INTERPLAY BETWEEN ECOSYSTEM STRUCTURE AND IRON AVAILABILITY IN A GLOBAL MARINE ECOSYSTEM MODEL

Transcription

1 ITERPLAY BETWEE ECOSYSTEM STRUCTURE AD IRO AVAILABILITY I A GLOBAL MARIE ECOSYSTEM MODEL Stephanie Dutkiewicz Fanny Monteiro, Mick Follows, Jason Bragg Massachusetts Institute of Technology Program in Atmospheres, Oceans and Climate

2 MOTIVATIO Observations of Iron (nm) (from Moore+Braucher, BG, 008) how does iron sets ecosystem structure and biomass? SeaWiFS Chl a (mg/m 3 )

3 MOTIVATIO : interplay between iron and ecosystem Observations of Iron (nm) (from Moore+Braucher, BG, 008) how does iron sets ecosystem structure and biomass? SeaWiFS Chl a (mg/m 3 ) how does community affect iron concentrations?

4 OUTLIE OF TALK model description: physical/biogeochemistry/ecosystem control experiment results theory: resource competition use theory to understand sensitivity experiments - changing iron availibility (solubility) - phytoplankton physiology parameters summary

5 MODEL DESCRIPTIO 3-D MIT ocean general circulation model (Marshall et al, JGR 997) global o x o horizontal, levels in vertical (0m-500m) physics: ECCO state estimates (Wunsch+Heimbach, Physica D 007) biogeochemistry:,p,si, ecosystem: phytoplankton zooplankton (Follows et al, Science, 007; Dutkiewicz et al, GBC, 009) Results: tenth year annual mean Model Primary Production (gc/m /y)

6 MODEL DESCRIPTIO IRO PARAMETERIZATIO: based on Parekh et al 004/005 (GBC) uniform ligand only free iron scavenged iron scavenging proportional sinking particulate matter sedimentary source proportional to sinking matter (Elrod et al, GRL 004) variable solubility of aeolian dust (Luo et al, GBC 008) sedimentary sources

7 MODEL DESCRIPTIO 3-D MIT ocean general circulation model (Marshall et al, JGR 997) global o x o horizontal, levels in vertical (0m-500m) physics: ECCO state estimates (Wunsch+Heimbach, Physica D 007) biogeochemistry:,p,si, ecosystem: phytoplankton zooplankton (Follows et al, Science, 007; Dutkiewicz et al, GBC, 009) Results: tenth year annual mean Model Primary Production (gc/m /y)

8 MODEL DESCRIPTIO genetics and physiology ECOSYSTEM MODEL: Follows et al, Science, 007; Dutkiewicz et al, GBC, 009 P P P P n D Z physical and chemical environment Z competition predation selection P i P j D Z Z n could by 00 s here reduced to 9 representative for computational reasons

9 MODEL RESULTS: COTROL EXPERIMET Iron solubility: variable, following Luo et al, GBC 008

10 MODEL RESULTS: COTROL EXPERIMET biomass (um P) Iron solubility: variable, following Luo et al, GBC 008 d (nm ) O3 (um )

11 MODEL RESULTS: COTROL EXPERIMET biomass (um P) Iron solubility: variable, following Luo et al, GBC 008 d (nm ) biomass is limited by different nutrients in different regions O3 (um ) limiting nutrient dashed line is boundary between nitrogen and iron limited regions

12 MOTIVATIO : interplay between iron and ecosystem Use this model as a laboratory to explore the interplay between the ecosystem and iron: sensitivity experiments but first look at a theory that can help us understand the system better

13 RESOURCE COMPETITIO THEORY Tilman 977, 98

14 RESOURCE COMPETITIO THEORY Tilman 977, 98 P dt t μ P mp + κ μ P + S + κ P μ( T, I) m( Z, w) S κ -> nutrient -> phytoplankton -> growth -> loss -> nutrient source -> nutrient half saturation

15 RESOURCE COMPETITIO THEORY Tilman 977, P dt t STEADY STATE: μ P mp + κ μ P + S + κ P μ( T, I) m( Z, w) S κ -> nutrient -> phytoplankton -> growth -> loss -> nutrient source -> nutrient half saturation P S m κ m μ m * R

16 RESOURCE COMPETITIO THEORY Tilman 977, P dt t μ μ STEADY STATE: S P m κ m μ m * R + κ + κ P mp P + S P μ( T, I) m( Z, w) S κ -> nutrient -> phytoplankton -> growth -> loss -> nutrient source -> nutrient half saturation Phytoplankton biomass not set by physiology Concentration of limiting nutrient set by physiology and loss terms Function of top down and bottom up controls Organisms with lowest R* excludes others

17 RESOURCE COMPETITIO THEORY Tilman 977, 98 P S m κ m μ m R * nitrogen limiting nutrient S P m κ m μ m R * In our model: theory works for most limiting nutrient, works best in relatively stable regions (Dutkiewicz et al, GBC, 009)

18 MOTIVATIO : interplay between iron and ecosystem Observations of Iron (nm) (from Moore+Braucher, BG, 008) how does iron sets ecosystem structure and biomass? P S m κ μ m m SeaWiFS Chl a (mg/m 3 ) how does community affect iron concentrations?

19 MODEL RESULTS: SESITIVTY EXPERIMETS Look at differences between simulations: ) % and 4% iron solubility: no diazotrophs ) % and 4% iron solubility: include diazotrophs 3) control and experiment where K fe non-diazotrophs halved 4) control and experiment where K fe diazotrophs halved P S m m κ μ m

20 MOTIVATIO Assumption about solubility of Aeolian iron dust Solubile Aeolian Iron Dust Flux (umol /m /y) % 4% modern iron solubility measured to range from <% to 80% Uniform solubility (using dust estimates of Luo et al., JGR 003)

21 MOTIVATIO: A ASIDE Assumption about solubility of Aeolian iron dust Solubile Aeolian Iron Dust Flux (umol /m /y) % 4% modern Uniform solubility (using dust estimates of Luo et al., JGR 003) Variabile solubility (Luo et al., GBC 008)

22 MOTIVATIO: A ASIDE Assumption about solubility of Aeolian iron dust Solubile Aeolian Iron Dust Flux (umol /m /y) % preindustrial 4% modern Uniform solubility (using dust estimates of Luo et al., JGR 003) Variabile solubility (Luo et al., GBC 008)

23 MODEL RESULTS: SESITIVTY EXPERIMETS () Difference between simulations (4%-%): no diazotrophs.5% increase in primary production (comparable to 3.5% with 0 times iron, Moore et al, GBC 004 % preindustrial to modern, Krishnamurthy et al, GBC 009) diff in biomass (um P) positiveincrease with increased iron solubility dashed line is boundary between nitrogen and iron limited regions Can we understand the spatial pattern from resource competition theory?

24 MODEL RESULTS: SESITIVTY EXPERIMETS () Difference between simulations (4%-%): no diazotrophs diff in biomass (um P) positiveincrease with increased iron solubility diff in iron (nm P) diff in nitrate (um P) dashed line is boundary between nitrogen and iron limited regions

25 MODEL RESULTS: SESITIVTY EXPERIMETS () Difference between simulations (4%-%): no diazotrophs diff in biomass (um P) increase in iron availability leads to increase biomass in iron limited regions diff in iron (nm ) S P m κ m μ m R * diff in nitrate (um ) no change to phytoplankton physiology, so no change to iron concentration additional nitrate consumed by increased biomass dashed line is boundary between nitrogen and iron limited regions

26 MODEL RESULTS: SESITIVTY EXPERIMETS () Difference between simulations (4%-%): no diazotrophs diff in biomass (um P) diff in iron (nm ) decreased supply of nitrogen leads to decreased biomass in nitrogen limited regions (Dutkiewicz et al, GBC, 005, Williams+Follows, DSR, 998) P S m κ m μ m R * diff in nitrate (um ) diff in source of nitrate (um /s) dashed line is boundary between nitrogen and iron limited regions

27 MODEL RESULTS: SESITIVTY EXPERIMETS Look at differences between simulations: ) % and 4% iron solubility: no diazotrophs ) % and 4% iron solubility: include diazotrophs 3) control and experiment where K fe non-diazotrophs halved 4) control and experiment where Kfe diazotrophs halved P S m

28 MODEL RESULTS: SESITIVTY EXPERIMETS () Difference between simulations (4%-%): with diazotrophs diazotrophs % log (um P) diazotrophs 4% log (um P) with increased iron availibility, increased region where diazotrophs can exist, total nitrogen fixation increases 5% diazotrophs exist where: ) on-diazotrophs are nitrogen limited ) AD iron is sufficient (Monteiro et al, in prep, 009) 50% increase with 0 times iron flux (Moore et al, GBC 004) 35% increase with LGM over modern dust (Moore and Doney, 007)

29 MODEL RESULTS: SESITIVTY EXPERIMETS () Difference between simulations (4%-%): with diazotrophs diff in biomass (um P) solid line: region of diazotrophs % dashed line: region of diazotrophs 4%

30 MODEL RESULTS: SESITIVTY EXPERIMETS () Difference between simulations (4%-%): with diazotrophs diff in biomass (um P) diff in biomass WITHOUT diazotrophs (um P) solid line: region of diazotrophs % dashed line: region of diazotrophs 4% region where diazotrophs and non-diazotrophs co-exist non-diazotrophs are nitrogen limited diazotrophs are iron limited need more complex theory here

31 RESOURCE COMPETITIO THEORY including phytoplankton types and nutrients This is set of equations for region where diazotrophs and other phytoplankton co-exist P dt P dt t μ μ μ t + κ μ + κ + κ + κ P m P P P + S R P m P μ + κ R P + S -> nitrogen -> iron P S P R μ m κ / / / / / -> non-diazotroph -> diazotroph ( T, I) ( Z, w) -> growth -> loss -> nitrogen or iron source -> or half saturation -> :P ratio Assume diazotrophs grow slower, lower K and higher :P

32 RESOURCE COMPETITIO THEORY / / / / / ), ( ), ( R S w Z m I T P P κ μ -> iron -> non-diazotroph -> growth -> loss -> nitrogen or iron source -> or half saturation -> nitrogen -> diazotroph -> :P ratio including phytoplankton types and nutrients Steady state solution for region where diazotrophs and other phytoplankton co-exist * * ) ( ) ( R m m R m m m R S R R m m m S P P R S S m R P R R m m m R S P m S P + + μ κ μ κ

33 MODEL RESULTS: SESITIVTY EXPERIMETS () Difference between simulations (4%-%): with diazotrophs biomass increases with increasing availibility of iron, AD source of nitrogen increases with increased diazotrophs diff in biomass (um P) P + P ( ) m m R κ m μ m S κ m μ m R R m * * R + S R m diff in iron (nm ) Diazotroph (sort of) control iron diff in nitrate (um ) on-diazotrophs control nitrogen and there is no change solid line: region of diazotrophs % dashed line: region of diazotrophs 4%

34 MOTIVATIO : interplay between iron and ecosystem Observations of Iron (nm) (from Moore+Braucher, BG, 008) iron sets ecosystem structure and concentrations increased availability of iron leads to: increased biomass in iron limited regions increased regions of nitrogen fixation and increased biomass in those regions increased biomass leads to decreased lateral nitrogen supply, and reduced biomass in some regions how does community affect iron concentrations? SeaWiFS Chl a (mg/m 3 )

35 MODEL RESULTS: SESITIVTY EXPERIMETS Look at differences between simulations: ) % and 4% iron solubility: no diazotrophs ) % and 4% iron solubility: diazotrophs 3) control and experiment where K fe non-diazotrophs halved 4) control and experiment where K fe diazotrophs halved m κ μ m

36 MODEL RESULTS: SESITIVTY EXPERIMETS (3) Compare with simulation where non-diazotrophs K fe halved Biomass sensitivity Biomass control Iron sensitivity Iron control no change to supply, so no change in biomass P S m κ m μ m R * half K fe,leads to halving iron concentration dashed line is boundary between nitrogen and iron limited regions

37 MODEL RESULTS: SESITIVTY EXPERIMETS Look at differences between simulations: ) % and 4% iron solubility: no diazotrophs ) % and 4% iron solubility: include diazotrophs 3) control and experiment where K fe non-diazotrophs halved 4) control and experiment where K fe diazotrophs halved

38 MODEL RESULTS: SESITIVTY EXPERIMETS (4) Difference between simulations where diazotrophs K fe halved no change to supply, so mostly no change in biomass Biomass sensitivity Biomass control Iron sensitivity Iron control P + P ( ) m m R κ m μ m S κ m μ m R R m * * R + S R m solid line: region of diazotrophs control dashed line: region of diazotrophs half Kfe half K fe,leads to halving iron concentration region where diazotrophs exist increases, since R*fe decreases

39 SUMMARY : interplay between iron and ecosystem Observations of Iron (nm) (from Moore+Braucher, BG, 008) iron sets ecosystem structure and concentrations change in source of iron leads to: increased biomass in iron limited regions increased regions of nitrogen fixation and increased biomass in those regions increased biomass leads to decreased lateral nitrogen supply, and reduced biomass in some regions community affect iron concentrations: change in phytoplankton physiology leads to change in iron concentration (but only where iron is limiting that phytoplankton) diazotroph physiology control where nitrogen fixation can occur SeaWiFS Chl a (mg/m 3 )

40 SUMMARY There is a strong link between ecosystem and iron cycling that can be understood through resource competition The source of iron is critical in determining: - total biomass of phytoplankton over much of the tropical Pacific - amount and region of nitrogen fixing in the sub-tropical Pacific The physiology and grazing of phytoplankton communities can largely regulate the iron concentration over much of the Pacific - non-diazotrophs where iron is main limiting nutrient - diazotrophs where others are nitrogen limited AD * > R From modelling perspective: we need to understand supply better before we can get ecosystems right, but also we need to get ecosystems right before we can get iron concentration right!

41 SUMMARY There is a strong link between ecosystem and iron cycling that can be understood through resource competition

42 simple 0-D theoretical system t P dt μ μ + κ + κ P + S P mp includes advection and diffusion remineralization etc 3-D simulation equations (for i nutrients and j phytoplankton) t P dt j i μ( T, I) min( μ( T, I) min( { + κ + κ PO3, PO + κ PO3, PO + κ 3 3 PO3 PO3, + κ, + κ ) P ) P j j + S mp i j sinking- grazing + S Pj growth function of temperature and light nutrient limitation minimum function of several nutrients more complex loss terms

43 SUMMARY There is a strong link between ecosystem and iron cycling that can be understood through resource competition The source of iron is critical in determining: - total biomass of phytoplankton over much of the tropical Pacific - amount and region of nitrogen fixing in the sub-tropical Pacific

44 SUMMARY There is a strong link between ecosystem and iron cycling that can be understood through resource competition The source of iron is critical in determining: - total biomass of phytoplankton over much of the tropical Pacific - amount and region of nitrogen fixing in the sub-tropical Pacific S advect + diffusion + S aeolian + S sed scavenging + re min eralization * ' S ( u ) + ( Κ ) + αfaeolian + Fsed kscav + rdofedo + r pofe POfe

45 ADDITIOAL SESITIVTY EXPERIMETS Change in assumption of iron solubility -> source of iron difference in biomass 4%-% solubility positiveincrease with increased iron source um P difference in biomass modern-preindustrial POC difference: modern versus preindustrial dust (from Krishnamurthy et al, GBC 009)

46 SUMMARY There is a strong link between ecosystem and iron cycling that can be understood through resource competition The source of iron is critical in determining: - total biomass of phytoplankton over much of the tropical Pacific - amount and region of nitrogen fixing in the sub-tropical Pacific S advect + diffusion + S aeolian + S sed scavenging S α * ' ( u ) + ( Κ ) + Faeolian + Fsed kscav GAPS I OUR KOWLEDGE: how to model iron chemistry, in particular solubility and ligand

47 SUMMARY There is a strong link between ecosystem and iron cycling that can be understood through resource competition The source of iron is critical in determining: - total biomass of phytoplankton over much of the tropical Pacific - amount and region of nitrogen fixing in the sub-tropical Pacific The physiology and grazing of phytoplankton communities can largely regulate the iron concentration over much of the Pacific - non-diazotrophs where iron is main limiting nutrient - diazotrophs where others are nitrogen limited AD * > R GAPS I OUR KOWLEDGE: physiological parameters of phytoplankton (e.g. K fe )

48 SUMMARY There is a strong link between ecosystem and iron cycling that can be understood through resource competition The source of iron is critical in determining: - total biomass of phytoplankton over much of the tropical Pacific - amount and region of nitrogen fixing in the sub-tropical Pacific The physiology and grazing of phytoplankton communities can largely regulate the iron concentration over much of the Pacific - non-diazotrophs where iron is main limiting nutrient - diazotrophs where others are nitrogen limited AD * > R From modelling perspective: we need to understand supply better before we can get ecosystems right, but also we need to get ecosystems right before we can get iron concentration right!

49 IRO SOURCES Total dissolved iron: sedimentatry + dust + remineralization % iron from sedimentary source % iron from aeolian source MODEL RESULTS % iron from remineralization (compare to Aumont et al, GBC, 003 who found 73% from regenerated)

50 IRO PARAMETERIZATIO Maltrud et al, in preparation (Atmospheric CO reduction from iron fertilization: a model intercomparison study)

51

52 MODEL RESULTS: COTROL EXPERIMET biomass (um P) Where do diazotrophs exist? d (nm ) diazotrophs log (um P) O3 (um ) limiting nutrient solid line region of diazotrophs

53 MODEL RESULTS: COTROL EXPERIMET biomass (um P) diazotrophs exist where: ) on-diazotrophs are nitrogen limited ) AD iron is sufficient (>R* fe ) (Monteiro et al, in prep, 009) d (nm ) diazotrophs log (um P) O3 (um ) limiting nutrient dashed line boundary between nitrogen and iron limited regions; solid line region of diazotrophs

54 simple 0-D theoretical system t P dt μ μ + κ + κ P + S P mp 3-D simulation equations (for i nutrients and j phytoplankton) t P dt j i μ( T, I) min( μ( T, I) min( { + κ + κ PO3, PO + κ PO3, PO + κ 3 3 PO3 PO3, + κ, + κ ) P ) P j j + S mp i j sinking- grazing + S Pj growth function of temperature and light nutrient limitation minimum function of several nutrients more complex loss terms

55 MODEL DEVELOPMET inclusion of diverse diazotrophs (Fanny Monteiro) different absorption spectrum/pigments (Anna Hickman) radiative transfer code (Watson Gregg) size-based trade-offs (Stephanie Dutkiewicz, Chris Kempes) mixotrophy/predation (Ben Ward) quota based model (Ben Ward) high resolution simulation (Oliver Jahn)

56 Sensitivity experiments to see: ) how important iron parameterization is on model community structure and iron concentrations (e.g. Moore and Braucher, BG, 008; Krishnamurthy et al, GBC, 009) ) such experiments can help us understand the linkage between community structure and biogeochemical cycling: - to what extent does iron supply control phytoplankton communities in the Pacific? (e.g. diazotrophs: Moore and Doney, GBC 007) and in turn - to what extent do phytoplankton communities control iron concentrations?

57 MODEL RESULTS: SESITIVTY EXPERIMETS () Difference between simulations (4%-%): with diazotrophs BUT WHAT IS HAPPEIG HERE? increase in iron supply leads to increase biomass in iron limited regions no change to phytoplankton physiology, so no change to iron concentration additional nitrate consumed by increased biomass, leads to lower supply to nitrogen limited region Diff in Biomass (mmol P/m 3 ) Diff in Iron (mmol /m 3 ) Diff in itrate (mmol /m 3 ) dashed line is boundary between nitrogen and iron limited regions

58 MOTIVATIO Change in assumption of availability of iron Effect on primary production and biomass: increased iron availibilty -> increase in PP PP increased 3.5% with 0 times iron flux (Moore et al, GBC 004) PP increased % preindustrial to modern (Krishnamurthy et al, GBC 009) POC difference: modern versus preindustrial dust (from Krishnamurthy et al, GBC 009) Effect on itrogen fixing: increased iron availibility -> increased fixing 50% increase with 0 times iron flux (Moore et al, GBC 004) 35% increase with LGM over modern dust (Moore and Doney, 007)

59 MODEL DESCRIPTIO ecosystem usually run with 00 s of phytoplankton types (and several simulations run to form ensemble) but here for computational reasons we reduce to 9 representative types, which fall into the functional groups : diatoms, other large, small, Prochlorococcus, diazotrophs