Modelling the export of biogenic particulates from upper ocean

Philip Boyd

Behrenfeld(OSU)

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 EXPORTPresent status

Primary ProductionBallasting agentsAlgal cells – large versus smallParticle transformations – aggregationFoodweb structure – different grazersMicrobial processes - solubilisation

Boyd and Trull (2007)

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

From Bishop (1989)

From Bishop (1989)

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?

Case study 3 – Michaels and Silver (1988) - what sets J100?

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)

• 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)

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

(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

Inorganic nutrients

DOM

Small PP

Large PP Filter feeder

Bacteria

Flagellates Ciliates

Carnivore

Detrital POC

Export

Modelled ef ratio

0 0.6

0.4

0.7

Ross Sea *

Polyna *

NABE *

OSP * * Peru-normal

* Peru El Nino

* Arabian

HOT *

* BATS*EqPac-EN

* EqPac

NPP (mg N m-2 d-1)

0 500 1000

0.4

0.7

Obs.efratio

Ross Sea *

* Polyna

NABE *

* OSP Peru-normal *

Peru El Nino *

* HOT

* BATS*EqPac-EN

* Arabian* EqPac

Combining ef ratio with satellite NPP and SST – global export is 20% of NPP

Case study 5 – export and ballast – Armstrong et al. (2002)

Ballast revisited

0 60

AA

CARBLIPID

Plankton - EqPac

Export 1000 m

Export 3500 m

Weight %

Hedges et al. 2001

Non-selective preservation within theInorganic matrix of biominerals

The mineral matrix

8 nm

S

S

S

5

5

5

POC flux

EqPac

S

S

S5

5

5

Depth (km)

0

5

0

5

Fraction OC by weight

Fluxes normalised to mass flux (OC/M) are much less variable than POCfluxes alone

POC export here is based on quantitative association of POC with ballast minerals

Martin curve

POC flux

Protected POC

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

Dashed line = excess POC fluxi.e. POC not associated with ballast minerals

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

Large phytoplankton augment small ones as production or biomass increases.

In this model, variability in NPP results in a biomass-modulatedswitch 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 primaryproduction (the pe ratio) using sea-surface temperature and chlorophyll concentrations(or primary productivity) as predictor variables.

Model NPP(Gt C yr-1)

Surface Export (Gt C yr-1)

Nutrient Inversion (of P, O2,

DIC, etc) Schlitzer (2002)na 9.6

Semi-prognostic (Temperature and e ratio) Laws et al. (2000)

52.1 12.9

Semi-prognostic (e ratio, NPP and SST – scaled using remote sensing data*) Laws et al. (2000)

52.1 11.1

Coupled Ocean Atmosphere Model (COAM) -LG (OPAICE – LMDS) Bopp et al. (2001)

na 13.1

COAM - LB (OPAICE – LMDS)! 64.7 11.1COAM - AG (OPAICE-ARPEGE)! Bopp et al. (2001)

na 9.5

Prognostic (COAM (NCAR) and offline ecosystem model) Moore et al. (2002)

45.2 7.9@

Prognostic (COAM (NCAR) and offline ecosystem model) Moore et al. (2002)

45.2 12.0$

13 models in OCMIP-II Doney et al (2004)

n.a. +40% range

Bo

Boyd and Trull (2007)

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.

SUMMARY(Boyd & Trull)

Observed versus predicted POC export (% error of fit –

((100*(predicted-observed)/(observed))

BATS -234

BN

910 Suess

979 Berger

-406 Pace

HOT 71 597 932 -265

NABE -25 67 96 -56

ST Atlantic

-57 0 -5 -77

T Atlantic

10 666 654 15

Papa -20 391 477 31