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![Page 1: Marine Biogeochemical and Ecosystem Modeling Michael Schulz MARUM -- Center for Marine Environmental Sciences and Faculty of Geosciences, University of.](https://reader035.fdocuments.us/reader035/viewer/2022062515/56649cec5503460f949b819b/html5/thumbnails/1.jpg)
Marine Biogeochemical and Ecosystem Modeling
Michael Schulz
MARUM -- Center for Marine Environmental Sciences
and
Faculty of Geosciences, University of Bremen
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9:15 - 9:45
1. Introduction (Lecture)- The global carbon cycle, CO2 in seawater
- “Biological pumps”
- Reservoir or box models
2. Modeling Marine Nutrient and Carbon Cycles (Box-Model Exercise)- Global oceanic phosphate distribution
- Nutrient – productivity interactions
- Oceanic carbon budget and large-scale ocean circulation
10:45 - 11:00 break
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11:00 – 12:30
2. cont'd- Circulation-productivity feedback in the global
ocean
3. State-of-the-art Biogeochemical Models (Lecture)- 2D and 3D Models- Included tracers and processes
4. Marine Ecosystem Models (Lecture)
- Why ecosystem models?
- Ecosystem models in paleoceanography
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Course Material
www.geo.uni-bremen.de/geomod/
staff/mschulz/lehre/ECOLMAS_Modeling/
This presentation
Box-model exercises
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Basic LiteratureNajjar, R. G., Marine biogeochemistry. in Climate system modeling, edited
by Trenberth, K. E., pp. 241-280, Cambridge University Press, Cambridge, 1992.
Rodhe, H., Modeling biogeochemical cycles. in Global biogeochemical cycles, edited by Butcher, S. S., R. J. Charlson, G. H. Orians and G. V. Wolfe, pp. 55-72, Academic Press, London, 1992.
Sarmiento, J. L., and N. Gruber, Ocean biogeochemical dynamics, pp. 503, Princeton University Press, Princeton, 2006.
Walker, J. C. G., Numerical adventures with geochemical cycles, 192 pp., Oxford University Press, New York, 1991.
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Ruddiman (2001)
For a climatologist biogeochemical cycles usually translates into carbon cycle.
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Sundquist (1993, Science)
Reservoir Sizes in [Gt C]Fluxes in [Gt C / yr]
Carbon-Cycle – Characteristic Timescales
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Thurman & Trujillo (2002)
Average surface-Water composition
CO2 0.5 %HCO3
- 89.0 %CO3
2- 10.5 %
- -
- -
23 3
22 3 3
TA [HCO ] + 2[CO ]
CO [HCO ] + [CO ]
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Biological Productivity in the Ocean
Ruddiman (2001)
Nutrients:P, N, (Si, Fe)
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Atmosphere
Ocean
Primary Production
Inorgan. C Organ. C
Particle-Flux
RemineralisationOrgan. C CO2
CO2
CO2
The Biological Pump
Fig. courtesy of A. Körtzinger
Sediments
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Photic Zone
Aphotic Zone
Sediments
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Biogenic Calcium Carbonate Production Raises Dissolved CO2 Concentration
2- -2 2 3 3CO + H O + CO 2HCO
pH Reaction:
(1) Biogenic carbonate uptake
(2) More bicarbonatedissociates
(3) More CO2 is formed
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Atmosphere
Ocean
CO2
The Calcium Carbonate Pump
CaCO3 Dissolution
Lysocline
Biogenic CaCO3
Formation3
CO32-
CO2
Fig. courtesy of A. Körtzinger
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Reservoir or Box Models
• Reservoir = an amount of material defined by
certain physical, chemical or biological
characteristics that, under the particular
consideration, can be regarded as homogeneous.
(Examples: CO2 in the atmosphere, Carbon in living organic matter in
the oceanic surface layer)
• Flux = the amount of material transferred from one
reservoir to another per unit time
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Single Reservoir Case
Reservoir (mass M)
Flux In Flux Out
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Basic Math of Box Models
(Rate of change of mass in reservoir) =
(Flux in) – (Flux out) + Sources – Sinks
Or, for concentration (C [mol/m3]) and water flux (Q
[m3/s]):
i o
dMF F SMS
dt
i i o
dCV QC Q C SMSdt
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Numerical Solution of Box-Model Equations
1 0
1 0 0 0 0
2 1 1 1 1
1
1 0
, ,
, ,
, ,
( )
( )
( )n n n n n
t ti o
t t i t o t t
t t i t o t t
t t i t o t t
M MdM MF F SMS
dt t t t
M M t F F SMS
M M t F F SMS
M M t F F SMS
Solution by finite-difference method (approximation!)
“Euler Method”
Initial Condition
Tim
e (
in s
tep
s of
t)
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Numerical Solution of Box-Model Equations
1 0
1 0 0 0 0
2 1 1 1 1
1
1 0
, ,
, ,
, ,
( )
( )
( )n n n n n
t ti o
t t i t o t t
t t i t o t t
t t i t o t t
M MdM MF F SMS
dt t t t
M M t F F SMS
M M t F F SMS
M M t F F SMS
Solution by finite-difference method (approximation!)
Initial Condition
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Numerical Solution of Box-Model Equations
1 0
1 0 0 0 0
2 1 1 1 1
1
1 0
, ,
, ,
, ,
( )
( )
( )n n n n n
t ti o
t t i t o t t
t t i t o t t
t t i t o t t
M MdM MF F SMS
dt t t t
M M t F F SMS
M M t F F SMS
M M t F F SMS
Solution by finite-difference method (approximation!)
“Euler Method”
Initial Condition
Tim
e (
in s
tep
s of
t)
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M(tn)
t
tn+1 tn t
M
“Prediction”
True Value
Error Slope = Fi(tn) - Fo(tn) + SMS(tn)
M(tn+1)
Euler Method
Assumption: Slope at time tn remains constant throughout time interval t
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Coupled Reservoirs
Reservoir 1 (mass M1)
F12
Reservoir 2 (mass M2)
F21
Principle of mass-conservation requires M1 + M2 = const.
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Large-Scale Ocean Circulation
(after Broecker, 1991)
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Box-Model ofOceanic PO4 Distribution
AABW_A(4 Sv)
AABW_P(20 Sv)
NADW(10 Sv)
Indo-Pacific Southern Ocean Atlantic
Surface(0-100 m)
Deep(> 100 m)
20 Sv 10 Sv20 Sv
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www.geo.uni-bremen.de/geomod/staff/mschulz/lehre/ECOLMAS_Modeling/bm1_po4_only.gsp
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Box-Model Experiment 1
• Vary the water transports and initial PO4
concentration and observe the final PO4
concentration and evolution (time series).
• Q1: How does the final PO4 distribution depend
on these settings?
• Q2: How do these settings affect the time it
takes to reach a steady state? (What
characterizes the steady state?)
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Inducing PO4 Gradients – Biological Productivity
• Assume an average export production of
12 g C/m2/yr
• With a “Redfield ratio” of C:P = 117:1
(molar ratio) and 1 mol C = 12 g C
Corresponding biological PO4 fixation is
1/117 mol P/m2/yr
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Box-Model of Oceanic PO4 Distribution with Productivity
AABW_A(4 Sv)
AABW_P(20 Sv)
NADW(10 Sv)
Indo-Pacific Southern Ocean Atlantic
Surface(0-100 m)
Deep(> 100 m)
Assumption: Biologically fixed PO4 sinks from the surface layer to the underlying deep layer, where the organic material is completely remineralized.
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www.geo.uni-bremen.de/geomod/staff/mschulz/lehre/ECOLMAS_Modeling/bm1_po4_fix_prod.gsp
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Box-Model Experiment 2
• Q: How does the inclusion of biological productivity affect
the PO4-concentration difference between Atlantic and Indo-
Pacific Oceans in the standard case?
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10 m water depth
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1750 m water depth
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Box-Model Experiment 2
• Vary the water transports (try max. and small values)
and observe how the PO4 distribution changes. Explain
the changes.
• Q: What happens if NADW = 0 Sv? (Keep the
remaining parameters at their default values.) Does this
result make sense in the real world?
• Q: For which initial PO4 concentration do no negative
concentrations result (with NADW = 0 Sv)? Is this a
reasonable increase for Late Pleistocene glacials?
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Avoiding Negative PO4 Concentrations – Nutrient-Dependent Productivity
• Assume that productivity scales with the PO4
availability in the surface layer (variety of relationships are possible: linear, non-linear with saturation…)
• PO4 fixation = [PO4]sfc * Volsfc / [mol/yr],
where is the residence time of PO4 in the
surface due to biological productivity
• Assume ATL = IPAC = 5 yr and SOC = 50 yr (Broecker
and Peng, 1986)
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www.geo.uni-bremen.de/geomod/staff/mschulz/lehre/ECOLMAS_Modeling/bm1_po4_dyn_prod.gsp
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Box-Model Experiment 3a
• Run the model for NADW of 0 and 10 Sv
and write down the PO4 concentrations for
the Atlantic boxes for each case.
• Calculate the difference between conc. in
deep and surface box. What do you
observe?
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Box-Model Experiment 3a – Atlantic
NADW
(Sv)
PO4 Surface
(mol/l)
PO4 Deep
(mol/l)
PO4
(mol/l)
10 0.24 0.69 0.45
0 0.18 0.88 0.70
Shift of PO4 content from surface to deep Atlantic as NADW drops
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Box-Model Experiment 3b
• Run the model for NADW = {0, 5, 10, 15,
20} Sv and write down the final PO4
fixation in the Atlantic Ocean.
• Sketch NADW vs. PO4 fixation.
• Q:What is the paleoceanographic
implication of this finding?
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NADW and Productivity in the Atlantic Ocean
2
2.2
2.4
2.6
2.8
3
3.2
3.4
3.6
0 5 10 15 20
PO
4 F
ixat
ion
[1011
mol
P /
yr]
NADW Flow [Sv]
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Including the Marine Carbon-Cycle
• Tracers: PO4 ( controls productivity)
DIC (dissolved inorganic carbon)
ALK (alkalinity)
• Aqueous CO2 partial pressure = f(DIC, ALK)
• Redfield ratio of organic matter (C:N:P = 117:16:1)
• Ratio between Corg and CaCO3 production (“rain
ratio”) assumed to be temperature dependent (a crude parameterization of ecosystem dynamics)
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0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
0 5 10 15 20 25 30
Rai
n R
atio
= P
CaC
O3
/ PC
org
Water Temperature [°C]
Rain-Ratio Parameterization
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Area-WeightedAverage
AtmosphericpCO2 ≈ Mean Oceanic pCO2
www.geo.uni-bremen.de/geomod/staff/mschulz/lehre/ECOLMAS_Modeling/bm1_c-cycle_fix_prod.gsp
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Box-Model Experiment 4C-Cycle with Fixed Productivity
• Run the model for the default setting. Identify the
sources and sinks with respect to atmospheric
CO2.
• Run the model for NADW of 0 and 10 Sv. Write
down the final global mean pCO2 and the
productivity in the Atlantic Ocean. (Neglect the
negative PO4 conc., identified in the previous exp.)
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Box-Model Experiment 4C-Cycle with Fixed Productivity
NADW
(Sv)
Prod. ATL
(Pg C/yr)
Prod. Glob.
(Pg C/yr)
Global pCO2
(ppm)
10 0.447 5.03 281
0 0.447 5.03 265
16 ppm Reduction
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Box-Model Experiment 5C-Cycle with Dynamic Productivity
• How will the response of the mean pCO2
change if productivity is no longer constant
but a function of PO4?
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www.geo.uni-bremen.de/geomod/staff/mschulz/lehre/ECOLMAS_Modeling/bm1_c-cycle_dyn_prod.gsp
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Box-Model Experiment 5C-Cycle with Dynamic Productivity
• Run the model for again for NADW of 0
and 10 Sv. Write down the final global
mean pCO2 and the productivity in the
Atlantic Ocean.
• Interpret your results.
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Box-Model Experiment 5C-Cycle with Dynamic Productivity
NADW
(Sv)
Prod. ATL
(Pg C/yr)
Prod. Glob.
(Pg C/yr)
Global pCO2
(ppm)
10 0.447 5.03 281
0 0.350 4.83 275
Only 6 ppm Reduction
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Box-Model Experiment 5C-Cycle with Dynamic Productivity
NADW = 0 DIC shifted from surface to deep Atlantic pCO2 reduced
BUT: PO4 is shifted to deep ocean too less nutrients in surface productivity decreases biological pump weakens pCO2 increases
Negative Feedback Mechanism
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Ruddiman (2001)
From Box-Models to 2D/3D-Models
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Structure of a Global Biogeo-chemical Model
Ridgwell (2001, Thesis)
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Ridgwell (2001, Thesis)
Modeling Deep-Sea Sediments
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Phosphate in the Atlantic Ocean [mol/l]
2D-Model
(Zonal Mean)
3D-Model
(N-S Section)
(Schulz and Paul, 2004)
(Heinze et al., 1999)
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-80 -60 -40 -20 0 20 40 60 80Latitude
Atlantic Ocean Export Production [gC/(m2 yr)]
(Schulz and Paul, 2004)
Horizontal Resolution in a 2D-Biogeochemical Model
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(Heinze et al., 1999)
Horizontal Resolution in a 3D-Biogeochemical Model
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A Modeled Sediment Stack in the North Atlantic
Heinze, C. et al., 1999: A global oceanic sediment model for long-term climate studies. Global Biogeochemical Cycles, 13, 221-250.
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Modeled and Observed Modern CaCO3 Content of Deep-Sea Sediments
Heinze et al. (1999)
Even the most sophisticated biogeochemical models allow only for a crude approximation of the real world. Discrepancies are largely due to an inadequate resolution (e.g. MOR) and a lack of knowledge of the processes being involved.
Model Observations
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Marine Ecosystem Models – Why?
• Productivity may depend on more than a single
nutrient (N, P, Si, Fe)
• Export production controlled by ecosystem
dynamics
• Understanding the preferential growth of
different algae groups (e.g. diatoms vs.
coccolithophores)
• Disentangling the seasonal imprint in biological
proxy records
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• 4 Compartments
• Coupled to carbon and
alkalinity
• Nutrients are
transported by ocean
circulation
• Efficient in predicting
seasonal patterns
NPZD-Type Ecosystem Model
(after Fasham et al., 1990)
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Marine Ecosystem Model Components (Moore et al., 2002)
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Marine Ecosystem Model Forcing
Output from global OGCM
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Global Foraminifera Model
Fraile et al. (subm.)
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Fraile et al. (subm.)
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Brown University Foraminiferal Database (Prell et al., 1999)
Modeled / Observerd Distribution of N. pachyderma (sin.)
Fraile et al. (subm.)
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Brown University Foraminiferal Database (Prell et al., 1999)
Modeled / Observerd Distribution of N. pachyderma (dex.)
Fraile et al. (subm.)
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Brown University Foraminiferal Database (Prell et al., 1999)
Modeled / Observerd Distribution of G. bulloides
Fraile et al. (subm.)
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Brown University Foraminiferal Database (Prell et al., 1999)
Modeled / Observerd Distribution of G. ruber (white)
Fraile et al. (subm.)
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Brown University Foraminiferal Database (Prell et al., 1999)
Modeled / Observerd Distribution of G. sacculifer
Fraile et al. (subm.)
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Modeled LGM shift in seasonality of G. bulloides
Fraile et al. (subm.)
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Benefits of Paleoecosystem Modeling
• To facilitate model-“data“ comparison
• To obtain a mechanistic understanding of
reconstructed shifts in species
• To assess the potential effect of altered plankton
successions on proxy reconstructions based on
organisms