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Modeling micronutrients in the ocean: The case of Iron Olivier Aumont Based on the work by: L. Bopp, A. Tagliabue, J.K. Moore, W. Gregg, P. Parekh, A.

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Presentation on theme: "Modeling micronutrients in the ocean: The case of Iron Olivier Aumont Based on the work by: L. Bopp, A. Tagliabue, J.K. Moore, W. Gregg, P. Parekh, A."— Presentation transcript:

1 Modeling micronutrients in the ocean: The case of Iron Olivier Aumont Based on the work by: L. Bopp, A. Tagliabue, J.K. Moore, W. Gregg, P. Parekh, A. Ridgwell, S. Dutkiewicz, D. Archer, L. Weber, C. Voelker, K. Flynn,... LOCEAN, Centre IRD de Bretagne, Plouzané, France

2 Nozaki Periodic Table Macronutrients Micronutrients

3 The case of Iron Iron: Used for many chemical and biological processes in phytoplankton cells N metabolismNitrate and Nitrite reductase Photosynthesis & RespirationCytochrome, ferrodoxin, … ProcessCatalyst … Iron has been demonstrated to play a critical role in large regions of the ocean Limits primary productivity Controls species composition Trophic structure Consequence: Iron is the only micro-nutrient that has been explicitely included in ocean biogeochemical models so far

4 (Short) History 1931: Gran suggested iron could be limiting in the Southern Ocean 1980: First reliable iron measurements in the open ocean 1980s: Martin showed that iron stimulates phytoplankton growth in incubations 1993: Ironex I 1997: First 1D biogeochemical models with explicit iron (Loukos et al, Johnson et al) 2000: First global 3D model with iron (Archer et al) 2007: At least, 6 global biogeochemical models include a description of the iron cycle

5 The Iron cycle in the ocean Fe coll Fe(III)FeL Fe(II) Fe part Dissolved iron Phyto. Bacteria Zoo. Sediments Rivers Dust (Simple view) sinking sinking

6 Observations Observations (Design/validate the models) Iron fertilization experiments Iron fertilization experiments + Natural system, detailed obs., … - Small scales, HNLC systems, … Lab experiments Lab experiments + Processes, detailed obs., parameters … - Artificial systems In situ measurements In situ measurements + Large spatio-temp. scales, natural system, … - Coverage, speciation, parameters, …

7 Iron distribution Dissolved iron (nM) Dissolved iron (nM) 0-50 m 0-50 m Dissolved iron (nM) Dissolved iron (nM) m m

8 Outline Introduction Iron chemistry Iron chemistry Biological uptake Biological uptake External sources of iron External sources of iron Past/Future scenarios Past/Future scenarios Conclusions/thoughts Conclusions/thoughts I won't discuss about iron cycle in sediments and in dust

9 Iron chemistry (I) From Archer and Johnson (2000) The simplest iron model The simplest iron model Fe T Fe part k sc This model is not used anymore The Johnson model The Johnson model FeFe part k sc FeL This model is still commonly used From Archer and Johnson (2000) < 0.6nM Iron at 2500m (nM)

10 Iron chemistry (II) Adsorption/coagulation model Adsorption/coagulation model FeFe part FeL (Aumont and Bopp, 2006;Moore and Braucher, 2007) k coag k ads Desorption/remin. Better agreement but: Better agreement but: - Predicted iron concentrations too uniform in the deep ocean - Parameters at the surface and in the deep ocean differ significantly - Desorption improves model results but is not demonstrated

11 Iron Speciation Iron speciation models Iron speciation models Fe(III)'Fe part FeLb Desorption/remin. Fe(II)' FeLa (Tagliabue and Arrigo, 2006; Weber et al., 2005;2007) Tagliabue and Arrigo, 2006 (Tagliabue et al., 2007, sub.) Iron speciation does matter !! Iron speciation does matter !! Impacts restricted to the upper ocean Expensive, many unconstrained parameters and processes

12 Iron Chemistry: Current state/challenges What we have learnt from models What we have learnt from models Fe coll Fe(III)FeL Fe(II) Fe part Critical for iron distribution in the deep ocean Critical for PP and surface Iron Future challenges Future challenges Dynamics of Iron colloids (Thorium, DOM analysis,...) Bioavailability of the different forms of operationnally defined dissolved iron Ligands

13 Phytoplankton growth Most biogeochemical models: NPZD-type models Most biogeochemical models: NPZD-type models Phytoplankton growth Phytoplankton growth N1, N2,... P1, P2,... D1, D2,... Z1, Z2,... μ = μ M L N (1-exp(- (Chl/C) E/ μ M ) Iron L N : Quota or Monod Approach (see for instance the work by Flynn and coauthors for discussion on both approaches) Constant parameters for iron limitation (except in Flynn, 2001)

14 Fe/C ratio First iron models : constant Fe/C ratios (5-10 First iron models : constant Fe/C ratios (5-10mol/mol) Currently, most (both quota and Monod) models have variable Fe/C Currently, most (both quota and Monod) models have variable Fe/C (Loukos et al., 1997; Lefèvre and Watson, 1999 ; Archer and Johnson, 2000;...) No luxury uptake, No Fe adsorbed onto the cell walls Values representative of the open ocean (Moore et al, 2002)

15 Iron Limitation All models predict similar patterns for Fe limitation All models predict similar patterns for Fe limitation (Aumont and Bopp, 2006) (Moore et al., 2004) Diatoms limiting factors They reproduce the main characteritics of HNLC regions but... They reproduce the main characteritics of HNLC regions but... 1D models and obs. suggest higher K Iron is not the whole story : light !!! (Gregg et al., 2003)

16 Other components Most of the modeling work has concentrated on phytoplankton Most of the modeling work has concentrated on phytoplankton Detailed mechanistic models: Flynn, 2001 ; Armstrong 1999 ;... In comparison, other components have received much less attention In comparison, other components have received much less attention Bacteria are not modeled. Bacteria are not modeled. Observations: in competition with phytoplankton for iron Zooplankton role in iron cycle is neglected Zooplankton role in iron cycle is neglected Constant Fe/C ratios or passively controled by its diet Never iron limited, nor affected by the Fe/C of its preys. Ideas from the work by Mitra et al. (2006,2007) Iron acquisition only from organic matter (preys) whereas studies have shown colloids consumption (e.g., Chen and Wang, 2001)

17 Sediment mobilization Rivers Dust deposition Hydrothermal vents External sources of iron

18 Dust deposition Historically, the external source which has received the first and main attention Historically, the external source which has received the first and main attention (Jickells et al., 2005) Large uncertainties in dust deposition to the ocean: Mt/year

19 Dust deposition in models All models include this source, but with very simple parameterizations All models include this source, but with very simple parameterizations Iron is a constant fraction of dust, typically ~3.5% Solubility is constant, typically between 1% and 5% Monthly-mean climatological fields Solubility is not constant (time/space) Solubility is not constant (time/space) (From Hand et al., 2004) Dust dissolves not only at the surface (Moore et al., 2004; Aumont and Bopp, 2006) Dust dissolves not only at the surface (Moore et al., 2004; Aumont and Bopp, 2006)

20 Weak sensitivity to increased iron flux Increased PP in HNLC region balanced by larger oligotrophic regions and scavenging Role of Dust Deposition Models have been used to estimate the contribution of aelion iron to new iron Models have been used to estimate the contribution of aelion iron to new iron Dust scenarios or estimating its impact on PP Dust scenarios or estimating its impact on PP About 20% to 30% of new iron in the euphotic zone comes from dust (Archer and Johnson, 2000; Moore et al, 2002 ; Aumont et al., 2003) But new iron is not necessarily completely used by phytoplankton Weak sensitivity to decreased iron flux at the beginning Strong iron flux over oligotrophic regions Strong sensitivity to decreased iron flux over long timescales Iron scavenging not balanced anymore by dust supply

21 Temporal variability of dust deposition Dust deposition extremely variable on all timescales Dust deposition extremely variable on all timescales Dust scenarios or estimating its impact on PP Dust scenarios or estimating its impact on PP Iron deposition at BATS (g Fe/d/m2) (from INCA2 atmospheric model) Iron relative variability Chlorophyll absolute variability (mgChl/m 3 ) Problem : iron variability seems underestimated with 1-2% solubility

22 Sediment mobilization A significant source to the ocean A significant source to the ocean Sediments in models Sediments in models Resuspension and diffusion generates high Fe in coastal regions (> 5nM) Influence far offshore along some transects (S. Polar Frontal zone, N. Pacific) (From Moore et al., 2007) Only very few models include this source (Moore et al., 2004; Aumont and Bopp, 2006;Tagliabue and Arrigo, 2006; Moore and Braucher, 2007) Estimated magnitude : ~ mol Fe/yr (very, very uncertain !!!!) Estimated magnitude : ~ mol Fe/yr (very, very uncertain !!!!) Without it, models are much too sensitive to variations in dust deposition

23 External sources: Thoughts Dust deposition Dust deposition The main unknown is the solubility (the rest is second order...) Processing in surface and deep waters Dust is not the only major source Sediment mobilization Sediment mobilization Potentially as important as dust We know very very little !! Oxic/anoxic, Diffusion/Suspension/Irrigation,... Rivers ?? Rivers ?? Hydrothermal vents ?? Hydrothermal vents ??

24 LGM: The iron hypothesis Atmospheric pCO2 about ppmv lower than preindustrial levels Atmospheric pCO2 about ppmv lower than preindustrial levels The Iron hypothesis (Martin, 1990) Ice cores suggest higher dust deposition in the Southern Ocean Higher aeolian input enhanced PP enhanced C sequestration Models Models ? A predicted CO2 drawdown between ~8 and ~30 ppmv (Archer and Johnson, 2000; Bopp et al., 2003; Parekh et al., 2006) Compensation between enhanced PP in HNLC regions and decreased PP due to lower N, P, Si levels An other Iron effect ? Lower sea level = lower sediment mobilization Maximum effect = +17 ppmv (Aumont et al., 2007, in prep.)

25 Future climate Dust deposition is predicted to decrease Dust deposition is predicted to decrease 40% decrease between 2000 and 2100 (Mahowald et al., 2006) Impact on the C cycle Impact on the C cycle Ocean sink is reduced by 0.5 Pg C/yr (Moore et al., 2006) Atmospheric pCO2 increased by up to 100 atm (Parekh et al., 2006) With Sediments, the impact is reduced to 0.2 Pg C/yr (Tagliabue et al., 2007, sub.)

26 Conclusions Models proved to be useful despite the oversimplications Models proved to be useful despite the oversimplications They are able to reproduce the basic features of the iron cycle They are able to reproduce the basic features of the iron cycle Major uncertainties in the iron cycle Major uncertainties in the iron cycle Scavenging/coagulation. In particular, what is the role of the colloids? Scavenging/coagulation. In particular, what is the role of the colloids? A lot to be learn from other metals, especially Th Characteristics of the ligands Biological processes. What is the role of zooplankton/bacteria? Biological processes. What is the role of zooplankton/bacteria? Iron on particles in the deep ocean External sources of Iron. What is the role of sediments? External sources of Iron. What is the role of sediments? Solubility of aelian iron – Data required both from process studies and from the field (Obvious !!!) Speciation of iron (truly dissolved, colloidal, particulate,...) Speciation of iron (truly dissolved, colloidal, particulate,...)


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