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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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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
slide4

FACTORS CONTROLLING EXPORT

Present status

Primary Production

Ballasting agents

Algal cells – large versus small

Particle transformations – aggregation

Foodweb structure – different grazers

Microbial processes - solubilisation

slide6

Case study 1 – Suess (1980)

a direct relationship between NPP, depth and export

slide7

Case study 1 – Suess (1980)

a direct relationship between NPP, depth and export

slide11

What does J100 represent?

Why is it a better predictor of export?

slide12

What does J100 represent?

Why is it a better predictor of export?

slide14

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)

slide18

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

slide19

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)

slide23

Ballast revisited

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 %

slide24

Fluxes normalised to mass

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

slide25

POC export here is based on quantitative

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”

slide26

Case study 6 Dunne et al. (2005)

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

slide28

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

slide29

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.

slide30

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.

slide32

SUMMARY

(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 (% error of fit – ((100*(predicted-observed)/(observed))
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