Marine Biogeochemical and Ecosystem Modeling Michael Schulz MARUM -- Center for Marine Environmental Sciences and Faculty of Geosciences, University of Bremen

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

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

Course Material www.geo.uni-bremen.de/geomod/ staff/mschulz/lehre/ECOLMAS_Modeling/ This presentation Box-model exercises

Basic Literature Najjar, 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.

For a climatologist biogeochemical cycles usually translates into carbon cycle. Ruddiman (2001)

Carbon-Cycle – Characteristic Timescales Reservoir Sizes in [Gt C] Fluxes in [Gt C / yr] Sundquist (1993, Science)

Average surface- Water composition CO2 0.5 % HCO3- 89.0 % CO32- 10.5 % Thurman & Trujillo (2002)

Biological Productivity in the Ocean Nutrients: P, N, (Si, Fe) Biological effect on DIC distribution; light-limited to near surface, nutrients (N, P, Fe) Ruddiman (2001)

The Biological Pump Atmosphere Ocean Sediments CO CO 2 CO 2 Primary Production Inorgan. C ® Organ. C Particle-Flux Ocean Remineralisation Organ. C ® CO 2 Sediments Fig. courtesy of A. Körtzinger

Photic Zone Aphotic Zone Sediments

Biogenic Calcium Carbonate Production Raises Dissolved CO2 Concentration pH Reaction: (1) Biogenic carbonate uptake Some marine organisms form shells of CaCO3 (Coccolith., Foram) (2) More bicarbonate dissociates (3) More CO2 is formed

The Calcium Carbonate Pump Atmosphere CO 2 CO 2 Biogenic CaCO3 Formation 3 Lysocline Ocean CaCO3 Dissolution CO 2- 3 Fig. courtesy of A. Körtzinger

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

Single Reservoir Case Reservoir (mass M) Flux In Flux Out

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]):

Numerical Solution of Box-Model Equations Solution by finite-difference method (approximation!) “Euler Method” Initial Condition Time (in steps of Dt)

Numerical Solution of Box-Model Equations Solution by finite-difference method (approximation!) Initial Condition

Numerical Solution of Box-Model Equations Solution by finite-difference method (approximation!) “Euler Method” Initial Condition Time (in steps of Dt)

Euler Method M M(tn+1) Slope = Fi(tn) - Fo(tn) + SMS(tn) M(tn) Dt tn “Prediction” Slope = Fi(tn) - Fo(tn) + SMS(tn) Error True Value M(tn) Dt tn tn+1 t Assumption: Slope at time tn remains constant throughout time interval Dt

Coupled Reservoirs F12 Reservoir 1 Reservoir 2 (mass M1) (mass M2) F21 Principle of mass-conservation requires M1 + M2 = const.

Large-Scale Ocean Circulation (after Broecker, 1991)

Box-Model of Oceanic PO4 Distribution Indo-Pacific Southern Ocean Atlantic Surface (0-100 m) AABW_P (20 Sv) 20 Sv NADW (10 Sv) 20 Sv 10 Sv Deep (> 100 m) AABW_A (4 Sv)

www.geo.uni-bremen.de/geomod/staff/mschulz/lehre/ECOLMAS_Modeling/ bm1_po4_only.gsp

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?)

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

Box-Model of Oceanic PO4 Distribution with Productivity Indo-Pacific Southern Ocean Atlantic Surface (0-100 m) AABW_P (20 Sv) NADW (10 Sv) Deep (> 100 m) AABW_A (4 Sv) Assumption: Biologically fixed PO4 sinks from the surface layer to the underlying deep layer, where the organic material is completely remineralized.

www.geo.uni-bremen.de/geomod/staff/mschulz/lehre/ECOLMAS_Modeling/ bm1_po4_fix_prod.gsp

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? Q4: approx. 2.4 umol/l 120m/3800m  3% vol. decr.  2 umol/l  2.06 umol/l only

10 m water depth

1750 m water depth

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? Q4: approx. 2.4 umol/l 120m/3800m  3% vol. decr.  2 umol/l  2.06 umol/l only

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 / t [mol/yr], where t is the residence time of PO4 in the surface due to biological productivity Assume tATL = tIPAC = 5 yr and tSOC = 50 yr (Broecker and Peng, 1986)

www.geo.uni-bremen.de/geomod/staff/mschulz/lehre/ECOLMAS_Modeling/ bm1_po4_dyn_prod.gsp

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?

Box-Model Experiment 3a – Atlantic NADW (Sv) PO4 Surface (mmol/l) PO4 Deep DPO4 10 0.24 0.69 0.45 0.18 0.88 0.70 Shift of PO4 content from surface to deep Atlantic as NADW drops

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?

NADW and Productivity in the Atlantic Ocean 3.6 3.4 3.2 3 2.8 PO4 Fixation [1011 mol P /yr] 2.6 neg. feedback on pCO2 at LGM ongoing prod. during HE 2.4 2.2 2 5 10 15 20 NADW Flow [Sv]

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)

Rain-Ratio Parameterization 0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 5 10 15 20 25 30 Rain Ratio = PCaCO3 / PCorg Water Temperature [°C]

Area-Weighted Average Atmospheric pCO2 ≈ Mean Oceanic pCO2 www.geo.uni-bremen.de/geomod/staff/mschulz/lehre/ECOLMAS_Modeling/ bm1_c-cycle_fix_prod.gsp

Box-Model Experiment 4 C-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.) 10 Sv: 281 ppm; Global 5.03 PgC/yr; Global 5.03 PgC/yr

Box-Model Experiment 4 C-Cycle with Fixed Productivity NADW (Sv) Prod. ATL (Pg C/yr) Prod. Glob. Global pCO2 (ppm) 10 0.447 5.03 281 265 16 ppm Reduction

Box-Model Experiment 5 C-Cycle with Dynamic Productivity How will the response of the mean pCO2 change if productivity is no longer constant but a function of PO4?

www.geo.uni-bremen.de/geomod/staff/mschulz/lehre/ECOLMAS_Modeling/ bm1_c-cycle_dyn_prod.gsp

Box-Model Experiment 5 C-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.

Box-Model Experiment 5 C-Cycle with Dynamic Productivity NADW (Sv) Prod. ATL (Pg C/yr) Prod. Glob. Global pCO2 (ppm) 10 0.447 5.03 281 0.350 4.83 275 Only 6 ppm Reduction

Box-Model Experiment 5 C-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

From Box-Models to 2D/3D-Models Ruddiman (2001)

Structure of a Global Biogeo-chemical Model Ridgwell (2001, Thesis)

Modeling Deep-Sea Sediments Ridgwell (2001, Thesis)

Phosphate in the Atlantic Ocean [mmol/l] 2D-Model (Zonal Mean) (Schulz and Paul, 2004) 3D-Model (N-S Section) (Heinze et al., 1999)

Horizontal Resolution in a 2D-Biogeochemical Model 10 20 30 40 50 60 70 80 90 -80 -60 -40 -20 Latitude Atlantic Ocean Export Production [gC/(m2 yr)] (Schulz and Paul, 2004)

Horizontal Resolution in a 3D-Biogeochemical Model (Heinze et al., 1999)

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.

Modeled and Observed Modern CaCO3 Content of Deep-Sea Sediments Observations  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. Heinze et al. (1999)

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

NPZD-Type Ecosystem Model 4 Compartments Coupled to carbon and alkalinity Nutrients are transported by ocean circulation Efficient in predicting seasonal patterns (after Fasham et al., 1990)

Marine Ecosystem Model Components (Moore et al., 2002)

Marine Ecosystem Model Forcing Output from global OGCM

Global Foraminifera Model Fraile et al. (subm.)

Fraile et al. (subm.)

Modeled / Observerd Distribution of N. pachyderma (sin.) Fraile et al. (subm.) Brown University Foraminiferal Database (Prell et al., 1999)

Modeled / Observerd Distribution of N. pachyderma (dex.) Fraile et al. (subm.) Brown University Foraminiferal Database (Prell et al., 1999)

Modeled / Observerd Distribution of G. bulloides Fraile et al. (subm.) Brown University Foraminiferal Database (Prell et al., 1999)

Modeled / Observerd Distribution of G. ruber (white) Fraile et al. (subm.) Brown University Foraminiferal Database (Prell et al., 1999)

Modeled / Observerd Distribution of G. sacculifer Fraile et al. (subm.) Brown University Foraminiferal Database (Prell et al., 1999)

Modeled LGM shift in seasonality of G. bulloides Fraile et al. (subm.)

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