplankton, consequently reducing the grazing pressure on Chl and leading to an increase in Chl. However, in the south, macrozooplankton biomass is too low to cause significant losses of smaller zooplankton. Hence, the high proto- and meso-zooplankton biomasses prevent a phytoplankton bloom from developing in that region. Although PlankTOM6 simulates some degree of trophic cascade with the presence of two zooplankton PFTs, our sensitivity tests presented in Fig. 11 show that the difference in growth rates between the two zooplankton PFTs is too small to impact the phytoplankton significantly.
The higher concentration of macrozooplankton biomass in the north compared to
the south is consistent with the observations, where the mean biomasses of
macrozooplankton were reported to be 3 times higher in the Northern
Hemisphere compared to the Southern Hemisphere (Moriarty et al., 2013). A
similar contrast is found between the Atlantic and Pacific sectors of the
Southern Ocean, where the high macrozooplankton biomass observed in the
Atlantic (Atkinson et al., 2004) would reduce the abundance of smaller
zooplankton, resulting in higher Chl concentrations in the Atlantic sector,
as simulated in PlankTOM10 (Fig. 4). Such trophic cascades have been observed
in diverse ecosystems on land and in the ocean (Casini et al., 2009).
Furthermore, many observational-based studies have highlighted the important
role of zooplankton grazing for controlling phytoplankton biomass (Atkinson
et al., 2001; Banse, 1996; Dubischar and Bathmann, 1997; Granĺi et al.,
1993). Although some processes are missing from the model (e.g. vertical
migration of zooplankton, which mostly contributes to downward export), the
model suggests that the primary cascading effect of grazing is sufficient to
account for a large part of the north
Our results indicate that zooplankton grazing exerts an important control on
Southern Ocean Chl. This propagates through to influence phytoplankton
biomass. Indeed, the north
There are a number of limitations to the current version of PlankTOM10, including simplified overwintering strategies for zooplankton, the use of a coarse Fe model, and the lack of representation of semi-refractory organic matter. In addition, the model does not include some ecosystem pathways, such as viral lysis (Evans et al., 2009), and the zooplankton representation does not include salps, pteropods, and auto- and mixo-trophic dinoflagellates. The nano- and micro-zooplankton are also combined into a single compartment. The realism of the simulations may also be affected by the relatively coarse resolution of the physical ocean model. However, these biases affect both PlankTOM6 and PlankTOM10, and thus the experiments still provide information on the processes that differ between the two models. Our work suggests that improved representation of the zooplankton components could help further constrain the processes that regulate Chl distribution in models. The effect of further ecosystem model developments will be explored in follow-up studies.
The development of global marine ecosystem models is hampered in particular because of our poor understanding of several critical ecosystem processes and food-web interactions (Smetacek et al., 2004), and the paucity of global-scale observation of physiological rates and biomass for parameterisation and validation (Le Quéré and Pesant, 2008; Barton et al., 2013). For example, the wide range in observed growth rates for the same temperature is an indication of the challenges met by marine ecosystem modellers, particularly in representing the within-PFT diversity, which is unaccounted for in our model. In addition, the lack in knowledge of trophic relationships means that semi-arbitrary choices have to be made to characterise the predator–prey relationships based on size. Much more work is needed to understand the specific pathways by which matter circulates within ecosystems, taking into account the regional distributions of zooplankton groups and interactions with the environment including seasonal mixed-layer dynamics.
The role of macrozooplankton highlighted here has implications for carbon export to depth because faecal pellets of some macrozooplankton have very fast sinking rates (Fortier et al., 1994; Turner 2002). Hence, a more explicit representation of the pelagic food web in global models is needed to capture the full range of interactions between marine ecosystems, marine biogeochemistry and climate. The synthesis and analysis of observations and model results by the MAREDAT and MAREMIP projects provide valuable insights into the processes that control marine ecosystems, including the contributions that different PFTs make to ocean biomass (Buitenhuis et al., 2013a; Hashioka et al., 2013; Sailley et al., 2013).
Our simulations examining the effects of grazing on phytoplankton biomass
raise questions about the biological and biogeochemical bases for the
current projections of the feedbacks between climate (and other
environmental changes) and marine ecosystems. It also highlights potential
complications for the large-scale proposed use of purposeful
Fe-fertilisation to enhance the deep ocean storage of CO
Our results on the important role of grazing do not contradict the results on the importance of Fe-fertilisation as highlighted in Fe enrichment experiments (Boyd and al., 2007), because additional Fe would trigger further growth provided that Fe were initially below an optimal concentration (Blain et al., 2007). However, our results suggest that low Fe concentrations by themselves are insufficient to account for the very low Chl levels observed in the Southern Ocean HNLC region in summer, and that differences in zooplankton trophic and community structure, and concomitant grazing dynamics play an important role in controlling phytoplankton blooms and maintaining very low Chl levels in that region. Although previous studies emphasised the role of phytoplankton community structure (Arrigo et al., 1999) and mixed-layer dynamics for nutrient supply and demand (Platt et al., 2003a, b) in ocean biogeochemical cycles, our analysis makes it clear that it is important to consider the whole pelagic ecosystem, including the zooplankton, when studying and predicting ecosystem responses to Fe (or any essential nutrient) fertilisation. This complex interplay has received less attention than either the drivers of primary production or the representation of Fe cycling in global biogeochemical modelling. Our results suggest that representing zooplankton interactions more explicitly in models would improve the representation of biogeochemistry–climate interactions, and could bring new insights to understand changing global biogeochemical cycles.
The structure of PlankTOM10 was developed through a series of seven international workshops funded in part by the Max Planck Institute for Biogeochemistry in Jena, Germany, and hosted by the Villefranche Oceanography Laboratory, France. We thank C. Klaas and D. Wolf-Gladrow for their input on model development and interpretation, S. Pesant for support with data compilations, G. Madec and the NEMO team for assistance with the physical model, A. Tagliabue for providing the iron database, N. Mahowald for providing dust deposition fields, V. Smetacek and one anonymous referee for their comments. C. Le Quéré and E. T. Buitenhuis were funded by UK-NERC projects NE/C516079/1 and NE/K001302/1, and European Commission project EMBRACE 282672. R. Moriarty was funded by EU FAASIS project MEST/CT/2004/514159. M. Vogt was funded by the Marie Curie Research and Training Network GREENCYCLES project MC-RTN-512464 and EUR-OCEANS project 282672. Model simulations were run on the High Performance Computing Cluster of the University of East Anglia. Edited by: G. HerndlReviewed by: V. Smetacek and one anonymous referee