Stiftung Alfred-Wegener-Institut f ¨ur Polar- und Meeresforschung
A Series of Planktonic Ecosystem Models with an Identical Kernel
Markus Schartau
1, Martin Losch, Claudia Sprengel, and Christoph V ¨ olker
Alfred-Wegener-Institut f ¨ur Polar- und Meeresforschung, Bremerhaven, Germany
1now at: MSRC at SUNY, Stony Brook, New York
1 Overview
For prognostic studies of the global carbon cycle it is critical to correctly reproduce primary productivity in different marine environments with the same model. Any given, single model will simulate biogeochemical processes with varying accuracy at different oceanic sites.
In general, we seek for different model formulations that are appropriate for different specific biogeochemical questions. These individual mod- els are required to share a common set of basic parameterisations (the kernel). The efforts put into evaluating and improving a common kernel greatly pay off with a simplified error analysis. Our strategy also helps to determine important processes and those which play only a minor role.
DetC DetN EOC EON
TEP-C
CO2flux
Remineralisation Respiration
Growth
Extracellular organic matter
Carbon of Transparent Exopolymer Particles
Detritus
Phytoplankton
Nutrients & alkalinity
ZooC ZooN
Zooplankton
PhyC CHL PhyN DIC
ALK DIN
Export
Figure 1: The basic model kernel decouples carbon from nitrogen fluxes. Most parameterisations for phytoplankton growth are adopted from Geider et al. (1998). Mass fluxes within the ecosystem are primar- ily regulated by the carbon-to-nitrogen (C:N) utilisation ratio of phyto- plankton, thus giving it the name C:N-Regulated Ecosystem model (CN- REcoM). As a further new and important feature, the model explicitly resolves the carbon content of transparent exopolymeric particles (TEP- C) and their role for particle formation and export.
2 CN-REcoM
0 0.2 0.4 0.6 0.8 1 1.2
J F M A M J J A S O N D 120
110 100 90 80 70 60 50 40 30 20 10 0
Depth / m
DIN observed / mmol m−3
0 0.2 0.4 0.6 0.8 1 1.2
J F M A M J J A S O N D 120
110 100 90 80 70 60 50 40 30 20 10 0
Depth / m
DIN modeled / mmol m−3
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4
J F M A M J J A S O N D 120
110 100 90 80 70 60 50 40 30 20 10 0
Depth / m
Chl a observed / mg m−3
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4
J F M A M J J A S O N D 120
110 100 90 80 70 60 50 40 30 20 10 0
Depth / m
Chl a modeled / mg m−3
0 2 4 6 8 10
J F M A M J J A S O N D 120
110 100 90 80 70 60 50 40 30 20 10 0
Depth / m
CPP observed / mg C m−3 d−1
0 2 4 6 8 10
J F M A M J J A S O N D 120
110 100 90 80 70 60 50 40 30 20 10 0
Depth / m
CPP modeled / mg C m−3 d−1
Figure 2: Near Bermuda (BATS site) oligotrophic conditions prevail near the surface except for the short period during the onset of the bloom. When applied to oligotrophic sites, conventional nitrogen or phosphorus based models produce systematic errors in carbon flux es- timates (Schartau and Oschlies, 2003). In the monthly averaged values of a CN-REcoM simulation at BATS, that are shown in the figure, the decorrelation of nitrate concentration (DIN), chlorophyll a (Chl a), and carbon based primary production (CPP) becomes significant.
Contact: Markus Schartau (markus.schartau@stonybrook.edu)
3 REcoM & Co
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1
2 3 4 5 6 7 8 9 10
mmol m−3
Month
REcoM&Co at OWS−INDIA (59N 19W) DIN
DIP*16
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 0.5
1 1.5 2 2.5 3 3.5 4 4.5 5
mg m−3
Month
REcoM&Co at OWS−INDIA (59N 19W) Total Chl a
Chl a Coccolithophores Chl a other phytoplankton
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 10
15 20 25 30 35 40 45
molar ratio
Month
DIN:DIP ratio
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2240
2260 2280 2300 2320 2340 2360 2380 2400 2420
mmol m−3
Month Total Alkalinity
including CaCO3 precip.
disregarding CaCO3 precip.
Figure 3: For carbon cycling it is of particular interest to determine the amount of cal- cification by phytoplankton. Here, we seek for a mechanistic model that can simulate coccolithophorid blooms. We postulate that the success of the calcifying algae (Emil- iania Huxleyi) is strongly regulated by the nitrogen-to-phosphorus (DIN:DIP or N:P) utilisation ratio of phytoplankton. The figure shows the success of coccolithophores over other phytoplankton (e.g., diatoms) under high N:P conditions at the site of Ocean Weather Ship India (OWS-INDIA).
Contact:
Claudia Sprengel (csprengel@awi-bremerhaven.de) Markus Schartau (markus.schartau@stonybrook.edu)
5 An Ocean General Circulation Model with REcoM & Dia
REcoM&Dia has been coupled to the general circulation model MITgcm (Marshall et al., 1997). Research activities include assessing the impact of physical processes, for example advection, mixing, mixed layer depth, on biological productivity and export fluxes into the deep ocean. For this purpose the modelling system is configured in a zonally periodic channel (Fig. 6 and Fig. 7), but it is also possible to design an experiment which simulates production and export of the global oceans.
0 10 20 30 40
depth [m]
DIN [mmol m−3], w/out Fe limitation
0 100 200 300 400 500
−200
−150
−100
−50 0
0 10 20 30 40 DIN [mmol m−3], with Fe limitation
0 100 200 300 400 500
−200
−150
−100
−50 0
0 5 10 15 20
depth [m]
DSi [mmol m−3], w/out Fe limitation
0 100 200 300 400 500
−200
−150
−100
−50 0
0 5 10 15 20 DSi [mmol m−3], with Fe limitation
0 100 200 300 400 500
−200
−150
−100
−50 0
0 0.2 0.4 0.6 0.8 1 Chlorophyll a [mg m−3], w/out Fe limitation
Y [km]
depth [m]
0 100 200 300 400 500
−200
−150
−100
−50 0
0 0.2 0.4 0.6 0.8 1
Y [km]
Chlorophyll a [mg m−3], with Fe limitation
0 100 200 300 400 500
−200
−150
−100
−50 0
Figure 6: In a 500 by 500 km channel at eddy permitting resolution, the im- pact of iron limitation is clearly visible after 10 model years in the zonal mean.
The white dashed line marks the mixed layer depth. On the left hand side, without iron limitation, mixing provides high nutrient concentrations (DIN and DSi, nitrogen and silica respectively). The choice of wind forcing causes an Ekman drift to the right where nutrients are depleted. On the right hand side, with iron limitation, chlorophyll concentrations are generally smaller and limited to the region where mixing recharges surface concentrations of iron.
Contact: Martin Losch (mlosch@awi-bremerhaven.de)
0.1 0.2 0.3 0.4 0.5 0.6 0.7 Chlorophyll a [mg m−3], w/out Fe limitation
X [km]
Y [km]
0 100 200 300 400 500
0 100 200 300 400 500
0.1 0.2 0.3 0.4 0.5 0.6 0.7 Chlorophyll a [mg m−3], with Fe limitation
X [km]
Y [km]
0 100 200 300 400 500
0 100 200 300 400 500
Figure 7: Top view of the same channel as in Fig.6.
With iron limitation (bottom figure), production is limited to regions where suitable mixing recharges surface concentrations of iron from below. Without iron limitation (top figure) production occurs over the entire surface area.
4 REcoM & Dia
On a global scale, diatoms contribute some 40% of oceanic primary production and are very efficient exporters of fixed carbon to the abyssal ocean. Diatoms consume silicon and iron, and Fe fertilization has been shown to lead to rapid Si and C drawdown in so-called High-Nutrient-Low-Chlorophyll regions of the ocean such as the Southern Ocean. However, Fe availability and residence time in the euphotic layer also depend on the chemical speciation of iron (Wells, 2003).
Based on CN-REcoM, we have constructed a regulatory model of diatom physiology, that depends on Si and Fe availability. Currently, we are coupling this model to a prognostic model of iron speciation (Weber et al., Modelling the biogeochemistry of iron at the Bermuda Atlantic Timeseries Station. In preparation) to simulate diatom blooms in the Southern Ocean.
Fe(II) colloidal iron
Fe−organics Fe(III)
dry and wet deposition
Figure 4: Schematic representation of the iron spe- ciation model: dissolved iron is partitioned into in- organic ferric and ferrous species, truly dissolved or- ganically complexed iron and colloidal iron. Direct or indirect photochemical reactions (yellow) drive a daily cycle in iron speciation. Ultimately iron is either taken up by phytoplankton or adsorbes onto sinking particulate matter.
0 5 10 15 20
0 1 2 3 4
total dissolved Fe (nM)
time (days)
Figure 5: Iron input to the surface ocean with dust deposition is episodic rather than continuous, and the residence time of iron within the surface layer after an iron addition has been observed to be days rather than years, probably due to loss of colloidal iron (Bowie et al., 2001). Such a behaviour can- not be simulated with simple speciation models that are tuned to reproduce the deep ocean Fe distribu- tion with scavenging residence times for iron be- tween years and centuries. Here we compare the temporal evolution of total dissolved iron as mod- eled by four different models of iron speciation af- ter increasing dissolved iron by four nM over one day. The models are the three models presented in Parekh et al. (2004) that successfully reproduce the interbasin gradients of deep iron concentrations (red, green and blue lines), and our iron speciation model (black line).
Contact:
Christoph V¨olker (cvoelker@awi-bremerhaven.de)
References
Bowie, A. R., Maldonado, M. T., Frew, R. D., Croot, P. L., Achterberg, E. P., Mantoura, R. F. C., Worsfold, P. J.,
Law, C. S., and Boyd, P. W. (2001).
The fate of added iron during a mesoscale fertilisation experiment in the Southern Ocean. Deep-Sea Res. II, 48:2703–2743.
Geider, R. J., MacIntyre, H. L., and Kana, T. M. (1998). A dynamic regulatory model of phytoplanktonic acclimation to light, nutrients, and temperature.
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Marshall, J., Adcroft, A., Hill, C., Perel- man, L., and Heisey, C. (1997). A finite-volume, incompressible Navier Stokes model for studies of the ocean on parallel computers. J. Geophys.
Res., 102(C3):5753–5766.
Parekh, P., Follows, M. J., and Boyle, E. (2004). Modeling the global ocean iron cycle. Global Bio- geochem. Cycles, 18(1):GB1002, doi:10.1029/2003GB002061.
Schartau, M. and Oschlies, A. (2003).
Simultaneous data-based optimization of a 1D-ecosystem model at three lo- cations in the North Atlantic Ocean:
Part II – Standing stocks and nitrogen fluxes. J. Mar. Res., 61(6):795–821.
Wells, M. L. (2003). The level of iron enrichment required to initiate diatom blooms in HNLC waters. Marine Chemistry, 82(1–2):101—114.
