1. Modelling the export of biogenic particulates from upper ocean Philip Boyd

2. Behrenfeld (OSU)

3. 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) NPP and temperature – Laws et al. 2000 Ballasting agents (Armstrong et al. 2001) Mechanistic models – (Dunne et al., 2005) Summary

4. FACTORS CONTROLLING EXPORT Present status Primary Production Ballasting agents Algal cells – large versus small Particle transformations – aggregation Foodweb structure – different grazers Microbial processes - solubilisation

5. Boyd and Trull (2007)

6. Case study 1 – Suess (1980) a direct relationship between NPP, depth and export

7. Case study 1 – Suess (1980) a direct relationship between NPP, depth and export

8. From Bishop (1989)

10. Case study 2 – replacing NPP with J 100 (Martin et al., 1987)

11. What does J 100 represent? Why is it a better predictor of export?

12. What does J 100 represent? Why is it a better predictor of export?

13. Case study 3 – Michaels and Silver (1988) - what sets J 100 ?

14. Different foodweb structures result in A range of export efficiencies (pe ratio) pe ratio = particle export/NPP

15. 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 NO 3 m -2 32.0 µmol kg -1 tCO 2 80% diatoms Microzoo grazing 1990 3.6 µg chla L -1 14.7 g C m -2 NPP -33.5 mmol NO 3 m -2 -33.5 µmol kg -1 tCO 2 70% diatoms Microzoo grazing

16. 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 NO 3 m -2 32.0 µmol kg -1 tCO 2 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 NO 3 m -2 -33.5 µmol kg -1 tCO 2 70% diatoms Microzoo grazing 410 mg POC m -2 export (3100 m)

17. 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)

18. 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

19. Modelled ef ratio 00.6 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 5001000 0.4 0.7 Obs. ef ratio Ross Sea * * Polyna NABE * * OSP Peru-normal * Peru El Nino * * HOT * BATS *EqPac-EN * Arabian * EqPac

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

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

23. Ballast revisited 060 AA CARB LIPID Plankton - EqPac Export 1000 m Export 3500 m Weight % Hedges et al. 2001 Non-selective preservation within the Inorganic matrix of biominerals The mineral matrix 8 nm

24. S S S 5 5 5 POC flux EqPac S S S 5 5 5 Depth (km) 0 5 0 5 Fraction OC by weight Fluxes normalised to mass flux (OC/M) are much less variable than POC fluxes alone

25. 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 flux i.e. POC not associated with ballast minerals

26. 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

28. 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

29. 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.

30. 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.

31. ModelNPP (Gt C yr -1 ) Surface Export (Gt C yr -1 ) Nutrient Inversion (of P, O 2, DIC, etc) Schlitzer (2002) na9.6 Semi-prognostic (Temperature and e ratio) Laws et al. (2000) 52.112.9 Semi-prognostic (e ratio, NPP and SST – scaled using remote sensing data*) Laws et al. (2000) 52.111.1 Coupled Ocean Atmosphere Model (COAM) -LG (OPAICE – LMDS) Bopp et al. (2001) na13.1 COAM - LB (OPAICE – LMDS)! 64.711.1 COAM - AG (OPAICE- ARPEGE)! Bopp et al. (2001) na9.5 Prognostic (COAM (NCAR) and offline ecosystem model) Moore et al. (2002) 45.27.9@ Prognostic (COAM (NCAR) and offline ecosystem model) Moore et al. (2002) 45.212.0$ 13 models in OCMIP-II Doney et al (2004) n.a.+40% range BoBo Boyd and Trull (2007)

32. 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)

33. Observed versus predicted POC export (% error of fit – ((100*(predicted- observed)/(observed)) BATS-234 BN 910 Suess 979 Berger -406 Pace HOT71597932-265 NABE-256796-56 ST Atlantic -570-5-77 T Atlantic 1066665415 Papa-2039147731