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Modelling the export of biogenic particulates from upper ocean. Philip Boyd. Behrenfeld (OSU). Outline Factors impacting export – selected models. NPP and export (Suess, 1980) J 100 (Martin et al., 1987) Algal cells and foodweb structure (Michaels & Silver, 1988); Boyd & Newton (1995)

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Modelling the export of biogenic particulates from upper ocean

Modelling the export of biogenic particulates from upper ocean

Philip Boyd


Behrenfeld

(OSU)


Outline factors impacting export selected models
Outline Factors impacting export – selected models

  • NPP and export (Suess, 1980)

  • J100 (Martin et al., 1987)

  • Algal cells and foodweb structure (Michaels & Silver, 1988); Boyd & Newton (1995)

  • NPP and temperature – Laws et al. 2000

  • Ballasting agents (Armstrong et al. 2001)

  • Mechanistic models – (Dunne et al., 2005)

  • Summary


FACTORS CONTROLLING EXPORT

Present status

Primary Production

Ballasting agents

Algal cells – large versus small

Particle transformations – aggregation

Foodweb structure – different grazers

Microbial processes - solubilisation



Case study 1 – Suess (1980)

a direct relationship between NPP, depth and export


Case study 1 – Suess (1980)

a direct relationship between NPP, depth and export




Case study 2 – replacing NPP with J100 (Martin et al., 1987)


What does J100 represent?

Why is it a better predictor of export?


What does J100 represent?

Why is it a better predictor of export?



Different foodweb structures result in

A range of export efficiencies (pe ratio)

pe ratio = particle export/NPP


Using michaels silver comparison of ne atlantic spring bloom signatures boyd newton 1995

1989

2.7 µg chla L-1

16.1 g C m-2 NPP

-27 mmol NO3 m-2

32.0 µmol kg-1 tCO2

80% diatoms

Microzoo grazing

1990

3.6 µg chla L-1

14.7 g C m-2 NPP

-33.5 mmol NO3 m-2

-33.5 µmol kg-1 tCO2

70% diatoms

Microzoo grazing

Using Michaels & Silver-Comparison of NE Atlantic spring bloom signatures (Boyd & Newton, 1995)


Using michaels silver comparison of ne atlantic spring bloom signatures boyd newton 19951

1989

2.7 µg chla L-1

16.1 g C m-2 NPP

-27 mmol NO3 m-2

32.0 µmol kg-1 tCO2

80% diatoms

Microzoo grazing

720 mg POC m-2 export (3100 m)

1990

3.6 µg chla L-1

14.7 g C m-2 NPP

-33.5 mmol NO3 m-2

-33.5 µmol kg-1 tCO2

70% diatoms

Microzoo grazing

410 mg POC m-2 export (3100 m)

Using Michaels & Silver-Comparison of NE Atlantic spring bloom signatures (Boyd & Newton, 1995)


Observed versus predicted poc export mg c m 2 d 1

Predicted

16.6 (Suess) 15.1

41.8 (Betzer) 36.7

19.2 (Berger) 17.5

4.4 (Pace) 4.0

9.5 (BN – Martin) 3.8

Observed

9.6

4.0

Observed versus predicted POC export(mg C m-2 d-1)

(1989 – black; 1990 – red)


Case study 4 Laws et al. (2000)

Temperature effects on export fluxes

Calculated ef ratios (export/NPP) as a function of NPP and temperature

Nutrients

Filter feeder

Carnivore

Large PP

Inorganic

nutrients

Detrital POC

Flagellates

Small PP

Ciliates

Export

DOM

Bacteria


0.7

0.7

Ross Sea *

Ross

Sea *

Polyna *

* Polyna

NABE *

NABE *

Obs.

ef

ratio

0.4

* OSP

0.4

OSP *

* Peru-normal

Peru-normal *

Peru El Nino *

* Peru El Nino

* Arabian

* HOT

HOT *

* Arabian

* EqPac

* EqPac

* BATS

* BATS

*EqPac-EN

*EqPac-EN

0

0.6

500

1000

0

Modelled ef ratio

NPP (mg N m-2 d-1)




Ballast revisited (2002)

Non-selective preservation within the

Inorganic matrix of biominerals

Plankton - EqPac

AA

CARB

LIPID

Export 1000 m

The mineral matrix

Export 3500 m

8 nm

Hedges et al. 2001

0

60

Weight %


Fluxes normalised to mass (2002)

flux (OC/M) are much

less variable than POC

fluxes alone

POC flux

0

S

5

S

5

Depth

(km)

S

5

EqPac

5

0

S

5

5

S

5

5

S

Fraction OC by weight


POC export here is based on quantitative (2002)

association of POC with ballast minerals

Protected POC

POC flux

Dashed line = excess POC flux

i.e. POC not associated with ballast minerals

Martin curve

“Using ballast mineral data markedly increases the ability to predict organic carbon fluxes”


Case study 6 Dunne et al. (2005) (2002)

Empirical and mechanistic models for the pe ratio

A synthesis of global field observations related to ecosystem size structure, NPP and particle export was used for model validation


Large phytoplankton augment small ones as production or biomass increases.

In this model, variability in NPP results in a biomass-modulated

switch between small and large phytoplankton pathways


The empirical model captures 61% of the observed variance in the pe ratio of particle export using SST and chlorophyll concentrations (or NPP) as predictor variables.


The empirical model captures 61% of the observed variance in the ratio of particle export to primary

production (the pe ratio) using sea-surface temperature and chlorophyll concentrations

(or primary productivity) as predictor variables.


Boyd and Trull (2007) the ratio of particle export to primary

Bo


SUMMARY the ratio of particle export to primary

(Boyd & Trull)

Similarity of global export estimates despite the diversity of approaches.

Either the problem has a relatively unique solution, or all models are

making similar approximations.

No models have yet included sufficient complexity to capture the

observed variability of export fluxes.

Determining which additional factors, beyond those of temperature,

chlorophyll and NPP, are, most critical is a high priority task.


Observed versus predicted poc export error of fit 100 predicted observed observed
Observed versus predicted POC export the ratio of particle export to primary(% error of fit – ((100*(predicted-observed)/(observed))


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